Research in Personnel and Human Resources Management, Volume 24

LIST OF CONTRIBUTORS Robyn L. Brouer

College of Business, Florida State University, Tallahassee, FL, USA

M. Ronald Buckley

Michael F. Price College of Business, University of Oklahoma, Norman, OK, USA

Jason A. Colquitt

Warrington College of Business, University of Florida, Gainesville, FL, USA

Rene´e E. DeRouin

Department of Psychology, University of Central Florida, Orlando, FL, USA

James H. Dulebohn

Michigan State University, 412 S. Kedzie East Lansing, MI, USA

Erich C. Fein

Department of Psychology, The Ohio State University, Columbus, OH, USA

Gerald R. Ferris

College of Business, Florida State University, Tallahassee, FL, USA

Barbara A. Fritzsche

Department of Psychology, University of Central Florida, Orlando, FL, USA

Stanley M. Gully

Department of Human Resource Management, Rutgers University, Piscataway, NJ, USA

Jonathon R.B. Halbesleben

Michael F. Price College of Business, University of Oklahoma, OK, USA

Howard J. Klein

Department of Management and Human Resources, The Ohio State University, Columbus, OH, USA

Janet H. Marler

School of Business, University at AlbanyState University of New York, Albany, NY, USA vii

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LIST OF CONTRIBUTORS

Marcia P. Miceli

The McDonough School of Business, Georgetown University, Washington, DC, USA

Janet P. Near

Department of Management, Kelley School of Business, Indiana University, Bloomington, IN, USA

Jean M. Phillips

Department of Human Resource Management, Rutgers University, Piscataway, NJ, USA

Quinetta M. Roberson

School of Industrial and Labor Relations, Cornell University, Ithaca, NY, USA

Eduardo Salas

Department of Psychology, University of Central Florida, Orlando, FL, USA

Anthony R. Wheeler

College of Business Administration, California State University, Sacramento, CA, USA

Cindy P. ZapataPhelan

Warrington College of Business, University of Florida, Gainesville, FL, USA

CONTENTS LIST OF CONTRIBUTORS

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OVERVIEW

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A MULTILEVEL APPLICATION OF LEARNING AND PERFORMANCE ORIENTATIONS TO INDIVIDUAL, GROUP, AND ORGANIZATIONAL OUTCOMES Stanley M. Gully and Jean M. Phillips JUSTICE IN TEAMS: A REVIEW OF FAIRNESS EFFECTS IN COLLECTIVE CONTEXTS Jason A. Colquitt, Cindy P. Zapata-Phelan and Quinetta M. Roberson STANDING UP OR STANDING BY: WHAT PREDICTS BLOWING THE WHISTLE ON ORGANIZATIONAL WRONGDOING? Marcia P. Miceli and Janet P. Near A MODEL OF EMPLOYEE SELF-SERVICE TECHNOLOGY ACCEPTANCE Janet H. Marler and James H. Dulebohn LEARNER CONTROL AND WORKPLACE E-LEARNING: DESIGN, PERSON, AND ORGANIZATIONAL ISSUES Rene´e E. DeRouin, Barbara A. Fritzsche and Eduardo Salas v

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GOAL PROPENSITY: UNDERSTANDING AND PREDICTING INDIVIDUAL DIFFERENCES IN MOTIVATION Howard J. Klein and Erich C. Fein ‘‘THE ELUSIVE CRITERION OF FIT’’ REVISITED: TOWARD AN INTEGRATIVE THEORY OF MULTIDIMENSIONAL FIT Anthony R. Wheeler, M. Ronald Buckley, Jonathon R.B. Halbesleben, Robyn L. Brouer and Gerald R. Ferris ABOUT THE AUTHORS

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OVERVIEW Volume 24 of Research in Personnel and Human Resources Management contains seven thought-provoking papers. The first two papers (Gully & Phillips; Colquitt, Zapata-Phelan & Roberson) address motivation and justice issues at multiple levels of analysis. The third paper (Miceli & Near) contains a review of the whistle-blowing literature, bringing us up-to-date on theoretical issues pertaining to the whistle-blowing phenomenon. The fourth and fifth papers (Marler & Dulebohn; DeRouin, Fritzsche & Salas) offer thought-provoking ideas and reviews of the use of human resource information systems and learning in e-environments. The final two papers (Klein & Fein; Wheeler, Buckley, Halbesleben, Brouer & Ferris) further advance our knowledge of person–organizational fit through an integration of alternative fit perspectives, and expand our thinking about employee motivation through the proposal of a construct called goal propensity. Gully and Phillips extend research and theory on learning and performance orientations to multiple levels of analysis. They begin by introducing a model describing the impact of individual learning and performance orientations on attentional focus, response to failure, experimentation, and motivation, and identify potential sources of these orientations. They then describe how learning and performance orientations are linked to incremental and profound change, and theoretically based propositions are presented to guide future research efforts. Leadership, organizational learning, and strategic human resource management are discussed in relation to the model and implications of the framework for future research and practice are revealed. Colquitt, Zapata-Phelan, and Roberson point out that the use of teams has increased significantly over the past two decades, with recent estimates suggesting that between 50% and 90% of employees work in some kind of team. Their paper examines the implications of this trend for the literature on organizational justice – the study of fairness perceptions and effects in the workplace. In particular, the authors explore three specific research questions: (1) Will the justice effects observed in individual contexts generalize to team contexts and member-directed reactions? (2) Will the justice experienced by specific teammates have direct or interactive effects on members’ own reactions? (3) Will the justice experienced by the team as a ix

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whole impact reactions at the team-level of analysis? Their review of almost 30 studies suggests that each question can be answered in the affirmative, illustrating that team contexts can magnify the importance of justice in organizations. Miceli and Near discuss that research on whistle-blowing has focused on the questions of who blows the whistle, who experiences retaliation, and who is effective in stopping wrongdoing. In this article, the authors review research pertinent to the first of these questions. Since the last known review (Near & Miceli, 1996), there have been important theoretical and, to a lesser extent, empirical developments. In addition, the U.S. law is changing dramatically, which may serve to promote valid whistle-blowing, and international interest in whistle-blowing is widespread and increasing. Unfortunately, evidence strongly suggests that media, popular, and regulatory interest is far outpacing the growth of careful scholarly inquiry into the topic, which is a disturbing trend. Here, the authors argue that the primary causes of the underdevelopment of the empirical literature are methodological, and that workable solutions are needed but are very difficult to implement. Marler and Dulebohn review the literature on individual acceptance of technology to show how organizations can improve the effective use of human resource web-based technologies. Integrating and expanding several theoretical models of technology acceptance, the authors develop a perceptual model of employee self-service acceptance and usage. Based on this model, they propose several key individual, technological, and organizational factors relevant to individual intentions to use ESS technology. The authors summarize these in several testable propositions and they also discuss implications for organizational researchers and practitioners. DeRouin, Fritzsche, and Salas review the literature on learner control and discuss the implications that increased control may have for training in elearning environments. The authors provide a comprehensive review of the learner control literature, focusing on adults and workplace training. They consider the instructional design factors that have been manipulated to provide learners with control and person issues that moderate the relation between learner control and outcomes. Then, the authors summarize developments in training research and in adult learning that relate to learner control in order to provide a theoretical context for understanding learner control in adult workplace e-learning. Klein and Fein propose the development of a compound personality trait termed goal propensity. Motivation is a key determinant of performance in

Overview

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virtually all contexts and personality has long been viewed as an important influence on motivation. Despite the long history of exploring how personality influences motivation, the authors point out that we do not have a clear understanding of the linkage between individual differences in personality and work motivation or the tools to reliably and accurately predict individual differences in motivation. Advances in our understanding of personality and the convergence of motivation theories around models of self-regulation present the opportunity to achieve that understanding and predictive efficacy. They maintain that goal propensity would be a theoretically derived trait that would explain the role of personality in self-regulation models of motivation as well as allow the prediction of tendencies to engage in self-regulation. This article provides the rationale for the development of this construct, articulates the nature of the proposed goal propensity construct, and explores the value of such a construct for theory, future research, and human resource practice. Wheeler, Buckley, Halbesleben, Brouer, and Ferris maintain that although ‘‘fit’’ as a human resources decision criterion has emerged as an active body of research in recent years, its ‘‘elusiveness’’ as a scientific construct, noted more than a decade ago by Judge and Ferris (1992), still remains. To address this issue, the authors propose an integrative theory of multidimensional fit that encompasses five relevant (and distinct) streams of current fit research: Person-Organization Fit (Chatman, 1989), PersonVocation Fit (Holland, 1985), Person-Job Fit (Caldwell & O’Reilly, 1990), Person-Preferences for Culture Fit (Van Vianen, 2000), and Person-Team Fit (DeRue & Hollenbeck, in press). They propose that these five dimensions of fit relate to an individual’s self-concept; moreover, an individual assesses multidimensional fit utilizing a social cognitive decisionmaking process called prototype matching (Cantor, Mischel & Schwartz, 1982). By assessing fit across multiple dimensions, an individual can both gain a social identity (Tajfel & Turner, 1986) and expand the self-concept (Aron & Aron, 1996), which explains the motive to fit. The authors advance testable propositions, and discuss implications for multidimensional fit across the employment life cycle. They conclude their paper with directions for future fit research. Joseph J. Martocchio Series Editor

A MULTILEVEL APPLICATION OF LEARNING AND PERFORMANCE ORIENTATIONS TO INDIVIDUAL, GROUP, AND ORGANIZATIONAL OUTCOMES Stanley M. Gully and Jean M. Phillips ABSTRACT The purpose of this chapter is to extend research and theory on learning and performance orientations to multiple levels of analysis. We begin by introducing a model describing the impact of individual learning and performance orientations on attentional focus, response to failure, experimentation, and motivation, and identify potential sources of these orientations. We then describe how learning and performance orientations are linked to incremental and profound change, and theoretically based propositions are presented to guide future research efforts. Leadership, organizational learning, and strategic human resource management are discussed in relation to the model, and implications of the framework for future research and practice are revealed.

Research in Personnel and Human Resources Management Research in Personnel and Human Resources Management, Volume 24, 1–51 Copyright r 2005 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 0742-7301/doi:10.1016/S0742-7301(05)24001-X

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INTRODUCTION As economies globalize and organizational environments become increasingly complex, learning organizations and adaptive workers are becoming more important for organizational performance. Theory and research suggest that in the presence of global competition and rapid technological advancements, modern organizations must be flexible, efficient, and continually adapt to changing environments to sustain a competitive advantage and survive (Barkema, Baum & Mannix, 2002; D’Aveni, 1994). Industries dependent on highly sophisticated technologies and firms engaged in multinational competition face a particularly strong need for continuous and rapid modification of their product features and the ways in which they conduct business (Teece, 1987). In turbulent environments, organizations that are more dynamic and successfully adapt to changing environmental conditions outperform more static, less resilient organizations that focus on performance through maintenance of the status quo and minimal risk taking. The movement toward total quality management in U.S. industry in the early 1990s is indicative of how modern organizations are responding to the demand for constant learning and improvement, as well as consistent quality and low error rates. The less imitable a strategy, the more durable it is as a source of competitive advantage (Barney, 1991; Porter, 1985). Innovation provides particularly useful forms of competitive advantage if the innovation process or the outcomes of innovation are difficult to copy (Lengnick-Hall, 1992). Thus, effective corporate innovation has become an increasingly important ingredient in sustaining competitive advantage. However, innovation and its implementation require varied forms of organizational learning, adaptability, and change. Change efforts can be incremental, focused on improvements in performance and reductions in errors, or profound, encompassing paradigm shifts. Incremental change can result in increased efficiency, lower costs, fewer errors, and simple improvements that build upon current organizational capabilities. Examples of incremental change include using total quality management techniques to reduce the amount of scrap produced in a currently existing manufacturing process or providing new colors or sizes of Post-It notes to customers. In contrast, profound change can result in new strategic directions, systems and processes, and product innovations. Profound change can be seen in Nokia’s transformation from its initial roots as a manufacturer of paper and rubber products to becoming a leading

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manufacturer of cell phones and other telecommunication products. Another example of profound change can be found in Kodak’s entry into the digital photo business after focusing nearly exclusively on chemically based photo products. Organizational survival in a highly competitive and changing world depends on the simultaneous achievement of both reliable performance and adaptability (Benner & Tushman, 2003; March, 1991). Thus, most organizations need to focus on both profound and incremental change for optimum performance and survival. We suggest that the notion of learning and performance orientations can help theorists and practitioners to better understand the processes and forces that generate profound and incremental change at the individual, group, and organizational levels of analysis. A performance orientation focuses on maximizing performance, reducing mistakes, and meeting the achievement expectations of key stakeholders. Change resulting from a performance orientation tends to be incremental in nature. A learning orientation focuses on learning through experimentation and error, and taking risks to create knowledge that will enable adaptability. Change resulting from a learning orientation tends to be profound. A fundamental tension exists between these two orientations. As mistakes are minimized and reliability is increased, as is the case with a performance orientation, opportunities for learning through experimentation or serendipity are reduced. Both orientations are important to organizations, but they tend to create varying emphases on the type of learning and change that occurs. The concepts of individual learning and performance orientation are rooted in theories of individual differences (Dweck, 1986), but they may be extended and applied across organizational levels to enhance our understanding of adaptability, efficiency, and learning. Individuals and collectives may adopt mastery or performance goals, which will affect the evaluative standards adopted, goal expectancies, perceived control, task choice, task pursuit, outcome attribution, satisfaction, and task interest (Dweck, 1989; VandeWalle, 2001). Although the early goal orientation research of Dweck and others was conducted primarily with children and adolescents in academic settings, there has been a growing interest in the implications of learning and performance orientations for adults working in organizations, and increasing evidence that the effects observed with schoolchildren replicate with adults in organizational settings (Bobko & Colella, 1994; Brown, 2001; Farr, Hofmann & Ringenbach, 1993; Hertenstein, 2001; Martocchio & Hertenstein, 2003; Potosky & Ramakrishna, 2002; Sujan, Weitz & Kumar, 1994; VandeWalle, Brown, Cron & Slocum, 1999).

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Learning and performance orientations manifest themselves at multiple organizational levels with different antecedents and outcomes. At the individual level, learning and performance orientations can reflect both dispositions and states, with dispositions and contextual effects such as group norms influencing states. At the group level, the two orientations are reflected in group climates and norms. Group leaders and the group’s functional purpose can have a strong influence on the development of these norms. At the organizational level, learning and performance orientations are evidenced by shared perceptions driven by the organization’s climate and culture. These shared perceptions can be created by key organizational leaders and the strategic direction of the organization (Ostroff, Kinicki & Tamkins, 2003). The interplay of these learning and performance orientations across levels has implications for individual, group, and organizational outcomes. The purpose of this chapter is to develop a framework that addresses the roles of learning and performance orientations at three organizational levels of analysis, beginning with the most micro, the individual level, then moving on to the organizational and group levels of analysis. The basic model is illustrated in Fig. 1. This model will be used to derive testable propositions that can guide future research at and across each level of analysis. Our model is a multilevel model because it specifies patterns of relationships that are partially or completely replicated across levels of analysis (Klein, Dansereau & Hall, 1994; Kozlowski & Klein, 2000; Rousseau, 1985). As we discuss the model, definitions and roles of learning and performance orientations will be introduced. Also, we will link different organizational

Organizational Leaders & Strategy

Organizational Goal Orientation

Group Leaders and Group Function

Group Goal Orientation

Individual Trait Goal Orientation

Fig. 1.

State Goal Orientation

Single-Loop Learning & Incremental Change

Attentional Focus Response to Failure Experimentation Motivation

Double-Loop Learning & Profound Change

Multi-level model of learning and performance orientations.

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strategies to learning and performance orientations and outcomes at different levels to provide a basis for generating new directions for applied interventions and for improving human resource management practices in organizations. The basis of the model revolves around the effects of learning and performance orientations on attentional focus, willingness to experiment and take risks, responses to failure, and maintenance of motivation. Performance orientations lead to a focus on demonstrating high levels of ability. Failure is regarded as a sign of incompetence. Thus, risk-taking and experimentation are avoided and motivation drops in the face of setbacks. Single-loop learning emphasizes learning about routine processes that operate within given parameters (Argyris & Scho¨n, 1978). The desire to demonstrate high levels of performance can create a motivation to improve efficiency and reduce errors, leading to single-loop learning and incremental improvements. In contrast, learning orientations lead to a focus on the development of knowledge and skills through risk-taking and experimentation. Failure is viewed as part of the developmental process and it is regarded as an opportunity for learning. Thus, motivation is formed by the desire for long-term development and is maintained in the face of mistakes and setbacks. This orientation leads to double-loop learning. Double-loop learning emphasizes the questioning of fundamental assumptions, strategies, and purposes, and it includes an ongoing examination of how to define and solve problems. Thus, learning orientations are more likely to lead to profound changes in strategies and procedures. The effects of learning and performance orientations depend on contextual factors like environmental uncertainty, stability, time horizon, and task structure. These factors will be discussed at appropriate points in this chapter. The extension of learning and performance orientation theory across organizational levels advances current thinking in several ways. First, it is more parsimonious in the sense that it connects a variety of different phenomena in one theoretical model. As a result, it provides an avenue for integration of individual-level learning and performance orientation research with other literatures, expanding our understanding of these constructs. Second, it identifies similarities in seemingly different processes, and identifies outcomes of learning and performance orientations that may influence other levels. Third, it provides suggestions for future research directions, which are not currently considered in the literature, including moderators and cross-level interactions. Finally, it may yield insights into leadership processes, and describe how the implementation of organizational strategy, teams, and human resource management systems can be

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made more effective. Thus, the model has implications for human resource practice and effective strategic human resource management.

LEARNING AND PERFORMANCE ORIENTATIONS ACROSS LEVELS The development of a multilevel model requires consideration of the evidence for relationships at each level of analysis: individual, group, and organizational. Before continuing, it is useful to describe what is meant by each of these levels. The concept of level implies a hierarchical relationship, and the level of analysis is the focal plane to which generalizations will be made (Klein et al., 1994; Rousseau, 1985). When we refer to the individual level, we mean the individual employees of an organization. These employees are likely to work within work groups, teams, or departments of the organization, which is what we mean by the group level of analysis. The organizational level of analysis refers to organizational characteristics that lead to consistent similarities and differences among organizations. Higher levels of analysis can be envisioned, including industry and national levels of analysis, but these levels are beyond the scope of this chapter. Previous theories and models of learning and performance orientation have typically focused on one level of analysis at a time. Organizational researchers have become increasingly divergent in their emphasis on microand macro-level constructs and they have given little attention to the need to bridge micro- and macro-level concerns (Cappelli & Sherer, 1991; Staw, 1991). This is unfortunate because organizations are inherently multilevel in nature (Klein et al., 1994). Focusing on a particular level limits our capability to understand the full range of processes that influence phenomena of interest. For example, organizational innovation is rooted in the motivations, learning, and behaviors of the individual employees who comprise the organization. Practically, learning or innovation interventions at one level of analysis may not be useful when affected by relationships at another level of analysis. An example is when training intended to increase individual researchers’ creativity and risk-taking is ineffective because of workgroup pressures to remain conservative and focus on cost-efficient performance. Interventions may also have unintended cross-level consequences. For example, introducing a new merit pay plan at the organizational level that rewards individual rather than group innovation may decrease group cohesion and the sharing of knowledge at the group level due to the

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competitive nature of the awards. Adopting a level of analysis perspective may further our understanding of how learning and performance orientations influence processes leading to different types of learning and change in organizations.

Individual Orientations Defined We will consider learning and performance orientations at three levels of analysis. At the lowest, or individual, level a learning orientation is characterized by a desire to increase one’s task mastery or competence, whereas a performance orientation reflects a desire to demonstrate high ability and to be positively evaluated by others (Ames, 1992; Dweck, 1986, 1989; Dweck & Leggett, 1988; Farr et al., 1993; Nicholls, 1984; VandeWalle, 1997). Because the focus for performance-oriented individuals is on normative performance, ability is evidenced by outperforming others, surpassing normative-based standards, or achieving success with little effort. Important to individuals with a performance orientation is public recognition that they have performed in a superior manner. Other components of a performance orientation are the belief that abilities are immutable (Dweck, 1986) and a fear of failing (VandeWalle, 1997). The focus of a learning orientation is on improving on one’s previous performance and mastering tasks through experimentation and learning from mistakes (Dweck, 1989; VandeWalle, 1997). A learning, or ‘‘mastery,’’ orientation entails a belief that abilities are changeable, and that effort will affect outcomes. Learning-oriented individuals are interested in developing new skills, understanding and improving their work, improving their level of competence, and achieving a sense of mastery based on self-referenced standards (Dweck, 1989; Farr et al., 1993). Mastery goals lead to questions like, ‘‘How can I understand this?’’ and ‘‘How can I do this better?’’. Outcomes At the individual level of analysis, learning and performance orientations create the mental framework within which individuals interpret and respond to situations (Dweck, 1991). Performance-oriented individuals tend to focus attention on task difficulty and ability, while learning-oriented individuals focus on identifying strategies to improve and master the task. Learningoriented individuals focus more on successful aspects of past situations, which can create conditions for building both abilities and confidence in

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those abilities. By increasing task efficacy, the focus on personal standards helps sustain the benefits of previous success for learning-oriented people even in the face of failure (Ames & Ames, 1991). These effects have been found with adults in work settings (Martocchio, 1994; Potosky & Ramakrishna, 2002). Learning and performance orientations can influence motivation, the interpretation of errors or mistakes, skill and knowledge updating, and perceptions of ability and performance. Each of these will be discussed next. Learning and performance orientations can affect motivation by influencing task choice, persistence, self-management, and self-efficacy. The individual’s resulting motivation is likely to influence his/her willingness and ability to contribute innovative and insightful ideas to improvement efforts. A learning orientation has been found to be related to an increased motivation to learn (Colquitt & Simmering, 1998), persistence in the face of difficulty (Elliot & Dweck, 1988), seeking of feedback (VandeWalle & Cummings, 1997), use of more complex learning strategies (Fisher & Ford, 1998), and performance under challenging task conditions (Kozlowski et al., 2001). Learning-oriented individuals have also been found to make greater use of self-regulatory strategies (Ford, Smith, Weissbein & Gully, 1998). Phillips and Gully (1997) found that learning orientation was positively related, and performance orientation was negatively related to self-efficacy. Self-efficacy was then found to positively influence goals and performance even after controlling for ability. An individual’s learning and performance orientations can also influence his or her interpretation of errors or mistakes. A higher performance orientation leads to a greater concern about the appearance of one’s performance compared with others and increases competitiveness. The focus on meeting normative standards and a fear of failure can also induce impression management behaviors for performance-oriented individuals. The attribution of failure to lack of ability by performance-oriented individuals leads to less persistence and more disruptions in the face of difficulty (Dweck, 1989). Attributing failure to low effort, poor strategies, or bad luck leads to the maintenance of high expectancies, a high level of persistence, and effective performance under failure for learning-oriented individuals (Dweck, 1989). Active engagement is characterized by effective learning and problem-solving strategies, and use of these strategies is dependent on a belief that effort leads to success, and that failure can be remedied by a change in strategy, which is consistent with a learning orientation. Performance-oriented people have been found to choose less subjectively difficult and challenging tasks that might threaten their perceived level of

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normative competence. Learning-oriented people are more likely to choose subjectively difficult tasks, and to persist in the tasks they choose (Farr et al., 1993; Nicholls, 1984). Learning-oriented people like high-effort experiences, which characterizes most novel situations, whereas performanceoriented people devalue such experiences. Learning and performance orientations may also be related to skill and knowledge updating. Learning-oriented individuals welcome opportunities to develop new skills, understand and improve their work, and improve their level of competence (Dweck, 1989; Farr et al., 1993). A learning orientation has also been found to be related to greater metacognition, which refers to the process of thinking about how one engages in cognitive activities, which is in turn related to increased knowledge and skill (Ford et al., 1998). A performance orientation is related to the use of superficial or short-term learning strategies like memorizing and rehearsing (Ames, 1992). The belief that abilities are immutable makes performance-oriented individuals less likely than learning-oriented individuals to seek out self-development opportunities. Individuals’ reactions to negative feedback are also influenced by learning and performance orientations. More learning-oriented individuals base their performance expectations on effort whereas more performance-oriented people base their expectations of their performance on perceptions of their ability relative to others. A performance orientation is associated with negative effect following failure accompanied by a judgment that one lacks ability, and positive effect following success with little effort. An orientation to learning or performance may also be an individual difference variable that moderates reactions to negative feedback. More learning-oriented individuals may view negative feedback as information concerning how to develop task mastery while more performance-oriented individuals may view negative feedback as evaluating their competence or ability and react more negatively. Performance-oriented individuals are less likely to seek feedback, whereas learning-oriented individuals are more likely to seek feedback (VandeWalle & Cummings, 1997). Learning-oriented individuals may bypass attributional processes for failure and simply search for more effective strategies (Dweck, 1989). A performance orientation is not necessarily a negative in most organizational contexts, because individuals must be concerned with meeting performance standards. Indeed, Hofmann (1993) found that having a performance orientation was not necessarily dysfunctional. A performance orientation was positively related to performance, but it was also positively related to task-related cognitive interference, which was negatively related to

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performance. Thus, in this study performance orientation had countervailing effects on performance. Because performance-oriented people focus on displaying competency and maximizing their performance, they are more likely to produce incremental change. A performance orientation is likely to be related to decreased flexibility, but should be related to higher, more consistent performance in the short-term or in stable situations. A learning orientation is likely to induce continual experimentation, and these individuals are thus more likely to produce profound change. Outcomes of a learning orientation at the individual level of analysis include increased adaptability, persistence, and creativity. Possible short-term negative outcomes of a learning orientation are lower efficiency and more errors due to greater risktaking and willingness to try new ways of doing things, but these should be offset in the long run by greater efficiency and lower error rates. Proposition 1. High learning-oriented individuals will be more likely than low learning-oriented individuals to produce profound change. High performance-oriented individuals will be more likely than low performanceoriented individuals to produce incremental change. Sources Learning and performance orientations have both dispositional and situational components (Button, Mathieu & Zajac, 1996). Originating with Dweck (1986) in the education literature, learning and performance orientations are often thought of as traits, or personality characteristics. However, Farr et al. (1993) note that situational characteristics can affect one’s orientation. Research has found that learning and performance orientations are situationally manipulable (Martocchio, 1994). For example, even though an individual’s personality may be predisposed to be lower in learning orientation and moderate in performance orientation, contextual factors such as the framing of instructions and rewards can be introduced that can raise one’s learning orientation or lower one’s performance orientation for a particular task or situation (Kozlowski et al., 2001). George (1992) discusses the distinction between traits and states and shows that states mediate the effects of traits on behavior with states essentially capturing the person – situation interaction. Similarly, Chen, Gully, Whiteman, and Kilcullen (2000) found that the impact of dispositional goal orientations on learning performance occurred primarily through their influences on more malleable states. Although individuals have been found to be predisposed to hold particular levels of learning and performance orientations, they have also been

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found to be situationally manipulable. For example, Martocchio (1994) found that adult trainees in an acquirable skill condition (learning-oriented state) had less anxiety and more efficacy than trainees in a skill is a fixed entity condition (performance-oriented state). Performance-oriented trainees did not show an increase in anxiety, but did show a significant decrease in efficacy between pre- and post-training assessments. External factors can therefore influence a person’s learning and performance orientation levels. Similarly, Stevens and Gist (1997) found that MBAs in a training program for developing negotiation skills who were given a mastery-oriented posttraining session engaged in more interim skill-maintenance activities, planned to exert more effort, and showed more positive effect than did performance-oriented trainees. Kozlowski et al. (2001) found that the types of mastery and performance goals provided during training on a complex decision-making task influenced the development of knowledge structures and self-efficacy. It is important to note that learning and performance orientations appear to be two separate dimensions rather than a single continuum anchored by the two orientations (Button et al., 1996; VandeWalle, 1997). A meta-analysis by Beaubien and Payne (1999) indicates that the average correlation between the two dimensions is quite weak (r ¼ 0:048). This suggests that it is possible for an individual to be high or low in both learning and performance orientations simultaneously, or have different combinations of levels of the two orientations. Despite the orthogonal nature of the two orientations, very limited research has been done on the effects of various combinations of learning and performance orientation on individual motivation, performance, efficiency, or adaptability. Although it is an important area for future research, because of the lack of existing research and the number of possible combinations of high, moderate, and low learning and performance orientations, a discussion of the implications of the multiple possible configurations of learning and performance orientations is beyond the scope and purpose of this chapter.

Organizational Orientations Defined At the highest level of analysis in an organization, the organizational level, learning orientation is a climate variable that indicates the degree to which members of an organization believe they should focus on experimenting, learning, acquiring new skills and knowledge, and mastering new or

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uncertain environments. Organizational performance orientation reflects the degree to which members focus on demonstrating high levels of performance, reliability, and competence. It is also associated with avoidance of errors or failure. Organizational learning and performance orientations manifest themselves through the behaviors, motivations, and perceptions of individuals, which are influenced by the individuals’ workgroups and leadership as well as organizational policies, procedures, and human resource management systems (Bowen & Ostroff, 2004). Just as individual orientations within an organization vary, systematic between-organization differences in context exist. These organizational contexts include climate and culture, which have been viewed as key drivers of organizational effectiveness and performance (Ostroff et al., 2003). Climate and culture are key contextual elements, which can have more impact than dispositional characteristics in terms of influencing individual attitudes and behaviors (Davis-Blake & Pfeffer, 1989). Climate is a perceptually based set of descriptors of relevant organizational features, events, and processes (Kopelman, Brief & Guzzo, 1990; Kozlowski & Doherty, 1989). It involves perceptions of the social setting or context of which the person is a part (Rousseau, 1988). Although climate is rooted in individual perceptions, we are interested in shared perceptions and values that characterize the organization as an entity and transcend the individual. Similarly, the content of organizational culture includes values, norms, ideologies, charters, and philosophies (Ostroff et al., 2003; Schein, 1990). Culture is based on interactionism, shared experience, normative beliefs, and consensus (Rousseau, 1988), so it is a characteristic unique to collective entities. Empirical work has linked organizational climate and culture to safe work behaviors (Zohar, 1980), customer service (Schneider & Bowen, 1985; Schneider, Wheeler & Cox, 1992), technical updating (Kozlowski & Hults, 1987), employee retention (Sheridan, 1992), and performance (Denison, 1984; Kozlowski & Hults, 1987; Pritchard & Karasick, 1973). An organization’s climate or culture for learning and performance will exert effects on how individuals believe they should allocate their effort and attention, and how they should behave. Research and theory on organizational characteristics provide indirect support for the existence of these orientations. For example, Chatman and Jehn (1994) collected data in 15 firms representing 4 industries in the service sector. They obtained evidence for 7 cultural dimensions, including orientations toward innovation (learning) and orientations toward outcomes or results (performance). Similarly, Benner and Tushman (2003) and March (1991) discuss the paradox between exploration and exploitation.

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Exploitation focuses on the utilization of current technology to generate immediate returns, while exploration of new technologies requires a longterm focus on environmental adaptation and prosperity. Levinthal and March (1993) argue that most organizations tend to focus on short-term adjustments to the immediate environment. As they point out, there is a tension between the exploitation of current capabilities and the development of new ones. Development requires a willingness to experiment and deal with profound change, whereas exploitation of current capabilities focuses on maintaining high performance and pursuing incremental change. Organizations must balance the competing goals of developing new knowledge and exploiting current competencies in the face of tendencies to emphasize one or the other (Benner & Tushman, 2003). These learning and performance orientations underlie processes and outcomes in many different organizational systems. Sitkin, Sutcliffe, and Schroeder (1994) explain how total quality management is undergirded by two fundamentally different goals. Control goals focus on enhancement of reliability, minimization of errors, and maintenance of control over systems and processes. Learning goals focus on development of new insights by going beyond control over familiar and stable processes to yield knowledge creation and innovation. Van de Ven and Poole (1995) describe the difference between change within prescribed and constructive modes. Change within a prescribed mode refers to development within a prescribed direction, typically maintaining and incrementally adapting in a stable or predictable way. Change within a constructive mode refers to generation of unprecedented, novel forms that are often discontinuous and unpredictable departures from the past. Similarly, Morgan (1986) identified rule following and enactment as two important aspects of organizational culture. In a culture based on rule following, emphasis is placed on establishing a set of cultural rules or social norms to which every organizational member is expected to adhere in order to attain organizational efficiency, stability, and predictability (Lado & Wilson, 1994; Morgan, 1986), resulting in incremental change. In a culture based on enactment, members proactively generate cultural variation through experimentation and improvisation to yield profound change. Outcomes Potential sources of sustainable competitive advantage include organizational culture (Barney, 1986; Fiol, 1991; Lado & Wilson, 1994) and climate (Ostroff et al., 2003; Schneider & Bowen, 1985). There are a variety of outcomes of learning and performance orientations, both positive and

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negative. Individuals in a performance-oriented organization will focus on improving their capabilities to perform jobs in a facile and cost-effective manner and to demonstrate immediate high performance. As networks of individuals in such organizations repetitively interact, they will learn how to respond to one another more effectively and the behavioral actions and responses of organizational members will become streamlined. Over time, these interactions can develop into a distinct set of role networks and routine patterns that increase efficiency (Nelson & Winter, 1982). This pattern of learning has been variously referred to as single-loop learning (Argyris & Scho¨n, 1978) and lower-level learning (Fiol & Lyles, 1985). Such learning enables organizational members to detect performance deviations and make incremental changes to enhance organizational performance (Lado & Wilson, 1994). To the extent that an employee’s learning and incremental improvements lead to reduced variability in the employee’s performance over time or result in increased gains in productivity, learning is economically efficient (March, 1991). Thus, performance orientations should result in short-term incremental increases in efficiency and reliability, and decreases in production costs on routine tasks. For example, Boeing introduced the 737 and 747 plane programs with great fanfare, but the planes had many problems. Boeing then commissioned an employee group to compare the development processes of the 737 and 747 with the 707 and 727. Over three years they learned many lessons that could be applied to other projects. When members of the team were transferred to the 757 and 767 startups, they produced the most successful and error-free launch in Boeing’s history (Garvin, 1993). Proposition 2. Organizations with stronger organizational performance orientations will be more likely to rely on incremental change to improve performance and efficiency, increase reliability, and reduce costs on routine tasks than will organizations with weaker organizational performance orientations. Because of the incremental nature of improvements, performance orientations will be particularly beneficial in relatively stable organizational environments. However, performance orientations can be detrimental, particularly in dynamic environments. For example, Elmes and Kassouf (1995) identified a number of themes about what prevents an organization from developing more profound forms of learning. The strongest of these was the pressure to meet aggressive deadlines. Competitive forces drive companies to produce rapid results, rarely giving individuals time for reflection on what they are doing. Nor do they have time to communicate

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effectively with their colleagues (Easterby-Smith, 1997). These patterns of behavior and the corresponding reduction in deeper learning will be particularly true in strong performance-oriented climates. Argyris and Scho¨n (1978) describe how people filter and manipulate information flows in organizations such that employees avoid passing on negative information to their superiors, and try not to be too closely identified with new projects in case they fail. The problem is that the salience of a single failure looms much larger than the salience of aggregate success. Shapira (1995) concluded, on the basis of data collected from over 700 managers, that although success is considered valuable, the occurrence of one failure can eliminate any perceived gains of success. The asymmetry between incentives associated with success and negative outcomes associated with failure will lead decision makers to focus on realized outcomes rather than potential opportunities. These dynamics will lead to a selection of conservative over creative actions (Ford, 1996), especially in performanceoriented organizations. Performance-oriented organizations focus on standardization through task specialization, task formalization, and work routinization through job design, written rules, and standard operating procedures, inhibiting the development of transformational competencies by promoting and reinforcing organizational ‘‘defensive routines’’ (Argyris, 1986; Lado & Wilson, 1994). Defensive routines are organizationally sanctioned behaviors for avoiding threats, contradictions, or surprises. Such routines are more likely in performance-oriented organizations that maximize efficient performance and cost effectiveness and reduce risk-taking and failure. Thus, performance-oriented organizations will focus more on exploiting existing competencies in preference to exploring and experimenting with the development of new ones (March, 1991). Proposition 3. Organizations with stronger organizational performance orientations will discourage creativity, experimentation, and risk-taking, and increase avoidance of failure and defensive routines compared with organizations with weaker organizational performance orientations. Evidence suggests that innovation and creativity are enhanced in environments where risk-taking is encouraged and supported. For example, Shapira (1995) discusses how a learning orientation supported by acceptance of risk is an important organizational influence on an organization’s ability and desire to support creative actions and innovation. Cultures that provide this support should increase innovation (Amabile, 1988; Burnside, 1990; Nystrom, 1990; Woodman, Sawyer & Griffin, 1993). Thus, learning

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orientations should result in increased creativity and product innovation, and more profound forms of change. Argyris and Scho¨n (1978) describe a process called double-loop learning that characterizes a nontraditional organizational learning approach (Weick, 1991). Organizations with learning orientations are more likely to have members engaging in double-loop learning who question and reassess the relevance of existing performance standards, norms, and current practices. An organizational learning orientation will encourage members to experiment and play with new ideas, and to improvise and adapt when necessary. Double-loop learning also may enhance organizational adaptability by enabling members to think and respond divergently to change (Lado & Wilson, 1994). A number of organizations have been able to develop learning orientations that encourage active experimentation, innovation, and risk-taking, which together provide a source of sustainable competitive advantage. For example, Capital One, a banking firm, has been growing rapidly since 1994, and it now ranks among the ten largest issuers of credit cards in the U.S. The secret to its success has been the constant pursuit of innovation, experimentation, and learning through testing (Fishman, 1999). In 1998 the company performed 28,000 experiments, including tests of new products, new advertising approaches, new markets, and new business models. Capital One’s informationbased strategy combines scientific testing and data collection with mass customization (one-to-one marketing) and delivery speed to determine how to deliver the right product, at the right price, to the right customer, at the right time (Fishman, 1999). Despite tough economic conditions, net income in 2003 was $1.1 billion, up from $900 million in 2002 and Capital One appears to be uniquely positioned as the industry leader in deploying datadriven marketing techniques across the full credit spectrum. As another example, 3M is an organization that supports and rewards experimentation and risk-taking to create a culture of innovation (3M, 2002). William L. McKnight is credited with helping to forge 3M’s innovative culture. He was president of 3M in 1929 and board chairman in 1949. McKnight stated that it requires tolerance to encourage men and women to exercise their initiative and he warned against micromanagement and the chilling effect that accompanies intolerance of failure, or ‘‘Management that is destructively critical when mistakes are made can kill initiative.’’ (3M, 2002) One of his basic rules of management was laid out in 1948: ‘‘yMistakes will be made. But if a person is essentially right, the mistakes he or she makes are not as serious in the long run as the mistakes management will make if it undertakes to tell those in authority exactly how they must do their jobs.’’ (3M, 2004)

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The philosophy of allowing employees to experiment and take risks led to one of 3M’s largest areas of business today. Dick Drew was an early architect of 3M’s innovation-focused culture. One of Drew’s early assignments was to go to local auto-body shops to test the new Wetordry sandpaper. While there he could not help but notice the problems people had painting the popular two-tone style on the car bodies. Paint would come off when painters tried to remove the plaster tape they had used or the tape’s adhesive would be softened by the lacquer solvent and would remain on the car’s surface. Drew decided to try to create a better, nondrying adhesive tape that could be removed more easily. After weeks of experimentation on the new tape, McKnight told Drew to get back to work on the Wetordry sandpaper. Drew persisted in his tape experiments and McKnight did not stop him. Two years later Drew’s ‘‘contraband’’ Scotch masking tape debuted and sparked 3M’s entry into the adhesives industry, a major source of revenue for 3M today. The culture of learning and experimentation continues to exist today. For example, 3M technical employees are encouraged to devote up to 15% of their working hours to independent projects (3M, 2002). Allowing for such experimentation led to the development of Scotch masking tape, Post-it notes, and many other products, providing product diversification and allowing 3M to enter new industries and lines of business. We suggest that Capital One and 3M are examples of organizations with strong learning orientations. We further propose that such learning orientations lead to experimentation, risk taking, deeper learning, and large-scale change. Proposition 4. Organizations with stronger organizational learning orientations will encourage and display greater active experimentation, risk-taking, innovation and creativity, and greater double-loop learning with profound change than will organizations with weaker organizational learning orientations. Organizational learning orientations can have detrimental effects as well. As organizations continually pursue innovation and experimentation, they must also implement discoveries and recover investments in time and money to remain viable in the long-term. Although learning orientations will result in more experimentation, innovation, and deeper learning, they are also initially less reliable and efficient. Experiments are, by definition, more likely to result in failure. For example, Merck and Johnson & Johnson constantly experiment to develop new drugs, but not every experimental drug they develop is ultimately successful. Similarly, constant innovation and changes to work processes can result in internal turmoil and disruptive work flow.

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Proposition 5. A strong organizational learning orientation will increase turmoil in the workplace, increase error rates, and reduce efficiency in the short term. Organizations must balance their learning and performance orientations to match the amount of situational uncertainty faced. A typical approach to learning is to focus on a few key problem areas that are encountered repeatedly. These problems can be analyzed systematically as they are routine and low in uncertainty (Sitkin et al., 1994). Such problems are easily tackled using a performance orientation with a single-loop learning process. Thus, performance orientations are well-suited for stable or routine situations. On a related point, Benner and Tushman (2003) argue that firms must focus on process management, which increases incremental change but reduces innovation, when the environment is more stable. Learning orientations, however, provide new insights by going beyond typical approaches, yielding deeper forms of learning and process innovation that can enhance flexibility and adaptability. As Sitkin et al. point out, double-loop learning might be better suited to uncertain and nonroutine conditions (e.g. identifying new product domains). Similarly, Benner and Tushman (2003) suggest that firms must engage in exploratory behaviors in environments characterized by technological ferment and uncertainty. For organizations to exploit or take advantage of innovations, they must implement and retain them long enough to reap the harvest of the seeds they have sown. Thus, organizations that have strong learning orientations may experience lowered initial efficiency and higher error rates, but they must retain the profound changes resulting from innovations long enough to increase efficiency and reduce error rates. Thus, the negative effects of experimentation and innovation can be offset in the long-term because of the resulting double-loop learning, and adaptability, which should lead to a long-term competitive advantage for organizations facing dynamic and turbulent environments. Proposition 6. Organizations must balance learning and performance orientations across tasks and innovations. Learning orientations are more effective for uncertain and nonroutine conditions with a long time horizon. Performance orientations are more effective for stable and routine conditions with a short time horizon. Sources As organizations develop, they can take on a variety of different structural forms along with corresponding strategies (Miles & Snow, 1978; Thompson,

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1967; Woodward, 1965). These strategies will be driven in part by the environment, critical stakeholders, previous successes/failures resulting from attempts at experimentation and innovation, and the organizational time horizon (short-term or long-term). Over time, organizational strategies and configurations will influence human resource management practices and the climate and culture of the business. For example, Chatman and Jehn (1994) found that organizational culture was associated with industry characteristics, and various researchers have suggested that various human resource management practices would be associated with various business strategies (Miles & Snow, 1984; Wright & McMahan, 1992). One source of an organization’s learning and performance orientations is its business strategy. Miles, Snow, Meyer and Coleman’s (1978) strategic typology that includes defenders, prospectors, analyzers, and reactors is useful in illustrating the role of strategy. Defenders include organizations that focus on reliability, competitive pricing, and high quality to maintain dominance in a narrow domain. Wal-Mart and Home Depot are good examples of defender organizations. These organizations strive to aggressively prevent competitors from entering their ‘‘turf’’ and compete aggressively with existing competitors. Based on this description, we expect that defenders would have a strong performance orientation and a weak learning orientation because they focus on high levels of efficient and error-free performance. Prospectors follow a strategy of product innovation and new market creation. Miles et al. (1978) argue that maintaining a reputation as an innovator may be as important as, or even more important than maintaining high profitability. Prospector domains are broad and in a continuous state of development. Continual change, adaptability, and innovation are some of the major tools used by prospectors to maintain a competitive advantage. Given these characteristics, prospectors should have a strong learning orientation and a weak performance orientation because they focus on experimentation, innovation, and profound change. Because they are continually developing and bringing new products to market, 3M is a good example of an organization pursuing a prospector strategy. Analyzers are a unique combination of prospectors and defenders. A true analyzer attempts to minimize risk while maximizing opportunities for profit. Thus, analyzers seek a position somewhere between the innovation of prospectors and the reliability and stability of defenders. Analyzers must learn how to maintain an equilibrium between technological flexibility and technological stability. As a result, analyzers typically have different structures within the organization that pursue innovation and production.

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Analyzers are more likely to show balance in the learning and performance orientations because they must focus on innovation and productivity at the same time. They are also more likely to exhibit more variance across different functional units within the organization because these units will have different goals (e.g. exploration versus production). Examples of organizations implementing an analyzer strategy include IBM and Microsoft. Prospectors, analyzers, and defenders can be proactive with respect to their environments but each does so differently. In contrast, reactors are those organizations that are primarily responding to environmental change and uncertainty. Such organizations often have little control over critical resources or fail to respond to changes in the organizational environment. Kmart is an example of a firm pursuing a reactor strategy. It has shown a tendency to react to what its competitors are doing rather than proactively identifying and pursuing a niche of its own. These organizations do not pursue a singularly consistent strategy, and are often characterized by constant turmoil and chaos. Reactors are unlikely to show a consistent learning or performance orientation, or they will be low in both orientations because they have no clearly defined strategic direction. Taylor and Giannantonio (1993) argued that these organizational characteristics will influence human resource management practices and employee adaptation. Similarly, we believe that these strategic configurations will influence the climate and culture for learning and performance. We expect that organizational strategic configurations will have a strong impact on learning and performance orientations within the organizations. Proposition 7a. In general, prospectors will exhibit stronger learning orientations than will analyzers, defenders, and reactors. Proposition 7b. In general, defenders will exhibit stronger performance orientations than will prospectors, analyzers, and reactors. Proposition 7c. In general, analyzers will exhibit a balance between both orientations, or alternatively, different functional areas will exhibit particular strength in either learning or performance orientations. Proposition 7d. In general, reactors will be below average or more inconsistent in both orientations over time than will prospectors, analyzers, and defenders. Another source of organizational learning and performance orientations is the organization’s top leadership. Leaders and their personalities can have a strong influence on the climate and culture of their organizations

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(Ostroff et al., 2003; Schein, 1990; Staw, 1991; Waldman, 1994) and the associated learning processes of the organization (Vera & Crossan, 2004). For example, researchers have demonstrated that the personality of CEOs is related to organizational characteristics. CEOs who are higher in internal locus of control, the extent to which individuals perceive they have control over the events in their lives, are more likely to lead organizations that pursue innovations, take greater risks, and set trends rather than follow them (Miller, Kets de Vries & Toulouse, 1982). CEO need for achievement has also been found to relate to organizational formalization, integration, and centralization, particularly for small firms (Miller & Droge, 1986). Staw (1991) suggested that such research could be extended to include other personality dimensions as predictors of structure and internal management of a firm. We believe that the learning and performance orientations of members of top management will have a strong influence on the orientation of the organization. CEOs with strong performance orientations are more likely to create an organizational culture valuing high levels of reliable and error-free performance. In contrast, CEOs with strong learning orientations are more likely to pursue innovation and value creativity and experimentation. Thus, we expect that leader personality will have a strong effect on organizational orientations. The influence of top leaders and their personalities on the climate and culture of organizations is likely to be enhanced if the firm is small or young. The smaller an organization, the easier it is for top management to communicate and reinforce desired values and behaviors, and the greater the resulting influence top management can have on the organization’s culture and values. If employees are only one or two levels from the top, or if the organization is small enough that the CEO is everyone’s direct supervisor, the values and priorities of the leader are likely to be better known by employees and better reinforced by the leader than if the organization is large and has many hierarchical levels between employees and the leader. Because it is more difficult to change an established culture than it is to shape than the emerging cultures of a new organization, the organizational orientations of younger firms are also more likely to be influenced by current top management. Proposition 8. Organizational learning and performance orientations will reflect the orientations of the organization’s top leaders. This effect will be particularly strong for small or young firms. One explanation for the development of shared perceptions of climate was offered by Schneider (1983, 1987) through the attraction–selection–attrition

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(ASA) framework. In this approach, organizational processes such as selection into the organization, and individual processes such as attraction to or attrition from the organization, create a relatively homogenous population in that organization. For example, similar individuals are attracted to a particular setting (Schneider, 1987). Individuals are more likely to desire an innovative and creative environment if they are themselves innovative and creative. Similarly, individuals who admire diligent performance and high quality will want to work in environments that value these outcomes. Also, current organizational members are more likely to select individuals who share common interests and goals, and because the person–environment fit is tighter, the individuals are less likely to leave the organization. These individuals are socialized in similar ways (Louis, 1990), and they share their interpretations with others in the setting (Kozlowski & Doherty, 1989). Over time, reality becomes socially enacted (Weick, 1979), and people come to view their environment in similar ways. According to this view, members of an organization have similar perceptions and attach similar meanings to organizational events because the members are in some ways similar to each other and they share similar experiences (Schneider, 1987; Schnieder & Reichers, 1983). Proposition 9. Individuals will be more attracted to, more likely to be selected into, and less likely to leave organizations that are similar to their individual dispositional learning and performance orientations. Group Orientations Although organizations can have general climates or cultures for learning and performance, subgroups within an organization may vary in their focus on learning, performance, and responses to failures (Cannon & Edmondson, 2001). As Chatman and Jehn (1994) note, focusing on organization-level values does not necessarily deny the existence or importance of subcultures within a firm (e.g. Jerimer, Slocum, Fry & Gaines, 1991; Ostroff et al., 2003; Sackmann, 1992). Emergent properties of interaction in groups create intricacies not apparent at the individual or organizational level. Thus, groups have characteristics that are distinct from individuals and organizations, and we expect that groups within some organizations will exhibit different norms, climates, and subcultures. These group characteristics can have a strong influence on individuals (Hackman, 1976, 1992). The term ‘‘group’’ implies a focus on meso-level entities that bridge the gap between individual and organizational relationships. Although we

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recognize there are differences among various types of meso-level units like departments, teams, groups, committees, and functional areas, these differences do not preclude an exploration of the impact of learning and performance orientations on various processes and outcomes. For the purposes of discussion, we define a group as a unit with three or more members, who are in some form of dynamic interaction with one another, and who share a clearly defined purpose. Defined Group learning orientation is a meso-level climate variable that indicates the degree to which the group is oriented toward improving capabilities, acquiring new skills and knowledge, and mastering new or uncertain environments. Group performance orientation is a meso-level climate variable that reflects the degree to which the group is focused on demonstrating high performance and avoiding failure as unit. Group climates will influence normative expectations for behavior that drive member interactions, forming group-level characteristics (Bunderson & Sutcliffe, 2003; Hackman, 1992). Thus, group learning and performance orientations refer to characteristics of the group as an intact unit. Although other conceptualizations of group learning and performance orientations are possible, our focus is on the group as an entity relative to other groups. Implied in our definition is some level of consensual valuation of learning and performance as outcomes of group endeavors. Systematic between-group differences have been found on various group climate-like variables. For example, George (1990) found that groups varied in their affective tone, which is a similar or homogenous affective reaction within a group. She found that positive affective tone was negatively related to absenteeism and negative affective tone was negatively related to prosocial behaviors. Researchers have found, among other things, that group climates systematically differ on task orientation and support for innovation (Agrell & Gustafson, 1994; Anderson & West, 1998). Using data from 51 work groups in a manufacturing company, Cannon and Edmondson (2001) found that people hold tacit beliefs about appropriate responses to mistakes, problems and conflict, and these beliefs are shared within and vary between organizational work groups. They also found that these shared beliefs vary in the extent to which they adopt a learning approach to failure and the extent to which they endorse openly identifying, discussing, and analyzing mistakes, problems, and conflicts. These beliefs, in turn, were found to influence group performance. Bunderson and Sutcliffe (2003), in one of the few studies to directly examine the impact of group learning orientations,

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found evidence for systematic between group differences in learning orientations, and these orientations in turn, influenced performance in a nonlinear fashion. Outcomes Groups with strong performance orientations will focus on productivity and obtaining results. As a result, they will work hard to maximize immediate efficiency and will rapidly identify routines and interactions that become implanted as the correct way of doing things. These interactions create networks of roles that influence group effectiveness (Kozlowski, Gully, Nason & Smith, 1999). As Gersick and Hackman (1990) note, groups easily slip into habitual routines that continue unquestioned. These routines streamline behavior and increase efficiency because the time and energy needed to coordinate among members is kept low. Over time, single-loop learning will result in incremental improvements to the group routines and network of interactions, improving efficiency and reliability. To the degree that stable role configurations and routines are appropriate to meet task demands, a strong performance orientation will result in increased effectiveness. This is most likely when the task is simple, well-learned, or low in demands for intergroup cooperation. Appropriate role networks and task routines are most likely to be easily identified on simple tasks. Thus, groups can get to work quickly and effectively on simple tasks because there are a limited number of possibilities for structuring the work. Similarly, when group tasks are well-learned, members know how to interact and what tasks need to be accomplished. In such situations, a strong group performance orientation can facilitate error-free transfer of learned skills to real-time performance environments (Kozlowski, Gully, McHugh, Salas & Cannon-Bowers, 1996). Research suggests that relationships external to a group are crucial for understanding group effectiveness (Ancona & Caldwell, 1992, 1998; Argote & McGrath, 1993). Such relationships involve boundary-spanning activities, including sharing of technical information across group boundaries, persuasive action with external constituents, intergroup cooperation, and resource allocation. Unfortunately, little theory and research has addressed the effects of external relationships on group outcomes. We believe that task demands for intergroup cooperation will have a strong effect on the relationship of group performance orientations to group performance. When demands for resource and information sharing across groups are low, each group is free to compete with the other groups with little detrimental effect on its performance. Thus, groups that have a strong performance

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orientation are more likely to compete with other groups, spurring them on to high levels of performance. As a result, group performance orientation will be positively related to performance when intergroup cooperation demands are low. This will be particularly true on simple or well-learned tasks. Proposition 10. Strong group performance orientations will result in more rapid establishment of routines for group interaction that can be incrementally modified through first-order learning than weak group performance orientations. Stronger group performance orientations are most likely to result in greater effectiveness when the group task is simple, welllearned, or low in demands for intergroup cooperation. Strong group performance orientations can have a negative effect on group effectiveness, particularly when the task is dynamic, complex, novel, or high in demands for intergroup cooperation. Groups quickly establish habitual routines that lead to automatic processing (Gersick & Hackman, 1990; Ginnett, 1990). However, strategic failures are often associated with automatic information processing (Starbuck, 1992). When behavior has become habitual, members are unlikely to attend to changes in the broader performance context. Once established, groups are generally unlikely to question current routines and norms, and this problem is exacerbated with reduced information processing. This effect is most likely to occur for groups with strong performance orientations because they will be concerned about reductions in efficiency and productivity, and they are more likely to constrict the processing of information necessary to make effective adjustments. Performance-oriented groups are more likely to view environmental change as a threat because the possibility of failure increases as the environment becomes less predictable. Staw, Sandelands, and Dutton (1981) suggested that perceived group threats are associated with reductions in information processing. Similarly, Gladstein and Reilly (1985) found that increased group threats led to restrictions in information processing. The appropriateness of network configurations of roles and current routines must be reevaluated and altered when necessary, and the likelihood that appropriate solutions are immediately identified is reduced on complex and dynamic tasks. These task characteristics can make it difficult to ascertain whether current solutions are appropriate and they require continuous environmental scanning and information processing. Without reevaluation, blind and repetitive application of current procedures is likely to lead to lower levels of group effectiveness. Indirect support comes from work by Bereby–Meyer, Moran, and Unger–Aviram (2004). They found that groups focused on performance goals with low learning values and no

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team discussions showed negative transfer on an integrative negotiation task when conditions changed. When faced with a new task component, not experienced before, these teams performed worse than teams that had no experience at all. More performance-oriented groups are more likely to compete with other groups to demonstrate high levels of performance. Although such orientations can have positive effects when demands for intergroup cooperation are low, they can have negative effects when demands for intergroup cooperation are high. As these groups compete for resources and information, each will try to outdo the other, creating conflicts between groups. This can eventually lead to breakdowns in work processes and performance. For example, we once consulted with a manufacturing firm that used three shifts of teams throughout the day that made parts on lathes. Teams were rewarded on the basis of how many parts were manufactured by each team as unit, fostering a strong group performance orientation. The evening crew set the configuration of the lathe to meet their preferences, which was a different configuration from the way the morning crew preferred. Each crew left the lathe set to their preference, costing the other shift time in redoing the setup. To make their own processes more efficient, the evening crew began to weld the lathe tool in their preferred configuration, which the morning crew would break and reset each day. Each lathe tool cost several hundred dollars and would take more than an hour to replace, reducing the morning crew’s productivity while they replaced it. The strong performance orientation and competitive production atmosphere between shifts created obvious inter-team conflict and reduced effectiveness. Proposition 11. Stronger group performance orientations will result in automatic and restricted information processing and reduce intergroup cooperation as compared with weaker group performance orientations. Stronger group performance orientations are most likely to result in reduced effectiveness when the group task is complex, novel, or high in demands for intergroup cooperation. Group learning orientation refers to the group’s desire to improve group capabilities and acquire new group skills or knowledge. Groups high in learning orientation are more likely to experiment, restructure role relationships, accept diverse points of view and challenge group norms and routines. This will lead to greater group creativity (Woodman et al., 1993). It will also create a willingness to challenge the way things are accomplished, eliminate inappropriate routines, discard counterproductive norms, and reduce the likelihood of groupthink. Groupthink is a breakdown in decision

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making due to in-group pressures that results from a lack of reality testing and reevaluation of assumptions and values (Janis, 1982). Increased experimentation and risk taking is likely to lead to double-loop learning that will result in improved innovations, new group processes, and a repertoire of different role configurations (Kozlowski et al., 1999). This will increase the flexibility and adaptability of the group in the long term. Supporting this view, Bereby-Meyer et al. (2004) found that high learning groups (learning goals, high learning values, and team discussions) outperformed low learning groups (performance goals, low learning values, and no team discussions) on an integrative negotiation task when conditions for task performance changed to become more complex. Mistakes and failures are more likely to be interpreted as opportunities for learning and improvement in learning-oriented groups, so the group is more likely to maintain collective efficacy. Collective efficacy is a belief among members that the group can be effective (Bandura, 1982). It is related to other similar constructs like group potency (Shea & Guzzo, 1987a, b) and team efficacy (Gully, Incalcaterra, Joshi & Beaubien, 2002; Lindsley, Brass & Thomas, 1995), which have been shown to affect collective performance (Campion, Papper & Medsker, 1996; Gibson, 1999; Guzzo, Yost, Campbell & Shea, 1993). Thus, collective efficacy is an important outcome of a strong group learning orientation. Proposition 12. Stronger group learning orientations will result in a greater willingness to challenge current norms, accept diverse points of view, and view failures as learning opportunities than weaker group learning orientations. This will increase creativity and collective efficacy and reduce the likelihood of groupthink. Strong learning orientations can have negative effects as well. Groups with a strong learning orientation are more likely to engage in experimentation, risk-taking, and self-questioning than groups with a weak learning orientation. As a result, they are more likely to reevaluate current norms and alter current routines and procedures. Reevaluation and alteration of the status quo invite the possibility of disagreements and conflicts among members, which can be beneficial or counterproductive depending on whether a task is routine or not (Jehn, 1995). Conflict is costly in time and effort because it hinders members’ abilities to focus on the task as opposed to questioning internal assumptions and procedures. As noted by Gersick (1989), groups that continue to discuss and explore ideas over extended periods are unable to move on to more productive forms of interaction. Thus, the reevaluation of norms and renovation of current procedures take time, and risk-taking

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and experimentation potentially lead to mistakes and failure. Supporting this perspective, using a sample of management team members from 45 business units in a Fortune 100 consumer products company, Bunderson and Sutcliffe (2003) found that it is possible for learning-oriented teams to compromise performance in the near term by overemphasizing learning, particularly when they have been performing well. Although strong learning-oriented workgroups might have higher adaptability and efficacy in the long term, it is likely that they will have less efficiency and more errors in the short term. Proposition 13. Stronger group learning orientations will lead to a focus on process rather than outcomes, increase the probability of group conflict, and reduce efficiency compared with weaker group learning orientations. Sources Potential sources of group learning and performance orientations include member self-selection, functional purpose, feedback and reward systems, and group leaders. Although organizations may express general patterns of learning or performance orientations, subgroups within the organization may vary. This can occur partly because individuals tend to be attracted to groups made up of members similar to themselves in some way (Tsui & O’Reilly, 1989). Similarly, group selection practices tend to favor applicants most similar to current members, and ill-fitting members tend to leave. This results in a reduction of diversity of group members (O’Reilly, Caldwell & Barnett, 1989) that is similar to the ASA process observed at the organizational level (Schneider, 1987). Proposition 14. Individuals will be more attracted to, more likely to be selected into, and less likely to leave groups that are similar to their individual dispositional learning and performance orientations. Differences in learning and performance orientations may also result from differences in a group’s functional purpose. This context is most likely in analyzer organizations because they have to balance both learning and performance orientations. One easy way to achieve this balance is by having subunits perform different functions. For example, an organization that has an established and stable market position may try to simultaneously develop new product lines. Its manufacturing division will emphasize high levels of productivity, quality, and efficiency but its research and development division will emphasize experimentation, innovation, and active risk taking. Similarly, a marketing group with incentives tied to customer sales will focus

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on sales productivity whereas an advertising group, with an emphasis on product image enhancement, will be focused on creativity. Proposition 15. Group learning and performance orientations will be influenced by the group’s functional purpose. Groups performing routine or stable tasks that require high levels of productivity, quality, or reliability will tend to have a stronger performance orientation than groups performing novel or dynamic tasks. Groups performing novel or dynamic tasks that require high levels of creativity, innovation, and adaptation will tend to have a stronger learning orientation than groups performing routine or stable tasks. Just as feedback and rewards can affect individual attitudes and behavior, we expect they will also influence group attitudes and behavior. Group feedback and rewards that are oriented toward outcomes are more likely to evoke a performance orientation, particularly when it involves a normative comparison among groups. Such a system will foster competition among groups that focuses on immediate outcomes and bottom-line performance. In contrast, feedback and rewards that are process oriented are more likely to result in a learning orientation, particularly if the system is developmentally oriented toward each group’s particular strengths and weaknesses. Proposition 16. Outcome feedback will increase group performance orientation and process feedback will increase group learning orientation. Cross-Level Interactions Individuals within the same workgroup may be similar to or different from their organization and co-workers in their tendencies to be learning and performance oriented. Individuals’ perceptions of the learning and performance orientations of their workgroups or departments may also influence the individual’s behavior in combination with their personal learning and performance orientations. For example, if I believe my supervisor is very performance oriented and discourages risk-taking behaviors (even if they might lead to the discovery of a more productive process), then I am unlikely to try a new approach even if I have a learning orientation. This response would be particularly likely if that supervisor controls salient rewards. Similarly, individuals with a high learning and a low performance orientation are not as likely as individuals with a high performance and a low learning orientation to perform well in an organizational context that promotes production efficiencies and low error rates. Kristof-Brown and

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Stevens (2001) found congruence of personal learning and performance orientations with team orientations affected individual satisfaction and contributions. Woodman et al. (1993) link organizational creativity to individual (e.g. personality, knowledge), group (e.g. norms, diversity, cohesiveness), and organizational (e.g. culture, strategy, structure, rewards) characteristics. For example, they discuss how individual characteristics interact with social influence processes and environmental influence processes at both the group and organizational levels. The instigation and operation of learning and performance oriented tendencies on the part of one individual may impact other individuals in the group, influencing group learning and performance orientations. Similarly, group learning and performance orientations may serve to suppress individual group members’ differences in these tendencies by reinforcing behaviors consistent with the group’s orientations. The context within which individual and group behaviors are enacted is created by the complex interactions of individual, group, and organizational characteristics. The lack of implementation of organizational initiatives may be partially due to factors such as a mismatch between individual or group orientations to learning and performance and the organization’s orientations. Proposition 17. Inconsistency between the organization’s orientations to learning and performance and the orientations of individuals and groups in the organization will lead to the implementation failure of organizational initiatives. Summary This model is the first to link individual, group, and organizational learning and performance orientations into a common framework. To some degree, research findings at one level of analysis can be applied to a different level of analysis; this occurs through the embedding of the constructs in a nomological network across levels. A primary advantage of a multilevel perspective on learning and performance orientations is that individuals, groups, and organizations are not treated as separate conceptual categories, but as parts of a whole, each affecting and being affected by the other. As 10–30% of the variance in individual responses may be explained by a change of organizational setting, this is an important consideration (Herman & Hulin, 1972; Rousseau, 1977, 1978). Another benefit of a multilevel perspective is that findings at one level may be applied to another level of analysis to

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generate testable propositions. Managerial interventions at one level may not be useful when affected by relationships at another level. Additionally, organizational interventions can have cross-level consequences, and the effectiveness of these interventions may be helped or impeded by factors occurring at other levels in the organization.

PROCESSES LINKING MULTIPLE LEVELS Leadership The effects of organizational learning and performance orientations on incremental and profound change are likely to be carried in part through group effects, which are influenced by the group’s leader. Group leaders and supervisors are a key source of feedback (Kozlowski et al., 1996). By implementing the feedback and reward systems of the organization, the leader influences the goals and values of the group. Emergent group norms might then make the group’s behaviors and values more permanent. Thus, work group leaders are likely to be key drivers of group learning and performance orientations. As Staw (1991) noted, middle-level executives influence the extent and manner by which organizational policies and strategies are enacted. Similarly, Gephart, Marsick, Van Buren and Spiro (1996) stated that leaders and managers have considerable power to create an effective learning environment by providing systems that encourage learning. They can enable the development of knowledge, skills, and abilities through personal development plans, job rotations, and assignments across divisions. Gephart et al. (1996) discuss how leaders and managers can create positive consequences for learning by including learning actions and outcomes in performance appraisals and by rewarding employees for learning from mistakes. Although any organization can be said to have a particular learning and performance orientation profile, different subunits and subgroups within the organization are likely to have different profiles, in part because of the differing values and behaviors of each subgroup’s leader. Imagine, for example, a research and development organization that has a strong learning orientation at the organizational level. Within the organization, the leader of one work group may have a stronger learning orientation than the leaders of the other work groups, resulting in a greater tolerance for risk taking and greater encouragement and rewards for employee learning and profound change in that leader’s group. A work group with a leader particularly low in learning orientation may receive less encouragement and

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reward from the group leader for innovation and profound change, resulting in fewer learning behaviors on the part of work group members. Similarly, a work group leader may have a stronger than average performance orientation for that organization, and encourage less risk taking and more incremental change in his/her work group than is the case in the work groups with leaders lower in performance orientation. Leaders have a great deal of influence over the behaviors and values that are exhibited by their work groups, reinforcing or counteracting the effects of organizational-level learning and performance orientations. Cannon and Edmondson (2001) found that effective coaching, clear direction and a supportive work context influence beliefs related to failure, supporting the notion that leaders can have a strong influence on learning and performance orientations. Proposition 18. Group leaders will have a strong influence on group learning and performance orientations. Leadership is an influence process whereby the leader seeks to have an impact on followers’ beliefs and values, resulting in subsequent behavior changes. Transactional leadership theories follow from contingent reinforcement theory (House & Mitchell, 1974; Podsakoff, Todor & Skov, 1982). Transactional leadership theories stress the importance of clear follower roles and goals, and ways in which the successful achievement of roles and goals will lead to favorable outcomes. The enhancement of self-efficacy and ideological values on the part of followers has been stressed as the key to transformational leadership (Bass, 1985; Eden, 1984; Gist, 1987). These elements are more characteristic of learning than performance orientations on the part of the leader. Howell and Avolio (1993) found that transformational leadership was positively related and transactional leadership was negatively related to business performance. They argue that in changing environments leaders may spend too much time focusing on meeting goals, achieving results, and controlling behavior as opposed to promoting freedom of action. Future research into the role of learning orientation in transformational leadership seems warranted. Sujan et al. (1994) found that managerial feedback and actions can influence salespeople’s learning orientation, which is positively related to sales performance. If leader feedback and reinforcement can influence individuals’ state learning and performance orientations, leaders have the potential to shape these orientations in their groups. Proposition 19. Leaders can influence subordinates’ learning and performance orientations through feedback.

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The degree to which leaders view situations as threats or opportunities may also be influenced by their learning and performance orientations. Leaders who view situations as threats have fears about the likelihood of a loss without a gain, and they have a concern about being underqualified. Leaders who view situations as opportunities perceive a high potential for gain, and they feel they have the autonomy and freedom to act (Jackson & Dutton, 1988). These perceptions are likely to be influenced by the leader’s orientation toward learning or performance. Leaders with a high learning orientation might be more likely to view environmental changes as opportunities to expand or improve, while highly performance oriented leaders might be more likely to view these same factors as potential failures and threats to performance. Research on the role of the leader’s orientation in the interpretation of ambiguous events might prove fruitful. Vera and Crossan (2004) argue that transformational leadership is more likely to lead to learning that challenges institutionalized learning and transactional leadership is more likely to lead to learning that reinforces institutionalized learning. Although there is no research linking dispositional learning and performance orientations to transformational and transactional leadership, it can be surmised that strong leader learning orientations are more likely to lead to challenges to institutional norms and routines whereas strong leader performance orientations are more likely to focus on reinforcement and improvement of institutional norms and routines. Furthermore, these dispositional leadership orientations are likely to act as filters of environmental data, influencing perceptions of environmental changes. Proposition 20. A strong leader learning orientation will be related to greater transformational leadership and a perception of environmental changes as opportunities as compared to a weak leader learning orientation. A strong leader performance orientation will be related to greater transactional leadership and a perception of environmental changes as threats as compared to a weak leader performance orientation. Upward Influence Processes In addition to leaders exerting downward influence on work groups to adopt particular learning and performance orientations consistent with those of the leader, upward influence process may also operate that translate individual-level learning and performance orientations to the group and organizational levels. Learning and performance orientations are climate

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variables that indicate the degree to which members of an organization believe they should focus on experimenting and learning and demonstrating high levels of performance, reliability, and competence. Because organizational learning and performance orientations manifest themselves through the behaviors, motivations, and perceptions of individuals, if enough individuals’ learning and performance orientations change, then so too will the group’s and organization’s learning and performance orientations. For example, the establishment of learning oriented tendencies at the individual level may have an impact on other individuals in the group. Previously, riskaverse employees who see a fellow group member trying new things and pursuing a more risk-tolerant learning strategy may be motivated to do the same, particularly if the initial member experiences success or high performance. An influential or charismatic individual may influence his/her fellow group members to change group norms regarding risk taking, learning, and change. Likewise, once an orientation is identified for particular groups, there are processes that enable these effects to flow from the group level to the organizational level. For example, if an external threat or change in the competitive environment causes enough groups in an organization to change from a pursuit of learning-oriented, profound change strategies to prefer more conservative, incremental-change strategies and behaviors, then the organizational climate is likely to become more performance- and incremental-change oriented as a result.

Strategic Human Resource Management Fundamental to the strategic human resource management perspective is an assumption that firm performance is influenced by the set of human resource management practices firms have in place. Recent empirical evidence supports this basic assumption (Arthur, 1994; Cutcher-Gershenfeld, 1991; Huselid, 1995; Huselid & Becker, 1996; MacDuffie, 1995). Strategic human resource management has been defined as ‘‘the pattern of planned human resource deployments and activities intended to enable the firm to achieve its goals’’ (Wright & McMahan, 1992: 298). For a resource to provide a sustained competitive advantage, the resource must (a) add positive value to the firm, (b) be unique or rare among current and potential competitors, (c) be imperfectly imitable, and (d) be unable to be substituted for with another resource by competing firms.

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Input-based competencies encompass an organization’s physical resources, capital resources, human resources, knowledge, skills, and capabilities that enable its transformational processes to create and deliver products and services that are valued by customers (Lado, Boyd & Wright, 1992). Achieving sustained competitive advantage depends upon the firm’s ability to utilize the existing stocks of resources and its ability to accumulate new resource stocks more efficiently and effectively relative to competitors (Mahoney & Pandian, 1992; Penrose, 1959; Prahalad & Hamel, 1990; Wernerfelt, 1984). Input-based competencies both influence and are influenced by managerial vision (Prahalad & Bettis; 1986; Prahalad & Hamel, 1990), ‘‘shape the scope and direction for the search for knowledge,’’ (Penrose, 1959: 77) and provide the ‘‘proverbial grist to the organizational mill for creating and delivering value to customers’’ (Wright & McMahan, 1992). Experimentation can be a critical element of an organization’s core competencies because it enables firms to create new frames of reference and heuristics that produce insights for problem-definition and solution (Argyris & Scho¨n, 1978; Hedberg, 1981). Dodgson (1991) looked at technology transfer and concluded that the ability to learn quickly is a key factor in the relative success of small and large firms in a rapidly changing environment, such as biotechnology. Because entrepreneurial talents are rare (Leibenstein, 1987), they are cultivated and nurtured over a long period of time, and are embedded in a firm’s historical context (Schein, 1983), the culture and processes of a learning oriented organization will be difficult to imitate, particularly in the short-term. Because they provide the impetus for resource mobilization and deployment, entrepreneurial skills have been said to be a nonsubstitutable strategic asset (Schein, 1983). It is likely that having a learning orientation is similarly a nonsubstitutable strategic asset. Tannenbaum (1997) found that organizations with the strongest learning environments also tended to exhibit the strongest overall organizational performance. The pursuit of process improvements such as zero defects can undermine the experimentation necessary for deeper innovation (Benner & Tushman, 2003). Sitkin et al. (1994) discuss the difference between total quality control (TQC) and total quality learning (TQL). TQC, including six sigma programs, is consistent with incremental change and a performance orientation. TQC consists of monitoring, benchmarking, exploiting existing skills and resources, increasing control & reliability, stimulating single-loop learning, providing incentives for error reduction and avoidance, and socialization practices designed to produce constructive conformity. TQL is consistent with a learning orientation and profound change. TQL involves scanning

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the environment for changes, exploring new skills and resources, increasing learning and resilience, stimulating double-loop learning, providing incentives for innovation, leadership that supports independent thinking, the provision of learning-related feedback, and socialization practices designed to produce creative autonomy. Pure organizational types are rare. The appropriateness of learning and performance orientations to organizations and human resource management practices may vary not only across, but also within organizations (Lepak & Snell, 1999). Complementary goals at multiple organizational levels and across the various groups and departments, and a balance between learning and performance are critical to obtaining and sustaining a competitive advantage. As noted, groups may have different orientations that are driven by their different functional purposes. In order for strategic human resource management initiatives to be effective, these different goals must be incorporated consistently throughout the human resource management system, affecting the recruitment, selection, training, and performance management systems across the different functional units. For example, research and development units may be rewarded for creativity and innovation, while manufacturing units may be rewarded for efficiency, productivity, and reliability.

ORGANIZATIONAL LEARNING In the 1995 National HRD Executive Survey, conducted by the American Society for Training and Development, 94% of respondents said that it is important to build a learning organization. A 1996 survey of almost 200 German companies, conducted by DEKRA Akademie with the Maisberger and Partner consulting firm, found that 90% consider themselves to be a learning organization, or in the process of becoming one (Gephart et al., 1996). An important question is, ‘‘What are they trying to learn?’’ Learning can be profound or incremental, and can be attached to different processes, human resource management systems, goals, and outcomes. The organization needs to define the type of learning it intends to adopt if it is to be successful. Incremental and profound change are produced by different processes. Learning is only part of the equation. Also important are innovation, implementation, and ultimate performance. Processes, inertial forces, and inabilities to adapt have been documented at the organizational level that are similar to the habitual routines which lead to automatic processing in

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workgroups (Gersick & Hackman, 1990; Hannan & Freeman, 1984; Louis & Sutton, 1991). Continual success decreases the active experimentation necessary for learning (March, 1976; Sitkin, 1992). A learning organization’s culture supports and rewards learning and innovation, promotes inquiry, dialogue, risk taking, and experimentation, allows mistakes to be shared and viewed as opportunities for learning, and values the well-being of all employees (Gephart et al., 1996). The contextual factors that influence learning styles include competitive strategies, organizational culture, industry and product life-cycles, and technology. The relevant sources and focus of learning can also vary with cycles related to the industry, technology, and product life (Gephart et al., 1996). ‘‘All learning takes place inside individual human heads’’ (Simon, 1991, p. 125), and an organization can only learn through its members. The link between individual and organizational learning thus occupies a critical position in any theory of organizational learning. Unfortunately, to date this relationship has only been explored by Kim (1993). Our model begins to link individual, group, and organizational learning processes into incremental and profound changes, but more theoretical attention and empirical research examining this issue from different perspectives are required.

IMPLICATIONS FOR PRACTICE Human resource management systems institutionalize the organization’s learning and performance orientations and shape and reinforce the learning and performance orientations of the other organizational levels. Human resource management systems can contribute to a sustained competitive advantage by facilitating the development of competencies that are firm specific, produce complex social relationships, are embedded in a firm’s history and culture, and generate tacit organizational knowledge (Barney, 1991; Bowen & Ostroff, 2004; Reed & DeFillippi, 1990; Wright & McMahan, 1992). Human resource management practices have been said to contribute to competitive advantage to the degree that they elicit and reinforce the set of role behaviors that result in lowering costs, enhancing product differentiation, or both (Schuler & Jackson, 1987). When attempting to influence organizational learning, it is important to consider the system as a whole. Learning is rooted in individuals, but individual learning, motivation, and behaviors are influenced by socially constructed reality against which experimentation, risk taking, and motivations are weighed. Snell and Dean (1992) found that firms that

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emphasized investments in specific human capital through selective staffing, comprehensive training, developmental performance appraisal, and equitable compensation were more likely to be successful in implementing advanced manufacturing technologies and total quality management systems than firms that did not emphasize such investments. Kazanjian and Drazin (1986) argued that human resource managers can contribute to the effective implementation of technological innovations by providing employees with a strong vision, linking idea generators with employees who serve as custodians of the existing knowledge base of the firm, and championing employees’ creative and innovative ideas to top management. Some human resource management systems (e.g. rules, policies, and procedures), performance appraisal systems, reward systems, task structure, and task difficulty can create ‘‘learned helplessness,’’ such that individuals feel passive, apathetic, and powerless after experiencing a series of frustrations or failures (Martinko & Gardner, 1982). Such human resource management systems are more likely to have a performance orientation because the focus is on the failure and not on the opportunity to learn (Diener & Dweck, 1978, 1980; Nicholls, 1984). Unfortunately, the strong internal and external demands for high levels of performance in most jobs and organizations are likely to create strong performance orientations. Without an accompanying learning orientation, a strong performance orientation can be maladaptive. For example, sales managers typically focus on short-term performance goals and seldom attempt to motivate or teach salespeople skills that benefit long-term performance. Sales people tend to seek favorable evaluations of their skills from managers and colleagues, which is usually supported by a commission-based compensation system. They are usually reluctant to experiment with new sales techniques, fearing that experimentation will result in poor outcomes and result in negative evaluations of abilities and performance (Sujan et al., 1994). Environmental conditions supported by human resource management practices can make a learning or performance orientation more salient. Research rooted in the ‘‘behavioral perspective’’ (e.g. Schuler & Jackson, 1987) has shown how human resource management systems can foster and facilitate innovation and profound change through eliciting and reinforcing employee role behaviors, such as creativity and innovation, a long-term orientation, cooperation and trust, risk taking, and tolerance of ambiguity. Accordingly, a human resource management system intended to reinforce a high learning orientation could emphasize idiosyncratic and interdependent jobs, participative decision making and problem solving, group-based work assignments, individual performance appraisal, specific compensation, and

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broad career paths. Selection, training, performance management, and compensation can each influence the learning and performance orientations of the groups and individuals in an organization, and will be discussed next.

Staffing Given that individuals are predisposed to various learning and performance orientation profiles to at least some extent, it might be possible to select individuals whose traits are consistent with the organization’s learning and performance orientations. Taylor and Giannantonio (1993) argue that prospectors, following a strategy of product innovation and new market creation, need staffs of creative experts who can think and produce at the frontier of new ideas. Such individuals are more likely to have a strong learning orientation. Similarly, analyzers seek a position somewhere between the innovation of prospectors in creating new markets and the reliability of defenders in maintaining stable ones. They require employees who are willing to take moderate risks and strive for new ideas, but who are also committed to maintaining the organization. Such organizations require individuals with a balance in both learning and performance orientations. Similarly, staffing systems could be designed to select organizational leaders whose dispositional learning and performance orientations match the strategies and orientations of the organization. The selection of a learning oriented leader may be particularly important during an organization’s transformation from a more performance oriented to a more learning oriented entity. Snow and Snell (1993) discuss more thoroughly how innovative selection systems that seek to identify individuals with the ability to learn and adapt to new situations can provide a firm with a competitive advantage. Team staffing might also be conducted with an awareness of the intended team goal orientation, and the orientations of the team members. Jackson, Stone, and Alvarez (1993) suggested that diversity uniquely shapes the experiences of individuals, dyads, and groups and Tsui, Egan, and O’Reilly (1992) found that increasing work-unit diversity on demographic variables like age and ethnicity was associated with lower levels of psychological attachment among group members. It is likely that diversity on characteristics like learning and performance orientation will have a strong impact on the interaction of team members. This is more likely on highly interdependent teams. Future research on the role of diversity in the team members’ orientations might also inform effective team staffing practices. A mix of both high learning oriented and high performance oriented members

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might help the team balance its need for production and incremental change with its goals to support and produce profound change.

Training Strategic objectives must serve to drive alignments throughout the organizational system (Beer & Walton, 1990), but must especially link to the personnel and training subsystem (Schuler & Jackson, 1987). Kozlowski and Salas (1997) discuss the importance of alignment between supportive contexts and well-articulated strategic objectives, both in the short- and longterm. An organizational culture that values innovative change, personnel development and continuous training can be developed if it is consistent with strategic objectives and organizational change. This can help create a general receptivity to training and the use of trained skills, which will support a learning orientation and profound change efforts. On the other hand, once skills are honed and highly refined, it is possible that a performance orientation will facilitate application and transfer of skills (Kozlowski et al., 1996).

Performance Management Typical approaches to performance management may constrict creativity, risk-taking, and exploration. Individual incentive programs assume that by enhancing individual task performance, the performance of the greater unit or organization will be enhanced. However, such programs may influence individuals to set or negotiate less challenging goals to obtain rewards. In fact, most people tend to set goals that stress only short-term productivity or short-term financial outcomes (Waldman, 1994). Human resource managers who focus on utility and analyzing ‘‘hard data’’ on employee performance may become trapped by a ‘‘paralysis by analysis’’ syndrome and enmeshed in a ‘‘numbers game’’ designing human resource management systems (e.g. performance appraisal) that overemphasize individual performance without regard for systems factors (Deming, 1986; Dobbins, Cardy & Carson, 1991). Deming (1986) discusses how traditional performance appraisals and associated reward mechanisms reward people who do well within the system, but do not necessarily support attempts to improve the system. Organizations interested in supporting profound change processes may be able to create performance management systems in which employees are evaluated on the basis of their ability to get approval from the appropriate people for a new

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venture, but the ultimate success of the venture has no effect on the individuals’ performance ratings. For example, Garvin (1993) described how an innovative steel producer kept expensive high-impact experiments off the scorecard used to evaluate managers. These experiments required prior approval from four senior vice-presidents, but once approval was given, failures had no impact on performance ratings. Amabile (1988) has identified conditions likely to inhibit innovation, and thus stifle profound change. These include an evaluation environment focused on criticism and external evaluation, severe time pressures for task accomplishment, and competition within work groups. These same factors are likely to create a performance orientation. Managers can influence performance-oriented individuals to innovate by providing support and noting individuals who engage in such activities. To support profound change, managers must be sure to not punish errors that occur during innovation, and should downplay immediate performance while stressing the importance of a longer-term increase in performance.

Compensation Reward systems that encourage personal development and skill utilization provide the concrete symbols necessary to create contexts that support training and skill (Beer & Walton, 1990). Reward systems that penalize and extinguish behaviors deemed critical to organizational success (e.g. innovation, risk taking, long-term orientation) may generate cognitive dissonance and heighten workers’ feelings of alienation, apathy, and resentment. Successful ongoing learning programs require an incentive system that favors risk taking. This creates a dilemma: organizations must maintain accountability and control over individual and group experiments while encouraging risk taking. Productive failure is not the same thing as unproductive success. Productive failure leads to insight, understanding, and adds to the knowledge base of the organization. Unproductive success is when something goes well, but nobody knows how or why (Garvin, 1993). Competitive reward structures, such as pay linked to a forced-distribution appraisal system, influence reactions to failure and perceptions of ability (Ames, Ames & Felker, 1977). Social comparison information influences self-evaluations for performance oriented, but not the self-evaluations of learning oriented individuals (Jagacinski & Nicholls, 1987). Providing outcome feedback and giving rewards for high performance is consistent with a performance orientation, while developmental feedback is consistent

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with a learning orientation (Butler, 1987, 1988; Kozlowski et al., 1996). Certain situations necessitate a focus on small wins and the reduction of errors. In uncertain situations, on the other hand, learning occurs through experimentation, mistakes, etc. Incentive systems designed to support profound change should reward thoughtfully planned, well executed, but inherently risky ventures that may lead to failures.

CONCLUSION The purpose of this chapter is to (1) use goal orientation to provide a parsimonious explanation of processes unfolding at multiple levels; (2) serve as a framework for reviewing and integrating theory and research on leadership, organizational learning, human resource management systems, and goal orientation; (3) highlight the consideration of levels of analysis when considering the effects of learning and performance orientations; (4) describe implications of learning and performance orientations for practice; and (5) illustrate reasons why there is no one best orientation or human resource management strategy for all situations, organizations, groups, or individuals. Learning and performance orientations must vary not only across organizations, but also within organizations and over a period of time. Different functional units should have different orientations to learning and performance, which influences the appropriateness of various human resource management strategies for each unit.

NOTES A previous version of this chapter was presented at the 14th annual Society for Industrial and Organizational Psychology Conference, Atlanta, Georgia, May 1999.

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JUSTICE IN TEAMS: A REVIEW OF FAIRNESS EFFECTS IN COLLECTIVE CONTEXTS Jason A. Colquitt, Cindy P. Zapata-Phelan and Quinetta M. Roberson ABSTRACT The use of teams has increased significantly over the past two decades, with recent estimates suggesting that between 50% and 90% of employees work in some kind of team. This chapter examines the implications of this trend for the literature on organizational justice – the study of fairness perceptions and effects in the workplace. In particular, we explore three specific research questions: (1) Will the justice effects observed in individual contexts generalize to team contexts and member-directed reactions? (2) Will the justice experienced by specific teammates have direct or interactive effects on members’ own reactions? (3) Will the justice experienced by the team as a whole impact reactions at the team level of analysis? Our review of almost 30 studies suggests that each question can be answered in the affirmative, illustrating that team contexts can magnify the importance of justice in organizations.

Research in Personnel and Human Resources Management Research in Personnel and Human Resources Management, Volume 24, 53–94 Copyright r 2005 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 0742-7301/doi:10.1016/S0742-7301(05)24002-1

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INTRODUCTION Consider the following scenario: An employee exits a performance evaluation session convinced that unfair treatment has just occurred. The employee received a lower than expected evaluation from a new boss, which makes the employee ineligible for anything more than a cost-of-living salary increase. The employee doubts the accuracy of the evaluation because the new boss has not been around long enough to accurately monitor and gauge job performance. The employee also wonders whether the boss has some sort of biased agenda, as the boss seems to treat other employees much differently. To make matters worse, the boss refused to explain the logic behind many of the ratings, arguing that such detailed explanations would take up too much time. In general, the boss was very disrespectful while communicating the disappointing results of the review. We can make a variety of predictions about what should happen next in this scenario based on the literature on organizational justice (see Colquitt, Greenberg & Zapata-Phelan, 2005, for a narrative review). The employee has doubts about distributive justice, the perceived fairness of decision-making outcomes. Distributive justice is fostered when outcome allocations adhere to relevant norms, such as equity (Adams, 1965; Homans, 1961; Leventhal, 1976). Concerns have also been raised about procedural justice, the perceived fairness of the decision making procedures that resulted in those outcomes. Procedural justice is fostered when procedures are consistent across persons and time, based on accurate information, unbiased, correctable (Leventhal, 1980), and afford individuals voice and control during the process (Thibaut & Walker, 1975). In addition, the employee believes that the results were not communicated fairly, in terms of the amount of dignity and respect shown (interpersonal justice) and the justifications and explanations offered (informational justice) (Bies & Moag, 1986; Greenberg, 1993). Doubts about these four forms of justice will likely harm several job attitudes, including job satisfaction, organizational commitment, and trust in the leader (see Colquitt, Conlon, Wesson, Porter & Ng, 2001, for a metaanalytic review). All else equal, the employee in the scenario should be less happy on the job, more likely to explore other employment opportunities, and more suspicious of the leader’s motives and intentions. Moreover, such treatment could also incite a number of negative job behaviors, including lower task performance, less citizenship behavior, and counterproductive or withdrawal behaviors (Colquitt et al., 2001). The employee might therefore be expected to struggle more frequently with job duties, restrict efforts

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devoted to aiding the organization, intentionally violate rules or standards, and waste time on the job. Other potential reactions to the scenario have been, until recently, relatively ignored by the justice literature. Will the unfair treatment experienced by the employee affect his or her behavior toward colleagues, who had nothing to do with the injustice? If he or she recounts the unfair experiences to those colleagues, will those reports of injustice alter colleagues’ attitudes and behaviors? Finally, if several of the employees in that unit ‘‘compare notes’’ about their treatment by the leader, will a shared consensus emerge regarding justice issues, and will that consensus affect the attitudes and behaviors of the overall unit? Each of these questions acknowledges that justice phenomena occur in collective contexts – that what happens to one employee may depend on (and influence) what happens to others. These sorts of questions are critical to our understanding of justice phenomena, because employees find themselves spending more of their work time in teams. Although estimates vary according to the type of team, it appears that somewhere between 50% and 90% of employees work in teams (Devine, Clayton, Philips, Dunford & Melner, 1999; Lawler, Mohrman & Ledford, 1995). That number has increased significantly over the past two decades, for a variety of reasons. For example, teams provide a forum for pooling complementary sets of technical expertise, potentially resulting in more innovative and adaptive task strategies (Kozlowski, Gully, Nason & Smith, 1999). Teams also allow for the sharing of multiple judgments and opinions, potentially resulting in more accurate, well-reasoned job decisions (Hollenbeck et al., 1995). Finally, teams can create a self-managing structure in which employees assume many of the operational and strategic responsibilities previously assumed by supervisors (Dunphy & Bryant, 1996). The purpose of this chapter is to provide a critical review of the emerging literature on justice in teams. Our review will cover a number of different questions, including whether justice effects generalize to team members and team-relevant outcomes, whether individual reactions are influenced by the justice experiences of teammates, and how justice phenomena emerge to impact aggregate, team-level outcomes. A conceptual roadmap for our review can be found in Fig. 1, which summarizes many of the topics covered in the following sections. In particular, we will use our review to defend the merits of the following claim: that team contexts enhance the importance of justice in organizations. Before beginning our review, however, it is important to acknowledge and discuss the primary differences between team settings and individual work contexts. An understanding of those differences is

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Team's/Teammates' Reactions to Authority Team's/Teammates' Justice Experiences

Research Question 3 Team's/Teammates' Reactions to One Another

Research Question 2

TEAM CONTEXTS

Team Member Reactions to Authority Team Member Justice Experiences

Research Question 1 Team Member Reactions to Teammates

Multiple Fairness Referents

Task Interdependence

INDIVIDUAL CONTEXTS

Social Contagion

Multiple Reaction Targets

Outcome Interdependence

Individual Justice Experiences

Social Identity

Individual Reactions to Authority

Fig. 1.

Organizing Model.

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Clear Social Comparisons

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needed to understand how team contexts could magnify the importance of fair treatment.

DIFFERENCES BETWEEN INDIVIDUAL AND TEAM CONTEXTS In describing the differences between individual and team contexts, we must first acknowledge that the term ‘‘individual contexts’’ is a bit of a misnomer. Virtually all employees are embedded in a larger organization, so justice experiences will almost always have a collective character to some extent. Indeed, many of the criteria for judging justice require the existence of other people. For example, Adams (1965) argued that distributive justice would be judged by comparing one’s own ratio of outcomes and inputs with those of some comparison other. The existence of some other is therefore required for judging distributive justice, from an equity theory perspective. Similarly, Leventhal (1980) argued that procedural justice would be judged by gauging the consistency of decision-making processes across persons and time. The former therefore requires the existence of other people in order for such a procedural comparison to be made. The Unique Qualities of Teams We would therefore argue that the existence of other people is not the critical difference between individual and team contexts. One concept that does begin to capture the difference is task interdependence, which is present when job duties require cooperation from multiple individuals to be completed (Wageman, 2001). Task interdependence is driven by a number of work features, including the rules and instructions that govern tasks, the physical technology used to do the work, and the degree to which requisite skills and abilities are spread across individuals. Moreover, these features of structural task interdependence can be supplemented or mitigated by behavioral task interdependence, which captures the amount of task-related interactions in which individuals actually engage (Kiggundu, 1981; Wageman, 2001). In this way, teams with a typically average level of structural interdependence can act in such a way that the actual level of interdependence is increased or decreased. Relative to individual work contexts, teams are also more likely to possess outcome interdependence, defined as the extent to which significant work

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consequences are contingent on collective task performance (Wageman, 2001). Outcome interdependence may exist in the form of goal interdependence, where team members are evaluated according to the achievement (or lack of achievement) of collective goals. Alternatively, outcome interdependence may take the form of reward interdependence, where bonuses or awards are doled out based on the team performance. In either case, team members work under a condition of common fate, providing increased incentive to engage in more frequent task-related interactions. In addition, teams and individual work contexts differ in issues of social identity, defined as the portion of an individual’s self-concept that is derived from their membership in a social group, together with the value attached to that membership (Tajfel, 1978). Teams possess a defined, formal boundary that makes them a meaningful unit to their members. As a result, a team member reacts to organizational events with the referent of ‘‘we’’ and ‘‘us’’ as well as the referent of ‘‘I’’ and ‘‘me’’ (Turner & Haslam, 2001). Because events that affect the team also exert some influence on individual identities, the importance of the group is magnified. However, social identities are likely to be weaker in more informal collectives, like departments or other work units, which possess less psychological meaning.

Effects on Justice Processes and Mechanisms Task interdependence, outcome interdependence, and social identity are facets of almost every definition of the term ‘‘team’’ (Cohen & Bailey, 1997; Guzzo & Dickson, 1996; Hackman, 1987). As a result, Fig. 1 uses those three concepts to distinguish individual contexts from team contexts. Moreover, Fig. 1 suggests that these three factors can alter the processes and mechanisms by which justice perceptions are formed and used to guide attitudes and behaviors. For example, the three team characteristics should alter the choice and operation of social comparisons used to form justice judgments. Adams’ (1965) equity theory details the key role that social comparisons play in judging distributive justice, and many of the same mechanisms are found in other justice conceptualizations (Crosby, 1984; Folger & Cropanzano, 1998, 2001). Given that a selected reference point becomes the lens for judging the fairness of decision events, the choice of a specific comparison other is critical. Unfortunately, scholars have struggled to predict the specific comparison others used to judge fairness, as individuals sometimes select counterintuitive reference points (Scholl, Cooper & McKenna, 1987). However, there

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are reasons to expect that the choice of comparison others will be more reliable and predictable in team contexts. Goodman (1977) advanced a model in which the choice of comparison other was directly affected by the availability of information about the referent and the perceived relevance or attractiveness of the referent. Such a model is applicable to team contexts, in which the increased interaction created by task and outcome interdependence should furnish more readily available information on fellow team members. In addition, the social identity aroused by teams should make members a more attractive option for comparisons. If team members do indeed choose one another for comparisons more reliably, then the importance of treatment differences within the team should be magnified. Because interdependence and social identities define the team as a meaningful unit, members will also have multiple referents for judging fairness: ‘‘is this fair to me?’’ and ‘‘is this fair to us?’’ While the justice literature has focused almost exclusively on the results of the former question, the latter one has received some attention. For example, the concept of fraternal deprivation is said to occur whenever an individual feels resentful about the treatment of his or her group, relative to some other group (Crosby, 1984; Rhodebeck, 1981; Runciman, 1966). Rhodebeck (1981) argued that this group-referenced feeling of injustice occurs when an individual: (1) acknowledges membership in a group; (2) sees that another group possesses X; (3) wants X for their own group; (4) feels that the group deserves X; and (5) feels that the group can attain it (p. 246; see also Crosby, 1984). The increased interaction brought about by task interdependence, together with the social identity fostered by the team experience, should increase the likelihood that group membership will be acknowledged. In addition, the common fate created by outcome interdependence should increase the likelihood that members will want beneficial treatment for their own team. Taken together, the three defining characteristics of teams should make feelings of fraternal deprivation more likely. Thus individual reactions may depend not only on individual justice perceptions, but also on ‘‘vicarious injustice’’ – cases in which team members are aware of unfair treatment toward fellow members (Kray & Lind, 2002). Of course, without research in team contexts, it remains unclear how impactful vicarious injustice can be relative to individual justice. In addition to providing multiple referents for judging fairness, these interdependence and social identity phenomena also provide a forum for social contagion. Degoey (2000) noted that ‘‘talk’’ about justice is an everyday social phenomenon, with such talk affecting how individuals think and feel about fairness issues. Justice can therefore become contagious through

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two processes: (1) cognitive contagion – where others’ opinions alter one’s perceptions of ‘‘reality’’, and (2) emotional contagion – where the feelings experienced by others spill over to one’s own attitudes. Social contagion therefore allows individual members’ own justice perceptions (whether derived from their own experience or vicarious experiences) to spread across the team. In detailing the contexts that should foster social contagion, Degoey (2000) discussed the concept of ‘‘ties’’ – connections among individuals as a function of friendship or work relationships. More specifically, Degoey (2000) argued that social contagion would be particularly intense when there were several ties within a network and when an individual occupied a central role in that network of ties. In team contexts, interdependence should increase the number of ties within the group as a function of workflow (Wageman, 2001), thus creating the mechanism for social contagion processes. As an example, this interdependence should allow members to engage in more frequent storytelling about justice experiences and to compare notes on the ‘‘justice reputations’’ of key organizational figures (Degoey, 2000). Past research has supported the importance of ties in the formation of justice perceptions by demonstrating that peers who occupy influential positions in an individual’s social network can influence their fairness judgments to a significant degree (Lamertz, 2002; Umphress, Labianca, Brass, Kass & Scholten, 2003). The unique characteristics of team contexts have one other effect on justice processes and mechanisms: they provide multiple targets for reactions to injustice. Most of the work on individual justice in work contexts has explored reactions to authority, a term which is used loosely in our review to refer to the source of the injustice. In some contexts, the authority is a particular manager or supervisor, while in other contexts the authority could be the larger organization or decision-making system. Given these varying forms of authority, what we term ‘‘reactions to authority’’ could range from increased trust in one’s supervisor to reduced levels of organizational commitment to more frequent counterproductive behaviors. The focus on authority-directed reactions in past research is natural, given that the injustice that is studied typically originates in the very same authorities. Past research has examined one reaction that is not necessarily authority directed: organizational citizenship behaviors (Organ, 1990). While citizenship behaviors may be explicitly directed at authorities (Masterson, Lewis, Goldman & Taylor, 2000; Rupp & Cropanzano, 2002), they may also be directed at coworkers. Coworker-directed reactions become particularly important in team settings, as some scholars have defined ‘‘teamwork’’ as

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discretionary citizenship behaviors targeted at fellow team members (LePine, Hanson, Borman & Motowidlo, 2000). In addition, given the task and outcome interdependence that characterize team contexts, virtually any kind of justice-related reaction will have some impact on the other members of the team.

Summary and Research Questions In summary, the task interdependence, outcome interdependence, and social identities found in team contexts alter the processes and mechanisms used to perceive and react to fair or unfair treatment. Social comparisons become more apparent, multiple referents for judging fairness are created, social contagion processes shape cognition and emotion, and reactions to injustice can be directed at authorities or one’s teammates. Taken together, these differences in justice judgments and reactions raise a number of research questions that will be discussed in the remainder of our review. As summarized in Fig. 1, these questions include: Research Question 1: Will the justice effects observed in individual contexts generalize to team contexts, and will those effects occur both for reactions directed toward authorities and reactions directed toward team members? Research Question 2: Will the justice experienced by other team members, or by the team as a whole, have direct or interactive effects on members’ own individual reactions? Research Question 3: Will the justice experienced by the team as a whole affect reactions at the team level of analysis?

GENERALIZING INDIVIDUAL JUSTICE EFFECTS TO TEAM CONTEXTS As noted at the outset, justice scholars have identified a wide variety of reactions to fair and unfair treatment in individual contexts. A meta-analytic review by Colquitt et al. (2001) showed that distributive, procedural, interpersonal, and informational justice were associated with important authority-directed attitudes, such as leader evaluation, organizational commitment, and trust. Those same dimensions also predicted behaviors that impact

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authorities, including job performance, supervisor-directed citizenship behavior, withdrawal from the organization, and counterproductive behaviors. Many of the earliest studies of justice in team contexts explored these very same relationships, allowing scholars to determine whether the justice effects found in individual settings generalized to collective settings. However, more recent studies have explored an additional question related to member reactions to justice in teams – specifically, does the treatment exhibited by authorities impact the way an individual member responds to others in the team? Table 1 summarizes the studies in both of these groups, arranged according to their use of laboratory or field designs. We grouped the studies by laboratory and field designs so that themes and commonalities in the streams of research would be more evident. For example, many laboratory studies focus on specific justice rules and utilize small teams taking part in a work simulation. In contrast, field studies often focus on a broader set of justice concepts while studying teams that range in size from very small to quite large. Regardless of their setting, the results of these studies are relevant to Research Question 1.

Laboratory Studies One of the first laboratory studies to examine justice effects in a team setting was conducted by Korsgaard, Schweiger and Sapienza (1995). The authors used 20 middle- and upper-level management teams from a Fortune 500 company participating in a strategic management training program, and conceptualized justice using the process and decision control criteria discussed by Thibaut and Walker (1975), which capture the ability to voice one’s opinion and influence the leader’s decision. Some team leaders were assigned to a high process control condition and were instructed to actively listen to all team members’ presentations and to acknowledge each member’s input. Other leaders were assigned to a low process control condition and were instructed to listen without comment and communicate their decision without considering other views. In the high decision control condition, leaders were instructed to alter their own final decision in accordance with any member’s decision, whereas in the low decision control condition, leaders were instructed to present their own decision as the final one. The results revealed a significant effect of process control and decision control on commitment to the leader’s decision and trust in the leader. Team members were more trusting of the leader and more committed to his or her decisions when higher levels of control were afforded. Moreover, the results

Authors

Type of Team

Studies Generalizing Justice Effects to Team Members. Justice Dimension

Outcomes

Results

(A) Laboratory Studies Korsgaard, Schweiger, and Sapienza (1995) Yorges (1999)

Phillips (2001, 2002)

Managers working in Procedural (process intact three–six-person control and teams performing a decision control) decision-making simulation Distributive Undergraduates in threeperson teams performing (payment level), problem-solving tasks procedural (arbitrariness) Undergraduates in fourProcedural (process person teams performing control and a decision-making decision control) simulation

Procedural (process Phillips, Undergraduates in threeperson teams performing control and Douthitt, and decision control) Hyland (2001) a decision-making simulation Simon and German undergraduates in Distributive five–eight-person (evaluation of Sturmer (2003) computer-mediated suggestions), teams suggesting interpersonal improvements to the (respectfulness) university

Authority-directed: Trust in team leader Commitment to decision

Control was positively related to fairness perceptions, which were positively related to trust and commitment

Member-directed: Citizenship behaviors

Distributive justice increased citizenship behaviors directed at other participants, though procedural justice had no such effect Control was positively related to fairness perceptions, which were positively related to satisfaction. Satisfaction predicted withdrawal

Authority-directed: Satisfaction with leader Member-directed: Withdrawal from team’s work Authority-directed: Satisfaction with leader Member-directed: Attachment to the team Member-directed: Collective identification Citizenship behaviors

Control was positively related to fairness perceptions, which was related to satisfaction and attachment Interpersonal justice increased collective identification and willingness to engage in citizenship. No effects for distributive justice

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Table 1.

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Table 1. (Continued ) Authors

Type of Team

Justice Dimension

Outcomes

Results

(B) Field Studies Dulebohn and Martocchio (1998) Shapiro and Kirkman (1999)

Colquitt (2001)

Employees working in Distributive, teams of at least 28 procedural members, manufacturing chemical products Employees working in Distributive (anticipatory), 10–14 person teams in computer and procedural textile companies

Distributive, procedural, interpersonal, informational Procedural (bias suppression)

Procedural (bias suppression)

Distributive, interactional

Authority-directed: Leader-member exchange Member-directed: Social loafing

Distributive and interactional justice were negatively related to social loafing. Leader-member exchange mediated the interactional effect

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Employees working in 10–15 person teams in automotive parts manufacturing plants McIntyre, Military employees Bartle, Landis, working in teams of and Dansby unspecified size (2002) Chansler, Employees working in Swamidass, E10-person teams in and Cammann a Harley—Davidson (2003) assembly plant Murphy, Wayne, Employees working in Liden, and E17-person teams in Erdogan a family-owned (2003) manufacturing firm

Authority-directed: Procedural justice was positively Organizational commitment related to commitment while Satisfaction with pay distributive justice was positively related to satisfaction Authority-directed: Anticipated distributive injustice was Organizational commitment negatively related to commitment Turnover intentions and citizenship and positively Member-directed: related to turnover intentions. Citizenship behavior Procedural justice mitigated these relationships Member-directed: Procedural, interpersonal, and Group commitment informational justice were Citizenship behavior positively related to the three Collective esteem outcomes, respectively Authority-directed: Procedural justice was positively Organizational commitment related to organizational Member-directed: commitment and work group Work group efficacy efficacy Member-directed: Procedural justice was positively Cohesion related to cohesion

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showed that most of these relationships were mediated by fairness perceptions. Specifically, perceptions of fairness partially mediated the effect of process and decision control on decision commitment, while fully mediating the effect of process control on trust. The results of this study were important because it was the first to show that relationships observed in individualized work contexts could generalize to team settings. Whereas Korsgaard et al. (1995) examined authority-directed outcomes, Yorges (1999) conducted a laboratory study assessing the impact of distributive and procedural justice on citizenship behaviors directed at other participants. The author randomly assigned an equal number of participants and confederates to either a two-group condition (confederates made up one group and the actual participants made up another group) or to an individual condition (everyone worked alone but in the same room). In addition, participants were randomly assigned to either a just payment condition (fair payment of $2) or an unjust condition, in which participants were told that they would be receiving $1 instead due to an arbitrary reason (the day of the week). During each experimental session, the confederates asked the participants’ for assistance with various tasks while subsequent helping behavior was videotaped. Participants in the two-group condition engaged in fewer instances of citizenship behavior (e.g. helping a confederate search for a lost contact lens, giving a confederate directions to the bathroom) and were less likely to share information with other participants. More importantly, participants assigned to the unjust payment condition were less helpful to the confederates, despite the fact that they were not responsible for the injustice. This finding suggests that individuals in team settings may transfer their reactions to injustice from the source of the treatment to the other members of a group. Phillips and colleagues conducted a stream of laboratory research that further examined the effects of justice on team member reactions. In two related studies with four-person teams of undergraduates, Phillips (2001, 2002) used a military decision-making simulation to operationalize the same control-based criteria used by Korsgaard et al. (1995). Specifically, team members who were given more control were more influential in the team leader’s decisions across multiple trials of the simulation. Similar to Korsgaard et al. (1995) findings, Phillips (2002) showed that member control was positively related to procedural fairness perceptions. Moreover, fairness perceptions were positively related to satisfaction with the leader, which in turn was negatively related to withdrawal behaviors during the task (e.g. daydreaming). This effect on withdrawal behaviors was notable given

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that it showed that fellow team members were in some sense penalized by the unfair treatment of an individual group member. In a subsequent study, Phillips, Douthitt and Hyland (2001) had undergraduates work on a dormitory decision-making simulation in three-person teams. Phillips et al. (2001) again examined control-based forms of justice by having a confederate leader vary the degree to which suggestions were considered and recommendations were followed in arriving at the final team decision. Phillips et al. (2001) showed that fairness perceptions mediated the relationship between member control and an authority-directed outcome (leader satisfaction) and a member-directed outcome (attachment to the team). Again, justice was shown to have implications for both the source of the treatment, in the form of the leader, as well as witnesses of that treatment, in the form of the other group members. The most recent laboratory study in this area focused exclusively on reactions to other team members, but with one critical difference. Simon and Sturmer (2003) examined the effects of fair treatment by fellow team members, as opposed to fair treatment from the team’s leader. Participants were seated at individual computer terminals and told they would be working with four other students to develop suggestions for improving the university. Once the participants completed the task, they were told that their suggestions were randomly chosen for evaluation by the other group members. In actuality, all participants were chosen for evaluation and the actual evaluations were preprogrammed. Interpersonal justice was manipulated by giving all participants feedback, allegedly from their group members, which was either respectful or disrespectful. Distributive justice was manipulated by the overall evaluation received from their group members. As predicted, results demonstrated that participants in the respectful condition had higher collective identification and were also more willing to engage in citizenship behaviors than those in the disrespectful condition. There were no significant effects for distributive justice. Moreover, collective identification mediated the relationship between interpersonal justice and willingness to engage in citizenship.

Field Studies One of the first studies to examine justice in actual teams in organizations was conducted by Dulebohn and Martocchio (1998). The authors surveyed employees from a Fortune 500 company’s chemical production facility immediately following a mandatory meeting. The company had initiated a

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work team incentive plan several years prior to the study. The plan created outcome interdependence by linking incentives to the performance of work teams, which had a minimum size of 28 members. The survey results demonstrated that perceptions of distributive justice were positively related to satisfaction with pay. In addition, perceptions of procedural justice were positively related to organizational commitment. Whereas Dulebohn and Martocchio (1998) explored justice in teams that were already in existence, work by Shapiro and Kirkman (1999) referenced fairness in the transition from individual to teamwork structures. Rather than focusing on actual distributive justice, Shapiro and Kirkman (1999) studied the anticipation of distributive injustice in the context of a teambased reorganization (see also Kirkman, Shapiro, Novelli & Brett, 1996). These authors surveyed newly formed (i.e., an average of 4 months) selfmanaged work teams from two Fortune 500 companies. As expected, the anticipation of distributive injustice was negatively associated with organizational commitment and positively associated with turnover intentions. This anticipatory form of justice also predicted citizenship behaviors in the form of helping directed at fellow team members. These relationships were mitigated when procedural justice was perceived to be high. As in Shapiro and Kirkman (1999), Colquitt (2001) explored the relationship between justice perceptions and helping behaviors directed at fellow team members. The author surveyed manufacturing team members as part of his validation of a new justice measure. More specifically, Colquitt (2001) explored the effects of procedural, interpersonal, and informational justice on helping behavior, group commitment, and collective esteem (i.e., the degree to which a member felt valued by his or her teammates). Data from over 300 individuals working in teams of 10–15 members demonstrated that perceptions of procedural justice were positively related to group commitment. Perceptions of interpersonal justice were positively related to helping behavior, while perceptions of informational justice were positively related to collective esteem. Again, members responded more favorably toward their teammates when they were fairly treated, despite the fact that those members were not responsible for the justice levels. More recent fieldwork has begun to link justice to many of the more venerable outcomes in the groups and teams literatures, such as work group efficacy perceptions, cohesion perceptions, and social loafing. For example, McIntyre, Bartle, Landis and Dansby (2002) used the Military Equal Opportunity Climate Survey (MEOCS) database to evaluate the effects of equal opportunity fairness on several outcomes. The MEOCS, which was originally developed to measure equal opportunity fairness perceptions in

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the military, seemed to primarily tap Leventhal’s (1980) bias suppression rule. The authors found that work group equal opportunity fairness was positively related to perceptions of work group efficacy, in addition to predicting authority-directed outcomes like organizational commitment. Chansler, Swamidass and Cammann (2003) were interested in antecedents of group cohesion in self-managed work teams at a Harley Davidson assembly plant. Within each plant, self-managed work teams were granted the responsibility and power to make decisions concerning plant operations. Surveys assessed cohesion and procedural justice using both global items as well as items pertaining to bias suppression. As expected, perceived procedural justice was positively related to member perceptions of cohesion such that members were more attached to one another when they were fairly treated by the authorities in the plant. Finally, Murphy, Wayne, Liden and Erdogan (2003) linked justice to another important construct in the teams literature: social loafing or the reduction of individual effort in a collective setting. The authors surveyed a sample of 124 team members from a midwestern manufacturing company on leader member exchange (LMX) and on distributive and interactional justice. In addition, supervisors were interviewed to assess the degree to which each employee engaged in social loafing. As predicted, results showed that both distributive and interactional justice were negatively related to social loafing. Moreover, LMX mediated the relationship between interactional justice and social loafing. These results imply that high perceptions of justice can help diminish negative member behaviors.

Summary With respect to Research Question 1, the studies summarized in Tables 1a and b illustrate that many of the justice relationships observed in individual work contexts do in fact generalize to team settings. Moreover, team settings increase the potential for member-directed effects, where the treatment received from an authority alters a member’s reactions to his or her teammates. This type of transference is one reason for the claim we advanced in the opening – that team contexts enhance the importance of justice in organizations. If an employee in an individual work context engages in withdrawal behaviors in response to an injustice, perhaps by daydreaming or taking longer breaks, those behaviors have some noticeable impact on the organization. In a team setting, however, those behaviors have a more dramatic impact because they can disrupt the workflow created

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by task interdependence while also endangering the collective goals created by outcome interdependence.

EXAMINING EFFECTS OF TEAMMATES’ JUSTICE ON MEMBER REACTIONS The studies reviewed in this next section illustrate a different reason why team contexts could enhance the importance of justice in organizations. Each of the studies explicitly acknowledges the multiple referents for judging fairness that are present within teams. Some of the studies explored whether the justice experienced by teammates had incremental effects on member reactions beyond the impact of their own justice perceptions. Other studies explored whether teammates’ experiences moderated the effects of members’ own justice experiences. Figs. 2 and 3 provide potential representations of both types of effects, for positive and negative reactions, respectively. The slope of the dotted line illustrates the typical main effect of a member’s own justice perceptions that would be seen in an individual context. A comparison of the altitudes of the high and low teammates’ justice lines illustrates a smaller main effect of teammates’ justice on member reactions, suggesting that reactions become more favorable when other members are fairly treated. Finally, the differences in slopes for the high and low teammates’ justice lines suggests that unfair treatment directed toward one’s teammates can neutralize the effects of members’ own justice levels (the white circle line), while fair treatment directed toward one’s teammates can have an amplifying effect (the black diamond line). Tables 2a and b summarize the laboratory and field studies that have examined one or more of the effects represented in Figs. 2 and 3. The results of these studies are relevant to Research Question 2. Laboratory Studies The first three studies reviewed in this section utilized groups of participants that lacked some of the defining characteristics of teams, including task interdependence and social identity. Still, their results are relevant to the question of whether the justice experienced by others impacts one’s own reactions. All three studies applied referent cognitions theory (RCT) to the potential effects of others’ justice levels (Folger, 1986a, b, 1987). RCT focuses on the feelings of anger that can accompany relative deprivation,

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JASON A. COLQUITT ET AL. 9 High Teammates' Justice

8 Main Effect

Member Positive Reactions

7 6

Low Teammates' Justice

5 4 3 2 1 0 i High

Low Member's Own Justice

Fig. 2.

Potential Representation of the Effects of Teammates’ Justice on Positive Reactions.

arguing that resentment about a decision event will be maximized when three conditions hold: (1) referent outcomes are high, meaning that a better state of affairs easily could be imagined; (2) the perceived likelihood of amelioration is low, meaning that there is little hope that future outcomes will be better; and (3) when justification is low, meaning that the event ought to have occurred differently. Ambrose and Kulik conducted two different studies that directly manipulated referent outcomes in the context of own and others’ justice (Ambrose, Harland & Kulik, 1991; Ambrose & Kulik, 1989). Both studies relied on a computer simulation where own process control was manipulated (either by allowing participants to choose task options or giving them input into specific task steps). The studies also told participants they would be paired with another subject with similar skills and backgrounds, though this pairing was

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9 8

Member Negative Reactions

7 6 5 4 3

High Teammates' Justice

2

Main Effect

1

Low Teammates' Justice

0 i High

Lo w

Member's Own Justice

Fig. 3. Potential Representation of the Effects of Teammates’ Justice on Negative Reactions.

in fact entirely fictional. Others’ process control was manipulated by giving participants information on the control afforded to the unseen other. Own and other outcomes were manipulated in the same fashion by giving information on task success (Ambrose & Kulik, 1989) or on a bonus earned (Ambrose et al., 1991). In the language of RCT, the manipulation of others’ process control impacted referent outcomes. For example, when an individual’s own control was low, finding out that another had been granted control made it easier to imagine a better state of affairs. The result should be more unfavorable reactions than would have occurred if control had been uniformly low, as in Fig. 2. What should the expected results be when one’s own control is high? In the context of RCT, referent outcomes work in one’s own favor when others’ control is low. This could be expected to result in the kind of guilt

Authors

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Table 2.

Studies Examining Effects of Teammates’ Justice.

Type of Team

Justice Dimension

Outcomes

Results

Undergraduates in twoperson groups with an unseen other, working on a computer simulation Undergraduates in twoperson groups with an unseen other, working on a computer simulation Dutch undergraduates in two-person groups with an unseen other, working on a computer pattern recognition task Undergraduates in threeperson teams working on a computer simulation with an unseen supervisor

Procedural (own and other process control), distributive (own and other outcome)

Authority-directed: Fairness perceptions Satisfaction perceptions Withdrawal

Procedural (own and other process control), distributive (other control)

Authority-directed: Fairness perceptions

Procedural (Study 1: own and other accuracy; Study 2: own and other process control)

Authority-directed: Fairness perceptions

Procedural (own and other denial of process control)

Authority-directed: Fairness perceptions Leader evaluations Task behaviors Member-directed: Commitment to group Authority-directed: Fairness perceptions Leader evaluation Member-directed: Coworker evaluation

Own procedural and distributive justice had significant main effects. Few significant main or interactive effects for others’ justice Own procedural justice had significant main effects. Own and other justice had significant interactions resembling Fig. 2 Own and other procedural justice had significant main effects. Own and other justice had significant interactions resembling Fig. 2 Own procedural justice had significant effects on several outcomes. Others’ procedural justice had smaller, though significant, effects

(A) Laboratory Studies Ambrose and Kulik (1989); Ambrose, Harland, and Kulik (1991) Grienberger, Rutte, and van Knippenberg (1997) Van den Bos and Lind (2001)

Kray and Lind (2002)

Undergraduates in twoperson groups with an unseen other, working on a computer simulation with an unseen supervisor

Procedural (own and other denial of process control), interactional (respectfulness of feedback)

Own procedural justice had significant effects on several outcomes. Others’ procedural justice had smaller, though significant, effects

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Lind, Kray, and Thompson (1998)

Procedural (Study 1: own and other global selfreport; Study 2: own and other process control)

Authority directed: Fairness perceptions Member-directed: Performance of team role Cooperation with others Conflict perceptions

Own procedural justice had significant main effects. Own and others’ justice had significant interactions resembling Fig. 2

Mossholder, Bennett, and Martin (1998)

Employees working in Enine-person teams in bank branches

Procedural (own and group average)

Authority-directed: Organizational commitment Job satisfaction

Naumann and Bennett (2000)

Employees working in 3–14-person teams in bank branches

Procedural (own and group average)

Authority-directed: Organizational commitment Member-directed: Citizenship behaviors

Liao and Rupp (2005)

Employees working in Enine-person teams from different companies in different industries

Procedural (own and group average), interpersonal (own and group average), informational (own and group average)

Authority-directed: Organizational commitment Supervisor commitment Citizenship behaviors

Own justice was significantly related to both outcomes. The group’s average justice explained incremental variance in job satisfaction Own justice was significantly related to both outcomes. The group’s average justice explained incremental variance in citizenship behaviors Own justice was significantly related to almost every outcome. The group’s average justice explained incremental variance in approximately half the outcomes

(B) Field Studies

Justice in Teams: A Review of Fairness Effects in Collective Contexts

Study 1: Undergraduates in five-person class project teams; Study 2: Undergraduates in four-person teams working on a computer simulation with an unseen supervisor

Colquitt (2004)

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and discomfort suggested by equity theory (Adams, 1965). Moreover, it could give participants doubts about whether the situation was justified, as participants might believe that the circumstances ought to have occurred differently. The result would be more favorable reactions when control is uniformly high, again as shown in Fig. 2. Contrary to predictions, others’ justice had few consistent main or interactive effects in the studies by Ambrose and Kulik (Ambrose et al., 1991; Ambrose & Kulik, 1989). The control afforded to others and the outcomes received by others had little impact on participants’ own perceptions. The lack of significant findings for others’ justice inspired a follow-up study by Grienberger, Rutte and van Knippenberg (1997). The authors also applied RCT and followed the same design used in Ambrose et al. (1991), with participants taking part in a computer simulation where control over task characteristics was manipulated to be high or low. Participants were again supposedly paired with an unseen other and given information on that person’s control level and outcome level (i.e., bonus). The only change to the experimental procedure was the reasoning given for the manipulation of own and other’s control. In Ambrose et al. (1991), participants had been instructed that control was varied by random computer assignment, while Grienberger et al. (1997) told subjects that the levels were varied on the basis of experimenter choice. As in Ambrose et al. (1991), others’ control levels did not have a main effect on participants’ own fairness perceptions. However, the predicted interaction effects did emerge, with the pattern closely resembling that of Fig. 2. Own level of control was more strongly related to fairness reactions when the other’s control level was high, in comparison to when the other’s control level was low. In other words, the main effects of own control levels were supplemented by an interaction that rewarded consistent treatment within the team. Grienberger et al. (1997) result is important because it was the first study to show that an individual’s own reactions could be affected by the procedural treatment of others. Van den Bos and Lind (2001) studied the effects of own and others’ justice using a design similar to Ambrose et al. (1991) and Grienberger et al. (1997). In two studies, undergraduate participants were asked to complete a computerized pattern recognition task and were paired with an unseen other. In Study 1, the accuracy of the scoring procedures for that task were manipulated to be either accurate (all 20 trials scored) or inaccurate (1 of 20 trials scored) for both own and other. In Study 2, own and other process control was either granted or denied, with the influence referencing the division of lottery tickets between the two participants. Both studied yielded

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all three of the effects summarized in Fig. 2. Own justice had significant effects on member reactions, but so did the justice experienced by the unseen other. Moreover, the two yielded a significant interaction, with own justice being more positively related to member reactions when other’s justice was high. As mentioned earlier, the four studies reviewed above are relevant to the subject of justice in teams, but none of the studies created true team contexts. An earlier study by Lind, Kray and Thompson (1998) came closer to creating a true team context, as three participants were grouped together to perform a computer simulation with an unseen, fictional supervisor. The three participants worked side-by-side, allowing some degree of social identity to develop, though the simulation did not seem to create task or outcome interdependence. The simulation required the participants to, at times, make requests of the supervisor. Procedural justice was manipulated by accepting or denying those voice attempts, thereby manipulating process control. In addition, the denials of voice were concentrated on one particular member in some conditions, while the denials were spread equally across the three team members in other conditions. The results of Lind et al. (1998) study showed the predictably strong effects for own denials of voice across a variety of outcomes. The more interesting result was for others’ denials of voice, with those results summarized as follows: ‘‘People do take into account the experiences of othersybut the weight accorded others’ reports of injustice was so much less than that accorded personal experiences of injustice that even three times as much injustice did not produce a parity of justice judgments. It is clear that it takes a great deal of reported injustice to equal even a little experienced injustice.’’ (p. 17, emphasis in original). Thus, there was a measurable main effect of others’ treatment, though the effect was smaller than the own treatment effect (as in Figs. 2 and 3). Also noteworthy was that when participants were brought together to discuss their combined justice experiences, a group-level reaction was quite susceptible to the unfair treatment of any one member. In other words, the treatment experienced by others became more impactful as members could interact face-to-face and compare notes on their treatment. True team contexts provide just that opportunity. A follow-up study employed a similar task and experimental paradigm, though participants never met face-to-face to discuss their justice experiences (Kray & Lind, 2002). Participants took part in the same computer task used by Lind et al. (1998), with justice manipulated by granting or denying process control attempts in an interactionally fair or unfair manner. The participants then read a packet of information on an unseen ‘‘coworker’’, which included information on whether that coworker had experienced the

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same sort of treatment from either the participant’s supervisor or a different supervisor. One of the significant results revealed that participants did take into account the other’s treatment in evaluating supervisors, though the other’s treatment had less impact when the participant could draw on their own personal experiences with the authority figure. These results underscored the key finding of Lind et al. (1998) – that vicarious justice is taken into account when evaluating authorities, although it is weighed less than one’s own personal experience. The most recent laboratory investigation of own and others’ justice was conducted by Colquitt (2004). The author conducted two separate studies that were notable in three respects. First, participants were placed into true teams working side-by-side, allowing some degree of social identity to develop. Second, the teams worked under high levels of outcome interdependence, with either grades or cash prizes being contingent on collective performance. Third, variation in task interdependence was either measured or manipulated, in order to serve as a moderator of the influence of others’ justice. This study therefore provided the three defining characteristics of teams as depicted in Fig. 1, while allowing the importance of one of them – task interdependence – to be assessed empirically. Colquitt’s (2004) first study focused on existing student project teams in a management course, with own and teammates’ levels of procedural justice measured with self-report variables, along with task interdependence. In the second study, undergraduates were placed into four-person teams and asked to participate in a computerized decision-making simulation with an unseen leader. Own and teammates’ justice levels were manipulated by giving participants feedback on the degree of control each member had over the team leader’s final decision on each of the 36 trials of the simulation. Task interdependence was manipulated by varying the degree to which participants needed information from one another to fulfill their role requirements. This study also included a number of behavioral reactions referenced to teammates, including the degree to which members cooperated with one another and performed their roles in the team. While neither study demonstrated main effects for teammates’ justice, both studies revealed a number of significant interactions between own and teammates’ justice levels, with the pattern of the effects resembling Figs. 2 and 3. The relationships between own justice and both attitudinal and behavioral reactions were stronger when teammates were treated fairly than when teammates were treated unfairly. In addition to authority-referenced fairness perceptions, the interaction effect was observed for cooperation and role performance. These findings illustrate that justice combinations within

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a team are capable of influencing behaviors toward one’s teammates, even though they are not responsible for the unfair treatment. Moreover, the effects of the various justice combinations were magnified when the teams possessed higher levels of task interdependence. Presumably, the increased interaction allowed differences in treatment to be more easily noticed, or made such differences seem more inappropriate.

Field Studies The first two field studies to examine the effects of teammates’ justice on individual member reactions are part of the literature on justice climate, which will be discussed at greater length in the next section of this review. Mossholder, Bennett and Martin (1998) studied the effects of own and teammates’ justice in bank branches averaging around six employees. The authors gathered self-report measures of procedural justice and two authority-referenced outcomes: organizational commitment and job satisfaction. Teammates’ justice was operationalized by aggregating the individual justice perceptions within the branch, effectively creating an index of average procedural treatment. The results of the study revealed that the average procedural treatment within the bank branch had a significant main effect on satisfaction (though not on commitment), independent of the effects of a member’s own treatment. Naumann and Bennett (2000) conducted a subsequent study of justice in bank branches. Individual member perceptions of procedural justice were gathered, and the group’s average treatment was again calculated, providing some information on teammates’ justice. In addition to the authority-referenced outcome of organizational commitment, Naumann and Bennett (2000) assessed the degree to which the branch employees helped one another. The results revealed that the group’s average treatment explained incremental variance in helping behavior, after controlling for members’ own justice perceptions. Once again, this finding illustrates that the justice experienced by others can impact an individual’s own actions toward those others. Liao and Rupp (2005) conducted a more recent study that followed the same general procedure. The authors assessed a number of justice dimensions (procedural, interpersonal, and informational, from both organizational and supervisory sources) in a sample of 231 individuals working in 44 teams from nine different organizations and seven different industries. Liao and Rupp (2005) also collected data on a number of outcomes, including organizational and supervisor commitment and citizenship behaviors

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directed at the organization and the supervisor. While the findings varied across justice dimensions and across justice sources, the results revealed several cases where the group’s average justice explained variance in the outcomes, independent of the team members’ own perceptions. Summary With respect to Research Question 2, the studies summarized in Tables 2a and 2b suggest that the justice experienced by specific teammates, or the team as a whole, does impact members’ own individual reactions. In some cases this impact is interactive, with member’s own justice having a stronger impact on reactions when teammates were also treated fairly, as in Figs. 2 and 3. In other cases, this impact is additive, with teammates’ justice explaining incremental variance in reactions beyond that explained by one’s own justice levels. Such results offer another reason for the claim advanced in the opening, as they illustrate how team contexts can enhance the importance of justice in organizations. If leaders of teams hope to retain and motivate an individual team member, it may not be enough to treat him or her fairly. Rather, that member’s attitudes and behaviors will likely depend on the treatment experienced by teammates – a contention that even seems to hold when attitudes and behaviors reference those very teammates.

EXAMINING JUSTICE AT THE TEAM LEVEL OF ANALYSIS The studies reviewed in this section illustrate yet another reason why team contexts could enhance the importance of justice in organizations. As previously discussed, task interdependence creates a context for interaction between members of teams. Such interaction can be viewed as a series of encounters and event cycles between team members, through which collective constructs eventually emerge (Morgeson & Hofmann, 1999). Accordingly, each of the studies in this section explicitly acknowledges the role that interaction – particularly, social contagion – can play in team contexts, as the authors examine the meaning and operation of justice at the team level of analysis. If justice is ‘‘contagious,’’ (Degoey, 2000) and if the members of an individual’s social network have an impact on members’ justice perceptions (Lamertz, 2002; Umphress et al., 2003), it follows that a shared consensus

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may emerge within a team about its overall treatment. If that shared consensus impacts outcomes at the team level of analysis (e.g. team productivity, team absenteeism), then justice takes on an increased importance in organizations that utilize teams. Table 3 summarizes the studies that have examined justice at the team level of analysis, all of which have occurred in field settings. Before discussing these studies, however, we must first review the theoretical and methodological issues involved in operationalizing justice at the team level of analysis.

Operationalizing Team-Level Justice Justice researchers have operationalized fairness at the team level by drawing on the climate literature, where climate is defined as a shared and enduring perception of the psychologically meaningful aspects of the work environment (Schneider, 1975). Justice climate has been defined as shared perceptions of work unit treatment by organizational authorities (Colquitt, Noe & Jackson, 2002; Mossholder et al., 1998; Naumann & Bennett, 2000). Originating in the cognition, affect and attitudes of unit members and amplified by their interactions (Kozlowski & Klein, 2000), justice climates are conceptualized to be similar to individual justice perceptions in their content and operation but with a focus on the unit as a referent. In order to understand justice climate, one must first understand why a shared consensus might emerge about the unit’s treatment. Researchers have generally assumed the emergence of climates occurs through two primary mechanisms: structuralism and attraction–selection–attrition (ASA) processes (Schneider & Reichers, 1983). According to the structuralist perspective, the organizational structures that create work unit groupings give rise to similar perceptions among organizational members responding to a particular unit structure (Payne & Pugh, 1976). Because members of work units experience the same leader and/or similar work rules and procedures, individual perceptions may be shaped and constrained, thus giving rise to shared perceptions. According to the ASA perspective, selection practices combine with individuals’ attraction to, and attrition from, the work unit to produce a relatively homogeneous membership with similar perceptions of the work environment (Schneider, 1983; Schneider & Reichers, 1983). The end result of structuralism and ASA processes is a work unit that experiences many of the same stimuli while being homogeneous on a number of important individual characteristics. These structural and compositional characteristics should amplify the social contagion and social

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Table 3. Field Studies Examining Justice at the Team Level. Authors

Type of Team

Justice Dimension

Outcomes

Procedural (climate level and climate strength)

Member-directed: Team performance Team absenteeism

Naumann and Bennett (2002)

34 bank branches (averaging 6 members)

Procedural (climate level)

Other-directed: Citizenship behaviors Team performance

Johnson, Korsgaard, and Sapienza (2002)

54 top management Procedural (climate level teams of international and aggregate decision joint ventures control) (unspecified size)

Dietz, Robinsons, 250 plants of a Folger, Baron, nationally operating and Schulz (2003) public service organization (averaging 680 employees)

Procedural (climate level)

Authority-directed: Organizational commitment

Member-directed: Workplace aggression

Procedural justice climate level was related to both performance and absenteeism. Climate level was more strongly related to performance and absenteeism when climate strength was high Procedural justice climate level was related to helping behavior. Helping behavior fully mediated relationship between procedural justice climate level and performance Procedural justice climate level was related to commitment. Decision control was not related to commitment. Procedural justice had a stronger relationship to commitment when decision control was low Procedural justice climate level was a marginally significant predictor of workplace aggression (depending on the controls entered into the analysis)

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Colquitt, Noe, and 88 work teams Jackson (2002) (averaging 20 members) in an automobile parts manufacturing firm

Results

Authority-directed: Organizational commitment Turnover intentions Turnover Service behavior

Procedural and interpersonal justice were directly related to commitment and indirectly related to service behavior and turnover

Member-directed: Citizenship behaviors

Procedural justice climate was related to two dimensions of citizenship behavior

Justice in Teams: A Review of Fairness Effects in Collective Contexts

Procedural (climate level), Simons and 111 properties interpersonal (climate Roberson (2003) (averaging 90 level) employees) and 783 departments (averaging 9 employees) run by a large hotel management company Ehrhart (2004) 249 departments of a Procedural (climate level) grocery store chain (averaging 10 employees)

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influence processes discussed and studied by justice scholars (Degoey, 2000; Lamertz, 2002; Umphress et al., 2003). As a result, the ties within the unit should be strengthened, both in terms of friendship and workflow connections. The stronger the ties, the more contagious justice can become as more stories and experiences can be shared (Degoey, 2000). Strong ties also amplify the social influence peers hold over one another, creating more shared justice judgments (Lamertz, 2002; Umphress et al., 2003). Building upon many of these arguments, Roberson and Colquitt (2005) proposed a social network model of justice in teams that articulates the social influence processes through which shared perceptions of justice emerge. Following social network theory, which defines a network as a set of social system members connected by ties that indicate the relationships (or lack of relationships) between them (Brass, 1995), the model views teams as a type of social network. Focusing on the team as the unit of analysis, the authors viewed the pattern of interactions between members, and the intensity of those interactions, as potential sources of sensemaking in the development of team justice perceptions. Consistent with traditional network effects models, they proposed that convergence of justice perceptions may occur through two primary mechanisms: (1) structural equivalence, which refers to the extent to which people occupy similar positions in a network (Erickson, 1988); or (2) cohesion, which refers to the extent to which people interact frequently and intensely, and are therefore influenced by those with whom they interact directly (Burt, 1987). From a structural equivalence perspective, similarity in the pattern of relations between team members may allow each member access to the same sources of social influence when interpreting the team’s experiences, and therefore may provide an opportunity for members to gain others’ perspectives on the fairness of the team’s treatment. As a result, shared team justice may develop in structurally equivalent teams. From a cohesion perspective, Roberson and Colquitt (2005) suggested that strong ties between members should develop in interdependent work environments and that this cohesion may facilitate the exchange of justice-relevant information among team members by providing an opportunity for members to discuss organizational policies and jointly interpret the team’s experiences. Consequently, team members’ interpretations and evaluations of the team’s experiences may converge, resulting in shared team justice. Of course, justice perceptions may not always converge within the team. Does this mean that the team does not possess a justice climate? Climate researchers have suggested that all forms of climate have two components – level (or quality), which refers to unit members’ average climate perceptions,

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and strength (or consensus), which refers to the level of variance in members’ climate perceptions (Lindell & Brandt, 2000; Schneider, Salvaggio & Subirats, 2002). Similarly, justice climate researchers have distinguished between justice climate level, which refers to the favorability of the unit mean regarding how the unit as a whole is treated (Naumann & Bennett, 2000), and justice climate strength, which is defined as the variation (or lack thereof) in team members’ justice ratings (Colquitt et al., 2002). Climate strength is capable of acting as a moderator of climate level, with level effects being stronger when climates are strong rather than when they are weak (Colquitt et al., 2002; Lindell & Brandt, 2000; Schneider et al., 2002). Given the potential moderating effects of justice climate strength, researchers have begun to consider barriers to the convergence of unit member justice perceptions. One such barrier is diversity. Degoey (2000) suggested that differences in disposition and personality could influence people’s sensitivity to social cues and their tendencies to seek out such cues, while also impacting the likelihood that they will be influenced by the opinions of others. He further suggests that units with more demographically diverse workforces will exhibit less social contagion with respect to justice. Roberson and Colquitt (2005) advanced a similar prediction, arguing that the social networks that develop in diverse teams will constrain the development of shared team justice. Recent research has supported these propositions, as the demographic diversity within a unit has been associated with less within-group agreement in justice perceptions (Colquitt et al., 2002; Naumann & Bennett, 2000).

Field Studies on Team-Level Justice The first study to examine the effects of justice at the team level of analysis was conducted by Colquitt et al. (2002). Using a sample of manufacturing teams, the authors examined the unique and interactive effects of procedural justice climate level and strength on team performance and absenteeism. The results showed that climate level was positively related to leader ratings of team performance and negatively related to company data on team absenteeism. Although procedural justice climate strength lacked a direct relationship with the study’s team effectiveness measures, it did moderate the effects of climate level. More specifically, procedural justice climate level was more strongly related to team performance and absenteeism in teams with higher climate strength.

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Naumann and Bennett (2002) also examined the effects of team-level justice on team performance. As in Naumann and Bennett (2000), the authors focused on bank branches while examining team-level citizenship and job performance. The authors hypothesized an indirect relationship between procedural justice climate level and team performance through team helping behaviors. The hypotheses were tested using two measures of performance – net profitability and supervisors’ ratings of the extent to which their subordinates met expectations. The results showed that group helping behavior fully mediated the relationship between procedural justice climate level and perceived work group performance. In a study of international joint venture relationships, Johnson, Korsgaard and Sapienza (2002) examined how joint venture and parent involvement in strategic decision-making influences the top management team’s commitment to the venture and parent firms. The authors hypothesized that the team’s commitment to each of the venture entities (i.e., the venture itself, the local parent, and the foreign parent) would be higher when decision processes were perceived to be procedurally fair and when the team had greater control over decision-making processes. The results showed that procedural justice climate level was significantly related to commitment. The results also demonstrated an interactive effect of procedural justice climate level and aggregate perceptions of decision control on commitment to the foreign parent, such that procedural justice had a stronger relationship to commitment when decision control was low than when it was high. Other studies in the justice climate literature are relevant to team-level justice, though the studies were conducted in units that are likely too large to possess the task interdependence, outcome interdependence, and social identity that characterizes true teams. For example, Dietz, Robinsons, Folger, Baron and Schulz (2003) conducted an intraorganizational study of justice climates designed to examine societal and organizational causes of workplace aggression. The study contrasted community violence rates and procedural justice climates (or lack thereof) as explanations for employee-instigated physical assaults and other severe incidents of aggression. In a sample of 250 geographically dispersed and operationally independent plants of a public service organization, procedural justice climate was found to have a marginally significant, negative zero-order relationship with workplace aggression incidents (though this relationship varied according to the number of community control variables entered into the analysis).

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Simons and Roberson (2003) explored the effects of procedural and interactional justice at the department and business-unit levels of analysis on key organizational outcomes. Using latent variable analyses of individualand departmental-level data from 4,539 employees at various hotel properties, the results showed that procedural and interactional justice uniquely influenced employees’ satisfaction with supervisor, organizational commitment, turnover intentions, and service behavior. Among the business units, the results showed support for a model in which justice climates influenced service behaviors and turnover intentions through the mechanisms of increased commitment and increased satisfaction with supervisor. Finally, Ehrhart (2004) examined leadership and procedural justice climate as predictors of unit-level citizenship behaviors in a sample of grocery store departments. Two sets of models were tested using aggregated employee ratings and manager ratings of department-level OCB. The results demonstrated a positive relationship between servant leadership, or leaders’ recognized moral responsibility to organizational stakeholders, and procedural justice climate level. In addition, the relationships between procedural justice climate and helping and compliance forms of citizenship received support. Procedural justice climate level was also shown to partially mediate the relationship between servant leadership and citizenship.

Summary With respect to Research Question 3, the studies summarized in Table 3 illustrate that justice can, when operationalized as a form of climate at the unit level of analysis, impact collective reactions. Three of the studies occurred in units that possessed high levels of the characteristics in Fig. 1 (task interdependence, outcome interdependence, social identity), while the remaining studies occurred in larger units (departments, plants, and hotels). Taken together, the studies linked unit-level justice to a wide range of unitlevel outcomes (e.g. team performance, team absenteeism, citizenship behavior, commitment). Such results offer one final reason for the claim advanced in the opening, as they again illustrate how team contexts can magnify the importance of justice in organizations. Not only can unfair experiences compromise the attitudes and behaviors of individual employees – they can also be shared across members to create a shared, collective sense of injustice. Such a climate of unfairness can then impact the unit as a whole, harming its commitment and performance.

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SUGGESTIONS FOR FUTURE RESEARCH While we hope this review has illustrated how the importance of fairness can be magnified in collective contexts, we should also note that much more needs to be learned about justice in teams. This section provides some suggestions for future research in an effort to further improve our understanding of the processes depicted in Fig. 1. First and foremost, future research should explore why one’s teammates are sometimes penalized for one’s own experiences of injustice. Several studies illustrated that unfair treatment by authorities resulted in members withdrawing from the team’s work, cooperating less frequently with other members, and performing their team’s role less successfully (Colquitt, 2001; Colquitt, 2004; Murphy et al., 2003; Phillips, 2001, 2002; Phillips et al., 2001; Shapiro & Kirkman, 1999; Simon & Sturmer, 2003). It may be that members did not consciously realize that their teammatedirected attitudes and behaviors were suffering as a result of perceived injustice. More specifically, it may be that authority-directed reactions subconsciously ‘‘spilled over’’ to teammate-directed reactions. If true, the effects of injustice on teammate-directed reactions would disappear if authority-directed reactions could be controlled. Alternatively, it may be that members did realize that their attitudes and behaviors were declining in the team context. Members could blame their teammates for not ‘‘sticking up for them’’ or not being more sensitive to their plight. The results could also be capturing, in part, the left portion of Fig. 2, where members are treated worse than their teammates. If so, withdrawal from the team or its work would mimic the reactions typically seen in tests of equity theory (Adams, 1965). More research is also needed on the related subject of ‘‘vicarious injustice.’’ Research has shown that the justice experienced by others has either a main or interactive effect on one’s own reactions (Colquitt, 2004; Grienberger et al., 1997; Kray & Lind, 2002; Liao & Rupp, in press; Lind et al., 1998; Mossholder et al., 1998; Naumann & Bennett, 2000; Van den Bos & Lind, 2001). Justice scholars have long noted that others’ treatment has an impact on the formation of fairness perceptions (e.g. Adams, 1965; Leventhal, 1980), but linking others’ treatment to job attitudes and behaviors is a more recent development. It would be interesting to explore whether the mediators of vicarious justice effects are more cognitive or more affective. It may be that individual members use the treatment of others to indicate whether to develop a more social or economic exchange relationship with the authority (e.g. Cropanzano, Rupp, Mohler & Schminke,

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2001). Vicarious injustice could also have a purely affective impact, as the unfair treatment of others could lower a member’s own mood or disrupt the overall affective tone of the group. Future research should also explore the moderators of vicarious justice effects in an effort to learn when others’ treatment matters more or less. We suspect that the team characteristics identified in Fig. 1 are likely to be significant moderators. Colquitt (2004) showed that others’ treatment matters more when task interdependence is high, but outcome interdependence and social identity have yet to be explored. Future studies could therefore measure or manipulate team-based goals or rewards in an effort to capture outcome interdependence, or team size and demographic diversity in an effort to capture social identity issues. Personality variables could also serve as potential moderators, with vicarious injustice mattering more for collective individuals (Colquitt, 2004) or individuals more sensitive to justice issues (Colquitt, 2004; Schmitt, Neumann & Montada, 1995). Agreeableness could be another personality-based moderator, given that agreeable individuals tend to be more sympathetic, selfless, and soft hearted (McCrae & Costa, 1987). A related issue concerns the specific reactions that members have to the individual (or individuals) who are singled out. If such treatment is particularly bothersome to members in interdependent teams, or members high in collectivism or agreeableness, how do the members react to those who have been singled out? On the one hand, they may feel empathy and seek to help their teammate cope with the situation. On the other hand, they may ‘‘blame the victim’’ and distance themselves from the member. It seems likely that this choice could depend on a complex series of attributional processes (LePine & Van Dyne, 2001), as the members seek to separate causes internal to the individual (i.e., he or she has repeatedly failed in the group) from causes external to the individual (i.e., the leader has a bias against that particular member). In addition, we should note that our focus on own and vicarious justice experiences glosses over a potentially significant question – does the meaning of justice change in team contexts? In other words, do team contexts alter the rules and criteria used to evaluate and judge justice experiences? A recent study by Colquitt and Jackson (in press) assigned participants to either an individual or team context using hypothetical vignettes, with participants asked to evaluate the importance of various justice rules (e.g. equity, equality, consistency, bias suppression, process and decision control, interpersonal sensitivity). The results revealed that participants judged the equality, consistency, and decision control rules to be more important in

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team contexts. If such results are replicated in actual teams, then future research may need to distinguish not just own and others’ justice perceptions, but also the rules and criteria that were used to arrive at them. While the suggestions above are mostly of an intrateam nature, other future directions center on justice at the team level of analysis. Given that collective constructs emerge and are transmitted through the actions of members of the collective (Morgeson & Hofmann, 1999), research on the nature of team members’ interdependencies and interactions may provide a better understanding of the mechanisms that drive team-level justice perceptions. Similarly, while research has begun to connect justice to constructs like cohesion, conflict, social loafing, and work group efficacy (Chansler et al., 2003; Colquitt, 2004; McIntyre et al., 2002; Murphy et al., 2003), much more work is needed to understand how team-level justice affects team effectiveness. The studies that have been conducted suggest that fair treatment could reduce inefficient process losses while increasing synergistic process gains (e.g. Hackman, 1987), but the impact of justice relative to other team and contextual characteristics is unknown. It also remains unclear which forms of justice are most predictive of specific process behaviors at the team level. Future work should attempt to include a wide variety of process variables in conjunction with multiple justice dimensions. One context for attempting such research is self-managing teams, where members possess autonomy over decisions that directly affect job tasks, but also have discretion over administrative decisions related to goal setting, discipline, and composition (Banker, Field, Schroeder & Sinha, 1996; Janz, Colquitt & Noe, 1997). While self-managing teams have become more common in the workplace, justice issues are likely to take on a unique character in such groups. The relative absence of a formal leader means that the hierarchical focus of most justice theorizing no longer applies. Instead, procedural, distributive, interpersonal, and informational justice should originate in fellow group members to a much greater degree. With the exception of Simon and Sturmer (2003), no studies have examined the effects of member-originating injustice. It seems likely that justice effects should be particularly strong in such cases, as the authority and member-directed reactions that are separated in Fig. 1 would now be focused on a single target.

CONCLUSION Consider again the scenario used to open this chapter, where an employee exits a performance evaluation convinced that several forms of injustice have

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occurred. Past research in individual contexts would suggest that this individual is less likely to be committed to the organization as a result of this experience, and may also engage in fewer behaviors that aid the organization or the supervisor in question. However, our review points to a number of other potential consequences that could be quite costly to the organization, if the employee in question is a member of a work team. The employee may be less likely to help fellow team members with their tasks, even though those members had nothing to do with the evaluation session. The employee may also ‘‘spread the word’’ about the injustice, harming the attitudes and behaviors of other team members. As this social contagion and influence spreads throughout the team’s network, a shared consensus may emerge about the leader’s unfair treatment. This lowered level of justice climate could then reduce the performance and effectiveness of the unit as a whole. This sobering scenario highlights the potential implications of the shift toward team-based work for the justice literature. The task interdependence, outcome interdependence, and social identity that affect team members are capable of altering the formation and impact of justice judgments. These work characteristics take a phenomenon that has always been inherently social and make it more so – by supplying multiple referents for justice, allowing social contagion to spread unfairness throughout the team, and creating additional targets for justice-related reactions. We would argue that the changes created by these team characteristics have conspired to make justice an even more important concern in team-based organizations.

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STANDING UP OR STANDING BY: WHAT PREDICTS BLOWING THE WHISTLE ON ORGANIZATIONAL WRONGDOING?$ Marcia P. Miceli and Janet P. Near ABSTRACT Research on whistle-blowing has focused on the questions of who blows the whistle, who experiences retaliation, and who is effective in stopping wrongdoing. In this article, we review research pertinent to the first of these questions. Since the last known review (Near & Miceli, 1996), there have been important theoretical and, to a lesser extent, empirical developments. In addition, the U.S. law is changing dramatically, which may serve to promote valid whistle-blowing, and international interest in whistle-blowing is widespread and increasing. Unfortunately, evidence strongly suggests that media, popular, and regulatory interest is far outpacing the growth of careful scholarly inquiry into the topic, which is a disturbing trend. Here, we argue that the primary causes of the underdevelopment of the empirical literature are methodological, and that workable solutions $

The research was supported in part by the Robert H. Steers Faculty Research Fellowship at The McDonough School of Business, Georgetown University and by the Coleman Chair Fund at the Kelley School of Business, Indiana University.

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are needed but very difficult to implement. By calling attention to these issues, we hope to help encourage more research on whistle-blowing.

INTRODUCTION Anyone who believes that the Enron scandal is an isolated case has probably not picked up a U.S. newspaper or business magazine in the past few years. More systematic research also suggests organizational wrongdoing is not limited to a handful of corporations headed by personally corrupt managers. While a few surveys suggest that perceived organizational wrongdoing is rare, most show that it is far too common. Surveys generally ask whether respondents have observed wrongdoing within the past year, a consistency that enables some comparisons. One study reported that the percentage of federal employees who observed wrongdoing decreased over that time period, from over 40% in 1980 to less than 20% in 1992 (Miceli, Rehg, Near & Ryan, 1999). However, about a third of 1,500 public and private employees surveyed nationally said they had personally observed misconduct, according to a report released in late 2000 by the Washington-based Ethics Resource Center, a nonprofit educational group whose board of directors includes many business leaders (Grimsley, 2000). A national telephone survey of 1,004 employees identified a similar proportion (Miethe & Rothschild, 1994). An even higher percentage (80%) of surveyed directors of internal auditing said they had observed wrongdoing by their organizations (Near & Miceli, 1988). And, there is some anecdotal evidence that whistle-blowing in response to perceived wrongdoing is becoming more common (Freed, 2003), with some empirical support, e.g. in 1980, 26% of federal employees who believed they observed wrongdoing blew the whistle on it, and this number increased to 48% in 1992 (Miceli et al., 1999). The media have hailed whistle-blowers as a courageous line of defense against organizational wrongdoing. Time magazine recently selected (and wrote admiringly about) three whistle-blowers as their ‘‘Persons of the Year’’ for 2002: Cynthia Cooper of WorldCom, Coleen Rowley of the FBI, and Sherron Watkins of Enron (Lacayo & Ripley, 2002). All three blew the whistle internally, with memos and e-mails to a top executive. In all three cases, their correspondence was leaked to Congress or the press and they found themselves forced to discuss their allegations in public. A similar scenario played out in the recent revelation of the shocking abuses at the Abu Ghraib prison in Iraq (Miceli & Near, in press).

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The purpose of this article is, generally, to describe what is known about the question, ‘‘who blows the whistle?’’ In it, we begin by documenting the importance of whistle-blowing, based on its increasing frequency and criticality to societal control of organizational wrongdoing. We then address some controversies concerning the definition of whistle-blowing and describe how it relates to other constructs. As legal protections for whistleblowers – which provide a context for and affect the decision to blow the whistle – have changed over time, we briefly review the historical trends and describe current protections. Next, we review the evolution of a model of whistle-blowing and existing research examining the predictors of the precursors to whistle-blowing, such as observation of wrongdoing, and to whistle-blowing, which has been conducted primarily within the U.S. Finally, we describe methodological challenges in studying whistle-blowing, which we argue are more difficult to overcome without creating other problems, than with most topics in the management literature. We argue that this is an important dilemma, because it may account for why there is so little research on whistle-blowing in leading management journals, despite obviously widespread and enduring practical interest in the topic. If solutions are not found, managers and regulators will be informed by little more than anecdotes and opinions about whistle-blowing.

Importance of Whistle-Blowing to Societies It is becoming increasingly clear that whistle-blowing is anything but a uniquely North American phenomenon. Whistle-blowing has been documented or investigated in an extraordinarily diverse range of countries, for example, Australia (e.g. De Maria & Jan, 1997; The Economist, 2002), India (e.g. Keenan, 2002a), Israel (e.g. Day, 1996; Seagull, 1995), Japan (e.g. Matsubara, 2004; Yoshida, 2001), The Netherlands (e.g. Bates, 1999), Russia (e.g. Knox, 1997), Somalia (e.g. Anonymous, 1996), and South Korea (Park, Rehg, & Lee, in press). Because few studies systematically examine whistle-blowing in cultures other than the North American cultures, and because legal environments vary greatly, what we know is based primarily on research conducted in North America, and we cannot say whether the statements in this article apply more broadly. Here we will focus on the U.S. In an early book on organizational dissent, Ewing contended that the U.S. was ripe for a huge increase of whistle-blowing among organizational members (Ewing, 1983). He argued passionately that Americans who came of age protesting the Vietnam war, raising civil rights issues, and fighting

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consumerism battles had developed an appreciation of dissent, including recognizing the importance of it and understanding mechanisms for engaging in it. As these individuals moved into and up in the workplace and became valued and increasingly higher-level organization members, he predicted that they would not check their opinions at the door, but would instead voice their views. In short, he believed that this generation was more likely than its elders to publicize organizational wrongdoing, if observed. Ultimately this would be healthy for organizations, leading to enhanced operations and early warning of wrongdoing that top executives may not have been privy to – before it led to expensive lawsuits and damaging press releases. In fact, there is some anecdotal evidence that American acceptance of whistle-blowing is increasing. About 48% of 2,800 workers surveyed in 2001, said they felt comfortable reporting misconduct by their employers, compared with 34% in 1999 (Tejada, 2001). Even as early as 1980, 80–90% of federal employees agreed with a series of questionnaire items proposing that hypothetical employees who observed wrongdoing should blow the whistle (U.S. Merit Systems Protection Board, 1981). These statistics seem to bear out Ewing’s (1983) predictions that U.S. workers would increase their support for whistle-blowing, at least in principle. Employees of the U.S. federal government provide useful information regarding the incidence of whistle-blowing, because large random samples of employees were surveyed four times between 1980, shortly after the implementation of the Merit Systems Protection Act, and 1992. The Merit Systems Protection Act was designed, in part, to offer protection from retaliation to federal employees who blew the whistle. Despite major concerns about the effectiveness of the Act, and the Merit Systems Protection Board it created to monitor retaliation against federal whistle-blowers (Johnson, 2003), the percentage of federal employees who observed wrongdoing in their agencies decreased and the percentage of observers who reported wrongdoing increased, over time (Miceli et al., 1999). We could even view the Time article, selecting whistle-blowers as the persons of the year in 2002, as the culmination of this trend to increased whistle-blowing and increased support for whistle-blowers. Clearly whistle-blowing is a force to be recognized in the U.S. in the 21st century.

Importance of Whistle-blowers as an Organizational Control Mechanism Whistle-blowers have increased not only in number but also in importance as a societal control mechanism over organizational misdeeds. In many

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cases, organizations have become so complex and so skilled at hiding wrongdoing, that only insiders are capable of finding the problems and revealing them (Miethe, 1999). In contrast to the last century, in which the U.S. media and ‘‘muckrakers’’ scrutinized corporate records and publicized misdeeds (e.g. Sinclair Lewis, in writing Babbitt), it may be that large corporations have grown so large, so diverse, and so complex, that only employees near the top, who see the full picture of their firms, are able to pinpoint their problems. Furthermore, union membership has declined dramatically, from 35% of the U.S. workforce in the 1950s to 13% in 2003 (Anonymous, 2004), which means that unions cannot serve the watchdog role as effectively as in the past. Interorganizational relationships between regulator and regulated may have reduced the policing power of the government regulators. Even when the media, the unions, and the regulatory agencies identify organizational wrongdoing, often the initial information has come from an internal whistleblower, in part because the information can be hidden from all except insiders, as in the Enron case. Thus, the whistle-blower serves an important need, i.e., ‘‘as a significant check on accountability’’ (Trapp, 1998). Legal changes to support whistle-blowers, such as changes in the Office of Special Counsel in 1994, have encouraged organizations to strengthen mechanisms for internal whistle-blowing (Johnson, 2003). For example, hotlines were first instituted by the Department of Defense in the early 1990s, and had resulted in $163 million in savings by 1993 and $391 million by 1997 (Johnson, 2003). Sexual harassment regulations aimed at providing incentives to firms that instituted reporting mechanisms may provide a strong model for legislation aimed at supporting whistle-blowers (Near, Dworkin & Miceli, 1993). New supports provided by the Federal Sentencing Guidelines and the Sarbanes-Oxley Act may further serve to enhance protections for whistle-blowers (Dworkin & Callahan, 2004), a point to which we will later return. Several dramatic cases reported in the media recently have suggested that organizations’ failure to listen to whistle-blowers and stop wrongdoing will have impact beyond the immediate organization. Huge class action pay and promotion discrimination cases based on sex and race at Wal-Mart, Morgan-Stanley and other Wall Street firms, and Boeing, could transform how business is done (Armour, 2004; Holmes, 2004; Holmes & France, 2004; McClam, 2004). This is particularly true where internal documents and other evidence show that whistle-blowers have been ignored, because plaintiffs can get larger awards in the form of punitive damages (Armour, 2004), providing more incentive for organizations to be responsive.

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This brief review of trends in whistle-blowing leads us to two conclusions: (a) the incidence of whistle-blowing, and public support for whistle-blowing, are growing; and (b) whistle-blowers represent an increasingly important mechanism for identifying and reporting organizational wrongdoing, in a world of complex organizations, and in fact may be one of the best ways for society to identify wrongdoing in its organizations. Streams of research on whistle-blowing can be organized around three general research questions: (a) what factors lead to whistle-blowing? (b) what factors lead to retaliation against whistle-blowers and what are its consequences? (c) what factors help whistle-blowers to be effective, that is, to succeed at getting wrongdoing stopped? Space does not permit us to address all three questions here, so instead we focus only on the first, and address the second and third elsewhere (Miceli & Near, in press; Near & Miceli, under review). Before reviewing research, we begin with a definition of whistle-blowing.

DEFINING WHISTLE-BLOWING In 1985, we defined whistle-blowing as: ‘‘the disclosure by organization members (former or current) of illegal, immoral or illegitimate practices under the control of their employers, to persons or organizations that may be able to effect action’’ (Near & Miceli, 1985, p. 4). Since that time, this definition has been widely used (King, 2001) and included in a dictionary of human resource terms (Miceli & Near, 1997a); other researchers have used definitions that are extremely similar (e.g. Sims & Keenan, 1999). Obviously, any definition can be questioned and this one is no exception. However, many questions and controversies it has engendered have been dealt with at length elsewhere, both empirically and conceptually (e.g. Miceli & Near, 1992; Near & Miceli, 1996), so we do not believe it is helpful to repeat most of the arguments here. Instead, we will discuss only those issues that seem most important, and on which agreement has not been reached. One important definitional element that continues to generate some controversy was that some researchers (e.g. Farrell & Petersen, 1982) argued that a whistle-blower who reported wrongdoing within the organization, but informed no outside authority, was not really a whistle-blower. In 1992, we provided a number of arguments supporting the view that reporting channels used do not define an act as whistle-blowing or not, but more appropriately, they are one means of differentiating among whistle-blowing cases (Miceli & Near, 1992). Among the arguments were (a) that empirical

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evidence supported this view (e.g. that nearly all whistle-blowers start the process by reporting wrongdoing within the organization rather than outside, and that there are more similarities than differences in predictors of internal vs. external whistle-blowing), and (b) that it was more consistent with definitions of whistle-blowing in legal statutes. Empirical and legal developments since that time have strengthened this position. However, a recent book on whistle-blowing still claims that, by definition, whistle-blowers act ‘‘with the intention of making information public’’ (Johnson, 2003, p. 3). But this criterion would exclude three of the most famous whistle-blowers – Cooper of WorldCom, Rowley of the FBI, and Watkins of Enron – whom Time Magazine honored as their ‘‘Persons of the Year.’’ All three whistle-blowers reported their concerns only to insiders, and it was only through other means that external parties became aware of their expressed concerns and asked for elaboration. Their information eventually was made public to outsiders, so perhaps this distinction is not so important after all. Other components of Johnson’s definition (Johnson, 2003) are in agreement with ours: that ‘‘nontrivial’’ wrongdoing is involved and that the whistle-blower is a current or former employee, not an outsider. Jubb (1999) argued that whistle-blowing must be distinguished from ‘‘informing,’’ but did not define ‘‘informing;’’ like Johnson, he requires that reports must be ‘‘public’’ to be whistle-blowing. Jubb did not address the consistent empirical finding that external and internal whistle-blowing do not differ greatly in dynamics, and did not offer a rationale for his position that reflected understanding of these research results. The other five of the six elements Jubb described (disclosing damaging news, the whistleblower agent, a disclosure subject – some type of wrongdoing, a target organization, and a disclosure recipient) seem generally congruent with our prior discussions of definition elements (Miceli & Near, 1992; Near & Miceli, 1985, 1987). Scholars have examined behavior that is in some ways conceptually similar to whistle-blowing. Researchers have considered whether whistle-blowing is a type of prosocial behavior, that is, behavior intended to benefit other persons (Staub, 1978), such as bystander intervention in crime and emergencies, altruism, or other types of helping. Prosocial behavior was first studied by social psychologists; for example, the theory of bystander intervention (e.g. Latane´ & Darley, 1968, 1970) ultimately formed the basis of social impact theory (e.g. Latane´, 1981). Researchers have generally concluded that most instances of whistle-blowing qualify, even though few if any whistle-blowers are purely altruistic (Brief & Motowidlo, 1986; Dozier & Miceli, 1985).

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Research shows that a decision model of whistle-blowing based on theories of prosocial behavior does a reasonable job of predicting whistleblowing (Miceli & Near, 1992). Recent refinements have added social information processing and attribution theories as bases for expanding the predictions of the original model (Gundlach, Douglas & Martinko, 2003), but still have focused on prosocial behavior as a critical component. Recent anecdotal evidence also seems to support this treatment. For example, all of the Time whistle-blowers appeared to be engaging in prosocial behavior, because they believed that the top executive was not aware of the organization’s wrongdoing and would take steps to halt it, if properly informed. It is hard to identify instances that would both meet our definition of whistle-blowing (that it is intended to stop wrongdoing) and the definition of antisocial behavior, which implies negative motives and bad processes (Miceli & Near, 1997b). For example, employees who have been fired may be motivated by revenge if they then raise allegations of organizational wrongdoing, but if it cannot reasonably be said that they acted to stop wrongdoing, then they are not whistle-blowers. Obviously, there may be cases that fall in-between. Concepts that may be related to whistle-blowing include climates of organizational silence, i.e., those characterized by two shared beliefs: (a) that speaking up about problems in the organization is not worth the effort, and (b) that voicing one’s opinions and concerns is dangerous (Morrison & Milliken, 2000, 2003); and taking charge behavior, i.e., ‘‘discretionary behavior intended to effect organizationally functional change’’ (e.g. Morrison & Phelps, 1999, p. 403). Whistle-blowing may also be similar to issue selling (e.g. Dutton & Ashford, 1993; Dutton, Ashford, O’Neill, Hayes & Wierba, 1997), and proactive behavior and personality (e.g. Bateman & Crant, 1993; Campbell, 2000; Crant, 1995; Crant & Bateman, 2000; Seibert, Kraimer & Crant, 2001; Seibert, Crant & Kraimer, 1999; Wanberg & KammeyerMueller, 2000). However, research is needed to clarify relationships and boundaries among all of these constructs.

WHISTLE-BLOWING: EVOLUTION OF A GENERAL MODEL While the popular press may convey the impression that whistle-blowers are everywhere, research results suggest that less than half of all employees who observe wrongdoing blow the whistle about it. There is great variation in the

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estimates of whistle-blowing incidence. As noted in the introduction section, in 1992, the most recent systematic survey of federal employees, 48% of observers blew the whistle (Miceli et al., 1999). In a subsequent survey of federal employees, 1,280 reported being the victim of sexual harassment, but only 67 (4%) blew the whistle (Lee, Heilmann & Near, 2004). In 1997, only 26% of the observers of wrongdoing at a large military base blew the whistle; this sample was composed of about half military and civilian employees (Near, Van Scotter, Rehg & Miceli, 2004). Among directors of internal auditing – whose job is to ferret out and report financial wrongdoing internally in their organizations – about 90% of those who observed wrongdoing reported it (Miceli, Near & Schwenk, 1991). These figures suggest that typical employees, who do not see whistleblowing as part of their jobs, are not inclined to act when they see wrongdoing. In contrast, employees whose jobs legitimate and even require whistle-blowing are willing to report wrongdoing. Of course, there are other differences too among these employees, but the basic question remains the same: why do so many employees who observe wrongdoing not blow the whistle? And what variables separate those who do from those who do not? The model described here evolved from an earlier model of the decision to blow the whistle (Dozier & Miceli, 1985), as modified (Miceli, Van Scotter, Near & Rehg, 2001a), which viewed whistle-blowing as prosocial behavior. To illustrate one element of prosocial behavior theory, Latane´, Darley, and their colleagues identified a ‘‘diffusion of responsibility’’ effect explaining why none of the more than 40 witnesses who heard a victim screaming while brutally attacked in New York City called the police; each witness knew that others must have heard, and consequently felt less personally responsible for reporting the act than if he or she were the only witness (e.g. Latane´ & Darley, 1970). Organizational researchers extended this theory into organizational contexts, that is, prosocial organizational behavior, such as whistle-blowing (Brief & Motowidlo, 1986; Dozier & Miceli, 1985). Latane´ and Darley proposed that multiple decisions are made when bystanders witness problems, and that the outcomes of these decisions will affect the ultimate decision about taking action; similarly, Dozier and Miceli argued that organizational members faced with questionable activity or omissions have more time to make similar decisions. Therefore, they proposed that theory from a variety of areas can be integrated with the model to identify many types of variables (e.g. demographic, dispositional, and situational) that could affect these decisions. For example, one decision concerns the extent to which one feels personally responsible for acting, as implied by the bystander intervention example above. Extensive theory and

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some empirical research on moral judgment development (e.g. Rest, 1979) suggest that people who reason at more complex levels of moral development are more likely to feel responsible for taking action when harm is being done, and will therefore be more likely to engage in prosocial behavior, than will those who reason at lower levels. Theory and empirical research have extended findings to whistle-blowing, though the relationships are complicated (e.g. Brabeck, 1984; Miceli, Dozier & Near, 1991; Miceli et al., 1991). The decisions initially suggested by the theory of bystander intervention, as later adapted to predict whistle-blowing, identified at least seven preaction decisions, although research has not determined whether all decisions are sequential or concurrent (e.g. Miceli & Near, 1992). For ease in communicating, we condense these seven decisions into three broad phases. In Phase 1, employees assess whether wrongdoing has occurred (within a specified time period, such as in the past year) by answering such questions as ‘‘do I believe wrongdoing is occurring?’’ and ‘‘is action warranted?’’ Those who believe no wrongdoing is occurring are non-observers. Thus, the dependent variable in this phase is the observation of wrongdoing. Some non-observers do not witness wrongdoing because, by any standard, none has actually occurred, but evaluating wrongdoing often involves subjectivity. Some employees do not perceive wrongdoing even when most others believe it does exist, and some employees define certain events as wrongful that others may not. The Dozier and Miceli (1985) model has been modified (Miceli et al., 2001a) to propose that, in Phase 2, observing wrongdoing negatively influences how employees view the organization, though this will be less true for wrongdoing that one believes has been reported or is being corrected. However, because we know of only one study examining this phase (Miceli, Van Scotter, Near & Rehg, 2001b), we will focus on the other phases. In Phase 3, observers of wrongdoing who believe some action is warranted must make further decisions (e.g. ‘‘am I responsible for acting? Is there something that I could do that might stop the wrongdoing? Will the benefits of a considered action outweigh the costs?’’). Non-observers cannot proceed to Phase 3, because by definition, observing wrongdoing deserving of action is a necessary condition for whistle-blowing. Those who do take action are whistle-blowers. Thus, the dependent variable in Phase 3 is whether one is an inactive observer, versus whether one blows the whistle. Theories of power, among other factors, are integrated into the model, because observers of wrongdoing who believe themselves to be more powerful are more likely to respond affirmatively to the above questions (e.g. Miceli et al., 1991). For example, those who hold important or credible

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positions – such as Sherron Watkins of Enron – may feel that they are obliged to act, that they can get someone to listen, and that they will escape negative consequences such as retaliation. In their social information processing model, Gundlach et al. (2003) proposed that additional variables affect the cost–benefit analysis of blowing the whistle, including the whistle-blower’s attributions about why the wrongdoer acted and the wrongdoer’s attempt to influence the whistle-blower through impression management. They argue that whistle-blowers who attribute the wrongdoing to internal actions of a stable nature, that are controllable and intentional, will view the wrongdoer as responsible for the wrongdoing, and therefore the wrongdoing is deserving of reporting. Further, to the extent that the wrongdoer does not take steps to manage the impression made, the potential whistle-blower is likely to feel anger and resentment, and to move to actually blow the whistle. This reaction may be tempered by fear, however, if the whistle-blower views the wrongdoer as powerful. These predictions extend the original model, and we look forward to the empirical tests.

THE LEGAL CONTEXT FOR WHISTLE-BLOWING Legal research has focused on changes in the legal landscape that were intended to increase valid whistle-blowing, and in some cases seem to have done so. We begin by reviewing the legal research, and then we address the theory and empirical findings produced by the social science research, first concerning observers of wrongdoing and second concerning whistleblowers. Most of the legal developments could be said to be directed toward providing more rewards for whistle-blowers and greater protection (lower costs) from retaliation, which comprise part of Phase 3 of the Dozier and Miceli and Gundlach et al. models (which we will treat as one model, simply to ease communication). But legal developments may also directly or indirectly affect the incidence of wrongdoing in the first place (Phase 1), thus affecting observation and earlier phases in the model. Therefore, we discuss legal developments before behavioral research focusing on Phases 1 and 3. As noted earlier, Phase 2, which is concerned with the effects of wrongdoing on employees’ feelings about their organizations is generally unexamined, empirically. Private employers in the U.S. are largely governed by the employmentat-will doctrine (e.g. Westin, 1981). Basically, this doctrine provides that either the employer or the employee may terminate the employment relationship at any time. Without limitations on this doctrine, employees

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who contemplate whistle-blowing recognize that they are in a precarious position, since employers could retaliate or fire them with impunity. In 1978, the passage of the Merit Systems Protection Board Act changed the legal environment facing federal employees (U.S. Merit Systems Protection Board, 1981). Specifically the new law encouraged whistle-blowing and prevented retaliation against whistle-blowers. In 1989, the Whistleblower Protection Act was passed and provided further protection to federal employees who blew the whistle (Dworkin & Callahan, 2002). Repeated surveys of federal employees provide a picture over time of the effects of these changes in the law. Results of a study (Miceli et al., 1999) comparing incidence of whistle-blowing and retaliation during this 25 year period show that respondents reported that organizational wrongdoing decreased and whistle-blowing increased, both positive consequences. Unfortunately, these effects were counterbalanced by negative consequences: retaliation increased over this time period and the percentage of whistle-blowers who chose to remain anonymous increased, presumably because of fears of retaliation. Thus, the law’s effects must be viewed as mixed. Beyond that, these results suggest an important question in assessing predictors of whistle-blowing: are all types of whistle-blowing (e.g. anonymous, internal, external) predicted by the same variables, or should types of whistle-blowing be examined separately, when attempting to identify predictors? While the Merit Systems Protection Board Act did not directly affect private employers, four other changes in the legal environment did so. First, the False Claims Act was enacted during the Civil War era, but was considerably strengthened in the 1980s (Phillips & Cohen, 2004). The False Claims Act provides financial rewards to whistle-blowers who successfully prosecute lawsuits against individuals or firms that defrauded the government, and many states have written their own versions of similar statutes (Dworkin & Callahan, 2002). Second, the Corporate Sentencing Guidelines (United States Sentencing Commission, 1991, Sentencing Guidelines, Chapter 8.) were written to encourage firms to develop effective compliance programs to prevent corporate wrongdoing. Firms that do so may qualify for reduced sanctions, should they be found to have committed wrongdoing (Dworkin & Callahan, 2002). Third, state courts have often supported employees of private firms who were fired for whistle-blowing, when the wrongdoing harmed the public at large. This ‘‘public policy exception’’ meant that even employees of private employers could sue for protection, if their employment-at-will relationship had been terminated because they blew the whistle against the employer for wrongdoing covered under the exception (Dworkin & Near, 1987). In fact,

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lawsuits filed under this existing tort law arrangement exceeded in number those filed under new state whistle-blower statutes, in the 1980s (Dworkin & Near, 1987). Fourth, Congress has added anti-retaliation components to many statutes aimed at particular types of employees and employers. For example, the National Labor Relations Act of 1935 (as subsequently amended) protects employees engaged in union-related activities from retaliation by their employers, specifically those who testify or file charges concerning illegal unfair labor practices. These kinds of protections are limited to those who blow the whistle about activities specifically regulated by the statute (Dworkin & Callahan, 2002). Another example is provided by the Federal Mine Health and Safety Act of 1977, which protects miners who report unsafe conditions (Dworkin & Callahan, 2002). These changes in the legal environment raise two questions: (a) have organizations changed their policies and operations so as to encourage whistle-blowing? and (b) have these laws directly encouraged employees to blow the whistle? Empirical results on both questions are mixed.

Effects of Legal Changes on Employer Behavior Preliminary research on corporate response to legal changes showed few changes, at least early on. A survey of human resource executives from Fortune in 1000 firms (Near & Dworkin, 1998) asked whether their firms changed their whistle-blowing policies in response to changes in new state statutes (see also Dworkin, Near & Callahan, 1995). The authors expected that firms might have created internal channels for whistle-blowing in response to the new legislation, but very few firms indicated that they had created their policies in responses to legal changes. For most, this meant reliance on an open-door policy as their primary mechanism for internal whistle-blowing. Unfortunately, most employees do not see such policies as effective or protective, and they have not been used successfully to encourage internal reporting of wrongdoing (Keenan, 1990). Researchers have found a positive correlation between increased internal whistle-blowing and having specific, identified routes for whistle-blowing, a particular person identified to receive and follow up the information, and a strong, nonretaliatory policy encouraging whistle-blowing (Barnett, Cochran & Taylor, 1993; Miceli & Near, 1992). Open-door policies do not meet these requirements. They are also unlikely to result in compliance under the Federal Sentencing Guidelines.

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Even if these legal changes have not had the desired effect, there is some evidence that private employers have changed their policies over time, in part because employees and citizens demand it. For example, a survey by the Ethics Resource Center found that 79% of employers have a written ethics standard, up from 60% in 1994 (Grimsley, 2000). We believe that employers will be more likely to take such actions in the future, because of pressures from individual employees who are increasingly responding to legislative changes aimed directly at potential whistle-blowers. We now turn our attention to results pertaining to direct effects of legislative changes that may affect the decision by employees to blow the whistle, once they observed wrongdoing.

Effects of Legal Changes on Employee Behavior As Phase 3 of the whistle-blowing model indicates, observers of wrongdoing consider the costs and benefits of acting, along with other factors. The simplest interpretation of motivation theory would suggest that valued rewards for internal whistle-blowing would lead to greater internal reporting, all other factors such as potential retaliation being equal or minimized. However, we know of no private sector U.S. organizations that provide direct financial incentives specifically to reward whistle-blowing, although whistle-blowing could be indirectly rewarded through a merit system or a suggestion bonus system. There are potential financial incentives for citizens who save the federal government money by informing it of fraud by contractors or other activity. The False Claims Act, dating to the Civil War, allows whistle-blowers to collect up to 30% of the damages (Callahan & Dworkin, 1992; Seagull, 1995), which has produced awards as high as $77 million (Haddad & Barrett, 2002). Prior to 1996, the behavioral literature generally did not examine whether financial incentives actually affected whistle-blowing. In the 1980 MSPB study, federal employees were asked whether they would be more willing to blow the whistle if they received financial rewards for reporting wrongdoing; a large majority said this would not affect their behavior (U.S. Merit Systems Protection Board, 1981). This referred to hypothetical behavior, so we have no clear indication of likely effects. However, four factors lead us to think that awards for whistle-blowing probably encourage it. First, given that financial incentives are exceedingly rare, as are whistleblowers, it is entirely possible to interpret the finding as showing that

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financial incentives are actually an essential part of any program to encourage the rectification and prevention of wrongdoing. The finding could indicate that whistle-blowers to date would be the minority of people who (a) could see far more non-financial benefits than usual, already in the situation, (b) are extremely selfless, or (c) process information in ways most people might consider ‘‘emotional’’ rather than a rational assessment of expected costs and benefits. They accurately say they do not require cash incentives, because their past action shows they do not. However, for organizations to induce the ‘‘typical’’ worker who observes serious wrongdoing to take action, it may be necessary to offer immediate, substantial positive rewards. Second, taboos in the U.S. and privacy concerns often discourage admissions that money is valued (even in business relationships). Even CEOs are expected to say that they live for the challenge of their work first and foremost, and millionaire athletes are not worthy of respect unless they play ‘‘for the love of the game.’’ Third, respondents may have viewed this question as asking whether they can be bribed into moral behavior. For either of these reasons, survey responses relative to incentives may bear little relationship to what employees actually think and do. Fourth, studies in the law literature reveal a different picture (Seagull, 1995). In 1986, the False Claims Act was revised, such that whistle-blowers were more likely to receive a reward (Callahan & Dworkin, 1992). Prior to 1986, about six false claims for government funds had been reported per year by whistle-blowers; this increased to 1,500 suits between 1997 and 1999 (Johnson, 2003). If potential whistle-blowers are motivated to act by financial rewards, then private employers are more likely to protect themselves by changing their policies and procedures to prevent wrongdoing in the first place or to terminate it when informed by their employees that wrongdoing is ongoing. While monetary incentive has not been a big factor in the past, we believe that these changes in the legal environment eventually will have a huge impact on encouraging employees who observe wrongdoing to blow the whistle. As will be discussed below, the type of wrongdoing (specifically wrongdoing that results in fraud against the federal government) will probably become an important predictor of whether employees who observe wrongdoing decide to blow the whistle. We now turn our attention to behavioral research. Table 1 provides a listing of the published studies addressing the question ‘‘who blows the whistle?’’ This table does not include articles dealing specifically with

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Table 1. Author(s), Year Brewer and Selden (1998) Dworkin and Baucus (1998)

Design

Data Source(s)

Sample

Secondary analysis of survey data Analysis of legal cases alleging wrongful firings of whistle-blowers in violation of a public policy Survey and scenarios

1992 MSPB data; stratified random sample Legal cases

Employees of 22 federal agencies Various

National sample from marketing research firm database Participants in Indian management seminars; ‘‘matching’’ U.S. sample drawn from Keenan (2002a) Employees of local hospitals MSPB data set

Managers from low to high levels

Keenan (2002a)

Survey and scenarios

King (1997)

Survey and scenarios Survey

Lee et al. (2004) Miceli et al. (1999)

Secondary analysis of survey data

3 MSPB data sets

No. of Subjects Subsamples of 13,432 employees 33 cases

Middle level managers in large manufacturing companies

725 non-differentiated; stated likelihood to blow the whistle rather than actual measured 31 Indian men, 45 U.S. men; stated likelihood to blow the whistle rather than actual measured

Nurses

261 nurses in a local hospital

Employees of federal agencies Employees of federal agencies

1,952 female victims of sexual harassment Respondents included 8,296 in 1980, 4,427 in 1983, and 13,432 in 1992

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Keenan (2002b)

Empirical Studies on the Prediction of Whistle-blowing, Published Since 1995.

Survey and organization records

Military base

Civilian and military employees

Near et al. (2004)

Survey

Military base

Rothschild and Miethe (1999)

Various samples from the public and private sectors, and the 1992 MSPB data set

Sims and Keenan (1998)

(1) Non-random accounts (2) Telephone surveys (3) Interviews (4) Secondary analyses Survey and scenarios

Civilian and military employees Various

Undergraduate and graduate students

Full-time employees

Sims and Keenan (1999)

Survey and scenarios

Mid-sized firms in Jamaica; ‘‘matching’’ U.S. sample drawn from a 1988 survey

Managers

1,828 non-observers, 97 inactive observers rectified who observed wrongdoing but believed someone else had reported it or it was already being resolved, 934 other inactive observers, 72 whistleblowers who reported wrongdoing only to their supervisors, 199 whistle-blowers who used internal channels but no external channels; and 50 whistle-blowers who reported through external channels 1,224 observers of perceived wrongdoing Number ranged from 27 to 292 employees, plus 13,432 respondents in the MSPB data set

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248 respondents; stated likelihood to blow the whistle rather than actual measured 42 Jamaican managers, 44 U.S. managers; stated likelihood to blow the whistle rather than actual measured

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the reporting of sexual harassment rather than the more general topic of whistle-blowing, but we do include those articles below. As noted earlier, the model proposes that there are three major decision phases that precede whistle-blowing. In Phase 1, the employee judges whether she or he has observed wrongdoing that has occurred (within a specified time period, such as in the past year) by answering such questions as ‘‘do I believe wrongdoing is occurring?’’ and ‘‘is action warranted?’’ Variables that are likely to affect the answers have been the focus of research.

PHASE 1: PREDICTORS OF OBSERVATION OF WRONGDOING Personal Characteristics Some non-observers do not witness wrongdoing because, by any standard, none has actually occurred, but evaluating wrongdoing often involves subjectivity, as noted above. Some employees do not perceive wrongdoing even when most others believe it does exist, and some employees define certain events as wrongful that others may not. Earlier models of the whistle-blowing decision, described above, postulated many factors that may affect the observation of wrongdoing, e.g. employee moral development, cognitive processing, and wrongdoing seriousness (e.g. Miceli & Near, 1992), which have not been examined empirically, as predictors of observation of wrongdoing. It is quite difficult to study this empirically, however. Most field studies of whistle-blowing have relied on observers’ perceptions of wrongdoing, rather than provide a separate measure (e.g. of others’ perceptions of the same triggering events), both of which may be colored by bias, or some objective measure, which in many cases (e.g. sexual harassment, mismanagement) does not exist. As a result, studies generally have not directly examined what personal or situational variables (e.g. the seriousness of the wrongdoing) cause observers to label an activity or omission as wrongdoing deserving of action. We know of only one study (Miceli et al., 2001b) examining the relationship between observation of wrongdoing and dispositional characteristics; it focused on negative affectivity (NA) (Watson & Clark, 1984). NA may influence perceivers’ views of questionable activity (Miceli et al., 2001a). NA is a general disposition to experience subjective distress; people high in NA are more critical of themselves and others, and experience more

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stress, anxiety, anger, fear, and guilt (Watson & Clark, 1984). Employees high in NA have more work-related stress than other people and interpret neutral or ambiguous situations more negatively (Parkes, 1990). Thus, they should be more likely to judge an activity to be wrongful than other employees. Consistent with this reasoning, NA differentiated non-observers from observers of wrongdoing (Miceli et al., 2001b). Fortunately, research on sexual harassment, one type of wrongdoing that occurs in organizations, sheds more light on the relevance of personal characteristics of the employee to whether she (less frequently he) observed the wrongdoing. Researchers have found that more ‘‘vulnerable’’ employees were actually more likely to experience sexual harassment, when compared with other employees (Fitzgerald, Drasgow & Magley, 1999). Perceived sexual harassment decreased with marital status (married or widowed less vulnerable than single), age and education level (Coles, 1986; Dougherty, Turban, Olson, Dwyer & LaPrese, 1996; Fain & Anderton, 1987; HessonMcInnis & Fitzgerald, 1997; LaFontain & Tredeau, 1986; Martin, 1984; Schneider, 1982; Tangri, Burt & Johnson, 1982), and rank (Bergman, Langhout, Cortina & Fitzgerald, 2002). This occurred presumably because these employees were less vulnerable to harassers (e.g. they might fear retaliation less because they were less financially dependent on the job). Young, unmarried women may be more likely to experience sexual harassment, especially if they are also not supervisors and have lower levels of income, because they are seen as powerless targets by harassers; thus, we would say that these employees were more likely to observe wrongdoing, when compared with other employees. These employees may also be less likely to blow the whistle because they view themselves as powerless in the organization, and therefore more likely to suffer retaliation than other employees – but that is a separate question. Future research is needed to identify variables that predict whether employees exposed to organizational wrongdoing actually label it as wrongdoing, apart from whether they then choose to blow the whistle about the wrongdoing. If self-censorship causes employees not to notice wrongdoing, then they cannot blow the whistle. For example, in addition to demographic variables, we might expect other variables, such as personality, to predict observation of wrongdoing. The Time article suggested that all three whistle-blowers were exceptionally loyal to their organizations, perfectionist in tendency, and aggressive about pursuing situations that they thought were wrong. Since many of their colleagues apparently saw the same evidence that confronted the whistle-blowers, but noticed nothing amiss, we must wonder what characteristics caused these whistle-blowers to

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notice wrongdoing when others around them did not. Without research to identify differences between employees who tend to notice wrongdoing and employees who ignore wrongdoing, we can only speculate about the type of personality characteristics and other individual differences that might differentiate observers of wrongdoing.

Situational Characteristics We consider three sets of situational variables, namely, characteristics of the perceived wrongdoing, characteristics of the job or organization, and country and culture variables. Characteristics of the Perceived Wrongdoing Research on sexual harassment provides some guidance about possible differences between non-observers and others. Empirical attention has been devoted to the question of how employees define certain actions to be sexual harassment, that is, the labeling process by which employees decide that what they have experienced actually constituted sexual harassment. Sociologists note that deviant behavior is not really recognized as ‘‘deviant’’ until people ‘‘label’’ it as such (e.g. Merton, 1957). Of course, each type of wrongdoing is in some way unique, and sexual harassment is no exception. For example, the employee who reports the harassment is usually the person who suffered the harassment – unlike other types of wrongdoing where the employee may observe the event but not suffer directly from its occurrence. The events surrounding the wrongdoing may be less ambiguous than in other cases of wrongdoing because the employee directly observes the events. On the other hand, whether the events constitute sexual harassment and therefore wrongdoing may be more ambiguous than in other cases of wrongdoing. For example, accounting errors are probably less ambiguously wrong than repeated sexual teasing, which may be viewed as sexual harassment in some contexts and not in others. For this reason, the importance of the labeling process is probably better recognized in research on sexual harassment than in research on other forms of wrongdoing. Sexual harassment research shows that more explicit and extreme sexual harassment is related to the perception that harassment occurred (Fitzgerald & Ormerod, 1991; Gutek, Morasch & Cohen, 1983; Reilly, Carpenter, Dull & Bartlett, 1982; U.S. Merit Systems Protection Board, 1981). That is, employees are more likely to ‘‘label’’ their experience as sexual harassment where triggering events are explicit, clear or severe.

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Frequency of sexually harassing behavior was found to increase perceived offensiveness, a mediating variable that directly predicted reporting of the harassment (Bergman et al., 2002; Brooks & Perot, 1991). These findings suggest that it is the perceived seriousness (i.e., frequency, offensiveness, explicitness) of the sexual harassment that causes employees to label their experience as harassing, a necessary first step to blowing the whistle. Characteristics of the Job or Organization The literature on sexual harassment has also identified characteristics of the job and organization that may later be shown to predict other types of wrongdoing, or the assessment of wrongdoing in general. Several studies demonstrated that organizational tolerance of sexual harassment is related to incidence of harassment (Bergman et al., 2002; Fitzgerald, Drasgow, Hulin, Gelfand & Magley, 1997; Hulin, Fitzgerald & Drasgow, 1996; Timmerman & Bajema, 2000; Williams, Fitzgerald & Drasgow, 1999). Women were more likely to encounter sexually harassing situations when they worked in a mostly male environment (Coles, 1986; Ellis, Barak & Pinto, 1991; Fain & Anderton, 1987; Fitzgerald et al., 1997; Fritz, 1989; Gutek & Cohen, 1987; Martin, 1984; Schneider, 1982), worked with male supervisors (Gutek, 1985), or performed stereotypically male tasks (Coles, 1986; Gutek, 1985; Gutek & Dunwoody, 1987; Koss et al., 1994; LaFontain & Tredeau, 1986; U.S. Merit Systems Protection Board, 1988). This may have occurred because these situations created greater tolerance of sexual harassment. Women with male supervisors or in non-traditional positions were more likely to believe that the organization tolerated sexual harassment than were women with female supervisors (Hulin et al., 1996). In one study, organizational tolerance and job gender context together affected level and frequency of sexual harassment (Fitzgerald et al., 1999). Job gender context was measured as the degree to which the respondent believed his/her job was usually held by a person of his/her gender and that personnel of same gender holding this job was uncommon; supervisor’s sex; and gender ratio among co-workers. As noted above, observers of sexual harassment may differ in some ways from observers of other types of organizational wrongdoing. What is similar in both cases, however, is the judgment process whereby employees who observe the event must decide whether it really constitutes wrongdoing. Often, they may find the stimulus event to be ambiguous, and need to discuss it with co-workers or friends, or to search for confirming evidence before they are convinced that it constitutes actual organization wrongdoing. Current

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research on silence among employees has generally assumed that some employees remain silent in the face of clear evidence of organizational problems (e.g. Morrison & Milliken, 2003) but often, in fact, part of the reason employees remain silent is that they are not sure that a problem really exists – or at least a problem of such significant magnitude that it warrants mentioning. Country and Culture Variables Unfortunately, due to the paucity of research, it is too early to develop a taxonomy of cultural influences on the observation of wrongdoing and whistle-blowing. Such a taxonomy must also include a comprehensive treatment of legal, economic, and organizational conditions that may differ across countries, as all of these may affect whistle-blowing. Recent attempts to specify such a typology have focused primarily on cultural influences as predictors of whistle-blowing behavior within countries (Rehg & Parkhe, 2002). The preliminary cross-cultural research that has been done on how wrongdoing is judged and whistle-blowing, while interesting, has relied heavily on scenario designs, in which participants are asked how they might behave if placed in a given situation, and has been limited by small samples. Nonetheless, we agree with Keenan and his colleagues (e.g. Keenan, 2002a; Sims & Keenan, 1998, 1999; Tavakoli, Keenan & Crnjak-Karanovic, 2003) that theories about cultural differences imply intriguing research questions about whistle-blowing. Perhaps the best known theory is that of Hofstede, who noted that management theories developed mainly in Great Britain and the U.S. are based on Western individualistic assumptions that may not apply for the majority of the world’s population in other continents (e.g. Hofstede, 1980, 1999; Hofstede, Neuijen, Ohayv & Sanders, 1990). The Hofstede model proposed that cultures vary across six key value dimensions, and has stimulated a great deal of recent research, some of which proposed alterations to the original dimensions (Sivakumar & Nakata, 2001). Although the Hofstede taxonomy and scales have been criticized (e.g. Yoo & Donthu, 2002), the developing research literature may inform research on whistle-blowing. In one scenario-based study comparing U.S. managers’ and Jamaican managers’ stated propensity to blow the whistle (Sims & Keenan, 1999), hypotheses were derived from all four of Hofstede’s dimensions. Specifically, Sims and Keenan argued that U.S. culture would be rated as higher than Jamaican culture on individualism and uncertainty avoidance and about the same on masculinity and power distance. While they did not measure these dimensions directly, they did find significant differences between the two samples on some variables. Jamaican managers were more likely than U.S.

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managers to view minor fraud as immoral, while U.S. managers were more likely to view major fraud and harm to others as immoral. Despite this, they differed in their stated likelihood to blow the whistle only for major fraud, in which scenarios U.S. managers said that they would be more likely to blow the whistle than did Jamaican managers. Finally, U.S. managers rated their organization’s propensity to encourage whistle-blowing lower than Jamaican managers but their own individual propensity no differently; both groups rated their fear of retaliation at nearly identical levels. Unfortunately, this study did not include data about actual whistle-blowing decisions, nor could the differences between samples be reliably ascribed to cultural differences on Hofstede’s dimensions, since the effects of other potential predictors (e.g. organizational characteristics) were not controlled. A second study attempted to link the Hofstede model to ethical decisionmaking; the authors provided preliminary evidence that culture scores on the ‘‘masculinity–femininity’’ dimension predicted negotiators’ emphasis on justice versus caring (French & Weis, 2000). This dimension may affect whether questionable activity is judged to be wrongful and worthy of action (Phase 1 of the model), and it may also affect evaluations of the costs and benefits of action (Phase 3). Whether this finding can be extended to observation of wrongdoing and whistle-blowing remains to be explored. Previous research has linked moral development and whistle-blowing (e.g. Miceli & Near, 1992). There has been some discussion in the moral development literature as to whether the stages of moral development (Kohlberg, 1969) are gender neutral, and these could be related to or affected by the masculine–feminine dimension of the culture. Whereas Kohlberg argued that the highest stage of moral development would involve a ‘‘principled’’ conception of justice, Gilligan found that women were more likely to reach a stage of moral development where they focused on preserving relationships as the highest moral good; for example, they would be more likely to engage in questionable behavior to save a friend in life-threatening circumstances than would men (Gilligan, 1982). She argued that Kohlberg’s characterization of this stage as lower-level suggested a misinterpretation of the data because the decision to help a friend by committing some unethical behavior (e.g. stealing drugs from a drug store for a dying, penniless spouse) in fact represented the greatest level of altruism. Given this argument, the question arises as to whether Hofstede’s labeling of cultures as more masculine or feminine could be related to questions of moral development. Would employees in more ‘‘masculine’’ cultures (such as in Japan and in the U.S.) be more concerned with the validity of the specific charges raised by the whistle-blower, whereas ‘‘feminine’’ cultures

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(such as France) would be concerned more for the consequences of the alleged wrongdoing on people in society (e.g. political corruption may be viewed as inevitable and relatively minor in consequence), or the consequences of whistle-blowing on people in the organization (e.g. ‘‘finking’’ is an uncaring and hence ‘‘bad’’ thing to do to one’s colleagues)? In other words, would the focus be on relational issues in more feminine cultures rather than on following the rules or focusing exclusively on legal issues? These and many other questions derived from Hofstede’s model could be examined.

PHASE 3: PREDICTORS OF WHISTLE-BLOWING Personal Characteristics Most research examining characteristics has controlled for observation of wrongdoing, by comparing the characteristics of inactive observers and whistle-blowers versus those of non-observers, or by providing scenarios describing the same wrongdoing to all respondents. While we recognize that these are imperfect controls, they do help to sort out how people who believe they have witnessed organizational wrongdoing may differ in their reactions to it, depending on a number of variables. Conceptually, in this phase, observers of wrongdoing must decide whether the costs of acting, including possible retaliation and time expended, outweigh the benefits likely to result, which may include the desired termination of the wrongdoing as well as cash awards and other benefits (Dozier & Miceli, 1985). This assessment may be either a cognitive or an emotional process, or a combination (Gundlach et al., 2003). Power theories have been integrated within this phase of the prosocial model, because, for example, employees who have more power may be more likely to believe they can bring about change and to escape retaliation (e.g. Near & Miceli, 1987). Demographic characteristics may serve as one indicator of power. Generally, consistent with these notions, empirical research prior to 1996 showed some tendency of whistle-blowers to be male, supervisors or above, and more senior, with higher pay and performance. But this finding was by no means clear-cut. Research rarely showed opposite findings – such as that whistle-blowers had relatively little power (junior, non-supervisory women, for example). But in many cases, there was no relationship between whistleblowing and demographic variables. Findings since that time are similar. Research since 1996 shows that whistle-blowing is positively related to being male (Miethe, 1999; Sims & Keenan,

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1998). Although some studies reported a positive correlation with age and years of service to the organization (Brewer & Selden, 1998; Dworkin & Baucus, 1998), others reported no relationship (Lee et al., 2004; Sims & Keenan, 1998). Education was positively correlated with whistle-blowing in one study (Brewer & Selden, 1998), but not in another (Sims & Keenan, 1998). Similarly, pay was positively correlated with whistle-blowing in one study (Brewer & Selden, 1998), but not in another (Lee et al., 2004). Supervisory status was unrelated to whistle-blowing (Lee et al., 2004), and in an interview study relying on a national sample that was not randomly selected, the authors found no socio-demographic characteristics that distinguished whistle-blowers from ‘‘silent observers’’ (Rothschild & Miethe, 1999). As of 1996, we found that whistle-blowing was related to values about whistle-blowing but not consistently related to moral judgment or personality characteristics; since that time very few personality and generalized personal belief factors have been investigated. We know of no addition to the one study published before 1996 that examined religious beliefs and whistle-blowing. However, the media sometimes report anecdotes in which whistle-blowers attribute their action at least partially to their religious or moral beliefs (e.g. Bates, 1999). Whistle-blowing was positively associated with having a proactive personality (Miceli et al., 2001b).

Situational Characteristics As before, we consider three sets of situational variables, namely, characteristics of the perceived wrongdoing, characteristics of the job or organization, and country and culture variables. Characteristics of the Perceived Wrongdoing In our previous review (Near & Miceli, 1996), we summarized research showing that characteristics of the wrongdoing generally have had significant relationships to whistle-blowing. The type of wrongdoing and its seriousness or magnitude have often, but not always, been associated with whistle-blowing. Research since then has supported this argument. In one field study, hospital nurses reviewed scenarios; wrongdoing severity, i.e. the nurses’ perception of danger or risk, was associated with greater reporting of wrongdoing to the supervisor, as prescribed by the organization, but not to other channels (King, 1997). Respondents indicated that they did not consider reporting to the supervisor to be whistle-blowing, but rather to be using the chain of command to report concerns about possible wrongdoing;

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in this industry, this kind of reporting was seen as legitimate and normal – and decidedly not as whistle-blowing (King, 1994). A study of whistleblowing about sexual harassment found that predictors of whistle-blowing were similar to those seen in studies where the whistle-blowing concerned other types of wrongdoing (Lee et al., 2004), including frequency and length of sexual harassment, number of types of sexual harassment, having multiple harassers and the organization level of harassers (inverse relationship). Finally, results from a study of differences in whistle-blowing by type of wrongdoing indicated that rates of whistle-blowing varied significantly by type of wrongdoing (Near et al., 2004). In particular, employees who observed wrongdoing involving mismanagement, sexual harassment or unspecified legal violations were significantly more likely to blow the whistle than employees who observed stealing, waste, safety problems, or discrimination; those who did not blow the whistle varied significantly as to their reason for not doing so. Further, type of wrongdoing was significantly associated with cost of wrongdoing and quality of evidence of the wrongdoing – both of which are variables found to predict whistle-blowing in earlier studies (Miceli & Near, 1992). Type of wrongdoing was not significantly related to whether the whistle-blower was identified or anonymous, whether the whistle-blower thought the job required whistle-blowing in cases of observed wrongdoing, or frequency of wrongdoing. In their conceptual discussion of the social information processing model, Gundlach et al. (2003) argued that characteristics of the wrongdoer influence the whistle-blower’s view of the wrongdoing, but some of the characteristics they cited also pertain to the nature of the wrongdoing, including whether the outcomes are under the control of the wrongdoer or stable, reflecting whether the wrongdoer engages in this wrongdoing more than once. Other characteristics pertain only to the wrongdoer, including whether the wrongdoing was intentional or caused by internal characteristics rather than external pressures. Finally, they argued that the wrongdoer’s attempt to manage the potential whistle-blower’s impressions of the wrongdoing will influence whether the individual decides to actually blow the whistle; again, this process may relate to characteristics of the wrongdoing as well as the wrongdoer. It is highly plausible that all of these variables do indeed influence whistle-blowing, but we know of no published empirical tests of these propositions to date (Gundlach et al., 2003). In our earlier review of the literature, we concluded that quality of evidence and supervisor support for the whistle-blower had been found to be positively related to whistle-blowing. The choice to use external channels seemed to be associated with having better quality evidence and with low

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social status of the wrongdoer. More recent research suggests that some of these variables may be confounded with type of wrongdoing. It is perhaps instructive in this regard that the Time selection of three whistle-blowers as persons of the year focused heavily on whistle-blowers who acted upon evidence of wrongdoing that was clear and unambiguous – accounting fraud in two cases (i.e. Enron and WorldCom) and misuse of data in the third (i.e. with the FBI). We wonder whether whistle-blowers who reported misdeeds that were more complex or ambiguous would have received the same level of commendation. Additional research is needed to untangle the web of predictor variables that are related to the type of wrongdoing being reported; findings to date are tantalizing but do not fully explicate the effects that type of wrongdoing has on the whistle-blowing process. Characteristics of the Job or Organization Our earlier review (Near & Miceli, 1996) indicated that some studies showed whistle-blowers were more satisfied and committed than inactive observers and perceive the organization to be more just. In a more recent scenario study of whistle-blowing, there was no relationship between whistle-blowing and job satisfaction or organizational commitment (Sims & Keenan, 1998). While affective reactions to the job or workplace may also be influenced by personal characteristics (Judge, Heller & Mount, 2002), we classify satisfaction and commitment as primarily situational, due to the commonly held view that satisfaction and commitment are primarily a function of the situation (e.g. Hodson, 2002). We recognize that some may prefer to label these variables as ‘‘individual variables’’, because scores will obviously vary from person to person. In our earlier review, we concluded that whistle-blowing was more likely in organizations that supported whistle-blowing. Organizations with higher rates of whistle-blowing seemed to be high performing, to have slack resources, to be relatively non-bureaucratic, and to cluster in particular industries or in the public rather than private or not-for-profit sectors. Group size was positively related to whistle-blowing, but quality of supervisor was not. Recent research has further elucidated the possible effects of industry on whistle-blowing. What may manifest itself as an apparent industry effect may be either personal or situational, or a combination. The public service motivation effect (Perry & Wise, 1990) suggests that individuals are drawn to work in the public sector because of their wish to serve the public. Thus, a selection effect (e.g. organizational recruiters may search for people who appear to

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share these values) or self-selection effect (e.g. job seekers choose organizations where they believe their values will be supported) could occur. If so, either could produce more whistle-blowing in the public sector than in the private sector, because whistle-blowing may be seen as consistent with serving the public or society-at-large (as opposed to disloyalty to the employer). Consistent with this notion, an examination of whistle-blowing incidents described in articles appearing in 30 major newspapers over a 7-year period showed that 70% of the incidents occurred in the public sector, a number much higher than would be expected, given that only about 20% of the U.S. workforce was during that period employed in the public sector (Brewer, 1996). Interview research with whistle-blowers solicited across the United States yielded the same conclusion (Rothschild & Miethe, 1999). Of course, alternative explanations could be offered and examined. Characteristics of the Country or Culture In some cultures, the term ‘‘whistle-blower’’ is sometimes viewed as derogatory, having a ‘‘snitch’’ connotation, whereas in others, ‘‘bell-ringer’’ or ‘‘lighthouse keeper’’, is preferred (Johnson, 2003). Various non-governmental organizations have focused on the goal of reducing corruption, including the American Bar Association, the Ethics Resource Center, GAP and Transparency International. Governmental agencies, including USAID, OECD, the Department of Commerce, the World Bank, and the Asian Development Bank, have embraced support for whistle-blowing as a way to reduce corruption, thereby improving the functioning of democracies around the world (Johnson, 2003). Clearly, by their encouragement of whistle-blowing, these institutions serve to legitimize it, in such a way that it may be more fully accepted even in cultures that traditionally have viewed the reporting of wrongdoing as akin to snitching. The question of whether dimensions from the Hofstede model (discussed earlier) might be relevant to whistle-blowing has been raised. An interesting study comparing the values of American and Croatian managers has appeared, but unfortunately, this study did not examine actual whistle-blowing (Tavakoli et al., 2003). It has been argued (Rehg & Parkhe, 2002) that whistle-blowing would be less likely in collectivist cultures, where it might be viewed as disloyalty against the group. In cultures where high power distance exists, such as in India, there is greater pressure to conform to accepted organizational practices. In such cultures, employees may have a greater fear of retaliation for blowing the whistle, though no support for this proposition emerged in preliminary studies (Keenan, 2002a). However, pressure to conform in organizations is further emphasized when the structure is

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bureaucratic (Weinstein, 1979). This suggests an interaction effect between societal cultural differences (high power distance versus low power distance) and organizational structure (highly bureaucratic versus low bureaucratic), such that organizational members in high power distance cultures or highly bureaucratic organizations would experience a ‘‘double whammy’’ and be more unlikely to blow the whistle than organizational members working under other conditions. Finally, workers in more individualistic cultures may be more supportive of whistle-blowing than would collectivistic cultures, where members might be more likely to judge the whistle-blower as a traitor to the employer and co-workers (Rehg & Parkhe, 2002). In other words, collectivistic cultures may view whistle-blowing as an attack on the collectivity, meaning the organization itself and its members. But it is also possible that individualistic cultures would be less supportive, because the collectivistic culture would value the benefit to society of whistle-blowing (e.g. to protect the public from unsafe products or financial fraud) above any concern for the potential repercussions for an employer viewed as harming society. Preliminary research on whistle-blowing in South Korea (Park, Rehg & Lee, in press) has examined conditions under which the whistle is blown, but has focused only on samples drawn from South Korea, which Hofstede characterized as a collectivistic culture, so no comparative data are available. Other research questions related to societal culture can easily be envisioned. The potential role of organizational culture should also be considered. For example, one study showed that the organization’s effects were stronger than the society’s effects on member behavior, although the researchers found that both were important (Hofstede et al., 1990). It is possible also that one effect might moderate the other, in other contexts. Unfortunately, this study did not include whistle-blowing or organizational dissent variables. Thus, much could be learned from investigation of whistle-blowing in settings outside North America.

Summary In 1996, we concluded that ‘‘empirical research to date has not shown that whistle-blowers are inherently different from those organization members who observe wrongdoing but chose not to report it. In other words, there is no evidence that whistle-blowers are typically crackpots. Basically, whistleblowers are employees who are in the wrong place at the wrong time – that is, they have the opportunity to observe wrongdoing, often because of the

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nature of their jobs. Although it is premature to draw conclusions, there is some evidence that if the wrongdoing is sufficiently serious and if potential whistle-blowers believe they can successfully cause the termination of the wrongdoing, they will act. Organizations can encourage the use of internal channels to blow the whistle by providing sufficient information to employees about the use of these channels and providing reassurance that they will not suffer retaliation if they use the internal channels. Preliminary research evidence indicates that whistle-blowers use external channels when they do not know about the internal channels and when they think the external channels will afford them protection from retaliation’’ (Near & Miceli, 1996, p. 515). Since 1996, our conclusion is largely the same, except that we now have a bit more evidence suggesting that individual variables play a stronger role than previously thought. But we are disappointed that, while a massive amount of information dealing with whistle-blowing has been published since 1996, most of it is anecdotal. Very little controlled research on whistleblowing has been published, particularly in the top journals; with more research, we might have more new directions to report. Further, there is evidence that changes in the legal and possibly the social environment are influencing the behavior of both organizations and potential whistleblowers, suggesting that findings from earlier periods may no longer apply. Finally, we recommend that future research direct more attention to the question of type of wrongdoing and industry as larger characteristics that may be confounded with other situational characteristics of the whistleblowing context, as well as with individual characteristics of whistleblowers. One possible explanation for the paucity of recent empirical work on whistle-blowing lies in the methodological difficulties inherent in studying the phenomenon. There are several problems, which we describe briefly below. Perhaps the foremost problem lies in reliance on the whistle-blower’s perception of events: while the whistle-blower’s recollection of the whistleblowing process may be flawed due to problems of recall or bias, asking for permission to validate the data against perceptions of other parties, such as an ombudsperson, requires that the whistle-blower respondent identify him or herself rather than completing a questionnaire anonymously. We are convinced that whistle-blower respondents would not respond to a questionnaire that required them to identify themselves, for reasons articulated below. This basic conundrum – how to verify data from one source when that source will respond only if permitted to do so anonymously – bedevils whistle-blowing research.

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CONCEPTUAL AND METHODOLOGICAL CHALLENGES Some difficulties in studying whistle-blowing have been discussed previously (e.g. Miceli & Near, 1992). Our purpose in discussing difficulties here is to try to show how many of the techniques that researchers typically utilize to enhance the internal validity of the study of most human resource management topics actually create greater internal validity problems when investigating whistle-blowing. This is important because we are concerned that this is the primary reason why more high quality research is not published on this topic. Authors aware of the issues may be reluctant to undertake what seem to be insoluble problems and abandon the topic altogether. Or, they may make difficult trade-offs in designing the study, but – after the fact – reviewers do not understand the nature of the trade-offs and view the study as simply failing to meet the usual standards because of the authors’ negligence. Our concern is that, if solutions are not found to this problem, then research on whistle-blowing will essentially stop, and there will be little increase in our knowledge. Organizational wrongdoing will continue to occur, as will whistle-blowing, but our field will have little more to offer to responsible managers or to legislators who want to change these events, other than collections of anecdotes. While it might be tempting to interpret these arguments as reflecting unfounded biases of disgruntled researchers, this interpretation is contradicted by the absence of empirical articles on whistle-blowing in the elite management journals (e.g. Academy of Management Journal, Administrative Science Quarterly, Personnel Psychology) by any authors since the 1996 review. There are a few recent articles on sexual harassment appearing in Journal of Applied Psychology, but most of these studies (a) focus on how victims come to perceive wrongdoing, and (b) end with the effects of sexual harassment on the victim’s well-being, rather than examining the decision factors that encourage the victim to report the sexual harassment, or the variables that predict what happens next. Nor can the absence of empirical publications on whistle-blowing in these journals be explained as reflecting the topic’s lack of relevance to managers and employees, or a lack of interest in the topic. Quite the contrary: there is substantial and growing interest in the topic as documented in the many articles in the popular literature, as we noted in our introduction, and some conceptual work on whistle-blowing in Academy of Management Review (e.g. Gundlach et al., 2003). We argue that when one dissects the empirical dilemmas inherent in studying whistle-blowing, it is easy to see the impasse, but the solution

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requires change both on the part of authors and on the part of editors and reviewers, which we will offer later. For example, reviewers often expect that authors will utilize multiple sources of data to overcome problems associated with perceptual biases, among others. But, as noted above, in order to obtain multiple sources of data and concatenate them, researchers must have identifying information on the respondent, and, obviously, the trust of the respondent. While this is a challenge in most studies, in the case of whistle-blowing, it is extraordinarily difficult to get respondents who have negative information about their companies to agree to be identified to unknown researchers and to other people in the company, who may be among multiple sources. There are not many topics in organizational research that involve such a high likelihood of career damage, job loss, or even death threats, as documented in movies such as The Insider, and many media accounts. But there are at least two resulting problems that threaten internal validity, that are much less severe with less sensitive topics. A study of the relationships among, for example, organizational benefit programs, gender, perceived organizational support, and later turnover, is unquestionably challenging. But there is nothing threatening in any of these data. In contrast, cases are rare where researchers are fortunate enough to obtain the cooperation of organizational leaders to collect data from employees about organizational wrongdoing and coax participants into identifying themselves and get supervisors, complaint recipients, etc., to provide information about these same cases and have valid company records pertinent to these cases. Will this likely result in a large enough sample for sufficient power to reject null hypotheses, particularly given that, unlike variables such as recruiting sources, satisfaction, performance – which can be measured for most if not all employees – whistle-blowing is a rare event? What assurance can the researchers offer to reviewers that the respondents who are willing to participate are representative of all whistle-blowers even in that same organization? Would organizations that would be willing to permit entre´e be representative of organizations in which wrongdoing occurs? As we have described in depth previously (e.g. Miceli & Near, 1992), laboratory and scenario studies, while quite useful for many other topics, are not the solution for studying whistle-blowing. Like the longitudinal or multi-source design, they address certain problems, but create others that are equally if not more likely to be ‘‘fatal’’ to internal validity, and raise questions of external validity as well. For example, it is well-understood that social desirability or a wish to please the experimenter leads many people to say they would do the ‘‘right thing’’ when it is described on paper, when in reality they would not. If we have no confidence that the dependent variable

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of whistle-blowing has been measured properly, then any reported relationships with predictors will be suspect. As another example, ethical considerations obviously prevent laboratory researchers from creating the kinds of retaliatory conditions that would realistically be faced by actual would-be whistle-blowers. And, laboratory researchers would find it very difficult to find a large, willing, representative sample of career employees to participate in an experiment on whistle-blowing (we know of no such study), yet college students likely cannot judge wrongdoing and weigh the kinds of career considerations that are crucial to understanding whistle-blowing decisions. To some extent the entire whistle-blowing process represents a social construction of reality, created by the players: the whistle-blower, the wrongdoer, the top executives who represent the organization’s interest, and the co-worker who observe the events. On the other hand, in any case involving legal issues, the task of the third parties (judges and juries) is to try to find the truth from the perceptions and evidence, some of which may be in conflict. They must, and usually do, come to a decision about whether the complaint is valid. Researchers, unfortunately, do not have the luxury of having access to all of the information, for reasons noted above. One of the great difficulties, therefore, in studying the decision to blow the whistle lies in assessing the accuracy of the whistle-blower’s allegation of wrongdoing. Most whistle-blowing research has relied on either case studies or surveys, with the whistle-blower serving as the informant, and thus has been forced to measure the whistle-blower’s perception of the process, rather than considering the views of other observers or other sources of evidence, such as records, photographs, and so forth. While some of the case studies have relied on interviews where the interviewees were known to the interviewer, but where the data were treated as confidential, all the questionnaire studies involved anonymous respondents, thus precluding any validation of the data obtained by cross-checking with other observers of the whistle-blowing, or even collection of longitudinal data from the same respondents, so as to permit analysis of causal relations. Where confidential interviews were conducted, they usually involved whistle-blowers who had already been identified by the media or in public data bases, so the whistle-blowers themselves had little to lose at that point through providing data to researchers (Perry, 1992). Unfortunately, of course, these cases may not be representative of whistle-blowing cases in general. Similar measurement problems appear when trying to assess the validity of the allegation of wrongdoing. Again, most whistle-blowing studies have relied on the whistle-blower’s perception of events. Some studies

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(e.g. Dworkin & Near, 1987) have used samples of whistle-blowers who have filed lawsuits (which would suggest that at least an attorney agrees that the case may have merit) but these then are limited to whistle-blowers reporting illegal behavior, as opposed to other forms of legal wrongdoing. Most studies of whistle-blowing and retaliation have used anonymous samples in order to preserve those respondents who would not participate in the study without the promise of anonymity. Respondents who have already suffered or fear future retaliation are unlikely to participate in samples where their identity is known, for obvious reasons. Thus, promises of confidentiality are unlikely to be sufficiently believable to attract respondents. Elaborate methods to link anonymous respondents’ questionnaires to later questionnaires or to other respondents who can verify the respondents’ views are unlikely to work effectively (e.g. studies where respondents are asked to assign themselves an identification number and then remember that number for use in a later questionnaire, some months or years later). While the randomized response technique has been used to assess the incidence of wrongdoing in survey data (e.g. Burton & Near, 1995), it does not allow researchers to examine predictors of respondents’ behaviors, because individual respondents cannot be separated from the aggregated data. If we are to continue to study organizational wrongdoing, we will be forced to rely on methods that permit respondents to remain anonymous – and that prevents researchers from validating their measures of organizational wrongdoing or retaliation or effectiveness of the whistle-blowing as they might otherwise wish to do. We believe that the solution is twofold. First, researchers must make careful trade-offs to address methodological dilemmas that we have identified, and adhere to the highest methodological standards. When submitting the paper for review, they must articulate clearly (a) what these trade-offs and their likely consequences were, and (b) the limitations of their studies. Second, editors and reviewers should become more familiar with the dilemmas in whistle-blowing research, and be supportive of researchers who have taken appropriate care, rather than presuming, as one example, that researchers are uninformed, negligent, or lazy, in relying on a single source. Editors and reviewers should impose consistent standards; it should be impossible to find – in the recent issues of the same journals – studies with the same design features to which they object in a whistle-blowing study, especially studies on other topics where trade-offs are far less challenging. Conveying a consistent message as to how trade-offs ‘‘should’’ be made will also help send a clear signal to authors in early stages of their work. And, where reviewers object to the trade-off decisions of authors, to be fair to authors and to encourage whistle-blowing research, editors should insist

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that reviewers offer real and workable solutions that are in fact superior to those decisions made by the authors that take into account problems created as well as those apparently solved; editors should not deem adequate a simplistic, standard reply. It is our belief that, once reviewers are put in this position, most of the time they may better understand that they may not be able to offer a superior design, which is helpful not only for authors but also a useful learning process. Certainly, this challenge would increase the likelihood that someday, perhaps as methodological advances (or legal developments) occur, an ingenious, realistic design for a future study will be developed. It is often said that there is a trade-off in research between relevance and rigor. Nowhere is this truer than in the whistle-blowing area of research. We believe that research in this area is sorely needed and has never been more relevant at any time in history. If it is true that large, complex organizations have unprecedented opportunity to commit wrongdoing, at a time in history when oversight is nearly impossible because of increasing organizational complexity and size (e.g. Miethe, 1999), and when members who decide to blow the whistle may be the best hope for identifying their organization’s wrongdoing, then understanding the whistle-blowing process has never been more important. Unfortunately, collecting data from representative samples of whistle-blowers requires holding in abeyance some of the field’s most stringent requirements for careful research design. In this instance, we believe that relevance justifies research, with appropriate rigor to the extent feasible.

CONCLUSION Research on whistle-blowing has focused on the questions of who blows the whistle, who experiences retaliation, and who is effective in stopping wrongdoing. In this article, we attempted to review knowledge pertinent to the first of these questions. Since the last known review (Near & Miceli, 1996), there have been important theoretical and, to a lesser extent, empirical developments. In addition, the U.S. law is changing dramatically, which may serve to protect and encourage valid whistle-blowing, but also raises the question as to how prior theoretical and empirical findings might change in the new environment. International interest in whistle-blowing is widespread and increasing. Unfortunately, evidence strongly suggests that media, popular, and regulatory interest is far outpacing the growth of careful scholarly inquiry into

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the topic, which is a disturbing trend. Here, we argued that the primary causes of the underdevelopment of the empirical literature are methodological, and that workable solutions are needed. There are some inherent difficulties in studying organizational wrongdoing that probably cause some authors to avoid the topic altogether, resulting in little research on whistleblowing. But equally important, it is nearly impossible to construct research designs on whistle-blowing that will ensure internal validity. The techniques researchers think best, and which are best for most topics, actually create more problems for research on whistle-blowing, making it difficult for even carefully designed studies to be published. We hope that, by calling attention to these issues, we can help encourage better research into whistleblowing.

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A MODEL OF EMPLOYEE SELF-SERVICE TECHNOLOGY ACCEPTANCE Janet H. Marler and James H. Dulebohn ABSTRACT We review the literature on individual acceptance of technology to show how organizations can improve the effective use of human resource webbased technologies. Integrating and expanding several theoretical models of technology acceptance, we develop a perceptual model of employee self-service (ESS) acceptance and usage. Based on this model, we propose several key individual, technological, and organizational factors relevant to individual intentions to use ESS technology. We summarize these in several testable propositions and also discuss implications for organizational researchers and practitioners.

INTRODUCTION The Internet has dramatically affected the management of human resources (HR). By using web-based technologies, the human resource function can now transfer much of its data management and transaction-processing responsibilities to employees and managers. Known as employee self-service Research in Personnel and Human Resources Management Research in Personnel and Human Resources Management, Volume 24, 137–180 Copyright r 2005 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 0742-7301/doi:10.1016/S0742-7301(05)24004-5

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(ESS), it is heralded as a revolutionary HR innovation (The Hunter Group, 2001). It is expected to transform the way human resource departments deliver their services (Zampetti & Adamson, 2001) by eliminating HR in the middle (Snell, Stueber & Lepak, 2001), duplication of data entry, and tracking of paper forms. As a result HR is freed to focus less on operational and more on managerial and strategic activities. ESS is a technological innovation that involves the use of Internet-based technology to permit all employees, throughout an organization, direct access to centralized human resource information databases through the use of computers that are connected to each other through the Internet. For example, many organizations deploy ESS to allow their employees to review internal job postings or access customized information about their compensation and benefits from work or at home using their PC. More advanced deployments of ESS also permit employees to personally access and update their personnel data, participate in open enrollment, change benefit selections, view their own performance information, and register for training. While only about 44% of U.S. companies have web-based ESS, over 80% plan to transition to web-based self-service during the next several years (The Hunter Group, 2001). Most organizations indicate that the major reasons for implementing ESS technology are to reduce labor costs and to improve HR services and employee satisfaction (The Hunter Group, 2001). In a revealing survey, however, Towers Perrin found that only 5% of the respondents had fully realized goals of improved HR efficiency and service (Brown, 2002). Research reveals that failure to get employees to voluntarily and fully adopt new IT innovations is often a major contributing factor to this lack of success (Swanson, 1988). Consequently, an important challenge for organizations is to figure out how to increase employee use of ESS. While the MIS literature contains abundant research on information system success factors, only recently have researchers focused particularly on individual use of technology (Taylor & Todd, 1995). Even this latest research stream, however, does not address the complexities of organization-wide internet-based IT innovations such as ESS. ESS is a significant investment in IT and organizational infrastructure (Walker, 2002) and given its size and strategic importance, organizations can benefit from research that identifies key predictors of ESS acceptance that should have implications for organizational efforts to increase ESS utilization. In this chapter we develop a model of ESS acceptance based on a review and synthesis of an extensive IT acceptance literature. Our model represents an integration of prominent models of technology acceptance found in the

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information science literature that are themselves derived from extant theories in social psychology, behavioral psychology, cognitive psychology, organizational theory, and sociology. We extend and synthesize these models to accommodate three significant aspects of ESS technology that differ from the typical IT contexts (and from which prior technology acceptance models were derived and empirically tested). First, unlike many IT applications, ESS is not directly related to the performance of an individual’s job. For example, by using ESS technology to enroll in employee benefits online, an employee whose job involves providing customer service does not perform their job more productively or effectively as a result of using ESS. In our model, we expand the notion of perceived usefulness of a new technology to accommodate this aspect of ESS technology. Second, ESS implementations do not automatically involve mandated use of the technology; much of it is ‘‘voluntary,’’ although clearly organizations want their employees to use it. For example, benefits enrollment using ESS is often not mandatory. An employee can use a call center or fill in the necessary forms manually for HR to process if they choose not to process their transactions using a PC. However, when employees voluntarily choose to use online ESS benefit enrollment technology, the organization realizes substantial savings in processing that transaction (Walker, 2002). Furthermore, when an employee fails to voluntarily use the system to update their benefit record, then an administrative employee must use this technology to perform that transaction. This latter use of ESS is mandatory. Ultimately, firms justify investments in ESS based on significant costs savings expected from the voluntary use of the system. Effective voluntary use of ESS reduces the organization’s labor costs and increases data accuracy and also improves HR service delivery by providing customized HR information. In such an environment, technology resources and organizational factors such as management influence, organizational support, and training are critical to realizing the full benefits of both voluntary and mandatory aspects of ESS functionality. Our model specifically addresses the likelihood of both mandatory and voluntary aspects of ESS technology and its effect on ESS acceptance. We consider key organizational factors that are likely to influence ESS acceptance under varying conditions. Finally, implementation of ESS technology is highly complex and organization-wide. Most research conducted on technology acceptance has focused on stand alone software such as PC operating systems (Agarwal & Prasad, 1998; Moore & Benbasat, 1991) or use of application software such as graphic software or word processing (e.g. Davis, Bagozzi & Warshaw, 1989). Organization-wide implementations involve a wide variety of

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employees performing a variety of tasks. Individual differences in IT background and in the kinds of tasks potentially performed using IT play a more prominent role in technology acceptance than in situations where students or a department of administrators have the opportunity to learn new stand alone software packages. Consequently, a model of ESS acceptance necessitates particular recognition of these important individual differences.

A MODEL OF ESS ACCEPTANCE In our model, individual perceptions of the ESS technology represent the primary antecedents of ESS acceptance. This aspect of our model is consistent with the most widely cited and empirically tested technology acceptance model (TAM) in the information sciences literature (Venkatesh, Morris, Davis & Davis, 2003). In addition, our model introduces several key individual, organizational, and technological characteristics, which we argue also significantly influence individual perceptions of ESS technology. By focusing on these characteristics and their relationships with ESS technology acceptance, our model enriches current models of technology acceptance and better clarifies how organizations can effectively manage ESS implementations.

Extant Technology Acceptance Models We derive our model of ESS acceptance from an extensive literature on individual use of IT that is based on four extant models of technology acceptance: the theory of planned behavior (TPB) models (Taylor & Todd, 1995), the social cognitive model (SCM) (Compeau, Higgins & Huff, 1999), the perceived characteristics of innovating (PCI) model (Moore & Benbasat, 1991), and the TAM (Davis et al., 1989). Each model has a different theoretical foundation, different constructs related to technology acceptance, and each has received substantial empirical support. Consequently, to provide a foundation for our proposed model, we begin with a brief overview of the four major technology acceptance models from which our model is derived. We begin with the least ‘‘popular’’ model, the TPB and end with the dominant model, the TAM. In the middle, we describe the SCM and the PCI model.

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Theory of Planned Behavior The TPB has its roots in social psychology and behavioral intention models (Eagly & Chaiken, 1992). In TPB, there are three belief categories: behavioral beliefs, normative beliefs, and control beliefs, which in turn determine three main attitudes toward technology and intention to use a technology. Behavioral beliefs determine one’s affective attitude toward performing the behavior. Normative beliefs determine attitude to social pressures to perform a behavior. Finally, control beliefs determine perceptions of behavioral control (Ajzen, 1991; Taylor & Todd, 1995). These three attitudes are antecedent to the fourth attitude, intention to perform a behavior. Applied to technology, TPB implies that three attitudes (attitude toward a specific technology, attitude toward social pressure to use the technology, and perceptions of their ability to actually use the focal technology) determine an individual’s intention to use that particular technology. The TPB introduces several important constructs and relationships into the technology acceptance equation. First, the TPB introduces the concepts of ‘‘perceived behavioral control’’ (PBC). PBC emphasizes the importance of an individual’s perceptions of their internal abilities and external facilitating conditions. Second, TPB relies on the concept of normative pressure, which emphasizes the effects of social influence over individual behavior. In our model, we adopt these constructs drawing from this model. A third aspect of the TPB model, however, makes it problematic for researchers as an empirical framework. Under the TPB, each situation warrants the development of a specific set of salient beliefs related to the three focal attitudes and because these belief sets are idiosyncratic it is difficult to generalize empirical results beyond a specific technology application. In order to apply the TPB model to technology acceptance, researchers must develop context specific belief sets for the particular technology. This problem is addressed and ameliorated in the more popular technology acceptance model, which we discuss last. The Social Cognitive Model With its roots in social cognitive theory (Bandura, 1991), the dominant focus of the SCM of technology acceptance is the role cognitive factors play in directly predicting behavior. The SCM is founded on social cognitive theory developed by Bandura and associates (Bandura, 1986; Compeau et al., 1999). Extending behaviorist foundations to recognize cognitive influences, social cognitive theory states that behavior, cognitive factors, and environmental events operate as interacting determinants, each influencing the other bidirectionally (Wood & Bandura, 1989).

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The SCM posits that computer usage is directly determined by three cognitive factors: outcome expectations, computer self-efficacy (CSE), and computer attitudes. The SCM introduces three aspects to understanding technology acceptance that the other models do not. First, it suggests that perceptions of ability to learn the new technology, especially CSE, are a key factor in determining computer use. Second, it points to the importance of individuals’ outcome expectations with respect to using the new technology in general, rather than being specific to job performance, in determining computer usage. Third, it implies these factors, self-efficacy (SE) and expected outcomes, have a direct effect on behavior rather than mediated by attitude or intention to behave as asserted in TPB. Our model draws on these notions, particularly the importance of CSE and an expanded notion of expected outcomes. Perceived Characteristics of Innovating Model The PCI model (Moore & Benbasat, 1991) is derived from innovation diffusion theory (Rogers, 1983), which has its roots in sociology. Rogers (1983) defined innovation diffusion as the process by which an innovation is communicated through certain channels over time among members of a social system. Innovation diffusion research suggests that within a social system, the number of individuals adopting an innovation over time follows a normal bell-shaped curve (Brancheau & Wethere, 1990) with those in the left tail considered early adopters and those in the right tail, laggards. The PCI identifies five characteristics of an innovation that affect the rate of innovation adoption over time. They are relative advantage, compatibility, complexity, observability, and trialability (Moore & Benbasat, 1991). In applying innovation diffusion theory to technology use, researchers model individual perceptions of these five characteristics as drivers of the adoption decision. The underlying premise is that these perceptions drive the rate of adoption with early adopters leading the way and influencing later adopters. The PCI model is similar to the TPB and SCM in that it too emphasizes the importance of perceived outcomes from using a new technology. In the PCI this aspect is captured in two of the five characteristics: relative advantage and compatibility. The PCI model also highlights the importance of social influence and context, noting that observability, the degree to which the results of an innovation are observable to others, is also an important characteristic to innovation adoption. What differentiates the PCI model from the other models, however, is that this model points to the importance of the innovation’s features that are separate from the individual. For

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example, in the PCI model, trialability, the degree to which an innovation may be experimented with before adoption, is an important characteristic. Technology Acceptance Model The dominant model in the empirical literature on technology use is the TAM proposed by Davis et al. (1989). With its initial roots in the theory of reasoned action (TRA) (Ajzen & Fishbein, 1977), the TAM was derived to apply to any IT thus overcoming the context-specific nature of the theory of reasoned action and its follow-on, the TPB (Davis et al., 1989). Theoretically related to the TPB, the TAM asserts that two salient beliefs determine technology acceptance and are the key antecedents of behavioral intentions to use IT. These two specific beliefs are: perceived usefulness and perceived ease of use (EOU) of the new technology. Perceived usefulness is the prospective user’s belief that the target technology will increase his or her job performance within an organizational context (Davis et al., 1989). EOU, the second key belief, is the degree to which a prospective user expects the target technology to be free of effort. In this model, intention to use technology, and not actual use (i.e. behavior), is the dependent variable. When first proposed, TAM diverged from the TRA in several respects. First, in TAM there are only two salient beliefs: perceived EOU and perceived usefulness (Taylor & Todd, 1995), whereas in the TRA there are often multiple beliefs, all of which are specific to a particular behavioral attitude. Second, the TAM does not include beliefs or attitudes about social expectations or norms, which are key constructs in the TRA and the TPB. Third, the TAM is not context or behavior specific. It is applied generally to intention to use any kind of technology, hence the name TAM. Finally, a substantial empirical literature supports this parsimonious model (see Venkatesh, 2003 for a review) indicating that the TAM is very robust across a wide spectrum of IT applications.

The ESS Acceptance Model Our model of technology acceptance builds on all four of the above models. Consistent with TAM as depicted in Fig. 1, we model technology acceptance as a function of two key cognitive beliefs about ESS. To this, we introduce the roles of individual technology orientation, technology resources, organizational environment, and task characteristics. As depicted in Fig. 1, however, as in TAM, two key proximal beliefs: ESS effort and ESS benefits fully and partially mediate these less proximal but key variables.

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Individual Technology Orientation

TAM ESS Effort

Technology Resources

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ESS Benefits

Task Characteristics

Fig. 1.

Perceptual Model of Employee Self-service Acceptance.

Our model is based on technology acceptance as it relates to the initial decision to use or adopt a new technology. An individual’s decision to first use a technology is different from the decision to continue to use a technology. Once, the employee has direct experience with the focal technology, factors that predict continued use change (Taylor & Todd, 1995b; Venkatesh, 2000; Venkatesh & Davis, 1996). For example, Venkatesh (2000) shows that direct experience with a target technology affects an individual’s general beliefs and also permits the individual to assess the usability of the system and the perceived enjoyment of the system. Consistent with the essential features of TAM, we posit that ESS acceptance, which we define broadly to include intention to use ESS and actual use, is determined primarily by individual cognitive perceptions of the ESS technology. As shown in Fig. 1, we posit two key individual perceptions: ESS effort and ESS benefits. ESS effort is defined as the degree of ease associated with the use of the technology (Venkatesh et al., 2003). ESS benefits represent the expected benefits that will accrue from technology adoption and is an expanded concept of perceived usefulness, shown in Fig. 2.

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TAM

Effort Expectancy

P1 Integrated Perceived Usefulness

ESS Acceptance P2

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P3 Personal Outcomes P4 Intrinsic Benefits

Fig. 2.

The TAM and other Perceived Employee Self-service Benefits.

Our model departs from TAM in three respects. First, we conceptually extend the three primary TAM constructs: behavioral intention to use technology, perceived usefulness, and perceived EOU. Second, we enrich the TAM by introducing four key technology constructs: individual IT orientation, technology resources, organizational environment, and task characteristics. Incorporating aspects of the three other technology acceptance models, these four additional constructs identify individual, technological, and situational characteristics that predict an individual’s acceptance of ESS technology. Third, we highlight the contingent nature of the relationships. Specifically the model proposes: (1) an individual’s technology orientation affects ESS acceptance indirectly through its effect on an individual’s perceptions of ESS effort expectancy; (2) an organization’s technology resources also affect ESS acceptance, directly, and indirectly through its influence on perceptions of ESS effort expectancy; (3) organizational environment affects ESS acceptance indirectly through its impact on individual perceptions of ESS benefits; and (4) the nature of an individual’s tasks will moderate the influence of these indirect relationships. As we discuss in greater detail in the next sections our model differs from TAM and the other three models in that we specifically identify individual, technological, and organizational factors that can affect ESS acceptance and highlight the

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direct, indirect, and contingent nature of these relationships based on varying task characteristics. In the next section we describe our model’s elements more completely. We begin with defining ESS technology acceptance, ESS effort, and ESS benefits.

EXTENDING TAM CONSTRUCTS ESS Technology Acceptance User acceptance is defined as the demonstrable willingness within a user group to employ information technologies for the tasks it is designed to support (Dillon & Morris, 1996). This construct is measured as either actual usage or as an intention to use a specific IT. The construct has been applied to a wide variety of information technologies such as use of new software applications (Brancheau & Wetherbe, 1990; Morris & Venkatesh, 2000; Moore & Benbasat, 1991), expert decision support software (LeonardBarton & Deschamps, 1988), computer resource room (Taylor & Todd, 1995), personal computers (Compeau et al., 1999), and an organization’s webpage (Mathieson, Peacock & Chin, 2001); or as intentions to use single software applications (Venkatesh et al., 2003) and new personal computer operating system software (Agarwal & Prasad, 1999; Karahanna, Straub & Chervany, 1999). Based on the theoretical premise that behavioral intention to perform a behavior is antecedent of actual behavior, research guided by the TAM focus only on an intention to use a specific technology as their measure of technology acceptance. This posited relationship between behavioral intention and actual behavior has been well established in the social psychology literature (see Ajzen, 1991; Eagly & Chaiken, 1993; Pinder, 1998 for reviews) and also in the technology acceptance literature (Taylor & Todd, 1995). However, as we discuss later, intention to use a technology assumes that every individual has complete control over their environment. This is clearly unrealistic in field settings where organizational characteristics exert considerable influence over individual behavior. Hence in this model we use, the term technology acceptance to mean both intention to use the technology and actual use of the technology. As discussed above, the technology acceptance empirical literature includes both the variables. Use of one or the other depends on the underlying theoretical framework. Studies using a TAM framework operationalize

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acceptance as an intention to use the technology. Studies that use an SCM framework include actual use, and other studies include both (Venkatesh et al. 2003). Since in our model we integrate aspects of multiple models, we subsume both dimensions of use under a more general latent construct, ESS technology acceptance.

ESS Effort Expectancy and Perceived Ease of Use ESS effort expectancy represents an individual’s subjective assessment of how easy it will be to competently operate an ESS technology. Four constructs from existing models capture the concept of effort expectancy: perceived EOU (TAM), complexity (PCI), computer self-efficacy (CSE) (SCM), and perceived behavioral control (TPB). The similarities among these four constructs have been noted in prior research (Plouffe, Hulland, & Vandenbosch, 2001; Venkatesh et al., 2003). In the TAM, perceived EOU is defined as the extent to which a person believes that using a technology will be free of effort (Davis et al., 1989). There are two basic mechanisms by which EOU influences technology acceptance: self-efficacy and instrumentality (Davis et al., 1989). The easier a system is to interact with, the greater should be the user’s sense of efficacy (Bandura, 1986). Higher self-efficacy in interacting with a new technology will lead to a sense of personal control and instrumentality with respect to carrying out the sequences of behavior needed to effectively operate the IT system. Perceived EOU is very similar to the complexity characteristic defined in the PCI model. Complexity is defined as the degree to which an innovation is perceived as being difficult to use (Moore & Benbasat, 1991), which is the inverse of the definition of EOU. Indeed, Moore and Benbasat (1991) acknowledge the similarity between their complexity construct and the TAM’s perceived EOU. In SCM, there is no EOU construct instead the model gives specific prominence to the notion of computer self-efficacy (CSE). Compeau and Higgins (1995) define CSE as a judgment of one’s capability to use a computer, typically related to future behavior. While this definition suggests a general concept of CSE, it is really specific to the target technology being evaluated, rather than a sense of general computer self-efficacy (GCSE) (Marakas, Yi & Johnson, 1998). The distinction between GCSE and specific CSE is an important one (Marakas, Yi & Johnson, 1998), which we discuss more fully in describing individual differences in technology orientation.

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Compeau and Higgins (1995) empirically tested their measure of specific CSE using individual reports of use of personal computers as the target technology. In TPB, there is no EOU, complexity, or self-efficacy (SE) constructs. Instead, there is the notion of perceived behavioral control (PBC). Ajzen (1991) defined PBC as a person’s perception of the ease or difficulty of performing a behavior of interest. Clearly this definition suggests it is quite similar to the EOU construct in TAM; however, Ajzen (1991) considered PBC to be closer to the concept of self-efficacy, stating that: Much of our knowledge about the role of perceived behavioral control comes from the systematic research program of Bandura and his associatesy These investigations have shown that people’s behavior is strongly influenced by their confidence in their ability to perform the behavior (i.e. by perceived behavioral control) (p. 184).

While Ajzen (1991) considers PBC to be similar to self-efficacy, other researchers applying the TPB in a technology acceptance context note that the PBC construct encompasses two dimensions: self-efficacy and external environmental factors (Taylor & Todd, 1995; Mathieson et al., 2001). The first dimension as described above concerns individual perceptions of ability to use the target IT or specific CSE. The second relates to perceptions of environmental resources and technology facilitating conditions (Taylor & Todd, 1995; Mathieson et al., 2001). This second dimension concerns factors external to the individual that may have a direct effect on an individual’s ability to enact the desired behavior. This aspect of PBC is different from EOU, which does not explicitly recognize this dimension. In our model we specifically separate these two dimensions. Rather than treating perceptions of environmental resources as a dimension of ESS effort expectancy, in our model we depict it as a separate construct, consistent with an extended TAM proposed by Mathieson et al. (2001). We discuss this aspect of the model later when we describe technology environment. In summary, all four constructs: EOU, complexity, CSE, and PBC are related in that they essentially describe an individual’s beliefs about their self-efficacy with respect to using a specific new technology. All constructs focus on an individual’s subjective cognitive judgments about hers or his capabilities and the likelihood of successfully gaining access to and achieving personal competence with a target new technology. Empirical research also shows that all four constructs are significantly and positively related to technology acceptance (Venkatesh et al., 2003). Venkatesh et al. (2003) empirically demonstrated that measurements of these constructs overlap significantly. We therefore assert that the same relationship will exist for our

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ESS effort expectancy construct, which encompasses the notion of specific ESS self-efficacy as well as beliefs regarding the physical and mental effort required to use ESS.1 As shown in Fig. 2, we therefore propose: Proposition 1. ESS effort expectancy will be positively related to ESS acceptance. ESS Perceived Benefits and Perceived Usefulness In the technology acceptance literature, the conceptualization of cognitive perceptions of benefits associated with technology acceptance is quite narrow. In particular, all four models of technology acceptance assume the target technology is designed to improve job performance and that this is the primary motivating factor. This conception of value is surprisingly limited. As IT becomes more widespread and organizations more dependent on its use, the benefits of using IT will not be limited to job enhancing software. The introduction of ESS provides an excellent example of the increasingly pervasive nature of IT. Use of ESS technology and its benefits can no longer be restricted to improving job performance. To address limitations in current conceptions of perceived usefulness, we integrate and expand existing perceived value constructs. This aspect of our model is shown in more detail in Fig. 2. First we draw from a recently integrated technology acceptance model that synthesizes three constructs from existing models: perceived usefulness (TAM), expected performance outcomes (SCM), relative advantage (PCI) (Venkatesh et al., 2003). Second, we propose two additional constructs that are not specifically related to extrinsic outcomes from job performance. These additional benefits, perceived personal outcomes and perceived benefits to others, expand the range of potential benefits an individual may derive from adopting ESS. Perceived Usefulness As with the concept of effort expectancy, there is also considerable overlap across the different models’ conceptions of perceived value in using a new target technology. In the TAM, perceived usefulness is a belief that represents the user’s subjective probability that using a specific application will increase his or her job performance within an organizational context (Davis et al., 1989). It is a cognitive assessment regarding a target technology’s usefulness in fulfilling intrinsic and extrinsic needs at work. In the empirical literature, however, perceived usefulness is operationalized as a cognitive

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belief about a new technology’s expected effect on job performance and its intrinsic dimension is ignored. In a broader conception of benefits using an SCM model, Compeau et al. (1999) argued that outcome expectancy has two dimensions: performance and personal. The first, called performance-related outcomes, is associated with improvements in job performance associated with use of computers. The second, called personal outcomes, relates to expectations of change in image or status or to expectations of rewards, such as promotions, raises or praise. This definition while broader than perceived usefulness is still, nevertheless, operationalized narrowly to reflect expectations about job-related extrinsic rewards. The PCI model’s relative advantage construct is defined as the degree to which using an innovation is perceived to be superior to using the existing offerings (Moore & Benbasat, 1991). Moore and Benbasat (1991) considered this construct to be very similar to perceived usefulness except that relative advantage is expressed in terms of perceived usefulness compared to an existing technology. This is an important distinction, particularly in situations when a new innovation is replacing an older technology. Part of the evaluation of the new technology’s usefulness will involve a comparison with its predecessor. All three benefit constructs, however, focus on the extrinsic rewards derived from using a new IT in the job context. Reinforcing this observation, Venkatesh et al. (2003) provided empirical evidence supporting the conceptual overlap and integrated them into one construct which they called ‘‘performance expectancy.’’ Performance expectancy is defined as the degree to which an individual believes that using the system will help him or her attain gains in job performance. In our model we adopt this integrated concept but to distinguish it from other benefit dimensions we continue to use the earlier label: perceived usefulness. This is shown in Fig. 2. Where IT has a job-related component, we expect that there will be a strong relationship between an individual’s perception of the value of the ESS technology to their job performance and ESS acceptance. Consistent with prior technology acceptance models, we therefore propose: Proposition 2. ESS perceived usefulness will be positively related to ESS acceptance. Personal Outcome Expectancy As noted earlier, ESS system technology can involve both job tasks and personal activities not directly related to performance of one’s job. Much of the research in technology acceptance focuses on business-related

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technology; consequently, the literature’s narrow focus on job-related outcomes is understandable. Because new web-based technologies such as Internet-based ESS provide potential value to employees over and above their job tasks, these model definitions of outcome expectancies are too narrow. Individuals are also motivated by other extrinsic outcomes that result in personal benefit such as making better benefit decisions that result in personal benefit. For example, Dulebohn, Murray, and Sun (2000) showed how plan features played an important role in individual choices and behavior related to retirement savings plan participation. Thus, while we expect that intentions to use ESS are influenced by perceptions of whether the technology augments an individual’s job-related outcomes, we also posit personal usefulness is another important facet of perceived ESS benefits. We define perceived personal outcomes as the degree to which an individual believes that ESS will help in attaining personal goals apart from the job such as increasing personal wealth and life satisfaction. In as much as attainment of personal outcomes is highly valued we propose the following relationship also shown in Fig. 2: Proposition 3. ESS personal outcome expectancies will be positively related to ESS acceptance. Perceived Intrinsic Value While job-related rewards are clearly relevant in technology adoption, a vast amount of motivation research (see Pinder, 1998) also suggests individuals engage in behaviors for other outcomes such intrinsic rewards (Deci & Ryan, 1985) as well as to meet social exchange expectations (Blau, 1964; Maurer, Pierce & Shore, 2002). Intrinsically motivated behavior is largely associated with the satisfaction of higher order needs (Maslow, 1954) or growth needs (Alderfer, 1972) compared to extrinsic outcomes that tend to relate more to existence (e.g. money, promotions) or relationship needs (e.g. acceptance, affiliation with peers). Intrinsic outcomes occur immediately upon the performance of the acts that produce them and therefore are self-administered rather than distributed by others (Pinder, 1998). Deci and Ryan (1985) further describe activities from which a person derives intrinsic value: When people are free from the intrusion of drives and emotions, they seek situations that interest them and require the use of their creativity and resourcefulness. They seek challenges that are suited to their competencies that are neither too easy nor too difficult. When they find optimal challenges, people work to conqueror them, and they do so persistently. (p. 32).

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Examples of intrinsic outcomes include positive feelings of accomplishment, a sense of mastery, competence (Pinder, 1998), and playfulness (Venkatesh, 1999). In relating intrinsic motivation to general computer usage, researchers have primarily used the construct of computer playfulness (Venkatesh, 1999; Webster & Martocchio, 1993). Venkatesh (2000) argued that higher levels of computer playfulness led to an internal focus of causality that in turn lowered perceptions of effort. Thus, he argued that perceived effort (EOU in TAM) fully mediated the relationship between expected intrinsic benefit and specific technology acceptance. He gathered data from students learning new software applications and demonstrated that computer playfulness was positively related to early perceptions of computer EOU and that EOU fully mediated the relationship between computer playfulness and intentions to use the specific software. We conceptualize intrinsic benefits as being an expectation of future benefits derived from gaining a sense of mastery and competence from using a new innovation such as ESS. In contrast, Venkatesh (2000) conceptualized intrinsic benefit more specifically as a belief about the playfulness attributes of computer technology, based on past experiences. We expect that there will be a high correlation between these two constructs, ESS intrinsic benefits and computer playfulness, because both are related to general computer self-efficacy (GCSE). An individual who is high in GCSE will perceive the use of a new technology to involve lower effort than an individual who is low in GCSE. In addition, an individual who expects to derive intrinsic benefits from using a new technology is also likely to have high GCSE. As we discuss in the next section, those with high general self-efficacy are also expected to have lower effort expectancies. Rather than proposing that ESS intrinsic benefits will be fully mediated by perceived effort expectancy, however, we propose that it will have a direct relationship with ESS acceptance. As shown in Fig. 2, we propose that similar to expected extrinsic benefits, expected intrinsic benefits will be directly related to specific technology acceptance. Proposition 4. ESS intrinsic benefits will be positively related to ESS acceptance. To summarize, in this section we have described how our model is based on and extends the three primary TAM constructs drawing from three other technology acceptance models. To differentiate these constructs from TAM’s behavioral intention, EOU and perceived usefulness constructs, we introduced the concepts of ESS acceptance, ESS effort expectancy and

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ESS benefits and posited direct relationships between these constructs. In the next section, we discuss three additional constructs that influence ESS acceptance indirectly through their effects on ESS effort expectancy and ESS benefits.

INDIVIDUAL TECHNOLOGY ORIENTATION, TECHNOLOGY RESOURCES AND ORGANIZATIONAL ENVIRONMENT Individual Technology Orientation Early empirical studies on the effect of individual differences on system success have been exploratory and therefore lack a clear conceptual framework that explains how and why individual differences are related to technology acceptance. In these studies, individual difference variables fall into four categories: cognitive style, personality, demographic, and situational (Alavi & Joachimsthaler, 1992; Harrison & Rainer, 1992; Nelson, 1990; Orr, Allen & Poindexter, 2001; Zmud, 1979). A meta-analysis of these categories of individual differences showed that of these four categories user-situational variables exerted the most influence on technology acceptance (Alavi & Joachimsthaler, 1992). In particular, prior similar IT experience, training, and user involvement had almost twice the effect size on technology acceptance as compared to cognitive style or personality differences. One drawback to this early literature is the use of widely different measures for the same construct. For example, Orr et al. (2001) measured personality type using the Keirsey temperament sorter that categorized individuals into four temperament types: guardian, artisan, idealist, or rational. Harrison and Rainer (1992) defined personality as various types of anxiety and attitudes that included measures of computer attitudes, computer anxiety, and math anxiety. Furthermore, both studies used very different dependent variables. Orr et al. used a measure of computer attitude while Harrison and Rainer (1992) used a measure of computer self-efficacy (CSE). With such methodological diversity, synthesized meaningful results are difficult to obtain. More recently, empirical studies have partially addressed these problems, providing conceptual frameworks and consistent measures that better explain why individual difference variables might affect IT adoption rates. However, these too are hampered by the use of different frameworks that

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come from different theoretical perspectives. For example, Agarwal and Prasad utilized a TAM framework in their study of individual differences while Compeau et al. (1999) adopted the SCM in their study. Fortunately, both are sufficiently similar that they can be integrated to gain a better insight into the role of individual differences. Based on features of learning and attitude formation theories, Agarwal and Prasad (1999) proposed that individual situational differences affect intention to use technology. Drawing on Fishbein and Ajzen’s (1980) perspective that belief formation should follow the laws of learning, Agarwal and Prasad used the TAM to show how individual differences influence system success through their effect on beliefs about a system’s usefulness and beliefs about the system’s ease of use. They explained how a learning process is involved as follows: In the human associative view of learning, it has been proposed that the law of proactive inhibition or interference (McGeoch & Irion, 1952) describes and helps predict the effects that individual difference variables have on the learning process. This law suggests that individuals’ prior knowledge and experiences interfere with their ability to learn to exhibit specific behaviors. The fundamental notion underlying this law is the extent of similarity or dissimilarity between an individual’s prior experiences and knowledge, and the new behavior being learned. Primarily through cognitive associative processes (Gick & Holyoak, 1987), similar prior experiences result in greater learning, and therefore might be expected to lead to more positive beliefs, whereas the opposite effect is expected for dissimilar prior experiences. (p. 369).

To test their hypotheses, Agarwal and Prasad (1999) selected individual difference variables that described the prior knowledge base individuals possessed at the time of interacting with a new IT. They found that level of education and prior similar IT experience were both positively related to beliefs about EOU and that the EOU construct fully mediated the effects of these two individual difference variables on computer attitudes. They also found that participation in training for the specific new software affected beliefs about the usefulness of the software and that this latter belief fully mediated the effect of training on computer attitudes. Thus using a TAM and learning process framework, Agarwal and Prasad provided a coherent model to explicate how individual human capital differences in education, prior experience, and training might affect technology acceptance. Using the SCM, Compeau et al. (1999) proposed that individual differences in CSE would affect computer attitudes, outcome expectations, and computer use. The concept of self-efficacy is defined as judgments of one’s capability to organize and execute courses of action required to attain designated types of performance (Compeau et al., 1999). SE reflects not only an

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individual’s perception of ability to perform a particular task based on past performance, but also forms a critical influence on future intentions (Marakas et al., 1998). Widely studied in other contexts, particularly in organizational training (e.g. see Gist et al., 1989; Martocchio, 1994; Webster & Martocchio, 1993), researchers have focused on computer self-efficacy (CSE) as an important individual difference affecting computer use. Compeau et al. (1999) found that an individual’s CSE was significantly associated with computer attitude, outcome expectation, and use. CSE can be operationalized at both a general level and at a specific application level. Indeed an self-efficacy estimate is strongest and most accurate when determined by specific domain-linked measures rather than general measures (Bandura, 1986). Thus distinguishing between general CSE and task-specific CSE is important to properly specifying the relationships between CSE and performance. Task-specific CSE refers to an individual’s perception of efficacy in performing specific computer-related tasks within the domain of general computing (Marakas et al., 1998) such as in using a particular computer application such as word processing software. General computer self-efficacy (GCSE) refers to an individual’s judgment of efficacy across multiple computer application domains (Marakas et al., 1998) and is more a product of a lifetime of related computer experiences. GCSE can also be thought of as a collection of all CSEs accumulated over time. As such, the concept of GCSE overlaps with the notion of ‘‘prior similar IT experiences’’ in Agrawal’s and Prasad’s (1999) model of individual differences and computer acceptance based on the TAM. Integrating these two frameworks, SCM and TAM, and based on the above empirical research, we assert that GCSE is a key individual difference that will influence ESS acceptance. We depict this in Fig. 3. GCSE represents an individual’s enactive experiences with computers and as such should have a significant influence the formation of an individual’s specific CSE (Marakas et al., 1998). Similarly, given its conceptual similarity to ‘‘prior experience,’’ we also expect that GSCE should be positively related to beliefs about ease of use of a specific IT. Finally, in our conceptualization of perceived effort expectancy, we assert specific CSE as its primary dimension. Since specific CSE is posited to be related to prior enactive experiences with computers, which is what GCSE represents, we expect there will be direct relationship between the two constructs, therefore we propose: Proposition 5a. An individual’s general computer self-efficacy will be positively related to ESS effort expectancy. Consistent with TAM, we also propose:

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GCSE

P5 P6

Anxiety

ESS Effort Expectancy

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P7b P7a Technology Resources

P11d Task Routiness

Fig. 3.

Relationship of Individual Technology Orientation and Technology Resources with ESS Acceptance.

Proposition 5b. ESS effort expectancy will fully mediate the relationship between an individual’s general computer self-efficacy and ESS acceptance. Computer anxiety which represents an emotional or affective state should also influence the formation of specific computer self-efficacy. Research shows that emotional state is one of four factors that can effect changes in self-efficacy (Marakas et al., 1998; Martocchio, 1994). Computer anxiety is an emotional state in which individuals experience stress because they believe that they cannot effectively manage computer technology. Several studies have shown that computer anxiety is negatively related to CSE (Harrison & Rainer, 1992; Martocchio, 1994; Orr et al., 2001) and that manipulation of computer anxiety in various training conditions resulted in changes in specific CSE. We therefore expect that an individual’s level of anxiety about computers in general will negatively affect their beliefs about the effort involved in using ESS technology. The relationship is depicted in Fig. 3. Proposition 6. Computer anxiety will be negatively related to ESS effort expectancy.

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In summary, as illustrated in Fig. 3, we conclude that the most salient individual differences proximally related to cognitive beliefs about ESS technology are GCSE and computer anxiety. We have focused on proximal and more influential individual differences, however, further research is needed to better conceptualize the roles cognitive learning and personality traits might play. While cognitive style and personality traits have been empirically shown to have a small effect on computer attitudes, their conceptual relationship to technology acceptance has yet to be well articulated and integrated with current attitude and intention-based models. As with GCSE and computer anxiety these factors are also likely to be mediated by more proximal beliefs and attitudes. Further, recent research on CSE suggests that it is also possible that cognitive and personality differences may moderate the relationship between GCSE and changes in specific CSE (Marakas et al., 1998; Martocchio, 1994). ESS Technological Environment In the context of information systems research, technology refers to computer systems (hardware, software, and data) and user support services (help lines, IT consultants, etc.). Individuals view technology in this context as a tool used for carrying out their tasks (Goodhue, 1995). In the technology acceptance literature, however, the nature of the technology is often dealt with in a very abstract manner, for example, as beliefs about its usefulness or EOU, and not as physical resources that comprises access to hardware, software, data, help lines, etc. Furthermore, in much of this research, there is a presumption that there are no barriers preventing individuals from using the technology if she or he chooses (Mathieson et al., 2001). Thus the presumption is that the hardware, software, and support services are there to enable voluntary use. In an ESS technology implementation, the characteristics of the technology system are crucial. Indeed, one of the often-cited drawbacks to implementing ESS is that many employees will not have access to adequate technological resources such as a personal computer or a high-speed Internet connection which is also needed to access ESS. Furthermore, ESS deployment assumes that the technology will be used by all of an organization’s employees, not just knowledge workers as is the typical assumption underlying other IT implementations (cf. Harrison & Rainer, 1992). Consequently, adequate technology support services are also an important aspect of the technology. In their seminal work introducing the TAM, Davis et al. (1989) theorized that consistent with the theory of reasoned action, EOU is determined by

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external variables that include computer system features that enhance usability, training, documentation, and user-support consultants. However, because they argued that these external variables would be completely mediated by an individual’s belief concerning the target technology’s EOU, they left this aspect to future research. Thus, the importance to user acceptance of having adequate technological resources has only recently been addressed in the technology-acceptance literature. In a study of antecedents of perceived EOU, Venkatesh and Davis, (1996) looked at the effect of an objective measure of a new technology’s characteristics, which they called objective usability. The concept of objective usability is derived from engineers who design hardware and software technologies and are interested in understanding how to design the computer interface to maximize user satisfaction (Dillon & Morris, 1996). Venkatesh (2000) however, argued that objective attributes of a new technology do not influence the initial acceptance decision when they wrote: In the absence of much knowledge about the target system and limited direct behavioral experience with the system, individuals will base their perceived ease of use of the target system on general abstract criteria. With increasing learning and direct experience with the target system, user judgments about the ease of use of the system are expected to reflect specific, concrete attributes that are a result of an individual’s direct experience with the system. (p. 345).

Data collected from students supported their hypothesis. They found that general ease of use perceptions of the target technology, in this case use of word processing and spreadsheet software applications, were positively related to intentions to use the software but that objective usability of the technology had no initial relationship to technology acceptance. However, after direct experience with the target new software, objective usability achieved significance. In this study, Venkatesh and Davis used a narrow measure of objective technology usability, which was the ratio of time it took for an expert to perform a task using the target software compared to a novice. In contrast, in another study of effects on user evaluations, Goodhue (1995) found that the underlying characteristics of technology do influence the user’s subjective evaluation of the target technology. Based on a cognitive cost/benefit framework (Payne, 1982) and organization theory’s structural contingency theory (Donaldson, 1996; Woodward, 1965), Goodhue proposed that technology that supports and matches the tasks individuals must perform will result in more favorable evaluations and also in better task performance. Goodhue identified a subset of objective measures of technology characteristics that included: (1) the extent to which

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the relevant major transaction processing systems accessed by the end-user were common systems with integrated data, (2) the number of PCs per user, (3) the ratio of assisters to users, and (4) the fraction of assisters who report to users rather than to an IS group. Collecting data from managers and staff outside information systems (IS) in 10 different organizations, Goodhue found that the number of PCs per user showed the strongest relationship to end-users’ evaluation of various systems. He also found significant relationships between positive evaluations and proportion of assisters devoted to end-user support, system reliability, and accuracy. Perceptual measures of physical characteristics of the target technology have also been used. Using a TPB framework, Taylor and Todd (1995) introduced the concept of resource-facilitating conditions. Resource-facilitating conditions consists of two dimensions: one relating to individual resource factors such as time and money and the other relating to the underlying technological characteristics that may constrain usage. Building on the notion of facilitating conditions in the TPB, Mathieson et al. (2001) extended TAM to include a new construct, which they called perceived resources. They defined perceived resources as the extent to which an individual believes that he or she has the personal and organizational resources needed to use an information system. Using data collected from an implementation of an organizational webpage, they empirically showed that perceived resources was directly and significantly related to individual’s perceived usefulness, EOU, behavioral intention, and actual system use of the organization’s web page. They also found that perceptions of accessibility of hardware and software to be the most critical predictor of perceived resources, followed by knowledge, and availability of time. The concept of perceived resources represents a perceptual approach to measuring technological characteristics that include perceptions of hardware and software accessibility, knowledge of the system and the time required to learn how to use it. Objective measures included a simple objective measure of technology usability measured as time to gain mastery of the technology, and Goodhue’s multidimensional objective measure, which tapped hardware and software accessibility and support for learning how to use it. The latter appears to more closely resemble Mathieson et al.’s (2001) perceptual measure of technology resources. All measures of technology resources were positively related to perceived EOU, although Venkatesh and Davis’s one item objective measure was significantly related to EOU only after an individual had an opportunity to actually try the system. Overall, the empirical literature supports the existence of a significant relationship between underlying technology characteristics and perceived

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ease of use. Consequently, the relationship between technology environment and technology acceptance should be at least partially mediated by perceived ease of use. Although there is little consensus in the literature concerning conceptualization or measurement of a technology resource construct, a broader conception of technology characteristics such as those used by Goodhue (1995) and Mathieson et al. (2001) seems to be more appropriate for an ESS technology implementation. An ESS technology implementation is a complex implementation, one that many members must use to benefit the organization (Leonard–Barton & Deschmaps, 1988) and requires greater management intervention and technical resources than introducing stand alone application software packages. In particular access to hardware and software to perform ESS is essential and also having supporting consultants to assist in quickly learning the technology are important antecedents of an individual’s perception of the mental, physical, and learning hurdles required to use ESS. Thus we propose the following relationships: Proposition 7a. Perceptions of technology resources will be positively related to ESS effort expectancy and indirectly related to technology acceptance through ESS effort expectancy. In addition, we argue, consistent with the TPB (Ajzen, 1991; Taylor & Todd, 1995) and Mathieson et al. (2001), that perception of effort expectancy and intention to use a new technology is a necessary requirement but not sufficient in itself for user acceptance. This is because access to the technology itself is also a requisite condition. Clearly, without access to the hardware and software, no matter how interested an individual is in using the technology, environmental obstacles will prevent execution of the required behavior. Consequently, we also propose: Proposition 7b. Perceptions of ESS technological resources characteristics will be directly related to ESS acceptance. Both these relationships are depicted in Fig. 3. Organizational Environment Organizations vary with respect to strategic management of their technology infrastructure, in their emphasis on the importance of employee use of IT, and in how much they invest in supporting the end-user through training. Consequently, in addition to technology resources, variation in an organization’s management environment can also influence individual acceptance of new technology (Cheney & Dickson, 1982; Leonard-Barton &

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Deschamps, 1988). Based on empirical technology acceptance research, we discuss the three dimensions of organizational environment that research evidence indicates are influential: management influence, perceived organizational support (POS), and training. These constructs and their relationship to ESS technology acceptance are explained in more detail below and depicted in Fig. 4. Managerial Influence In organizational settings, new technology introductions are often not voluntary and often introduce significant change. In these contexts, the presence of champions or powerful managers supporting an innovation has been consistently associated with higher levels of success in the areas of change and innovation (Leonard-Barton & Deschamps, 1988; Rogers, 1983). Research on power dynamics in organization also shows that individuals are likely to comply with others’ expectations when those referent others have the ability to reward the desired behavior or punish non-behavior (French & Raven, 1959; Keller & Szilagi, 1976). Finally, in the theory of reasoned

Voluntariness Management Influence

Perceived Usefulness

P8b

P8a

Personal Outcomes Perceived Organizational Support

Benefit to Others

P9b

P9a

ESS Acceptance

Intrinsic Benefits P10b Training

P10a

ESS Effort

P11c Task Significance

Fig. 4.

P11a/P11b Task Importance

Relationship of Perceived Organizational Environment and ESS Acceptance.

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action and the TPB, a person’s intention to behave (e.g. accept a new technology) can be altered by perceived normative pressure from powerful sources such as ones supervisor or senior management. Existing empirical results in the technology acceptance literature, particularly those based on the TPB, indicate that reliance on others’ opinions appears to be significant only in mandatory technology use settings and only in the early stages of adoption when actual knowledge of the system is limited (Venkatesh & Davis, 2000; Venkatesh et al., 2003). These results are consistent with a compliance perspective of managerial influence. Managers who have legitimate power to reward employee behaviors exert control over an employee’s intention and use of technology especially when they make its use mandatory. Thus, managerial influence has a direct effect on technology acceptance in the case where managers make use of the technology mandatory. As depicted in Fig. 4, we propose a moderated relationship between the effect of managerial influence on ESS acceptance. Proposition 8a. The relationship between managerial influence and ESS acceptance is moderated by the perception of voluntariness of the ESS technology adoption such that when adoption is mandatory there will be a high level of technology acceptance and when adoption is voluntary there will be no direct effect on ESS technology acceptance. When new technologies are introduced that are not mandatory, individuals are likely to rely on information from others who have greater experience or more knowledge of the particular technology to form an intention regarding potential adoption. The more complex and uncertain the environment, the more individuals rely on others for information (Salancik & Pfeffer, 1978). In this way the social environment in which an individual operates influences what information is noticed, encoded and/or retrieved. Social information comes from coworkers, supervisors, those in proximal social networks, or those who are similar to the individual, and can provide the most relevant information (Marler, 2000). As Venkatesh and Davis (2000) argued: yif a superior or co-worker suggest that a particular system might be useful, a person may come to believe that it actually is useful, and in turn form an intention to use it. In French and Raven’s (1959) taxonomy, the basis of internalization is expert power where the target individual attributes expertise and credibility to the influencing agent. (p. 189).

In empirical tests of a proposed relationship between perceived usefulness and perceptions of normative pressure to adopt IT voluntarily in two different organizations, Venkatesh and Davis (2000) found a significantly positive relationship. Consistent with the TAM, they also found that

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perceived usefulness completely mediated the relationship between perceived social influences and system acceptance. In their empirical specification, however, they did not distinguish between social referents (e.g. co-worker or supervisor). They simply asked respondents to report to what extent ‘‘people who influence my behavior think that I should use the system.’’ In their study of a voluntary adoption of complex decision support software, Leonard-Barton and Deschamps (1988) found that managerial influence was only positively related to adoption among employees who had low levels of personal innovativeness, for whom the use of the technology was not critical to job performance, and for whom the value of the innovation was low. In these situations, they reasoned, managerial influence was important because it persuaded those employees with initial low levels of ability and perceived value to give the new technology a try. Theory and evidence suggest that management influence is an important factor in both mandatory and voluntary implementations. In a voluntary context, however, the process through which individuals come to accept the ESS technology will be through their enhanced perception of the instrumentality of using ESS in achieving valued job-related outcomes. Thus, in situations where managers are able to legitimately persuade their employees as to the value of ESS, acceptance of ESS technology will be influenced through changing perceptions of perceived usefulness. We therefore propose the following relationships shown in Fig. 4: Proposition 8b. When ESS use is voluntary, the effect of managerial influence on ESS acceptance will be fully mediated by perceived usefulness. Perceived Organizational Support An important consideration in fostering participation in voluntary learning and development activities, such as fostering voluntary ESS acceptance for non-job related tasks is the extent to which the organization provides an environment that facilitates these behaviors. Perceived organizational support (POS) has been shown to be significantly related to intentions to participate in career related learning and development workshops. POS is also posited to engender prosocial behavior, which benefits others (Maurer et al., 2002). Prosocial behavior includes activities that are meant to benefit the organization (Maurer & Tarulli, 1994). Perceived organizational support is particularly instrumental in situations where an employee’s actions result in outcomes, which benefit others such as the employee’s supervisor or the organization generally, rather than the employee personally. Maurer, Pierce and Shore (2002) proposed a model of the employee’s decision-making process that features the supervisor and the

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organization as beneficiaries of an employee’s decision to undertake learning and development activities. They stated that in many cases, employees might not perceive themselves to be the primary or direct beneficiary of their development activities but rather a supervisor or their organization. An example where involvement in development activities does not directly benefit the employee, but rather the organization, is when an incumbent’s development activities are not related to his/her current job performance. The voluntary adoption of new technology may be characterized similarly. Use of a new technology introduced in the work setting and related training activities that are voluntary on the employee’s part and not required for the performance of current job tasks represent activities that more immediately benefit the organization than the employee. This is true, particularly in the case of initially using ESS, where often usage does not directly affect job performance but does benefit the organization by lowering its transaction costs. In the situation where the benefit accrues to others, Maurer et al. apply social exchange theory arguing that development behaviors take place in the context of existing exchange relationships with the supervisor and the organization. Employees consider whether the supervisor and or organization will benefit from their development activities and whether these seem appropriate given the nature of the relationships. They suggest that voluntary employee development is an acceptable commodity for exchange. Such an exchange can occur in the context of reciprocating behavior between employee (voluntary development) and organization (POS). An employee may perceive their organization as caring and contributing to their well being above and beyond the need to earn a profit. Such policies have been defined as POS, as a global belief concerning the extent to which an organization values employee contributions and cares about their well being (Eisenberger, Huntington, Hutchinson & Sowa, 1986). Motivated to reciprocate, employees undertake prosocial behaviors. Individuals may undertake activities that are of low benefit to themselves but are perceived to benefit the organization, which is a form of individual prosocial organizational behavior (Maurer, Pierce & Shore, 2001). Organizations implement ESS with the expectation of saving money by shifting tasks typically done by HR employees to all employees. ESS activities do not form a formal part of an employee’s job, however, by diligently adopting and effectively using this technology employees contribute to the organization’s profitability. Maurer et al.’s model of employee decision-making suggests that technology use may also be related to individual and organizational prosocial behaviors. Individuals who perceive a high level of organizational support will be more willing to use ESS technologies

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not because they perceive the technology has benefits for them but because they perceive it will benefit the organization. Thus as shown in Fig. 4, we posit that the following relationship between POS and the expectation that use of ESS will benefit the organization. Proposition 9a. Perceived organizational support will be positively related to ESS perceived benefit to others. Proposition 9b. Perceived organizational support’s relationship to ESS acceptance will be fully mediated by perceived benefit to others. Training The third dimension of organizational environment that is associated with successful technology implementations is training. Training is one of the most cited critical success factors in complex IT implementations (Kale, 2000). Training is important to orient users to new IT technology and also to help in the organizational change process. Successful IT system training can achieve at least two important objectives. At the minimum, it familiarizes potential users with how to use the system and helps reduce user uncertainty and anxiety. Secondly and more importantly, however, organizations can use training programs to communicate the value and benefits of the new system to gain users’ commitment and acceptance. In the technology acceptance literature, training has been studied in the context of its relationship to perceived ease of use (Venakatesh, 1999) and with respect to what role CSE plays in achieving successful computer training outcomes (Gist, Schwoerer & Rosen, 1989; Martocchio, 1994; Webster & Martocchio, 1993). Venkatesh (1999) found that training yields favorable ease of use perceptions which lead to greater intention to use. In testing an extended TAM, Venkatesh and Davis (1996) found CSE is related to perceived ease of use of various software programs. Other researchers have also found that CSE is directly related to individual decisions to use personal computers (Compeau & Higgins, 1995). Prior training has also been shown to be related to perceived ease of use (Agrawal & Prasad, 1999). Moreover it is through prior training and experience on other computer systems that individuals develop their general beliefs about using computers that reduce anxiety, trigger recall of prior sense of computer competency (Venkatesh, 2001; Pinder, 1998) and contributes to beliefs about GCSE (Marakas et al., 1998) and perceived resources (Mathieson, Peacock & Chin, 2001). While prior training is related to GCSE, training that is specific to teaching employees how to use ESS to perform job and personal tasks is expected

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to result in a change in the specific self-efficacy with respect to capability to use ESS. As shown in Fig. 4, we therefore expect: Proposition 10a. ESS technology training will be positively related to ESS effort expectancy. Proposition 10b. The relationship between training and ESS acceptance will be fully mediated by ESS effort expectancy.

THE MODERATING EFFECT OF TASK CHARACTERISTICS The technology acceptance literature to date has focused on refining models to improve prediction of intention to use a target technology, particularly research examining extensions of the TAM. Researchers in this domain have introduced antecedents of key constructs: EOU (Vankatesh & Davis, 1996, Venkatesh, 2000) and perceived usefulness (Venkatesh & Davis, 2000) and incorporated other related research models to validate and enrich the parsimonious TAM (Venkatesh et al., 2003). Research is still needed, however, that maps out major contingency factors that moderate the effects of these constructs (Venkatesh et al., 2003). Preliminary research on such contingency factors suggests a key moderator is the nature of the tasks individuals perform using the technology (Goodhue, 1995; Leonard-Barton & Deschamps, 1988). To date, technology acceptance models have assumed little variation in the nature of work performed by individuals adopting a new technology. In fact, much of the empirical research conducted is in situations where the subjects perform the same tasks, such as students learning a new word processing and graphics packages (Davis et al., 1989), financial professionals adopting a new portfolio management software (Venkatesh & Davis, 2000); or customer service representative using a new database (Morris & Venkatesh, 2000). In organization-wide technology adoptions, such as an ESS technology, the technology is used by individuals who perform a wide variety of job tasks from senior managers to entry level blue-collar workers. Furthermore, tasks will vary not only among individuals, but will also vary based on context, whether the task is personal or job related. Consequently, the underlying premise of common tasks certainly does not hold in an ESS technology implementation.

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Given the expected variation in tasks across members of an organization implementing ESS technology, we introduce the notion of task as a moderating factor drawing from the task-technology fit perspective (Goodhue, 1995). Goodhue identified three dimensions of task variation related to use of technology: routineness, interdependence, and ‘‘hands-on’’ tasks suggested by Fry and Slocum (1984). Other researchers have proposed task relevance based on innovation diffusion theory (Rogers, 1983) and task variety, task identity and significance from the job characteristics model (Hackman & Oldham, 1975). As shown in Figs. 3 and 4, our model depicts task importance, task significance, and task routineness as key moderators. There is limited empirical evidence regarding the effect of these task dimensions on perceptions of usefulness and ease of use. In a study of a complex technology implementation, Leonard-Barton and Deschamps (1988) found that task importance and task significance moderated the effect of managerial support on end-user acceptance. Goodhue (1995) found that his three dimensions of tasks (routineness, interdependence, and handson) had significant moderating effects on perceptions of end-user evaluations of the systems. Goodhue assessed user evaluations using 12 dimensions and tested 192 possible interactions of which 22 were significant. While these evaluations were not the same as the TAM perceptual constructs (EOU and perceived usefulness), many of the dimensions probably would correlate with perceived EOU. Based on these findings, it appears that task characteristics can be a contingency factor in complex technology implementations. While researchers use different terms to identify different dimensions of task characteristics, there appears to be some overlap with the three core job dimensions related to experienced meaningfulness of work that Hackman and Oldham (1975) introduced in their job characteristics model: task variety, task identity, and task significance. Hackman and Oldham defined task variety as the degree to which a job requires the worker to perform activities that challenge his or her skills and abilities. This definition appears to correspond to the task routineness dimension conceptualized by Goodhue (1995) as a combination of task variety and difficulty. Task identity is defined as the degree to which a job requires completion of a whole and identifiable piece of work-doing the job from beginning to end with a visible outcome (Hackman & Oldham, 1975). This dimension bears resemblance to job importance (Leonard-Barton & Deschamps, 1988) and the ‘‘hands-on’’ dimension by Goodhue (1995). Finally task significance corresponds to Leonard-Barton and Deschamp’s conception of identity with the task.

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Task Importance Leonard-Barton and Deschamps (1988) defined task importance as the degree to which a potential user performed a task and perceived a high need and relevance of the technology in performing this task. Such a perspective corresponds with Goodhue’s hands-on dimension which he defined as tasks that utilize the technology in non-preprogrammed ways that are integral to their job needs. While both definitions of the task appear vague, each focuses on the degree to which the technology is critical to key task performance. The concept is also similar to notion of job relevance introduced by Venkatesh and Davis (2000). Job relevance is a cognitively determined evaluation of the tasks that are important in achieving task-specific plans. Task-specific plans guide behavior through a matching process linking instrumental acts to goals (Venkatesh & Davis, 2000). The extent to which certain tasks are important to achieving goals and the degree that the target system is capable of supporting these tasks forms the basis of the concept of job relevance. Venkatesh and Davis (2000) proposed and found empirical support for an antecedent relationship between job relevance and perceived usefulness. In the context of ESS technology, we propose a moderating effect of task importance on ESS acceptance. In the situation where the tasks performed using the technology are important components of job performance, it is expected there will be a relationship between an individual’s perception of the value of the ESS technology to job performance and hence ESS acceptance. In the case where the tasks are not critical to job performance, where task identity is low because these tasks do not relate to a visible outcome, then there should be a limited relationship between perceived usefulness and technology acceptance. As shown in Fig. 4, we propose: Proposition 11a. The relationship between perceived usefulness and technology acceptance will be moderated by perceived task importance such that where task importance is high there will be a strong positive relationship and where task importance is low there will be a weak relationship. The concept of task importance and identity does not have to be limited to a job context. The same cognitive evaluation is most likely to occur with respect to whether there is a fit between the technology and accomplishing tasks that have personal relevance such as making employee benefit selections or tax withholding adjustments. In the situation where making annual benefit selections is critical to an individual’s personal outcomes, in this case, perhaps selecting the correct medical insurance plan, then an individual is likely to quickly accept a new technology that is a necessary tool

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to accomplish this important activity. Where such a task is not of particular importance to an individual’s personal outcomes such as keeping their home telephone number updated in the organization’s HRIS database then use of the technology will be limited. Thus the level of task importance is particularly relevant to ESS technology implementations and user acceptance. We therefore propose the following moderating relationships based on the preceding empirical evidence and depicted in Fig. 4: Proposition 11b. The relationship between personal outcome expectancy and technology acceptance will be moderated by perceived task importance such that where task importance is high there will be a strong positive relationship and where task importance is low there will be a weak relationship. Task Significance Task significance is another dimension of task characteristics that appears to moderate the relationship between beliefs about the technology and technology acceptance. Task significance is defined as the degree to which a task has a substantial and perceivable impact on the lives of other people, whether in the organization or the world at large (Hackman & Oldham, 1975). Increased task significance is likely to increase an individual’s involvement in the task and increase the individual’s perceived meaningfulness and thus resulting in higher motivation to perform the task. Consequently, where the technology is needed to complete a task, the significance of the task itself will predispose an individual to accept the technology. LeonardBarton and Deschamps (1988) hypothesized that task significance might also moderate the relationship between organizational environment and perceived usefulness of a new technology. They asserted that if the task itself had less significance, then individuals may have a lower perceived need for the innovation and hence require a push from management. They found empirical support for this proposed moderating effect of task significance on management influence. In an ESS implementation, task significance could also moderate the relationship between organizational environment and perceived usefulness. In complex organizations with many interdependencies, an individual may not realize the significance of their task in the whole scheme of things. For example, keeping one’s contact information up to date on the HR database may not seem significant. However, in times of a significant emergency, both the individual and organization benefits from having a current relevant

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database of information. Consequently, for many of the apparently less significant tasks involved in ESS technologies, the technology may not be used. In the case where this significance is clear to an individual there is little need for management influence and thus there will be little relationship between management influence and perceived usefulness for individuals who already perceive the significance of their task. On the other hand, for those for whom task significance is not clear or low then management’s influence and support of the innovation will have a positive effect on perceived usefulness. As shown in Fig. 4, we propose Proposition 11c. The relationship between managerial influence and perceived usefulness is moderated by task significance such that where significance is high there is no relationship and where task significance is low there is a positive relationship. Task Routineness Task routineness is the last task dimension which we propose will have a moderating effect on ESS acceptance. Goodhue described task routineness as tasks that are repetitive and simple. Task routineness has two dimensions: variety and difficulty. Nonroutine tasks involve a great variety of issues, need for a lot of information, and must be analyzed in new and innovative ways. There is some overlap between this concept and Hackman and Oldham’s (1975) conception of skill variety. Tasks that require an employee to perform activities that challenge skills and abilities bear a resemblance to nonroutine tasks. In his empirical study of task-technology fit, Goodhue found that for users engaged in nonroutine tasks, increasing the number of PC terminals had a greater impact on users’ positive evaluation of the new technologies ability to provide easily accessible, accurate and useful data and perceived ease of use of the hardware and software. We propose that task variety will have a similar moderating effect on the relationship between organizational technology resources and perceived effort expectancy. Where an employee’s task is simple and repetitive the employee will have little need for a barrage of technology resources, such as a high ratio of help consultants to employees or multiple different PC kiosks. Thus in this case the relationship between technology resources and perceived effort expectancy is likely to be very limited or null. On the other hand, for employees who have a variety of challenging tasks, the availability of PCs both at work and home and high investment in technology consultant support is likely to have a significant impact on an individual’s perceptions of

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how much effort will be required to use the technology in accomplishing task performance. Consequently, as depicted in Fig. 3, we propose: Proposition 11d. The relationship between technology resources and ESS effort expectancy will be moderated by task variety such that for individuals who have high task variety there will be a significant positive relationship between technology resources and effort expectancy and where task variety is low there will be a weak relationship between technology resources and effort expectancy.

IMPLICATIONS FOR PRACTICE AND FUTURE RESEARCH The literature on technology acceptance provides an initial framework for identifying factors that predict individual technology acceptance. We build on this foundation providing an enriched model that addresses how existing theories might be applied to investigating key predictors of ESS acceptance. In our review and synthesis of the literature, we highlight the key individual differences and the important contextual differences that affect ESS acceptance. Specifically, we argue that in addition to the perceptual factors highlighted in the parsimonious TAM, differences in individual technology orientation, organizational, technological, and task environments have significant effects on individual acceptance of new web-based self-service technologies. Our model has several theoretical and practical implications which we discuss in this section. We begin with a summary of the practical implications of our model for the HR practitioner. Subsequently, we discuss theoretical and empirical implications and avenues for future research. Implications for Practice The benefits employees expect to derive from using ESS are important drivers of ESS acceptance. Expected benefits can be extrinsic or intrinsic for the employee user and ESS acceptance is also affected by organizational support efforts that can result in prosocial behaviors. In organizational settings, extrinsic outcomes are typically most salient and therefore we expect that individuals will use ESS if they see it as instrumental in achieving existence (e.g. more money, time, or resources) or relationship needs (e.g. social approval). Employees must perceive that ESS will improve their job performance (or at least maintain performance

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relative to any earlier technology), and thus develop into a salary increase or bonus or increase their chances of promotion. If ESS does not result in work related benefits then it should be perceived as providing some other tangible personal benefit such as saving time, facilitating access to needed information, or improving personal wealth. Our model suggests that organizations must think broadly and creatively about communicating the value of this technology to their employees and also insuring that employee experiences with the new technology reinforce these positive expectations. For example, communicating that ESS will enable the employee to directly access and change critical HR, payroll, and personal information anywhere, anytime through a web browser, and spend less time in these activities than prior to the implementation of the ESS, would be part of communicating the value of ESS. Insuring positive experiences with the ESS would involve creating, prior to implementation, easy to navigate user interfaces that are intuitive to all employees as well as including immediate email verification to the employee of transactions such as W-4 payroll changes. Our model of ESS acceptance also indicates that where there is no apparent extrinsic benefit, then the use may also be motivated by expectations of some kind of intrinsic benefit such as gaining a sense of enjoyment and mastery over using a new Internet technology. Barring this, employees might be drawn to adopting the technology to benefit the organization. This latter possibility is likely in organizations that are employee-oriented and where there is high positive organizational support. In such contexts, employees are more likely to use the technology simply to benefit the firm. Acceptance of ESS is also influenced by what others think. Managers and immediate supervisors, in particular, indirectly affect technology acceptance through influencing their employees’ perceptions of how the use of a new technology is instrumental in achieving valued outcomes. Thus, when an organization introduces ESS, managers should pay attention to the image that the technology develops within the organization. How others describe the technology in the organization can play an influential role in how employees develop perceptions of ESS’s usefulness. Involving employees from different organizational levels in the implementation process, who then in turn communicate to employees the benefits of the system, can contribute to employee perceptions of the system. Consequently, organizations can increase the likelihood of greater ESS acceptance through conscientiously managing the formation of cognitive and affective attitudes across employees at all levels of the organization. This is important no matter whether ESS technology is mandatory or voluntary.

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Our model of ESS use suggests that managing perceptions of how much effort it will take to use ESS, for example, effort expectancies, also can increase ESS acceptance. Managers can do this by managing perceptions of the organization’s technology resources, particularly hardware and software availability, technical consulting, and end-user support. Organizations must also be aware of their employees’ levels of general computer self-efficacy (GCSE) and computer anxiety. Identifying those employees with low GCSE and high computer anxiety and investing in adequate technology resources and training are important first steps. Training has been shown to increase specific computer self-efficacy (Martocchio, 1994; Tannenbaum & Yukl, 1992), and reduce computer anxiety (Orr et al., 2001). Thus early investments in computer training targeted at specific employee groups may, over the long run, make a significant difference. Finally, our model indicates that task characteristics are important moderators of the technology belief–acceptance relationship. Not all employees will respond similarly to an organization-wide implementation of ESS technology. We have discussed individual differences in perceptions of capability but we also argue differences in the tasks individuals engage in will also impact acceptance. Technology acceptance will most likely occur in situations where the technology facilitates those tasks that are instrumental in achieving desired outcomes. For example, an important task is filing or updating tax withholding forms. If this task can now be completed, easily and quickly, online through a personal computer then employees will learn to use ESS in order to accomplish this important task. On the other hand, where the task has little instrumentality to employees, such as keeping an emergency contact address updated, no matter how useful the technology, chances of individual acceptance will be low. Organizations that are able to communicate the importance of tasks can increase the chances that a new technology will be accepted. As noted above, task importance refers to the degree to which a potential user perceives a high need and relevance of the technology in performing a particular task. For example, keeping employees’ home address and telephone data updated is likely to be of low importance to individual employees unless the organization can convince them this belief is not true. If the organization does not make an effort to communicate this then employees are unlikely to embrace such a task using ESS technology. They might simply continue to call HR and ask them to make the change or might ignore the task altogether. Managers can also increase the likelihood of acceptance if they are able to show the significance of a task that involves using ESS. As noted earlier, task significance is defined as the degree to which a task has a substantial

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and perceived impact on the lives of other people. Employees who see their task as having low significance are not likely to see the usefulness of performing such a task. While an employee may view keeping his/her personal home address updated as inconsequential, he/she may view tasks such as adding a dependent to their employer sponsored health care plan as imperative because the failure to do so would result in the other person not being covered by the health plan. Managers are likely to have the most influence on employees who perceive the task as having limited significance and less influence on tasks that are already considered significant. In the latter case, management influence is superfluous because employees will already know how significant that task is to both themselves and the organization. On the other hand, where a task is less significant, management influence can have a greater effect because managers can communicate how keeping personal information updated can affect the ability of the organization to make better HR-related management decisions. Our model suggests that task variety may play an important contingency role in technology acceptance. Our model posits that the impact of investments in technology resources on ESS acceptance (such as number of ESS PC kiosks) will depend on the degree of task variety with which the technology is used. If the tasks are of a routine nature, simple and repetitive, then increasing the amount invested in technology resources is unlikely to have a large impact on ESS acceptance. This suggests that employee selfservice systems should be sophisticated and allow the employee user to perform variety of transactional and even personal decision support tasks previously performed for the employee only by the HR department. Examples would include, submitting open-enrollment choices, making benefit and life-event changes, accessing and changing payroll information such as W-4 forms, direct deposit, and providing employees with online tools to calculate net pay, bonus payouts, retirement eligibility, and benefits, enabling them to view internal job postings and submit applications to those positions electronically, file grievances electronically, etc. If the tasks that can be performed are not limited and routine then investing in technology resources will be appropriate. Implications for Research From an empirical perspective, our model suggests there are several avenues for future field study research. These include verification of the model empirically, further refinement of its multi-level context, and development of new construct measures. While our ESS model is based on technology acceptance

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models that have received strong empirical support, our proposed extensions to these models need to be tested in lab and field settings. In particular new constructs such as perceived personal outcomes and perceived benefit to others and their relationship to technology acceptance need to be verified. Second, several antecedents to the construct, perceived effort expectancy, need confirmation and include general computer self-efficacy, general computer anxiety, and objective technology resources. Finally, various antecedents of perceived benefits such as managerial influence, perceived organizational support (POS), and training must also be tested and relationships to technology acceptance, both direct and mediated, need to be confirmed. Our model proposes many factors, both direct and indirect, that influence technology acceptance but it is silent about the relative importance of these factors. Prior research on the TAM suggests that perceived usefulness is the most influential construct, explaining more variance in intentions to use a target technology than perceived EOU (Davis, 1989; Venkatesh et al., 2003). However, we do not know which of the antecedents to these beliefs has the most impact. Such knowledge will also have useful application in HR practice. Our model specification suggests a multi-level perspective is needed to better understand individual technology acceptance. We posit important roles for organizational and technological contexts. There may be others such as industry, function, and team. These multi-level effects are best identified when data are collected from individuals in organizations in different industries, from different functions, and differing types of work units. This future empirical research would yield important information about multi-level contextual factors that we suggest affects individual ESS use. Finally, from an empirical perspective, based our review of the existing research and in our proposed model, we have identified several constructs that need more precise measurement. These include constructs that have existing measurement scales such as perceived EOU, perceived usefulness and computer self-efficacy. Perceived EOU is a multidimensional construct which in the empirical literature is typically measured quite narrowly. Our research suggests that a key dimension of this construct is specific CSE. Existing measure of perceived ease of use (cf. Venkatesh et al., 2003) need to be enhanced to better capture this aspect of the perceived effort expectancy construct. Measures of perceived usefulness have similar problems. We have proposed additional constructs as a way to address the current narrow operationalization of this construct. These new constructs (which include perceived benefit to others, expected intrinsic benefit, and expected personal outcomes), also require scale development. Finally, while there are several measures of computer self-efficacy in the literature, none properly addresses differences

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between general computer self-efficacy and specific computer self-efficacy (Marakas et al., 1998), which in our model is a critical distinction. From a theoretical perspective, our model suggests there is a need for further research in several areas. First, investigation needs to be made into the processes by which more distal individual difference factors such as dimensions of personality and cognitive style may affect perceptions of a new technology. A second area for theoretical investigation is the role of contextual moderators. We considered how differing tasks could moderate the relationships between individual perceptions and technology acceptance. Other moderating factors might include such demographic differences such as age and gender. The general expectation is a negative correlation between age and computer self-efficacy, but empirical support is mixed (Gist et al., 1989; Morris & Venkatesh, 2000; Orr et al., 2001; Webster & Martocchio, 1993), suggesting the relationship is more complex. The same may be true for gender. Another avenue of future research is to gain a better understanding of how organizational culture, social referents, and employee demographics affect ESS use. Questions exist such as which factors in an individual’s environmental context, such as family, organization, occupation, or supervisors, have the most effect on intentions to use ESS? Related is the issue regarding how these contexts influence pressure to use ESS? A third avenue for future investigation concerns role of organization-wide training. How much training, what type, and to whom are three main questions that are important to answer both from a theoretical point of view and for practice. Our review highlights several recent studies that have made contributions with respect to type of training (e.g. Webster & Martocchio 1993; Gist et al., 1989; Venkatesh, 2001) but more research is needed, particularly addressing how much training, when training should be provided, and to whom. Finally, our model focuses on an individual’s early intentions and initial adoption of ESS technology. Recent research indicates that the factors that are implicated in initial decisions to use a new technology differ from those that influence continued use of the technology (Karahanna et al., 1999; Venkatesh, Speier & Morris, 2002). This aspect of our model needs to be augmented so that it can be applied in those contexts where ESS has already been introduced but initial users no longer use it or fail to use it effectively.

CONCLUSION HR practitioners are facing a challenging array of technology choices in today’s web-based technology environment as well as issues regarding

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augmenting employee use and acceptance of new technologies. Increasingly popular, ESS technology has developed a reputation for facilitating HR’s transformation from a transactional-focused function to a strategic partner. ESS technology frees HR from administrative details and allows HR to push transactional processes, such as data entry and keeping HRIS databases updated to operational managers and their employees thus enabling HR to focus on strategic tasks. Further, ESS enables employees and managers to conduct business, initiate transactions, and access needed HR information through a web browser at any time. Despite its potential for increasing HR services, support capacity, and lowering costs, ESS technology implementations have low rates of success in achieving expected goals. Our model of ESS technology acceptance identifies the key success factors HR practitioners must focus on and manage in order to increase ESS technology acceptance. On the research front, our model provides new avenues for HR researchers to expand knowledge of employee behavior in a leading edge technological context. With technology management representing a potential strategic differentiator, knowledge of how theories can be applied in these contexts is critical. Research based on the social – technical approach, beginning in the 1950s, reminds us of the importance of the human factor in technology implementation (Scott, 1987). Specifically, the best technology system without consideration of the end-user may result in an under-utilization of the technology and a failure to achieve its intended benefits. In this paper we have provided researchers with a model and testable propositions focused on the ESS end-user. We encourage researchers to use this model to increase our knowledge of ESS technology acceptance and practitioners to use it in order to maximize its effective deployment.

NOTES 1. ESS effort expectancy is also expected to be more salient in the early stages of a new behavior, when information, process, and performance uncertainties represent primary hurdles to actual technology use (Venkatesh et al., 2003).

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LEARNER CONTROL AND WORKPLACE E-LEARNING: DESIGN, PERSON, AND ORGANIZATIONAL ISSUES Rene´e E. DeRouin, Barbara A. Fritzsche and Eduardo Salas ABSTRACT In this paper, we review the literature on learner control and discuss the implications that increased control may have for training in e-learning environments. The purpose of this paper is to provide a comprehensive review of the learner control literature, focusing on adults and workplace training. We begin by reviewing the literature on learner control, focusing on the positive and negative effects associated with providing adult learners with control in e-learning environments. We organize our review into instructional design factors that have been manipulated to provide learners with control and person issues that moderate the relation between learner control and outcomes. Then, we summarize developments in training research and in adult learning that relate to learner control in order to provide a theoretical context for understanding learner control in adult workplace e-learning.

Research in Personnel and Human Resources Management Research in Personnel and Human Resources Management, Volume 24, 181–214 Copyright r 2005 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 0742-7301/doi:10.1016/S0742-7301(05)24005-7

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INTRODUCTION To remain competitive in an ever-changing, global, and high-tech environment, organizations are investing more money in training than ever before (Bassi & Van Buren, 1999; Salas & Cannon-Bowers, 2001). In addition to this added investment, the focus of training itself has evolved as organizations realize the importance of having a workforce that is not only skilled but also adaptable. The dynamic nature of the current work world requires that employees be able to perform under circumstances that are often unpredictable and ill defined (Smith, Ford & Kozlowski, 1997). Consequently, as technology advances and offers an efficient alternative to traditional instructor-led training, more organizations are adopting e-learning as a way to meet their training needs (Brown & Ford, 2002; Kosarzycki, Salas, DeRouin & Fiore, 2003). E-learning refers to ‘‘a wide set of applications and processes, such as web-based learning, computer-based learning, virtual classrooms, and digital collaboration. It includes the delivery of content via internet, intranet/ extranet (LAN/WAN), audio- and videotape, satellite broadcast, interactive TV, and CD-ROM’’ (Kaplan-Leiserson, 2002, para. 85). Designed to be adaptive, the format of e-learning can be either very structured or very flexible. When flexible, e-learning allows trainees to accept considerable control of the learning process. Intuition suggests that learners themselves are the best judges of their own skills and deficiencies (Carrier, 1984). By providing learners with control over their learning, it is logical to assume that learners will accept more responsibility for learning and will adjust training to their specific learning styles. As a result, learner control can potentially increase learning, motivation to learn, satisfaction with the training, and transfer of training. Contrary to intuition, the results of 40 years of research suggest that shifting control to learners is not always desirable. Therefore, in this paper, we review the literature on learner control and discuss the implications that increased control may have for training in e-learning environments. The purpose of this paper is to provide a comprehensive review of the learner control literature, focusing on adults and workplace training. Because researchers have traditionally examined learner control from an educational perspective, the literature is potentially limited in its applicability to trainees in organizational settings. Our paper addresses these limitations and offers suggestions for moving learner control research out of the classroom and into the workplace.

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LEARNER CONTROL Although learner control research began over 40 years ago (e.g. Mager, 1961; Mager & McCann, 1961), no consistent definition for learner control has emerged (Merrill, 1984). In fact, learner control now appears to be a catch-all term for any instructional strategy in which learners assume some form of control. A broad definition was posed by Wydra (1980) in which learner control was described as, ‘‘a mode of instruction in which one or more key instructional decisions are delegated to the learner’’ (p. 3). In a recent chapter by Brown and Ford (2002), learner control was defined as, ‘‘learners, in partnership with computer programs, become their own trainers through the choices they make on the content they focus on, the sequence of their learning, and the learning strategies they invoke’’ (p. 204). As Goforth (1994) points out, ‘‘learner control input is implicated in management of the learning experience and is independent of the topic of the tutorial’’ (p. 3). As these definitions imply, learner control is a multidimensional construct. Some researchers have empowered learners to create their own instructional sequences or to work at their own pace; others have offered learners control over various task elements, such as the context, method of presentation, provision of optional content, task difficulty, and incentives provided. Still others have viewed learner control as a set of strategies that learners use in response to changing informational needs (Ross, Morrison & O’Dell, 1988). Often, learner control is thought of as a state that is induced by situational cues. For example, the training might be designed in a way that suggests to learners how they might sequence the topics, complete additional practice exercises, or delve more deeply into particular subject areas. Thus, learners are encouraged to take control through the way the instruction is designed. Alternatively, learner control could also be conceptualized as a more stable, individual differences variable. That is, learners differ in the amount of control that they are inclined to take when they are in learning environments. Clearly, research on metacognition and metacomprehension suggests that learners differ in their propensity to question themselves on their knowledge and understanding of new material (e.g. Maki & Berry, 1984; Osman & Hannafin, 1992; Schraw, 1998). Moreover, learners differ in their skills at regulating and monitoring their own learning (Ertmer & Newby, 1996).

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Regardless of whether an instructional designer builds learner control features into a training program, some learners will spontaneously take more control over their learning than others by questioning themselves, pacing themselves, and finding additional help when they realize that their understanding of a topic is poor. Most research that we have read conceptualizes learner control as a situationally induced variable, but the possibility exists that learners also differ dispositionally in their potential to accept control over the learning process. Although these two distinctions have not been directly tested, the multidimensionality of learner control suggests these two possibilities.

THE CHANGING NATURE OF WORK, WORKPLACE E-LEARNING, AND LEARNER CONTROL As organizations become more globally oriented and the workforce becomes increasingly geographically dispersed, organizations are finding it more and more efficient to offer training through e-learning than through traditional classroom instruction. Moreover, the current trend in many organizations is toward career-related continuous learning. Career-related continuous learning requires learners to take a dynamic role in their own training and to actively seek out and participate in developmental opportunities (London & Smither, 1999). Workplace e-learning can be a particularly powerful tool for training a global workforce involved in career-related continuous learning, because it has the benefit of being available on-demand, at any time and at any place. Workplace e-learning also offers a unique opportunity for learner control, because the training environment of workplace e-learning is such that learners are encouraged to make their own choices about the content and information that they need to study. In general, the purpose of e-learning is to provide employees with training on specific skills with immediate application (Brown & Ford, 2002). The provision of learner control in workplace e-learning gives trainees the ability to focus on selected topics (i.e. topics that need to be learned immediately to improve job performance) and to customize the format of instruction so that it best meets their needs (i.e. to control such things as the sequence of instruction so that they can focus on specific material). Adult learning theories, such as those by Sugrue and Clark (2000) and Clark and Wittrock (2000), and training principles, such as those proposed to improve learning retention and transfer by Schmidt and

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Bjork, (1992), suggest that adult trainees might benefit from increased control in workplace e-learning (see the organizational issues section for more information on these learning theories and principles). Conversely, in more formal educational settings, learner control is typically provided to motivate learners to engage in the learning process (Kinzie, 1990). The topics in education are often more general and have less immediate application than those in workplace training. As a result, the learner control that is utilized by workplace e-learners may be quite different than the learner control employed by students in formal educational settings.

WHAT DO WE KNOW ABOUT LEARNER CONTROL? INSTRUCTIONAL DESIGN ISSUES Sims and Hedberg (1995) created a taxonomy of learner control in which they organized the types of learner control into control of sequence, pacing, content, context within which to learn, method of presentation, provision of optional content, and locus of control. We used their taxonomy to guide our review of the research on adult learners that has been published from 1961 to 2002. Although most adult learner control research fits into these categories, we found that a few research studies involved manipulating learner control of task difficulty and incentives. We, therefore, added these two forms of control to our review of learner control. In addition, we found that researchers sometimes offer several different types of learner control at once without independently manipulating each type. These studies provide important information about the effects of learner control, but they often cannot tell us which aspect or aspects of learner control may have caused the effects. As a result, in the following sections, we review these studies but separate our discussion of them from studies that provide information about the causal effect of a particular aspect of learner control. Furthermore, we address some of the newer instructional design issues of learner control research and practice, including hypermedia applications and learner advisement strategies. Types of Learner Control We examine several types of learner control, including learner control of: sequence, pacing, content, context, method of presentation, optional content, task difficulty, and incentives.

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Learner Control of Sequence Learner control of the instructional sequence was among the first learner control variables studied. In 1961, Mager offered electronics instruction in which the sequence of tutoring was under the complete control of the learner. Although this was not a computer-based training course, learners were allowed to direct the path of instruction and to choose the areas of electronics that they wished to study. At first, learners were hesitant about the amount of freedom that they possessed and assumed traditional student roles (e.g. sitting in seats rather than actively walking around). After the first session, however, the students began to be more proactive in their learning (e.g. one learner brought in a radio to examine, learners walked around more, some asked for a review before moving on with the lesson). Mager’s observations of his participants led him to conclude that learners often do not create the same instructional sequences as their teachers. However, Mager was unable to determine from this study whether the instructional sequences created by learners were, in fact, more effective than the instructional sequences created by their teachers. Since Mager’s (1961) study, at least 11 computer-based studies have been conducted on learner control over sequence (e.g. Burwell, 1991; Carlson, 1991; Ellermann & Free, 1990; Gay, 1986; Gray, 1987, 1989; Hintze, Mohr & Wenzel, 1988; Lai, 2001; Milheim, 1990; Tennyson, 1980; Tovar & Coldevin, 1992). In these studies, learners are given the opportunity to choose their own instructional path through the program. Dependent variables have generally included immediate achievement on learning tests, delayed achievement (retention) on learning tests, amount of instruction utilized by learners, and student satisfaction with the instruction. The effects of providing learners with sequence control on immediate learning test scores have been mixed. Four studies manipulated sequence control independently from other types of learner control. One found positive results (e.g. Gray, 1987), one found negative results (e.g. Gray, 1989), and one found no effect (e.g. Milheim, 1990). The fourth study (Tovar & Coldevin, 1992) found that immediate learning test scores differed depending on the learning outcomes measured; for recall of facts, learner control of sequence had positive learning results, whereas for recall of procedures, learner control had no effect. The effects on immediate learning outcomes were also mixed in studies that provided sequence control to learners in conjunction with other types of learner control. For instance, one study found positive results (e.g. Ellermann & Free, 1990), two found negative results (e.g. Lai, 2001; Tennyson, 1980), and three found no effect (e.g. Burwell, 1991; Carlson, 1991; Gay, 1986).

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In two studies, self-sequencers spent less time on instruction (Tennyson, 1980; Lai, 2001), although four studies (i.e. Ellermann & Free, 1990; Gay, 1986; Milheim, 1990; Tovar & Coldevin, 1992) did not find any difference in time spent for participants in program and learner control conditions. And, although program and learner control participants viewed the same number of screens (Gray, 1989), in studies requiring multiple instructional sessions (e.g. Ellermann & Free, 1990), self-sequencers tended to require less time to complete later sessions than they did to complete earlier sessions. It is unclear how self-sequencing affects learner satisfaction with instruction. One study found positive effects on learner satisfaction (e.g. Hintze et al., 1988), two studies found negative effects (e.g. Carlson, 1991; Gray, 1987), and two studies did not find between-group differences (e.g. Gray, 1989; Lai, 2001). Findings from these studies indicate that certain learners may prefer self-sequencing more than others. For example, higher-ability learners (e.g. Gray, 1989; Lai, 2001) and learners who are more familiar with computers (e.g. Hintze et al., 1988) may enjoy more sequence control than others. In addition to enjoying more sequence control, higher-ability learners may also learn more effectively when allowed to construct their own instructional sequences than lower-ability learners (e.g. Gray, 1989; Lai, 2001). As these studies suggest, sequencing may be one aspect of learner control that instructors should control, particularly for lower knowledge and lower ability learners as well as for training situations in which one knowledge or skill builds on another. Learner Control of Pacing Learners are often given control over the amount of time they spend on the instructional task. In particular, we found five studies that manipulated learner control over the pace of instruction (i.e. Burwell, 1991; Carlson, 1991; Ellermann & Free, 1990; Lai, 2001; Milheim, 1990). Two of the studies (i.e. Lai, 2001; Milheim, 1990) manipulated pacing independently from other aspects of learner control. On the one hand, Milheim found that self-pacing enhanced immediate posttest results in a study of instruction on basic photography information and skills, such as film types, lighting, and parts of a 35 mm camera. On the other hand, Lai (2001) found that self-pacing hindered performance in a study of Common Business Oriented Language (COBOL) programing instruction given to college freshmen enrolled in a computer literacy course. In this instruction, participants learned programing language concepts through computer animation of programing sequences. Each programing sequence was first displayed at the top of the screen and then the

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sequence’s meaning was presented in animation form at the bottom of the screen. It may be that participants with control over pacing were less motivated to engage in learning because the topic was difficult and they had the ability to rush through the course. Interestingly, participants with control over pacing did spend significantly less time on instruction than did participants in the program control condition. Lai concluded that when learners are novices and the task is difficult, program control is the best option. Learner control conditions in the other three studies not only included selfpacing, but they also included control over sequence (e.g. Burwell, 1991; Carlson, 1991; Ellermann & Free, 1990) and the amount of instruction (e.g. Burwell, 1991). In Burwell’s and Carlson’s studies, there were no differences in immediate posttest achievement scores between learner control and program control conditions. However, Ellermann and Free found that those with control over pacing and sequence performed better than those without control. In Ellermann and Free’s (1990) study, Dutch-speaking participants were taught Japanese through a paired-associate learning technique in which a Japanese stimulus word was matched to its Dutch equivalent. Unlike the results of Lai’s (2001) study, learner control was found to be helpful when novice learners completed a difficult task. However, a key difference between the two studies is that Lai’s participants spent a significantly greater amount of time on instruction when they were in the program rather than the learner control condition and Ellermann and Free’s participants spent the same amount of time in both conditions. Perhaps motivating novice participants to spend more time on task is the critical factor. This idea is consistent with the prescriptions offered by Chung and Reigeluth (1992) and Milheim and Martin (1991). They suggested that learners should be given control over pacing when the learners are confident that spending more time on the instruction will lead to better learning outcomes. Based on the belief that self-pacing is beneficial to learning, self-pacing has been routinely incorporated into many instructional programs. Because selfpacing is often viewed as intrinsic to computer-based instruction, several studies mentioned that self-pacing was a characteristic of their learning programs without manipulating it as a learner control variable (e.g. Fitzgerald, 1995; Gay, 1986; Pridemore & Klein, 1991; Ross et al., 1988; Steinberg, Baskin & Hofer, 1986; Tovar & Coldevin, 1992); thus, no comparisons can be made between the learner control groups and the program-controlled groups on self-pacing vs. program-pacing. Moreover, because control over pacing is not always studied separately from other aspects of control (e.g. sequence), it is often difficult to make conclusions about the independent effect of self-pacing on learning. Despite the mixed findings that we found,

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Carrier (1984) maintains, ‘‘the only major principle that has consistently guided attempts to individualize instruction or to accommodate differences is that learners should be allowed to work at their own pace’’ (p. 15). Learner Control of Content Control over the instructional content is another form of control that has been offered to learners; the exact type of content control has, however, differed across studies. For example, content control in one study (Judd, Bunderson & Bessent, 1970) referred to allowing learners to select which course objectives they wanted to complete. The content chosen by the learners, therefore, affected the information presented by the program. In two other studies (Gay, 1986; Sasscer & Moore, 1984), content control referred to learner choice of the types of content (e.g. rules, key ideas, examples, or practice) that they preferred to view. In these studies, learner decisions only affected the way in which the information was presented to the learners. Judd et al. (1970) found that giving learners control over content resulted in lower posttest performance on a mathematical logarithm test but learners spent the same amount of time on instruction regardless of condition. Gay (1986), on the other hand, found no performance or time differences for those with learner control over content (and sequence, amount of practice, or mode of presentation) vs. those without control. Alternatively, learner choice of viewing rules, examples, or practice items was found to differ depending on the subject matter of the instruction provided. Sasscer and Moore (1984) found that learners in math instruction chose to see the course information presented as rules and practice more often than did learners in English courses. Learners in English courses, however, chose to view examples more often than learners in math courses. No choice pattern differences were found between students who completed the instruction successfully and those who did not. The results of this study and the other two previously mentioned suggest that content control does not appear to offer any useful advantages to learners in terms of posttest performance. In addition, time spent on the instructional task does not appear to be significantly reduced when learners are given content control. Learner Control of Context Learners may also control the setting in which the problem-solving task is embedded in order to create a more meaningful context for the problem. We found one study that manipulated this type of control (Ross, Morrison & O’Dell, 1989). In Ross et al.’s study, undergraduate students were granted

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control over the context of their examples in a statistics lesson. Learners could choose to frame their examples in terms of four different contexts: education, business, sports, or no-context. When learners controlled the context in comparison to computer-controlled contexts, no differences were found in achievement as measured by three separate sections of the posttest, including a knowledge subtest (recall of the lesson material), a calculation subtest (computation of measures of central tendency), and a transfer subtest (moving the principles of central tendency gleaned from the training to new items). However, students who chose their contexts did view a greater number of example problems and enjoyed the context control. Learner Control over Method of Presentation Matching learner preference to the method of presentation has been viewed as a form of learner control (Freitag & Sullivan, 1995). Generally, learners complete a measure of their preferences for the amount of information they receive during learning (Freitag & Sullivan, 1995) or the mode of instruction (i.e. video, audio, graphics, or text; Gay, 1986; computer-based or printed instruction; Ross et al., 1988). After completing the measure, learners are then either matched to their self-reported preferences or randomly assigned to a condition that is consistent or inconsistent with their preference. Although a few studies described earlier in the content control section (i.e. Gay, 1986; Sasscer & Moore, 1984) also appear to fall under control of the method of presentation, these studies were listed under content control, because comparisons were not made between matched and unmatched groups. Instead, participants in content control conditions were compared to participants in program control conditions. Research on control over the method of presentation suggests that achievement increases when learner preference is matched to the amount of instruction (Freitag & Sullivan, 1995) but not to the mode of instruction (e.g. Gay, 1986; Ross et al., 1988). Moreover, learners matched to their preferences spend less time in instruction (e.g. Freitag & Sullivan, 1995), although in Ross et al.’s (1988) study, this time-on-task reduction occurred only for participants assigned to the computer-based and not to the print instruction. Freitag and Sullivan found that learners had more positive attitudes about the instruction when their preferences were matched, although Ross et al. found no differences in attitudes toward the instruction for matched and unmatched groups. Ross et al., therefore, concluded that matching learner preference (i.e. presentation mode preference) to instruction may not be as practically useful as simply choosing the media type that best provides the instruction.

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Learner Control over Optional Content Control over optional content, such as the feedback received, additional instruction, or use of organizational/memory tools, is the most frequently studied type of learner control in our review. We found 15 studies on this topic (i.e. Burwell, 1991; Gay, 1986; Hicken, Sullivan & Klein, 1992; Hintze et al., 1988; Judd et al., 1970; Mattoon, 1994; Murphy & Davidson, 1991; Pridemore & Klein, 1991; Ross et al., 1989; Ross & Rakow, 1981; Schnackenberg & Sullivan, 2000; Shute, Gawlick & Gluck, 1998; Steinberg, Baskin & Matthews, 1985, Steinberg et al., 1986; Tennyson, 1980). Interestingly, despite considerable interest in this form of learner control, no studies were found that suggested that learner control over optional content improved learning outcomes. When optional content was studied independently of other types of learner control, studies found poorer outcomes when learners were given control (i.e. immediate learning or retention; Judd et al., 1970; Ross & Rakow, 1981; Steinberg et al., 1985) or no difference (i.e. Judd et al., 1970; Mattoon, 1994; Murphy & Davidson, 1991; Pridemore & Klein, 1991; Ross et al., 1989; Schnackenberg & Sullivan, 2000). Likewise, when learner control of optional content was provided along with other forms of learner control (e.g. control of sequence, pacing, or incentives), results were similar (e.g. Burwell, 1991; Gay, 1986; Tennyson, 1980). However, in two out of the three studies that examined learner attitudes (i.e. Hintze et al., 1988; Pridemore & Klein, 1991; Schnackenberg & Sullivan, 2000), learners reported that they liked having control over optional content. One possible reason for the poorer outcomes for learner control over the provision of optional content is that learners with control have been found to request fewer options (e.g. Ross & Rakow, 1981; Tennyson, 1980) and to spend less time on the task (e.g. Murphy & Davidson, 1991; Tennyson, 1980) than learners under program control. However, these findings may be more complex than they initially seem. Generally, spending more time in training results in better learning (Brown, 2001; Driskell, Willis & Copper, 1992). Yet, Shute et al. (1998) found that learners with control over the amount of practice were more efficient (i.e. achievement relative to time on task) in the short term than learners assigned to practice conditions that involved abbreviated–extended practice, extended–abbreviated practice, and extended–extended practice but not abbreviated–abbreviated practice. When long-term efficiency was examined, however, learners with control were more efficient than participants in all other practice conditions. Moreover, Hicken et al. (1992) found that how time was spent on instruction differed depending on whether participants were given control

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over adding instructional content, such as examples, practice problems, and review items (i.e. LeanPlus programs) or skipping additional instruction (i.e. FullMinus programs). In LeanPlus programs, learners are given abbreviated forms of training but can ‘‘add’’ supplementary items. In FullMinus versions, however, learners are generally given extensive instruction but can ‘‘skip’’ items that they do not wish to view. Overall, Hicken et al. found that the posttest performance and total task time spent by learners did not differ between FullMinus and LeanPlus conditions. Yet, FullMinus learners utilized more options and spent more time on the program’s optional sections than LeanPlus learners who spent more time on the mandatory sections. The results of Shute et al. (1998) and Hicken et al. (1992) suggest that there may be a complex relation between learner control over optional content, time spent, and learning outcomes. In addition, length of training may moderate the relation between learner control and time spent. That is, learners with greater control tend to spend more time on instruction when the training is shorter in duration. Schnackenberg and Sullivan (2000) found that learners in full programs spent more time on the instruction when they were under program control, whereas learners in lean programs spent more time on the task when they were under learner control. Similarly, Mattoon (1994) examined whole- (task is presented in its entirety without being separated into lessons) and part-task (parts are presented in individual lessons) training combined with learner control options. Learners in the whole-task condition utilized more time if they were under program control; yet, learners in the part-task condition utilized more time if they were under learner control. Finally, the benefits of providing learner control over optional content may be realized over time. In Steinberg et al.’s (1986) study, those who had the option to use a tool that provided performance feedback initially, used the tool less often than the participants who were forced to use the tool. By the third training session, however, participants with control used the tool more often than those who were forced to use it. It may be that learners with control of the tool did not realize its usefulness until after they had completed sessions without it. Learner Control of Task Difficulty Only one study was found that examined learner control of task difficulty. In an instructional aircraft simulation, Mattoon and Klein (1993) studied the effects of giving learners control over the challenge level (i.e. control over the speed and accuracy required) of locating a target and orienting an airplane toward that target. During practice, learners who were afforded control achieved higher accuracy scores but selected lower challenge levels than

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learners in the program-controlled condition. An examination of the immediate posttest scores, however, revealed no differences between learners in the two groups. When immediate posttest scores were compared with delayed scores, however, researchers found that learners with control scored significantly lower on a delayed test than they did on an immediate test of learning. The researchers noted that learners in the learner control condition set lower standards than learners in the program control condition. They concluded that when a task is novel and learners are unaware of their ability to perform on the task, learners with control over the challenge level are likely to set low standards, to maintain those low standards, and to avoid risk. Learner Control of Incentives Research on learner control of incentives is scant; only one study was found (Ross & Rakow, 1981) in which learners were allowed to control how many points would be assigned for test questions about each of five mathematical rules that they learned. For those not receiving control of incentives, point values were assigned equally across the five rules. Despite the researchers’ attempts to motivate learners, learner control of incentives was not found to significantly increase or decrease the amount of time spent on instruction compared with program control of incentives. In addition, immediate and delayed posttest scores as well as scores obtained during instruction did not differ significantly from the scores found under program control.

New Instructional Design Issues for Learner Control So far, we have examined computer-based instruction as the primary means of offering learner control; however, new forms of technology may afford learners even greater amounts of instructional control. For instance, hypermedia can be used to offer learners increased new and different forms of learner control. In addition, learners can also be guided in their use of learner control options by adaptive guidance and learner advisement strategies. In this section, therefore, we address these new forms of learner control and the implications they have for learner control in workplace e-learning. Learner Control and Hypermedia The Internet has expanded the capabilities for interactivity between learners and the instructional program far beyond that which was available with traditional teacher-led and computer-based instruction. Hypertext and

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hypermedia applications allow users to create the paths and relationships within the instructional program that best fit their learning needs (e.g. Large, 1996; Smith & Weiss, 1988). Federico (1999) defined hypermedia as ‘‘on-line settings where networks of multimedia nodes connected by links are used to present information and manage retrieval’’ (p. 662). These nodes offer users instructional texts, graphics, videos, audios, animations, models, simulations, and visualizations that serve to enhance the learner’s instruction (Federico, 1999). Within hypermedia applications, hypertext refers not only to information that is presented in static, textual form but also includes such visual stimuli as photographs, diagrams, tables, and sketches (Tolhurst, 1995). In other words, hypermedia and hypertext applications allow learners to select the nodes of information that interest them. Once a node is selected, the learner’s instructional path takes shape as links to other nodes appear (e.g. Large, 1996). Hyperspace is designed for learners to take control. Instruction via hypermedia applications is in some sense very similar to natural, ‘‘realworld’’ learning in that it offers only enough structure to ensure that the fundamental aspects of the content are covered; learners then select and explore areas within this learning environment that interest them. Because learners are generally responsible for selecting the links to view at each step of the instructional program, the relationships among the instructional elements are created by the learners. As Blanchard (1989) points out, ‘‘The power of hypermedia and hypertext applications resides in the links or relationships that students weave as they explore information’’ (p. 24). Despite the relatively unsupportive findings for increased learner control in computer-based instruction, the assumption that more control is beneficial to learning appears to remain unchallenged in the design of hypermedia instructional systems. However, if this assumption is true, then why has increasing learner control through hypermedia not led to improved learning outcomes? A potential answer to this question lies in the issue of getting lost in hyperspace (e.g. Daniels & Moore, 2000). Hypermedia instruction has been praised for its nonlinear capabilities and for the sequencing control it offers learners (e.g. Large, 1996; Smith & Weiss, 1988). However, because hypermedia environments lack an ordered linear structure, learners often find themselves lost in the instructional program and unable to retrace their steps to the program’s origins (e.g. Large, 1996; Nielsen, 1990). Given too many control options, learners tend to become disoriented (e.g. El-Tigi & Branch, 1997; Marchionini, 1988) and, even

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worse, to feel that they have lost control over their learning experience (e.g. El-Tigi & Branch, 1997). Researchers have tried to reduce the navigational problems of hypermedia by suggesting that learners be given maps signifying what nodes and links they have already completed (Large, 1996) and be allowed to backtrack through the program by clicking a return arrow (Nielsen, 1990). In addition, researchers have recommended that hypermedia instruction minimize the cognitive load on learners. Because learners, especially novices, need to assimilate vast amounts of information in addition to making learning decisions in hypermedia, it is important that they not be required to remember too much information when moving from one node to another (Kearsley, 1988). As Dillon and Gabbard (1998) concluded from their review of the literature on learner control and hypermedia, learning outcomes with hypermedia only surpassed those of computer-based and traditional instruction (e.g. paper-and-pencil) when learners were of high ability. And, just as ability level has been found to be a consistent predictor of performance in hypermedia instruction, previous experience predicts linear and nonlinear navigation through hypermedia environments. For example, learners with greater experience in hypermedia tend to create more nonlinear paths, whereas learners with greater computer experience tend to follow more straightforward, linear sequences in hypermedia instruction (Reed & Oughton, 1997). Reed and Oughton note that learners who are high in computer experience may be more accustomed to following a linear structure, because computer programs typically follow linear instructional sequences. Learners who are familiar with hypermedia applications, however, may be more aware of the nonlinear capabilities of hypermedia and may, therefore, be more apt to follow nonlinear sequences in hypermedia settings. Thus, learner control in hypermedia applications may be beneficial to learners high in ability and prior experience but not necessarily to all learners. In addition, the increased control offered by hypermedia may only add weight to the learners’ already overburdened cognitive load. The navigational problems associated with hypermedia suggest that learners may feel a lack of control when they become lost in hyperspace. This issue is a serious one considering that one of the goals of hypermedia is to increase the control that learners perceive they have over instruction. To combat these navigational problems and to adequately provide control to learners of differing ability levels and experience, more prescriptions for learner control in hypermedia settings are needed.

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Learner Control with Advisement Giving learners control is a necessary but insufficient condition for effective learner-controlled training. Learners must also know how to effectively use the control they are given to their advantage. Advisement has been offered as a supplement to learner control and is meant to help guide learners in their use of learner control strategies. Advisement can be given to learners in many forms. For instance, it may be provided prior to or during instruction (i.e. adaptive advisement). And, it may consist of diagnostic feedback or recommendations regarding the amount or sequence of instruction. Generally, learner control with advisement has led to increased learning outcomes in comparison with both program control (e.g. Shyu & Brown, 1992) and pure learner control without advisement (e.g. Tennyson, 1980), although some researchers have found no differences (e.g. Mattoon & Klein, 1993). Results for time on task, however, have been inconsistent. For instance, Tennyson found that learners receiving advisement completed the instruction significantly faster than learners receiving program-controlled adaptive instruction. Mattoon and Klein, on the other hand found no differences in the practice times of any of their learner groups. When Shyu and Brown examined time on task in their study, they too found different results: learners given advisement and control took significantly longer than learners under program control. Shyu and Brown’s results are unusual, because, generally, researchers have found that learners given control spend less time on instruction than learners under program control. The differences in time on task between these three studies may be due to the fact that the program control condition was adaptive in the Tennyson (1980) study but was not adaptive in either the Shyu and Brown (1992) or Mattoon and Klein (1993) studies. In other words, learners who performed less competently in the Tennyson study were required to complete more practice problems and instruction in the program control condition than learners under program control in either of the other two studies; this could have led to the increased on-task times for Tennyson’s program control participants relative to the learner control participants. Learner control with advisement, therefore, appears to increase learners’ time on task in comparison to program and learner control except when program control is adaptive. Improving upon current advisement strategies, Bell and Kozlowski (2002) proposed that learners be given advice on the content and skills to study and practice during instruction, a technique they termed ‘‘adaptive guidance.’’ Rather than simply providing feedback and recommendations as to the number of practice problems to complete, adaptive guidance directs learners

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in their choice of material and offers suggestions as to where they should concentrate their efforts. Comparing pure learner control to learner control with adaptive guidance, Bell and Kozlowski (2002) found that the provision of adaptive guidance focused learner attention on relevant training topics by significantly increasing the time learners spent examining these subjects; accordingly, learners’ basic performance early in training and their strategic performance later in training increased. Interestingly, learners with adaptive guidance had higher self-efficacy early in training and lower self-efficacy later in training than learners not receiving guidance. As a result, the learners who received adaptive guidance may have had more realistic beliefs regarding their abilities than learners with little or no means by which to judge their performance (Bell & Kozlowski, 2002). In view of these findings, it appears that providing some form of direction to learners in the form of advisement strategies or adaptive guidance may benefit trainee performance in learner-controlled instruction. As we mentioned before, learners are not always the best judges of their own skills and abilities (Carrier, 1984). When offering learners control of their instruction, then we may best assist their learning by also advising them of their learning progress and guiding them in their training efforts.

Conclusions for Instructional Design Issues of Learner Control From our review of the various learner control types, we can conclude that learner control is beneficial to adult learning in some situations. In general, we found that the particular type of control that learners are given may be less important to learning outcomes than the context in which learner control is offered. For example, when instructing learners on novel and difficult tasks, it is important that instruction is designed in such a manner that learners are confident that spending more time and effort on instruction will lead to better learning outcomes. Learners with control who believe that the instruction is too difficult are likely to spend less time on instruction, set and maintain lower goals for themselves, and avoid risk. In addition, results from studies using multiple instructional sessions suggest that the benefits of providing learner control may be realized over time. That is, learners who have been given control may need time to practice using it, so that they can understand how it can benefit their learning, particularly if they are accustomed to more traditional, instructor-led training.

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Perhaps the most consistently positive finding from learner control research is that learners generally enjoy having control over their instruction. Learners particularly like having control over the context of instruction and the provision of optional content. For the other types of learner control, at the very least, learners reported that they enjoyed learner-controlled instruction as much as program-controlled instruction.

WHAT DO WE KNOW ABOUT LEARNER CONTROL? PERSON ISSUES It is fairly well documented that greater learner control results in greater variability in learner outcomes. This is because all learners do not respond similarly to different forms of learner control. Research on learner control has examined such individual difference factors as ability, prior experience, learning styles, goal orientation, pretask attitudes, locus of control, and learner sex. This section will describe whether or not these individual difference factors moderate the relationship between learner control and learning outcomes. Individual Differences that have been Supported as Moderators We know of at least four individual differences that have been supported as moderators between learner control and learning outcomes: ability and prior experience, learning styles, goal orientation, and pretask attitudes. Ability and Prior Experience The effects of learner ability and prior domain experience on learner-controlled instruction are two of the most common individual difference factors studied. The abilities that have been investigated include reading comprehension and reading rate (e.g. Ross et al., 1988, 1989; Seidel, Wagner, Rosenblatt, Hillelsohn & Stelzer, 1978), mathematical ability (e.g. Lai, 2001), and general cognitive ability (e.g. Schnackenberg & Sullivan, 2000; Seidel et al., 1978). In general, higher-ability learners outperform lowerability learners on learner-controlled tasks regardless of whether they are in learner or program control conditions (e.g. Borsook & HigginbothamWheat, 1991; Ross et al., 1989; Schnackenberg & Sullivan, 2000). However, greater performance differences have been found between higher- and lower-ability learners in learner control conditions (e.g. Lai, 2001).

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Two other dependent variables affected by ability level are time on task and learner attitudes. Generally, lower-ability learners spend more time on the learning task and more time per screen than higher-ability learners (e.g. Lai, 2001; Ross et al., 1989; Schnackenberg & Sullivan, 2000), although this is not always the case (e.g. Ross et al., 1988). Moreover, higher-ability learners tend to have more favorable attitudes toward the instructional task (e.g. Ross et al., 1989) and toward having learner control (e.g. Lai, 2001) than lower-ability learners, although one researcher did not find a relationship between ability level and attitudes (Schnackenberg & Sullivan, 2000). Lai, for example, found that higher-ability learners performed better and enjoyed having more control over instruction than lower-ability learners who performed more poorly and did not enjoy having the control as greatly. In addition to learner ability, prior experience has been found to be a moderator of the relationship between learner control and learning outcomes. Experience has been measured as prior achievement and knowledge in the domain (e.g. Gay, 1986; Ross et al., 1988, 1989; Shute et al., 1998), computer experience (i.e. familiarity with using the computer; e.g. Brown, 2001), grade point average (GPA; e.g. Gray, 1989), and education level achieved (e.g. Brown, 2001). In general, learners with greater prior experience in the domain tend to outperform learners who are lower in prior experience (e.g. Gray, 1989; Ross et al., 1989; Shute et al., 1998), and lowerexperienced learners perform less well under learner control than under program control conditions (Gay, 1986). Alternatively, Brown (2001) found that learner education level and prior computer experience did not significantly predict the selection of learner control options; in other words, regardless of education or computer experience, learners chose similar practice levels (i.e. chose to complete comparable numbers of practice items), spent similar amounts of time on task, and engaged in similar amounts of off-task attention. Ross et al. (1989), however, found that high experience was associated with less time on instruction, and Gay (1986) found that this was especially true under learner control conditions. Learning Styles Individual learning styles may also impact the effectiveness of learner control efforts. In the method of presentation section presented earlier, learning styles were treated as a form of learner control when they were matched to the type of instruction provided. In this section, however, we demonstrate how learning styles may serve to moderate the relationship between learner control and learning outcomes.

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In the early 1990s, field independent vs. field dependent (e.g. Burwell, 1991) learning styles were examined in a learner control study. Field-independent learners tend to see objects as distinctly separate from their environment; in contrast, field-dependent learners see their surroundings in a global context and are often incapable of disconnecting specific objects from their environment (Witkin, Dyk, Faterson, Goodenough & Karp, 1962). Burwell found that the field independents in his learner control study performed better in program-controlled settings and that the field dependents performed better in learner-controlled environments. In addition, the field dependents spent more time on the instructional task than field independents. The results of Burwell’s study suggest that providing instructional control that complements individual learning styles can lead to better learning outcomes.

Goal Orientation Mastery and performance goal orientations have also been studied in the learner control literature. According to Maehr (1989), learners with a mastery orientation direct their learning goals and behavior toward the development of their own individual knowledge bases. Alternatively, learners with a performance orientation believe that their intelligence levels are fixed (Dweck, 1986) and, consequently, focus their learning goals and behavior on achieving positive social comparisons with others rather than on mastering the topic at hand (Maehr, 1989). Using a computerized corporate training course on problem solving, Brown (2001) found that goal orientation predicted the learner control choices that trainees made. Because mastery-oriented learners are likely to view practice exercises as a chance to improve their overall understanding of a topic, Brown predicted that learners high in mastery orientation would complete more practice exercises than learners low in mastery orientation. Contrary to expectations, a higher mastery orientation was negatively related to the amount of practice that learners completed. In addition, a higher mastery orientation was associated with poorer performance on a knowledge posttest but less off-task attention. A high performance orientation, in contrast, was related to greater off-task attention. Performance orientation was also found to interact with self-efficacy to predict the amount of practice learners chose to complete. Trainees with a high performance goal orientation and high learning self-efficacy completed more practice problems than trainees with a high performance orientation and low self-efficacy.

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From Brown’s (2001) study, we can conclude that goal orientation does appear to predict learner choices of amount of practice and off-task attention. However, results for off-task attention were weak and, despite expectations, goal orientation did not significantly predict the amount of time trainees spent on task. Trainees appeared unable or unwilling to effectively manage their control during instruction and may have skipped practice or spent less time on task than was necessary for an understanding of the topic. Overall, Brown concluded from the results that, ‘‘despite the appeal of computer-based training as a way to make learning more efficient, employees may not use control over their learning wisely’’ (p. 290). Pretask Attitudes Attitudes toward computer-based instruction and the subject to be taught (Ross et al., 1988) were also studied as individual differences variables believed to be associated with learner control. Ross et al. found that pretask attitudes toward the subject and toward computer-based instruction significantly predicted total posttask attitudes. The posttask attitudes scale measured participants’ affective responses to specific aspects of the control provided, including the task’s pace, appeal, difficulty, and readability. In addition, Ross et. al included an item that measured learner preference for the type of instruction provided over lecture. Learner control did not significantly predict posttask attitudes toward control in this study; only the participants’ pretask attitude score did. Thus, when offering learner control of text density and media preference, simply knowing the participants’ attitudes toward the subject and toward computer-based instruction will improve the prediction of their posttask attitudes.

Individual Differences that have not Yet been Supported as Moderators Two individual differences variables that researchers reasonably might expect to moderate the relationship between learner control and learner outcomes include locus of control and the sex of the learner. Locus of Control Learner locus of control does not appear to moderate the relationship between learner control and learning outcomes. Gray (1989) measured students’ locus of control, provided them with instruction on poverty reduction and inflation control, and gave them either program or learner control of sequence. Following instruction, she measured their attitudes

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toward computer-based instruction, their retrieval (i.e. scores on 10 questions students completed throughout the lesson), and their retention. Participants scoring high in internality outperformed participants scoring high on externality, regardless of whether they were under learner or program control of the instructional sequence. Learner Sex Gray (1987) did not find performance differences between males and females in learner control environments, but others have found sex differences in preferences for learner control (e.g. Hintze et al., 1988; Ross et al., 1989). For example, Hintze et al. found that males preferred a fully learner-controlled structure more than females. The authors suggested that this finding may have been due to a general tendency for the males to be more familiar with computers and, thus, to be more comfortable using the features provided by learner control. In addition, Ross et al. (1989) found sex differences in context control choices. In their study, learners were allowed to choose the context of examples in an instructional lesson. The researchers found that the male participants chose to view sports examples more often than did the female participants and that the female participants chose to view education examples more often than did the male participants. In addition, the female participants were significantly more likely than the male participants to change their context selections across lessons.

Conclusions of Person Issues of Learner Control Our review of the person issues of learner control reveals that learner control might be beneficial for some learners. For instance, learners high in ability and/or prior experience generally perform as well or better under learner rather than program control. On the other hand, other learner characteristics are not consistently found to impact learning outcomes under learner control. In fact, many learner characteristics often function independently of learner control. In a majority of studies, personal characteristics tend to predict learning outcomes, but the provision of learner or program control usually does not matter. Research suggests that matching learner goal orientation or learning styles to the type of control offered might be more important than simply giving learner or program control. Learners often prefer a certain amount of control in their instruction and matching their preference to the amount of

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control they are given might lead to better learning outcomes. In the future, researchers might focus on giving learners the amount and type of control they would like to have rather than requiring them to accept either total program or total learner control.

WHAT DO WE KNOW ABOUT LEARNER CONTROL? ORGANIZATIONAL CONTEXT ISSUES From our review of learner control, we hoped to evaluate the potential benefits and drawbacks to its use in adult workplace e-learning. The current nature of learner control research, however, limits the conclusions that we can make regarding the application of learner control to workplace training programs. The limitations of this research include the following: (a) learner control research is primarily carried out in educational contexts; (b) control is frequently presented in an all-or-nothing approach; (c) research methods and analyses often constitute pseudoscience; (d) dependent variables consist mainly of posttest scores; and (e) few prescriptive guidelines are available for implementing learner control in workplace training. Due to these limitations, the generalizability of learner control research to adult workplace e-learning programs is uncertain. An examination of our studies reveals that a majority of the research on adult learner control is conducted with college students in educational contexts. Bates, Holton and Seyler (1996) cited similar findings in their review of learner control research. The potential differences in learning support and motivation in educational vs. work settings are numerous enough to warrant moving research out of the classroom and into the workplace. For example, educational contexts may differ from workplace training contexts in the types of learning incentives offered; whereas in educational settings, incentives are often extra credit or course grades, in workplace settings, pay raises or promotions might depend upon trainees successfully acquiring the skills needed. As a result, trainees may have different motivations and goals for training (i.e. learning to apply a skill on a job) than college students. Moreover, trainees may also be able to more quickly and easily see the application of training to their jobs (Bates et al., 1996). For these reasons, the literature suggests that the time has come to study learner control in the ‘‘wild.’’ Halpern (2002) recommends that, because of the differences between adults and college students, ‘‘it is desirable to sample adults across the entire adult age span and to move out of colleges

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and into the workplace, military, home, and other settings where adults learn’’ (p. 34). In addition to being embedded within an educational context, most of the studies in our review presented learner control in an all-or-nothing approach. In other words, they either provided learner control or they withheld it. Both Sims and Hedberg (1995) and Newkirk (1973) described learner control as lying on a continuum ranging from complete learner to complete instructor (or program) control. Research, however, has often neglected to examine differing degrees of learner control (Sims & Hedberg, 1995). As a fairly new technology, learner control in web-based learning may still be somewhat novel to learners. If learners do not understand the benefits of the new technology or how to use it, then the capacity for them to perform well under learner control decreases. Moreover, if learners also have limited prior experience with the domain, performance may be reduced even further. Therefore, some learners may benefit from program control at the start of the task and learner control further along in the instruction (e.g. Steinberg, 1989). Providing for this transfer of control may be particularly relevant for learner control of the instructional sequence. For example, before they are familiar with an instructional task, learners may be unable to best organize and make sense of their learning experience. Perhaps providing for learner control of sequence after the learner has become acquainted with the task content would be more effective than simply providing total learner or total program control. A further limitation of the studies in our review is that learner control research is often pseudoscience. Reeves (1993) argued that research on learner control has been conducted unsystematically, without a clear definition and theoretical basis and with both methodological and analytical flaws. Because learner control research is purported to be embedded within a scientific framework, it then follows that the research conducted within this field should abide by the rules for scientific investigation. In other words, learner control research needs to be based upon a solid theoretical framework (e.g. the one proposed by Milheim & Martin, 1991) and accepted definition, should include appropriate sample sizes (most studies have used relatively small samples which may account for their inability to find effects), and should provide instructional tasks that are involving and long enough for learners to adjust to the new instructional techniques (Reeves, 1993). When learner control research follows these prescriptional guidelines, more quality research will be produced and the alternative explanations for research findings can be more easily ruled out.

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Bell and Kozlowski (2002) note still another limitation of previous learner control research. That is, the dependent variables in learner control studies are typically posttest scores. Considered to be a measure of learning, these posttests usually consist of items similar to the examples provided by the instruction (Bell & Kozlowski, 2002). However, posttest scores often do not provide an accurate evaluation of a training program unless considered along with several other measures. Kirkpatrick’s (1976) framework for training evaluation recommends the use of four different evaluative levels, including trainee reactions, learning, behavior, and results. Many of the studies we examined do, in fact, report learner reactions to the instructional task and to the control given along with posttest scores; on the other hand, the two highest levels of evaluation (i.e. behavior and results) are frequently overlooked in learner control research. Behavior change and improved organizational results (e.g. reduced cost, increased quality) are critical indices of organizational training effectiveness; therefore, their inclusion in any program evaluation is essential. Future research should provide evaluations of behavior and results as dependent variables in addition to posttest scores and trainee reactions. Finally, learner control research has offered few instructional prescriptions for implementing adult learner control. A few researchers (e.g. Brown & Ford, 2002; Chung & Reigeluth, 1992; Hamel & Ryan-Jones, 1997; Hannafin, 1984; Merrill, 1988; Milheim & Martin, 1991; Ross & Morrison, 1989) have provided design prescriptions for learner control in computerbased instruction, but generally these prescriptions have focused on students in educational contexts. The role of organizational support and other factors surrounding the implementation of learner control in workplace training environments have not been addressed. In their review of the last 10 years of training research, Salas and Cannon-Bowers (2001) noted several antecedent, intermediate, and post-training conditions found to influence training’s effectiveness. In particular, the circumstances affecting training transfer are mentioned, including such factors as social, peer, subordinate, and supervisor support for transfer and the delay between the training and the use of the skill(s) on the job. In order for learner control to be administered to adult trainees effectively, the influence of such organizational characteristics on learner control must be addressed in future research. Although adult e-learning has a fairly short history relative to other training methods, its use in organizational training programs, is projected to increase substantially over the next few years (Moe & Blodget, 2000). And, as e-learning becomes the standard for organizational training programs, so will the degree of learner control it offers. As we mentioned

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previously, the vast majority of research on adult learner control comes from an educational perspective despite the fact that adult, skill-based learning in work organizations may be different (e.g. different motivations, adults learn differently, organizational support issues, and different goals) from learning in educational contexts. We, therefore, cannot assume that the existing literature on learner control applies to training in workplace settings. The following sections will discuss the ways in which we expect learner control issues to be different for adult, skill-based, e-learning methods based upon our review of some of the recent theories and principles of adult learning and training research.

Adult Learning Theories and Learner Control Over the past several years, researchers have continued to increase our understanding of adult learning and training by applying what we already know about adult cognition to training theory. For instance, Sugrue and Clark (2000) recently developed a six-part model of cognitive process (i.e. goal elaboration, information, practice, monitoring, diagnosis, and adaptation) in which they suggest that learners should be given control over the cognitive strategies they use in learning unless they require instructional support. Alternatively, they suggest that if learners are not given control over the cognitive strategies they invoke, they are likely to replace effective cognitive strategies with less effective ones. Sugrue and Clark point out that any mix of media selected to deliver training should have the capability to monitor trainee performance and to adapt interventions to the needs of individual trainees. Adapting interventions to individual trainees may involve offering trainees a high degree of learner control at first and then subsequently monitoring their performance. If performance is low, control can be withheld until trainees are ready to take it back. If performance is high, control levels can be maintained or increased. From Sugrue and Clark’s (2000) model, we can conclude that learner control may enhance learning outcomes but only when learners are prepared to use it. What is more, learner control may, in fact, serve to make the learning process more engaging and motivating for adults (Stoney & Oliver, 1998). By making learning fun, Halpern (2002) suggests that adult learners may choose to engage in more learning and to complete more practice exercises. And, as Brown (2001) and Driskell et al. (1992) report, spending more time in training generally leads to better learning outcomes.

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Another theory of adult learning has been offered by Clark and Wittrock (2000). These researchers observe that learning during training is a ‘‘generative process’’ (p. 60). In other words, learners actively search for meaning in the training material and for the application of the training to their jobs. The key, Clark and Wittrock suggest, is to encourage trainees to generate summaries, analogies, or interactive pictures that relate concepts to one another and to the trainees’ own knowledge and experience. Support for this generative process during training may allow for learner control to be more effectively provided to learners. From these theories, it appears that learner control can be successfully offered to trainees in the workplace if administered under the appropriate conditions and if trainees are prepared and motivated to use it. The idea of learner control is intuitively appealing, and many organizations are choosing to adopt e-learning strategies because of the learner control it offers. In fact, learner control is often associated with the term ‘‘self-directed learning’’ (Candy, 1991) and, learning theorists consistently emphasize self-direction as a goal of many adult learning programs (e.g. Merriam & Caffarella, 1991).

Adult Training Principles and Learner Control In addition to learning theories, researchers have also recently developed new organizational training principles that have the potential to impact the effectiveness of learner control in workplace e-learning. For instance, according to Schmidt and Bjork (1992), an organization’s goals for training should include supporting both the long-term retention and transfer of learned skills. Of the studies in our review of adult learner control, most failed to mention any measure of training transfer and few offered results for retention over periods longer than 2–3 weeks. In order to accomplish training retention and transfer, Schmidt and Bjork recommend the application of three training principles. First, the training task needs to be structured so that the practice for each topic is offered randomly rather than in one massed block. In other words, practice on a particular topic should be offered throughout the instructional program and interspersed with practice on other topics. Second, the training task should fade out the feedback it offers learners on their performance. Too much feedback may be harmful to long-term retention. In fact, the authors suggest that if feedback is given too frequently, it may actually serve to disrupt skill acquisition. Third, Schmidt and Bjork suggest that practice examples be offered under varying conditions. For example, if the training task involves a driving simulation,

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trainees should be exposed to several different driving conditions (e.g. slippery, gravel, or dirt roads; rain, snow, sleet, or hail) that may potentially influence transfer of the learned skills to novel situations. These three principles have important implications for the use of learner control in organizational training programs. For example, it appears that the program should maintain control of the amount of feedback provided, because trainees who are allowed to control the amount of feedback may come to rely on the feedback too much, compromising their long-term retention of the material. In addition, the program should offer practice on each topic repeatedly throughout the instructional task so that trainees will not forget instruction on topics they have already completed. Trainees, however, may still be allowed to control the sequence of their instruction, because practice on each topic is offered throughout the instructional program. And, although the program should provide practice to trainees under varying conditions, trainees can still be allotted control over the example contexts. We can, therefore, conclude that the program should maintain control over certain aspects of the instructional task; trainees, however, can also be empowered to take control over other instructional elements. In addition to the training principles proposed by Schmidt and Bjork (1992), researchers have also recommended that organizations using learner control strategies adopt an action learning approach to training (e.g. Bowerman, 2000). Rather than teaching general job knowledge and skills, action learning focuses on solving real problems that employees face in their day-to-day tasks. The trainees then, not the instructional designers, are considered to be the experts of the training material. And, as a result of having a greater involvement in the learning process, control of the learning content is effectively given back to the trainees (Bowerman, 2000). By applying these training principles, the goals of organizational training (i.e. long-term retention and transfer) can be supported and learner control can more effectively be offered to trainees. Trainees, however, also need to align their personal training needs with those of their job and organization. As Knowles, Holton and Swanson (1998) point out, ‘‘When the individual’s needs are consistent with the organization’s, there is no tension. When the individual’s needs and goals are not congruent with the organization’s performance requirements, and the organization is providing the required learning experience, a tension exists and inevitably results in some degree of organizational control’’ (p. 122). Therefore, when trainees accurately match their personal needs to the organization’s training needs and when the aforementioned training principles are successfully applied, offering trainees’ control of instruction may lead to greater learning outcomes.

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CONCLUSION In this paper, we reviewed the literature on adult learner control, integrating findings from the adult learning and training literature as well as from educational research. Organizing our review into instructional design issues, person issues, and organizational context issues, we focused on the implications increased learner control holds for adults and workplace training. Overall, we concluded that learner control appears only to benefit some learners in some situations. And, the type of learner control provided might not be as important as the context in which the control is offered. From our review, we also offered suggestions for future learner control research. In particular, because the learner control literature has traditionally focused on students in educational contexts, we look forward to seeing more research being done with trainees in e-learning environments. We also hope to see research begin to report information on training transfer and organizational results as a function of increased learner control. If technology continues to outpace instructional science (and we think it will), learners will continue to be flooded with a barrage of new tools and options during instruction. We hope, however, that adult learner control research will soon catch up to new developments, such as hypermedia and hypertext, and will provide us with some firm guidelines for offering learner control in adult workplace e-learning.

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GOAL PROPENSITY: UNDERSTANDING AND PREDICTING INDIVIDUAL DIFFERENCES IN MOTIVATION Howard J. Klein and Erich C. Fein ABSTRACT This chapter proposes the development of a compound personality trait termed ‘‘goal propensity’’. Motivation is a key determinant of performance in virtually all contexts, and personality has long been viewed as an important influence on motivation. Despite the long history of exploring how personality influences motivation, we do not have a clear understanding of the linkage between individual differences in personality and work motivation or the tools to reliably and accurately predict individual differences in motivation. Advances in our understanding of personality and the convergence of motivation theories around models of self-regulation present the opportunity to achieve that understanding and predictive efficacy. Goal propensity would be a theoretically derived trait that would explain the role of personality in self-regulation models of motivation as well as allow the prediction of tendencies to engage in self-regulation. This chapter provides the rationale for the development of

Research in Personnel and Human Resources Management Research in Personnel and Human Resources Management, Volume 24, 215–263 Copyright r 2005 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 0742-7301/doi:10.1016/S0742-7301(05)24006-9

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this construct, articulates the nature of the proposed goal propensity construct, and explores the value of such a construct for theory, future research, and human resource practice.

INTRODUCTION ‘Look here, this is a book he had when he was a boy. It just shows you’ He opened it at the back cover and turned it around for me to see. On the last fly-leaf was printed y Read one improving book or magazine per week Save $5.00 [crossed out] $3.00 per week Be better to parents ‘I come across this book by accident,’ said the old man. ‘It just shows you, don’t it? It just shows you. Jimmy was bound to get ahead. He always had some resolves like this or something.’ F. Scott Fitzgerald, The Great Gatsby

A primary purpose of human resource management is to efficiently and effectively engage the employees within an organization in a concerted set of actions aimed at facilitating the accomplishment of that organization’s goals. Toward that end, many human resource activities are designed to facilitate the productivity of individual employees in order to increase the effectiveness of the organization. Individual productivity is, among other factors, a direct function of employee motivation. As such, many human resource activities are intended to enhance employee motivation, either directly or indirectly. It is widely recognized that there are individual differences in motivation, but there is currently no theoretically derived and empirically supported individual difference construct reflective of work motivation. This chapter attempts to begin filling that gap by proposing the development of a ‘‘goal propensity’’ construct. This stable, compound personality trait would reflect the tendency to engage in all aspects of self-regulation, which is at the core of most current conceptions of work motivation (Vancouver, 2000). As discussed in this chapter, the development of such a construct has important theoretical and practical implications. Conceptually, the goal propensity construct will uniquely integrate the motivation and personality literatures. While there has been a long line of research examining the relationship between personality and motivation, much of that research predates the recent resurgence of interest in personality and the convergence of motivation theories around models of self-regulation. Advances in our understanding of personality have led to

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calls for renewed research on the role of personality as it relates to motivation and task performance (Austin & Klein, 1996; Kanfer, 1990; Kanfer & Heggestad, 1997, 1999; Mount & Barrick, 1995; Tokar, Fischer & Subich, 1998). The emergence of the five-factor model (FFM) of personality (Costa & McCrae, 1992), in particular, has helped clarify the conceptual disarray of past attempts to examine the dispositional basis of motivation (Judge & Ilies, 2002). For example, while past efforts failed to yield consistent findings (Locke, Shaw, Saari & Latham, 1981), more recent research suggests that goal setting mediates the relationships between some FFM traits and performance (Judge & Ilies, 2002; Klein & Lee, 2003). The growing consensus is that distal predictors of performance, like personality, affect behavior in part through their influence on more proximal self-regulatory mechanisms (e.g. Barrick, Mount & Strauss, 1993; Kanfer, 1990, 1994; Lee, Locke & Latham, 1989). Despite this consensus, the linkage between personality and self-regulation is not well understood because most of this research has been inductive in nature. As such, theoretical explanations have not been provided for the processes by which individual differences in personality influence motivation. Austin and Klein (1996) advocated the development of theoretically derived and empirically supported individual difference constructs reflective of work motivation. The aim of this chapter is to propose a construct that more precisely articulates the relationship between personality and motivation. By developing the goal propensity construct based on a strong theoretical foundation that reflects advances in the conceptualization of personality and motivation, the opportunity exists not only to better understand the relationships between personality and motivation, but also to predict the tendency to engage in self-regulation based on personality assessments. It is that potential to predict the predisposition to self-regulate that is the basis for the practical implications of the goal propensity construct. For example, human resource selection concerns the assessment of the knowledge, skills, abilities (KSAs) and motivation of job applicants in order to evaluate the suitability of that applicant for employment. It is widely recognized that employees must be both capable and willing to expend the effort necessary to perform the required tasks. However, having the requisite KSAs is necessary but not sufficient for high performance. We are not discounting the value of assessing KSAs in making selection decisions. What we are suggesting is that in many cases, a valid assessment of motivational tendencies may be just as important and explain substantial incremental variation in job performance beyond KSAs alone. As noted by Sackett, Schmidtt, Ellingson and Kabin (2001), the focus in selection

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research has been on measures of maximum performance, determined largely by ability, rather than on measures of typical performance, which is more related to motivational factors. Unfortunately, existing selection techniques are much better at predicting KSAs than they are at predicting motivation (Kanfer, 1990; Kanfer & Ackerman, 1989). Many employers try to assess the motivational level of applicants, but most of the selection techniques available with the highest predictive validity (e.g. cognitive ability tests, measures of physical abilities, and work sample tests) target the KSAs rather than the motivational domain (Ree, Earles & Teachout, 1994; Schmidt & Hunter, 1998). Global personality and interest inventories are sometimes used as proxies for assessing individual differences in motivation (Hough & Oswald, 2000), and prior research offers some evidence for the usefulness of personality, particularly conscientiousness, to add incrementally to ability in predicting job performance (e.g. Hattrup, O’Connell & Wingate, 1998). However, the gains in validity are modest and it is not entirely clear that the incremental validity is attributable to the assessment of motivation. For example, when performance is predicted from individual differences in both ability and general personality constructs, only slight incremental validity evidence exists for those global personality variables related to motivation (e.g. Sackett, Gruys & Ellingson, 1998). In fact, Sackett et al. (1998) conceptualized motivation in terms of personality characteristics and failed to find evidence for the often-assumed ability by motivation interaction. Kanfer and Heggestad (1997) concluded that motivational traits are underrepresented in many current personality inventories, which may explain why using general personality measures as a proxy for individual differences in motivation have not been more successful. We believe that higher levels of incremental validity should be attainable with the identification of a trait, that more directly reflects the motivational determinants of performance. Because goal propensity would be a more precise, theoretically sound construct reflecting individual differences in self-regulation, the assessment of goal propensity should prove to be a more valid predictor of applicant motivation than previously examined personality measures or other currently available selection tools. As such, we feel that goal propensity could meet the call made by several researchers (e.g. Anderson & Herriot, 1997) for new selection tools. The practical implications of goal propensity extend beyond selection to other human resource practices as well. In articulating the goal propensity construct, this chapter begins with a review of the role of motivation in models of performance and past efforts to link individual differences in personality to motivation. The proposed

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nature of the goal propensity construct will then be articulated along with situation factors that must be considered in understanding the impact of this personality trait on self-regulation. Attention will then be given to the value of such a construct for human resources practice, particularly selection. In doing so, the primary current approaches to assessing motivation will be reviewed and contrasted to the potential use of goal propensity. Finally, attention is given to future research directions for both the further specification of the goal propensity construct and the development and validation of a measure of goal propensity.

ROLE OF MOTIVATION IN MODELS OF PERFORMANCE In order to develop a personality construct that will explain and predict individual differences in motivation and in turn performance, it is important to first clearly define both performance and motivation and understand the relationship between the two. Despite the widespread use of performance as a criterion, it is rarely defined clearly. Consistent with Campbell, McCloy, Oppler and Sager (1993), we define performance as synonymous with behavior, things people do rather than the results of what they do. We further view performance as multidimensional consisting of the full range of both in-role and extra-role behaviors. Finally, we view behavior related to each of those dimensions as directed toward goals that are relevant to organizational effectiveness (Campbell et al., 1993). It is these goals that provide the standard against which the behavior is judged. Motivation, in turn, refers to the willingness to engage in those behaviors. As traditionally defined, motivation concerns the arousal, direction, and persistence of behavior (Mitchell, 1982). In the context of the above definition of performance, motivation concerns the choice to expend effort, the choice of how much effort to expend, and the choice to persist in expending that level of effort (Campbell et al., 1993). Performance has long been viewed as a function of the interaction between individual differences in abilities and motivation (Campbell & Pritchard, 1976; Kanfer & Ackerman, 1989; Maier, 1955; Vroom, 1964). Some models of performance further differentiate the abilities category (e.g. abilities, skills, declarative knowledge, procedural knowledge, etc.), or add situational factors that enable or hinder performance as an additional external determinant, but the ability by motivation interaction remains

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central to most models of performance. The ability by motivation interaction indicates that for a behavior to be enacted, an individual must both be capable and willing to enact that behavior. As such, motivation is always a determinant of performance, because an employee with all of the necessary skills and abilities will not exhibit the desired performance unless that individual chooses to expend the necessary effort on the needed behaviors for the required length of time. Similarly, no matter how much effort is allocated by an employee lacking the requisite skills, performance will not result. In the Campbell et al. (1993) theory of performance, personality is viewed as one of several factors that influence both declarative and procedural knowledge. Subsequent research on this model found that declarative knowledge is primarily determined by cognitive ability while motivation is linked to personality (McCloy, Campbell & Cudek, 1994). The goal propensity construct proposed here would directly influence the motivational determinants of performance. As stated above, performance is viewed as multidimensional. Prior research suggests that personality relates differentially to different dimensions or components of job performance (e.g. Motowidlo & Van Scotter, 1994). It is thus likely that the goal propensity construct proposed here will similarly relate differentially to different dimensions of performance. However, goal propensity should be a valid predictor of all dimensions of performance given that (a) goal propensity will be directly reflective of motivation, (b) motivation is a key determinant of all dimensions of performance, and (c) all dimensions of performance are defined in terms of goal-directed behavior. Future research will, however, need to determine the relative strength of the relationship between goal propensity and different dimensions of performance. Motivation has been studied from a variety of perspectives and there are many different theories of motivation. Those theories have been grouped and categorized in various ways over the years but the focus recently has been on integrative, comprehensive theories (Klein, 1989; Kanfer, 1994). For example, Kanfer (1994) arranged constructs from specific theories within a broader antecedent structure of environmental factors, person factors, and factors based on person/environment interactions and according to the conceptual proximity of the construct from actual behavior. In that framework, a construct such as need for achievement is representative of a noncognitive individual difference that is a distal determinant for a potentially large number of goal-specific actions. In contrast, the difficulty level of a particular goal is a proximal construct, heavily affected by environmental factors while shaped by personal factors. Within the vast

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motivation literature, goals have emerged as the central, proximal construct, particularly among the current theories that emphasize self-regulation (Austin & Vancouver, 1996). Self-regulation is a comprehensive term that refers to self-generated thoughts, feelings, and actions that are planned and cyclically adapted for the attainment of personal goals (Zimmerman, 2000).

Self-Regulatory Theories of Motivation A number of specific theories that focus on explaining goal-directed behavior can be considered self-regulation theories. Some of the more prominent self-regulation theories include control theory (Carver & Scheier, 1998; Klein, 1989), task goal theory (Locke & Latham, 1990), social cognitive theory (Bandura, 1986, 2001), and Kanfer and Ackerman’s (1989) multiple resource allocation model. Although there are differences among these theories, the similarities are substantial. In a recent, comprehensive review of the state of the field of self-regulation, Zeidner, Boekaerts and Pintrich (2000) argued that, at the core of self-regulation, there is a consensus among researchers on several basic cognitive, volitional, affective, and behavioral constructs and processes. Those constructs and processes include goals, planning processes, feedback, and navigational processes (e.g. comparing goals to feedback and evaluating discrepancies between goals and performance) to facilitate the allocation of effort toward and adjustments to the goals being regulated. Another common element is the explicit incorporation of goal hierarchies composed of subgoals, focal or midrange goals, and higher-level goals of the self. Given this consensus, a general self-regulation framework is used here as the basis for articulating the goal propensity construct. There are three main phases of the self-regulatory cycle, which center around the use of goals and goal-feedback comparisons (Carver & Scheier, 2000; Zimmerman, 2000). These phases are illustrated in Fig. 1 and described below. The first phase, termed as forethought (Zimmerman, 2000), goal setting, or the judgmental subfunction (Bandura, 1997), consists of goal selection, strategic planning, and the activation of self-related performance beliefs. It is during this phase that the task is analyzed, goals are set, planning and the selection of strategies occurs, and the choice is made to actively engage in pursuing the goal. The second phase, termed as volitional control (Zimmerman, 2000), performance, goal striving, or the self-observation subfunction (Bandura, 1997), includes focusing attention on various tasks during goal pursuit. This phase concerns the management of thoughts and

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FORETHOUGHT Task analysis Goal selection Strategic planning Belief activation Goal engagement

SELF-REFLECTION

VOLITIONAL CONTROL

Self-evaluation of performance Performance attributions Performance/goal comparisons Performance satisfaction Adaptive or defense inferences

Attention focusing Self-instruction Strategy enactment Self-monitoring Feedback capturing

Fig. 1.

The Three Phases Self-Regulation and the Core Processes within Each Phase.

actions while pursuing a goal (Gollwitzer & Brandstatter, 1997) and includes such processes as focusing attention, self-instruction, allocating effort to enact previously planned strategies, and self-monitoring. The final phase, self-reflection (Zimmerman, 2000) or the self-reaction subfunction (Bandura, 1997), centers on evaluating one’s performance, the attributions made about the causes of that performance, comparing performance to goals, and the subsequent satisfaction or dissatisfaction emanating from that comparison. The resulting conclusions made about any needed changes in goals or strategies to attain those goals take the individual back to the forethought phase of self-regulation. A general, self-regulatory theory of motivation offers several key advantages over earlier motivational theories that focus just on needs, cognitive choice, or reinforcement. These advantages are gained primarily through the inclusion of additional constructs and processes including future-time orientation, flexibility in planning and implementing strategies to achieve goals

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through the use of multiple subgoals, and the ability of individuals to develop goal arrays, which are organized hierarchically by personal values and the short- and long-term connections between focal goals and higherlevel goals of the self. Furthermore these gains are made while retaining the key elements of those earlier motivational theories, with those elements (e.g. rewards, needs and values, evaluating the expectancy value of alternative actions, etc.) placed in the cyclical context of self-regulation. Two of the above characteristics of self-regulation theories are particularly advantageous in understanding and explaining motivation and the real world complexity of human behavior. The first is the flexibility in the choice and implementation of strategies and subgoals that provides equifinality in goal attainment. This flexibility explains how individuals can rapidly perceive and implement alternative plans based on the obstacles and resources afforded by the environment. The second main advantage afforded by self-regulation theories is through the recognition that goals are hierarchically organized. Through these systems of goals, self-regulation theories can explain how people work toward the simultaneous achievement of multiple goals as well as why individuals continue to persist in the face of obstacles. When focusing on hierarchies of goals, people are set free from the immediacy of current stimuli and are able to be oriented toward the future (Pervin, 1989). This fact can be especially motivating for individuals when the current environment is unfavorable for actions leading to the achievement of desired goals. Given the prominence of self-regulation theories, the integrative nature with which self-regulation theories encompass and extend earlier perspectives of motivation, and the growing consensus around the basic constructs and processes that constitute self-regulation, we contend that the full range of motivational issues (i.e. the arousal, direction, and persistence of behavior) can best be understood through an integrative model of selfregulation. As such, the most appropriate approach to explore individual differences in motivation is to assess individual differences in the propensity to engage in self-regulation. The constructs and processes that represent selfregulation have been shown to be important in the motivation literature, but they have been demonstrated from an experimental perspective rather than from the individual differences perspective taken in this chapter.

Personality and Motivation The linkage between personality and motivation is as old as the study of personality itself, and attention to this issue has waxed and waned with

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interest in the study of personality. The resurgence of interest in personality over the past 20 years has renewed interest in linking these two phenomena. Few would take this issue with the assertion that there are individual differences in motivation or that those differences are traceable to stable dispositions (Judge & Ilies, 2002). A substantial amount of research links personality with work behavior (Barrick & Mount, 1991; Hogan, 1991; Tett, Jackson & Rothstein, 1991), but prior attempts to empirically link personality with motivational variables have yielded highly inconsistent results (Gellatly, 1996). Most of the available information regarding the role of personality in self-regulation concerns how individuals choose self-set goals and persist in their pursuit of those goals because historically, the aspects of self-regulation that have received the most research attention are those concerning the basic elements of task goal theory (Locke & Latham, 1990). When goal setting emerged as a motivation theory in the 1970s, a number of researchers added measures of individual differences constructs to studies of goal difficulty and then tried to explain any observed relationships (Locke et al., 1981). The fact that a few of these studies examined individual differences in a theoretical manner or with a clear sense of the exact roles and functions they serve has since been noted by several other authors (Kluger & DeNisi, 1996; Locke & Latham, 1990; Weiss & Adler, 1984). Despite this recognition that the use of non-relevant personality variables is a plausible explanation for the historically mixed results, many studies continue to investigate the effects of personality variables on goal setting and other self-regulatory processes with little regard for support from theories of self-regulation. Fortunately, as noted by Heggestad and Kanfer (2000), progress in understanding the structure of personality and advances in self-regulatory theories of motivation have created the opportunity to more precisely identify how personality influences self-regulation. In a review of individual differences and behavior in organizations, Murphy (1996) proposed a basic distinction between cognitive and non-cognitive individual differences. Within the non-cognitive domain, personality is offered as the highest level and most general type of construct that subsumes many other individual difference variables. Non-cognitive variables that are subsumed by personality include the broad dimensions of behavioral consistencies and affective responses. Affective responses are further divided into referent-specific affective responses – such as values and interests – and global affective responses such as an individual’s general disposition and temperament (Murphy, 1996). Our interest is specifically in the global affective responses and resulting behavioral consistencies. After articulating the nature of those responses and consistencies as they relate to work motivation, prior

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efforts aimed at linking personality and motivation are briefly reviewed and differentiated from the goal propensity construct proposed here.

DEFINING GOAL PROPENSITY The first and most critical step in establishing construct validity is to provide a concisely stated definition of the focal construct. This definition should clearly articulate the elements that compose the construct and relate the focal construct to other constructs (Stone-Romero, 1994). Accordingly, goal propensity is defined as a personality construct, a non-cognitive individual difference variable at the highest level in its domain. More specifically, goal propensity is defined as a multidimensional, compound personality trait that emerges from lower-order basic personality traits. The specific lower-order traits that constitute goal propensity reflect the behavioral consistencies and affective responses required for all phases of the self-regulatory cycle and explain variance in those processes unaccounted for by situation-specific factors. As for relations to other constructs, goal propensity should be strongly related to the constructs and processes that constitute self-regulation (e.g. goal setting, goal-directed effort, feedback seeking, goal revision, etc.) as well as correlate with other non-trait factors that have been found to consistently relate to self-regulation (e.g. commitment to specific goals, task-specific self-efficacy). In total, goal propensity is defined as a multidimensional, compound personality trait reflective of individual differences in the tendency to engage in all phases of self-regulation.

Individual Differences in Goal Propensity The idea that individuals may vary in their goal propensity has been raised in the literature (Adler, 1987; Campbell, 1982) but has not been thoroughly explored conceptually or examined empirically. Campbell (1982) proposed that an individual’s consistent choice pattern in choosing the difficult level of personal goals could be reflective of a general personality trait. He further suggested that such a goal-setting trait would influence a variety of goal-setting behaviors including the propensity to set goals, the content of those goals, and the typical difficulty, specificity, and stability of those goals. Campbell also proposed that individual differences in sensitivity to feedback and competition might prove to be influential antecedents to the effective use of goals. The goal propensity construct proposed here is completely

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consistent with and builds upon Campbell’s ideas by (a) expanding the criterion space of interest to encompass all aspects of self-regulation, not just goal choice and (b) suggesting that a compound personality trait is needed to reflect that criterion space. In defining goal propensity, we described it as a compound personality trait. Within the realm of personality, two kinds of personality traits can be distinguished. These are basic personality traits and compound or ‘‘emergent’’ personality traits. On a general level, basic personality traits are internally consistent, temporally stable, and conceptually coherent. In contrast, compound personality traits are a composite of multiple basic personality traits (Hough & Schneider, 1996). Compound personality traits arise or ‘‘emerge’’ when researchers identify specific criterion constructs they want to predict and then identify the set of basic personality traits that will best predict those criteria of interest (Hough & Oswald, 2000). In the case of goal propensity, those criteria are derived from a composite model of selfregulatory theories of work motivation. That is, the specific basic personality traits that will constitute goal propensity need to be identified, based on their ability to, relate to and predict all phases of the self-regulatory cycle. While compound traits and multifaceted traits like the broad factors that make up the FFM typology are both collections of narrower traits, there are important differences between the two. Compound personality traits differ from the broad factors that make up the FFM typology, in which the component traits are theoretically chosen to reflect a specific criterion rather than empirically grouped, based on factor analytic procedures. Another difference is that multifaceted traits are said to cause their constituent facets, whereas compound traits emerge from their facets. Like FFM, however, the composite trait of goal propensity will likely be multidimensional, particularly since it is designed to predict all aspects of self-regulation. We have chosen to view goal propensity as a compound trait because (a) prior evidence suggests that any single basic trait is unlikely to satisfactorily explain the full range of constructs and processes associated with self-regulation and (b) combining basic personality traits into compound traits designed to predict particular criteria has been shown to increase criterion-related validity (Hough & Schneider, 1996). Because compound traits are constructed by identifying the criterion first, and then selecting a heterogeneous set of basic traits expected to predict that criterion, they have great potential to more strongly relate to the criterion of interest than individual basic traits or multifaceted traits. Examples of compound traits that have proven to have greater predictive validity than the basic personality scales they comprise include measures of customer service

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orientation, managerial potential, and sales potential (Hough & Schneider, 1996). Core self-evaluations in some ways resemble a compound or emergent personality trait but is better viewed as a latent construct (Bono & Judge, 2003) or a multifaceted trait (Borman et al., 2003). Regardless, research here again suggests that composite measures of core self-evaluations almost always predict better than the four individual traits that make up that composite measures (Erez & Judge, 2001). A final example of the superiority of a system of trait constructs is McClelland and Boyatzis’ (1982) finding that the profile of an individual across achievement, affiliation, and power needs predicted long-term leadership effectiveness better than any of those single traits alone. To reiterate, goal propensity is a compound personality construct, which is a composite of several basic personality traits. The component basic traits that constitute a compound personality trait should be selected based on theoretical as well as empirical evidence that these basic traits will predict the specific criteria of interest. Together, the composite of traits that is goal propensity should predict all phases of the self-regulation cycle. Articulating the goal propensity construct will therefore identify a small, conceptually relevant set of personality variables with strong effects on all phases of the self-regulatory cycle and, in turn, performance. While the specification of the component traits that should be included in goal propensity is beyond the scope of the current chapter, we need to further stipulate the specific criterion that should drive the identification of these traits. As stated previously, the referent criterion for goal propensity is derived from a composite model of self-regulatory theories of work motivation. These self-regulation criteria, the constructs and processes underlying the self-regulation cycle that should serve as the reference point in the development of goal propensity, are explicated below and summarized in Table 1. Beginning with the forethought phase, the basic traits that will constitute goal propensity should predict the tendency to (a) set and revise goals, (b) develop hierarchies or goal arrays around these goals, (c) have more complex goal arrays in terms of interrelatedness and temporality, and (d) be planful in terms of developing strategies for goal attainment. For the volitional control phase, basic traits are needed that will predict the tendency to (a) self-monitor, (b) focus attention on task relevant activities while blocking out distractions and debilitating thoughts, (c) exhibit persistence even under protracted negative conditions, (d) generate and solicit feedback, and (e) allocate greater attention to that feedback. Finally, for the selfreflection phase, the basic trait components of goal propensity should be able to predict the tendency to (a) compare performance to goals, (b) make

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Self-Regulation Criteria for Selecting the Component Traits for Goal Propensity.

Self-Regulation Phase

A Component Trait of Goal Propensity Should be Associated with the Predisposition to:

Forethought

Spontaneously set goals Develop hierarchical subgoals Have complex connections within goal arrays Have temporally rich goal arrays Develop strategies to attain subgoals Spontaneously modify goals and goal arrays Spontaneously modify goal attainment strategies Self-monitor Allocate attention to task relevant activities Block out distractions Persist during goal pursuit Capture, solicit, and attend to external feedback Generate internal feedback Persistence during goal pursuit Make goal-performance comparisons Engage in causal attribution searches Perceive satisfaction from achieving or making progress toward goals React adaptively to feedback by recognizing needed changes in goals or strategies Avoid defensive reactions such as helplessness, avoidance, and disengagement Infer appropriate change points for goal-pursuit strategies

Volitional control

Self-reflection

accurate attributions regarding the causes of performance, and (c) react adaptively based on goal/performance comparisons. The scope of these criteria further support the need for a composite trait as it is unrealistic to expect a single basic trait to be associated with this full range of self-regulatory processes. To achieve a parsimonious set of traits, the basic traits that make up goal propensity should account for variance in multiple criteria, but not necessarily the criteria across all three phases of self-regulation or all the criteria within a given self-regulation phase.

Constructs Related to Goal Propensity Next, to further articulate the nomological net for goal propensity, prior efforts aimed at linking personality and motivation are briefly reviewed and

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these constructs differentiated from goal propensity. These constructs include need for achievement, the FFM, action-state orientation, mastery orientation, and core self-evaluations. Need for Achievement In the 1950s, McClelland attempted to measure motivation-related worker needs using the thematic apperception test. These attempts at measuring worker motivation resulted in the inductively derived need for achievement construct (McClelland, Atkinson, Clark & Lowell, 1953). According to McClelland (1961), there are three major components of need for achievement, which take personal responsibility for finding solutions to problems, exhibiting consistency in setting goals and in taking calculated risks to achieve them, and a desire for feedback related to progress toward goals. Interestingly, these components are similar to the facets of dutifulness, achievement striving, and deliberation identified some 30 years later within the FFM. A need for achievement has been shown to influence goal choice (e.g. Yukl & Latham, 1978) but other studies have failed to find such effects. It is clear that the need for achievement is a personality variable that at some level and to some degree is relevant to self-regulation. As such, we believe that achievement traits such as the need for achievement will be positively related to goal propensity. However, it is also clear, both conceptually and from empirical research that need for achievement does not encompass enough potentially relevant traits to reliably predict all phases of self-regulation. The Five-Factor Model Over the past 15 years, FFM of personality has received considerable attention and is largely responsible for the current resurgence of interest in personality. FFM is a taxonomy of broad personality traits that has been widely replicated through factor analysis (e.g. McCrae & Costa, 1999) and has emerged as a common conceptual scheme for describing personality (Tokar et al., 1998). The consensus from several meta-analytic reviews is that FFM can be a useful tool in predicting performance from personality, and this is particularly the case for conscientiousness. Barrick et al. (1993) suggested that conscientiousness may be the most important trait-motivation variable in the work domain (p. 721) and conscientiousness has been empirically associated with some aspects of self-regulation. For example, in a meta-analysis, Judge and Ilies (2002) estimated a true score correlation of 0.22 between conscientiousness and goal setting (operationalized as goal level or difficulty).

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Conscientiousness is clearly a valuable predictor of a number of important criteria including motivational constructs such as goals. However, it has been argued (e.g. Kanfer & Heggestad, 1997) that FFM is not the best model to use in examining motivational processes because the factors are relatively broad and thus encompass more than motivational processes. For example, while some facet-level traits under conscientiousness are directly relevant to self-regulation, others are not. Using only the higher-order factors of FFM ignores, confounds, and obscures the understanding of the facet-level personality variables that are combined under the higher-level factors (Hough, 1997, 1998). Furthermore, because the higher-order factors of FFM contain facets with both high and low criterion-related validities, using the higher-order factors cannot yield maximal prediction of motivational criteria (Hough & Oswald, 2000; Paunonen & Ashton, 2001). In addition, FFM does not adequately capture or sufficiently differentiate several important personality dimensions that have been shown to be predictive of important employee and organizational outcomes (Funder, 2001; Hough & Schneider, 1996; Perrewe & Spector, 2002). Because some of the facet-level traits within FFM are associated with selfregulation (e.g. achievement striving), we believe that some of the broad FFM traits (e.g. conscientiousness, neuroticism, and agreeableness) will be positively related to goal propensity. Within those broad factors, some of the facet-level traits should be even more strongly related to goal propensity. For example, some facets of agreeableness imply receptivity to feedback from others, while some elements of neuroticism could be associated with maladaptive patterns of attribution, such as habitually making fixed and internal attributions for failure during goal pursuit. Action-State Orientation Another trait associated with motivation is action-state orientation, derived from Kuhl’s (1994) theory of action control. Individual differences in action control concern the ability to regulate one’s behavior and cognitions to accomplish goals. Action-state orientation is composed of three dimensions, each reflecting a tendency to fail during the initiation of action toward goals (Kuhl, 1994). The preoccupation dimension reflects the tendency of individuals to have persistent and intrusive debilitating thoughts regarding states of failure and other aversive experiences associated with failure during goal pursuit. The hesitation dimension assesses the tendency of individuals to initiate action on tasks. Finally, the dimension of volatility is designed to measure the tendency to persist on tasks despite distractions. Action-oriented individuals, those low on these failure tendencies, are thought to be

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better at initiating action toward goals, maintaining effort on goal-directed activities in the face of distractions, and in disengaging from negative thoughts and affective states (Diefendorff, Gosserand, Hall & Chang, 2002). Action-state orientation should therefore be useful for understanding and measuring some, but not all aspects of self-regulation. This is because the action-state orientation construct, while theoretically derived, comes exclusively from Kuhl’s (1994) theory of action control, which is not a complete theory of self-regulation. Action control focuses exclusively on just one phase of self-regulation – volitional control. In contrast, the proposed goal propensity construct will be theoretically derived from multiple, comprehensive self-regulation theories and will encompass all elements of the selfregulation cycle. Because action-state orientation does reflect one phase of self-regulation, action-state orientation could be expected to overlap with those elements of the proposed goal propensity construct designed to tap volition control. As such, we expect that action-state orientation will be positively correlated with goal propensity. Regarding volitional control, empirical research has supported both individual differences in the allocation of attention (e.g. Gernsbacher & Faust, 1991; Tipper & Baylis, 1987) and the important role that attention plays in constraining perceptions, decisions, and responses (e.g. Simon, 1994). Because action-state orientation does not address the forethought and self-reflection phases of self-regulation; however, goal propensity will provide a more complete conceptual linkage between personality and motivation and should allow the better prediction of motivation through the tendency to engage in self-regulation. Mastery Orientation Another personality characteristic with motivational implications is mastery orientation. Based on work originating with Dweck (1986) in the education literature, goal orientations are dispositions toward developing and demonstrating one’s ability in achievement settings. These dispositions are relatively stable and have both state and trait qualities (Button, Mathieu & Zajac, 1996). Individuals with a learning or mastery orientation focus on learning, gaining competence, developing new skills, understanding their work, learning from experience, and achieving a sense of mastery based on self-referenced standards (Ames, 1992; Dweck, 1986; VandeWalle, Cron & Slocum, 2001). The result is an adaptive motivational pattern in which individuals seek out challenges, effectively strive toward goals, and persist in the face of obstacles (Dweck, 1986). In contrast, individuals with a performance orientation focus on gaining positive judgments or avoiding negative judgments of competence based on external standards (Ames, 1992;

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Dweck, 1986). This results in a maladaptive pattern in which challenge is avoided and persistence is low in the face of difficulty (Dweck, 1986). More recently, performance prove and performance avoid have emerged as relatively independent forms of performance goal orientation (VandeWalle et al., 2001). Empirical research, conducted largely in training and academic contexts, has found a mastery orientation to be associated with motivation to learn and other motivational constructs (e.g. the choice of goal level) but such relationships have not been consistently demonstrated (e.g. Colquitt & Simmering, 1998; Lee, Sheldon & Turban, 2003; VandeWalle et al., 2001). Research findings regarding the performance orientation dimension have been even more inconclusive. Kanfer and Heggestad (1997) argued that goal orientations do not fully capture all motivationally relevant personality variables and sought to provide a more encompassing framework. They started by identifying traits from a variety of research streams thought to have motivational significance. Those traits were then organized based on their similarity using a trait clustering approach (Snow, Corno & Jackson, 1996). Kanfer and Heggestad (1997) identified two broad categories of motivationally relevant traits: achievement traits, which are associated with striving for success, and anxiety traits, which are associated with fear and avoidance of failure. Each of these superordinate traits, in turn, encompassed several more narrowly defined motivationally significant traits. Specifically, the achievement trait consisted of personal mastery, competitive excellence, and hard work (added by Heggestad & Kanfer, 2000) whereas the anxiety trait consisted of general anxiety, achievement anxiety, and failure avoidance. Heggestad and Kanfer (2000) tested the motivational trait questionnaire, a measure developed to assess the component traits in this typology. Results supported a threedimensional solution consisting of personal mastery, competitive excellence, and anxiety. Heggestad and Kanfer (2000) further examined the relationships between these traits and motivational skills, a measure reflective of generalized self-efficacy for learning. They found a strong negative relationship between anxiety and motivational skills, but only weak positive relationships for personal mastery and competitive excellence. In some respects, Kanfer and colleagues have successfully integrated the classic approach and avoidance motivational framework with facet-level personality variables such as assertiveness (Costa & McCrae, 1992) and elements of goal orientation (Dweck, 1986). While extremely helpful as a point of integration between these categories of personality traits, the implications of this motivational trait taxonomy for self-regulatory processes need further explication. An important difference between Kanfer and

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Heggestad’s (1997) approach and what we are proposing here is that they started with the identification of traits thought to have ‘‘motivational significance.’’ As discussed earlier in this chapter, we propose starting with a more specific set of criteria, the constructs and processes that comprise the self-regulation cycle, and then working from those criteria to identify the traits that are most likely to be highly predictive of those constructs and processes. Similar to the previous traits discussed in this section, both a mastery goal orientation and Heggestad and Kanfer’s (2000) motivational traits should be positively related to but distinct from goal propensity. Achievement traits are central components of the motivational traits framework developed by Kanfer and Heggestad (1997). As noted when discussing need for achievement, we believe that achievement traits will be positively related to goal propensity because they are relevant to some aspects of self-regulation. However, because Heggestad and Kanfer’s (2000) motivational traits do not conceptually account for all the elements of effective self-regulation, they do not fully capture the proposed goal propensity construct. Goal propensity should thus provide both a more complete conceptual explanation of the link between personality and all phases of self-regulation than the motivational trait taxonomy and should yield stronger relationships with motivational skills than those observed for the motivational trait questionnaire. Core Self-Evaluations A final previous attempt to link personality and motivation that needs to be recognized and contrasted to goal propensity is the work by Judge and colleagues (Erez & Judge, 2001; Judge, Bono & Locke, 2000; Judge, Erez & Bono, 1998; Judge, Locke & Durham, 1997; Judge, Locke, Durham & Kluger, 1998) on core self-evaluations. Core self-evaluation is a multifaceted, higher-order trait composed of four lower-level personality traits: self-esteem, generalized self-efficacy, neuroticism, and locus of control. Together, these traits amount to a fundamental appraisal of one’s ‘‘worthiness and capability as a person’’ and reflect one’s bottom-line appraisal of people, events, and things in relation to oneself (Judge et al., 1997). The core self-evaluations framework began with propositions about the effects of personality on job satisfaction. Judge and colleagues felt that some personality traits were more central to persons’ views of the world, themselves, and others and that these traits influenced judgments in deeper and more fundamental ways, producing ‘‘core evaluations’’ (Judge et al., 1997). Using a set of rational inclusion criteria, Judge and colleagues identified a number of self-referent traits and verified that those traits loaded on

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a single, primary factor. Subsequent empirical research has replicated a single factor structure for these four traits and has shown that the combined core self-evaluations construct generally explains more variance than the individual component traits (e.g. Erez & Judge, 2001). While initially developed as a general dispositional predictor of job satisfaction, the relationships between core self-evaluations and motivation have been examined. Erez and Judge (2001) found that core self-evaluations were related to task motivation, goal setting, and performance and that the motivational constructs partially mediated the effects of core selfevaluations on performance. While the empirical evidence for core selfevaluations is promising, the conceptual relationship between this trait and self-regulatory processes is not clear. This lack of clarity is mainly due to the manner in which the trait was derived, an issue that has led to other conceptual questions. For example, Perrewe and Spector (2002) questioned the omission of positive affectivity given that core self-evaluations were initially developed as a general dispositional predictor of job satisfaction. It should be noted that while the process used to identify the component traits of core self-evaluations differs from the process we recommend for goal propensity, and subsequent efforts to verify the dimensionality of the core selfevaluations construct, assess its relationship with predicted correlates and development of a reliable and valid direct measure are to be commended and could serve as a model for subsequent empirical work on goal propensity. Similar to the other traits we have reviewed, the basic traits that comprise core self-evaluations, and hence the higher-order trait, are likely to correlate with goal propensity. Generalized self-efficacy, for example, should be positively related to goal propensity. In spite of that association, the proposed goal propensity construct is distinct from core self-evaluations in several respects. First, goal propensity will be a theoretically derived construct created in direct reference to all phases of self-regulation. In contrast, core self-evaluations were developed to capture the dispositional causes of job satisfaction (Bono & Judge, 2003). While core self-evaluations have been shown to relate to some motivational constructs and to job performance (Erez & Judge, 2001; Judge & Bono, 2001), the magnitude of those relationships has been considerably smaller than relationships with job satisfaction. Second, because it will encompass all aspects of self-regulation, goal propensity will be a broader construct, reflecting more than just selfevaluations. Thus, relative to core self-evaluations, there will be a stronger conceptual linkage between goal propensity and self-regulation. Consequently, we expect goal propensity to provide better prediction of motivation through the tendency to engage in all aspects of self-regulation.

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Convergent Validity As articulated in the previous section, we believe there are numerous personality constructs that should be positively related to but conceptually distinct from goal propensity. In addition to need for achievement, some FFM traits and facets, action-state orientation, mastery orientation, and core self-evaluations, we expect other non-personality individual differences to also exhibit positive relationships with goal propensity. Within the domain of skill differences, we believe that planning and organizational skills will show strong positive relationships with goal propensity. Research supports that such skills are critical elements of long-term goal attainment (Friedman & Scholnick, 1997). In addition, differences in interpersonal skills are also likely to be related to goal propensity. This is because individuals with high levels of social skills may be more effective at collecting resources that can be used during goal pursuit. Such resources may include time, social support, financial resources, and domain-specific expertise. The domain of cognitive individual differences should also contain constructs with positive relationships to goal propensity. Because individuals with high levels of goal propensity are likely to construct complex goal arrays and effectively use such goal arrays during goal pursuit, we believe that individuals with higher general cognitive ability will exhibit higher levels of goal propensity. Also, high levels of knowledge necessary for goal pursuit, which would be reflected in the construct of crystallized intelligence, are also likely to be positively related to goal propensity due to greater numbers of options and pathways to goals. Thus, we believe that general cognitive ability and crystallized intelligence will be positively related to goal propensity.

Discriminant Validity Having identified constructs that should be related to goal propensity, we now turn to explicating some constructs that we would not expect to be related to goal propensity. We maintain that there are particular individual difference variables and situational characteristics or situation-dependent characteristics for which there is unlikely to be any relationship with goal propensity. Individual Differences For example, some cognitive individual differences, such as visual-spatial ability, should not be related to goal propensity. There are also many

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non-cognitive individual differences that we would not expect to be related to goal propensity. There is no reason, for example, to expect demographic differences in age (among adults), race, gender, or education to be associated with goal propensity, as these factors have not been found to relate either to the self-regulation processes that goal propensity is designed to predict or to basic personality traits that will constitute goal propensity. In addition, while we identified many personality variables that should relate to goal propensity, there are also personality variables that should have little or no relationship to self-regulation. For example, the facet-level variables under the openness factor of FFM have little conceptual connection to self-regulation. Therefore, we would not expect openness or the specific facets that comprise openness to be related to goal propensity. We also expect that referent-specific, non-cognitive individual differences such as values and interests would be largely unrelated to goal propensity because they are not conceptually connected to self-regulation criteria. These variables may be related to the content of personal goals and the contexts in which individual choose to set and pursue goals but not with goal propensity. A number of task-specific factors, even though they are often related to performance, would similarly not be expected to relate to goal propensity. Because they are task specific, not conceptually linked to the self-regulation cycle, and independent of the personality traits that will constitute goal propensity, constructs such as task complexity, the amount of task inherent feedback, and job characteristics including task identity, task significance, and skill variety (Hackman, 1979) should be unrelated to goal propensity. Situational Considerations As a personality trait, the conceptual domain of goal propensity excludes attributes that are largely goal or task specific. In addition, there are specific affordances unique to particular situations that impact the manifestation of all personality differences, and goal propensity. Some of the key situational factors that must be considered in understanding the relationship between goal propensity and self-regulation are discussed below. Situational Strength. In general, personality traits explain more of the variance in behavior in ‘‘weak’’ situations, while the effects of personality are highly attenuated under ‘‘strong’’ situations (Hogan, Hogan & Roberts, 1996). In ‘‘strong’’ situations, the demands of the environment are so structured that all individuals interpret them and respond to them in largely the same way. Because there is little autonomy or latitude as to how one should

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act, behavior is determined by the strong situational requirements and not by individual traits. In contrast, behavior is largely attributable to individual traits rather than situational cues in weak situations. Weak situations provide substantial latitude as to what tasks need to be completed and in what manner. As noted by Weiss and Adler (1984), goal-setting researchers typically examined individual difference measures while attempting to find effects for goal difficulty. The experimental manipulations of goals in these studies likely created strong situations that may have overwhelmed any effects of individual differences being studied. In exploring goal propensity, situations must be chosen that allow the manifestation of individual differences in self-regulation. In the context of self-regulation, a strong situation would be one where individuals are either assigned goals or have little choice in selecting their goals. In addition, the means to achieve these goals, the availability of feedback, and other aspects of self-regulation are tightly constrained. In weak situations, individuals would have significant autonomy as to what goals to set, if any, the means to accomplish these goals, and whether to modify these goals. It is under these conditions that the behavioral effects of goal propensity are likely to be strongest because in these situations, the choices individuals make are likely to be a reflection of their individual differences in goal propensity. While goal propensity can be expected to explain less variance in strong situations, as the influence of personality factors is lessened, goal propensity may still have some effects in these constrained situations. For example, even in strong situations goal propensity may be associated with the internalization of assigned goals, focus of attention and persistence during goal striving, and reactions to feedback. Goal Context. While goal propensity should predict the general tendency to set goals, it is not expected to explain variance in the particular contexts in which individuals choose to strive for goals. This is because individual differences in the choice of contexts in which to pursue goals are based on variations in interests and values across individuals and in the degree of corresponding goal-related affordances offered to individuals across different situations. Such variables are likely based on the situation as well as in the individual’s system of values and interests, both of which are factors outside the domain of goal propensity. Goal Commitment. Goal commitment, often described as an individual’s determination to achieve a goal, is clearly a critical element in the relationship between goals and task performance. In fact, the central finding within

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goal-setting theory – that specific, difficult goals lead to higher levels of performance relative to vague or easy goals – is based on the assumption that there is commitment to a specific, difficult goal (Klein, Wesson, Hollenbeck & Alge, 1999). The fact that commitment is defined in reference to a particular goal, is the reason goal commitment is discussed here and not as part of the self-regulation cycle. The meta-analysis conducted by Klein et al. (1999) confirmed that a variety of task-specific expectancy constructs and the attractiveness of the attainment of particular goals were the strongest antecedents of goal commitment. However, certain personal factors, such as ability and need for achievement, had significant positive relationships with commitment to specific goals. Thus, variance in commitment toward a particular goal is dependent on the situation as well as on the individual. It is likely that goal commitment will generally be correlated with goal propensity, yet the strength of this correlation will be moderated by the degree that commitment is based on situation-specific factors. Task-Specific Self-Efficacy. Self-efficacy beliefs refer to self-judgments about one’s capabilities to perform the specific tasks required to produce particular outcomes (Bandura, 1986). Bandura initially viewed self-efficacy as entirely context-dependent but others have come to the conclusion that there are elements of self-efficacy that generalize across tasks (e.g. Chen, Gully & Eden, 2001). Generalized self-efficacy, because it is context free and is an individual difference trait, may be a correlate of goal propensity. However, task-specific self-efficacy is defined in reference to a specific task and goal propensity is defined as reflecting aspects of self-regulation not accounted for situation-specific factors. Variance in task-specific selfefficacy is dependent on the situation as well as on the individual. It is for this reason that we have excluded task-specific self-efficacy from our discussion of the constructs and processes that constitute the self-regulation cycle even though task-specific self-efficacy is often included in studies of self-regulation and is a central variable in social cognitive theory (Bandura, 1997). Because task-specific self-efficacy is situationally dependent, like goal commitment, it cannot be expected to consistently relate to individual differences in goal propensity.

Summary As outlined above, several researchers have offered theories or approaches to link individual differences in personality and motivation. Yet none of those

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approaches is satisfactory, in terms of providing both the desired conceptual linkage and consistent empirical evidence connecting personality and motivation. Need for achievement, action-state orientation, and core selfevaluations are too narrow to capture the full range of self-regulation while the FFM factors are too broad to cleanly relate to self-regulatory processes. Both core self-evaluations and mastery orientation lack the desired theoretical connection to self-regulation. As suggested by Austin and Klein (1996), stable individual differences in personality should be related to all phases of self-regulation including goal establishment (e.g. the adoption of a particular goal, goal attributes), goal pursuit (e.g. monitoring the environment, processing feedback, detecting discrepancies, and discrepancy tolerance), and goal revision (e.g. the evaluation and choice of strategies to reduce discrepancies). None of the above approaches conceptually connects individual differences in personality with all phases of self-regulation. The proposed goal propensity construct would do just that. While it remains to be shown that such a construct will yield consistent empirical evidence, goal propensity should be more strongly related to all phases of self-regulation than the other traits reviewed above because it will be designed specifically to do so.

GOAL PROPENSITY AND HUMAN RESOURCE MANAGEMENT Having proposed the nature of goal propensity, we next turn our attention to the value of such a construct to human resource management. While the assessment of goal propensity has implications for a number of practices including training and performance management, we feel that the most significant potential contribution lies in the area of human resource selection. Because motivation is a component in the performance of all jobs, goal propensity could potentially be used in selecting applicants across a wide variety of positions and organizations. Goal propensity may, however, be particularly valuable for jobs that could be characterized as reflecting ‘‘weak situations’’ with regard to self-regulation as described earlier. In general, we expect the predictive validity of goal propensity to be greater when the nature of work is variable or otherwise heavy with demands for effective self-regulation. Managerial positions, for example, may be positions for which the assessment of goal propensity would be particularly useful, as there are very clear targets that need to be met but often significant discretion as to how to meet them. In such situations, individuals that are

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prone to spontaneously set subgoals, develop multiple strategies for attaining these subgoals, focus their attention on task-relevant activities, solicit and attend to feedback, monitor their progress, persist in pursuit of their goals, and make adaptive changes in their strategies and subgoals are likely to be much more effective than those who are not. The similar case can be made for sales positions, although the amount of discretion allowed in determining how goals are met depends on the type of sales position.

The Assessment of Motivation To establish the potential superiority of goal propensity for assessing and predicting the motivation of job applicants, the most common techniques currently used by organizations to assess motivation, either directly or indirectly through personality traits, are reviewed and critiqued below. Specifically those methods are interviews, biographical data, and personality assessments. Interviews Employment interviews are probably the most commonly used method to assess the motivational level or tendencies of job applicants. Interviewers will often attempt to make inferences about applicant motivation even if motivational factors are not formally designated to be evaluated during the interview. Employment interviews are generally defined as face-to-face exchange of information between applicants and agents of organizations. Interviews are one of the most widely used selection tools, following only reviews of resumes and application blanks in frequency of use (Dipboye, 1997). The popularity of interviews, despite their limitations, stems from the distinct advantages they offer over other selection methods, particularly the opportunity afforded for face-to-face interaction with job applicants. Traditionally, the interview has been among the most biased and inaccurate of selection procedures. Because most interviews are unstandardized (Graves & Powell, 1996) and because most interviewers are poorly trained, it is not surprising that average correlations between non-scripted selection interviews and later job performance have historically been low (Dunnette, 1972; Hunter & Hunter, 1984). However, the process of structuring and standardizing interview questions can add significant benefits in terms of increased reliability and validity (Conway, Jako & Goodman, 1995; McDaniel, Whetzel, Schmidt & Maurer, 1994) and reduced susceptibility to rating biases (Kataoka, Latham & Whyte, 1997). While structured

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interviews have fared better, standardization of interviews is often difficult due to their extemporaneous and open-ended style. In spite of the widespread recommendation that interviews be more structured, organizations still use unstructured interviews substantially more often than structured interviews (Graves & Powell, 1996). Furthermore, when structured interviews are used, they are typically not used to assess applicant motivation as most prescriptions for the development of structured interviews focus on assessing KSAs with questions developed from a job analysis. As such, if an interviewer conducting a structured interview attempts to assess motivation outside the prescribed scoring system for that interview, the reliability and validity of that assessment of motivation will be no better than if the interview was unstructured. The problems with unstructured interviews have been well documented (e.g. Arvey & Campion, 1982; Dipboye, 1997) and those problems are compounded when the focus is on assessing motivation, rather than KSAs. This is because KSAs are more concrete concepts and interviewers are often more familiar with requirements of the job than the nature of motivation. Interviewers are rarely trained to understand how patterns of individual differences interact with situational factors to lead to high levels of work motivation. Nor do most interviewers understand which individual differences will account for motivational variance (Wiggins, 1973). Questions asked during interviews to assess motivation or personality often reflect projective techniques in that the questions represent a highly ambiguous stimuli. Unfortunately, most interviewers are no better prepared to evaluate the responses to these questions than if they were administering inkblots. The exception to these comments would be psychological interviews conducted by trained psychologists or consultants aimed at measuring specific psychological traits. We consider such interviews to be a form of personality assessment, discussed below, rather than the typical employment interview. Posthuma, Morgeson and Campion (2002) noted that with the resurgence of interest in personality, researchers have started looking at the relationships between personality traits and interviewing behavior. Research has shown that traits such as need for achievement, conscientiousness, and extraversion are modestly related to applicant performance in the interview. Those findings, however, do not mean that interviewers are capable of reliably and accurately assessing these or other personality traits. Findings regarding social perceptions suggest that interviewers do often make such judgments and that those judgments are made on the basis of very little data and are often inaccurate (Posthuma et al., 2002). There is not much evidence to suggest the implicit theories of motivation held by interviewers

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adequately reflect valid, job relevant factors. As with implicit theories of personality or implicit theories of job requirements (Dipboye, 1997), the implicit theories of motivation held by interviewers are usually erroneous. Despite the likelihood that interviewers often attempt to do so, we are unaware of any research explicitly evaluating the validity of interviews in assessing the motivational level of job applicants. However, just as there is little evidence to support the validity of the interview for assessing personality traits (personality interviews aside), it is unlikely that evidence could support the validity of the interview for assessing motivation. While low reliability and validity are the primary reasons why the interview is not a viable means for assessing motivation, another concern is the potential for negative applicant reactions. While often assumed to be positive, reactions to interviews can be negative, particularly when the questions are viewed as invasive or inappropriate or when the interviewer is perceived as incompetent or a ‘‘jerk’’ (Rynes, Bretz & Gerhart, 1991). Questions aimed at assessing implicit theories of motivation could well be viewed as invasive or inappropriate. Avoiding negative applicant reactions is important for a number of reasons including applicant acceptance of offers, the enhancement of an organization’s reputation, and minimizing legal challenges (Gilliland & Steiner, 2001; Ryan, Ployhart, Greguras & Schmit 1998; Smither, Reilly, Millsap, Pearlman & Stoffey, 1993). Biographical Data The second method sometimes used to infer applicant motivation is the use of biographical data or biodata. Nickels (1994) defines biodata as a measurement strategy that requires people to report behaviors and events that have occurred earlier in their lives. As with interviews, the biographical data provided in resumes and application blanks may often be used implicitly to make inferences regarding applicant motivation (e.g. extracurricular activities, working during school, or holding multiple jobs). In discussing biodata here, however, our focus is on the use of biographical information blanks rather than the background information provided on resumes and application blanks. The core attribute of such biodata measurement is the assessment of historical events that have shaped a person’s development and identity (Mael, 1991). It is undisputed that biodata works well in predicting job performance. Biodata measures have proven to be among the best predictors of job performance available, typically yielding validity coefficients in the 0.30–0.40 range (Dunnette, 1972; Hunter & Hunter, 1984; Mumford & Owens, 1987). Despite the empirical superiority of biodata, its use is not without problems.

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Although biodata is one of the best predictors of subsequent job performance, it is not clear why biodata is such a good predictor. One explanation, developmental in nature, suggests that biodata is an effective predictor because it signifies prior development of required KSAs and other characteristics (Owens & Schoenfeldt, 1979). From this perspective, the assessment of biodata captures previous manifestations of constructs that contribute to predictive relationships with performance criteria (Nickels, 1994) and motivational tendencies would be captured in the ‘‘other characteristics’’ category. That is, biodata may asses a variety of attitudes, beliefs, and interests that account for a degree of behavioral consistency over time (Shaffer, Saunders & Owens, 1986) and motivational tendencies may be among those constructs indirectly assessed as underlying that behavioral consistency. Another explanation is ecological in nature (Mumford, Stokes & Owens, 1990), holding that a persons past experiences reflect a pattern of choices the individual has made to select situations in which to engage based on their resources (e.g. KSAs) and affordances (e.g. needs, desires). Yet others have argued that biodata is reflective of personality (Mael, 1991). The lack of coherent theory to explain how biodata constructs determine predictive relationships with criteria often leads to charges of ‘‘dustbowl empiricism,’’ particularly when empirical keying, the traditional and most common approach to developing biodata inventories is used. In response to these problems, researchers have proposed using ‘‘rational,’’ construct-oriented item-generation procedures for biodata (Mumford & Stokes, 1992). Biodata scales that are generated in this manner produce items that are theoretically related to the criterion of interest (Laurence & Waters, 1993). While empirically based biodata forms are keyed for a specific job in a particular organization, rationally keyed inventories are more stable (Mumford & Owens, 1987) and it is possible to develop a generalizable biodata form and scoring key (Rothstein, Schmidt, Erwin, Owens & Sparks, 1990). With a reduced focus on empirical prediction, however, there is some evidence that biodata scales generated using a rationale approach have lower predictive validity (Laurence & Waters, 1993) although others have found validities to be comparable (Mumford, Costanza, Connelly & Johnson, 1996). To balance the high validity of empirical keying with the stability and conceptual soundness of rational keying, a number of hybrid approaches have been developed in which construct-oriented development is followed by empirical-keying and factor-analytic scale development (Hough & Oswald, 2000). Biodata forms can also vary widely in the nature of the items used. Originally, biodata items assessed relatively factual and verifiable aspects of

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an applicant’s background. Biodata items now take a variety of forms (Mael, 1991), some of which are indistinguishable from personality test items. While a number of distinctions can be made between most biodata forms and personality assessments (Borman et al., 2003), biodata forms can be rationally designed to capture prior manifestations of personality characteristics and biodata forms developed in such a fashion have demonstrated good convergent validity with traditional personality measures (e.g. Mumford et al., 1996). In addition to concerns over just what biodata assesses, there are also concerns about faking, applicant reactions, and adverse impact. Research indicates that the applicants can and do fake their answers to biodata items to appear as a more desirable candidate (Stokes, Hogan & Snell, 1993). However, faking and other types of response distortion can be minimized by choosing items that induce the recall of potentially verifiable and objective events (Mael, 1991). Regarding the potential for adverse impact, the concern is that individuals who have not had the chance to obtain the appropriate life-history experiences will score lower on biodata questionnaires. Research suggests that subgroup differences in validity and mean scores are typically low (Reilly & Chao, 1982) although a purely empirical approach to developing the biodata form can result in the inclusion of items that are highly related to race or gender. In response to this concern, a rational or blended approach can be used and items written to reflect life events that should be equally accessible to all applicants (Mael, 1991). As for applicant reaction, it is thought that the individuals will be less defensive when describing past behaviors than when completing personality inventories. However, studies examining applicant reactions to various selection methods indicate that biodata inventories are neither viewed as valid by applicants nor well received (Smither et al., 1993). Also, it may be possible to minimize negative applicant reactions by writing biodata questions that are visibly job related and relatively non-intrusive (Mael, 1991). While some of these concerns can be minimized through the careful construction of biodata items, the use of biodata instruments presents a number of challenges and are not widely used despite their predictive efficiency. Furthermore, the use of biodata to assess motivation is still indirect and unproven. Personality Assessment In contrast to interviews or biodata, the use of personality assessments appears to be a more straightforward approach to assess individual differences in motivation. In fact, much of the current resurgence in research on personality theory can be attributed to its potential as an indirect method

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for measuring motivation (Hogan, 1991). Prior to the 1990s, personality assessment had a dismal history of predicting performance in work settings, with average correlations less than 0.10 (Dunnette, 1972; Hunter & Hunter, 1984). With a better understanding of the nature and structure of personality, there are now several meta-analyses supporting the validity of using personality assessments in predicting a variety of job performance criteria across multiple settings (Barrick & Mount, 1991; Salgado, 1997; Tett et al., 1991; Tett, Jackson, Rothstein & Reddon, 1999). For example, in their meta-analyses of various selection instruments, Schmidt and Hunter (1998) found that conscientiousness offered some of the highest incremental validity over cognitive ability alone. While personality assessment is likely the most valid of the current approaches to evaluating applicant differences in motivation, the validities are still modest, and currently used personality inventories are still indirect or inadequate indicators of individual differences in motivation and often have other limitations as well. As described earlier in discussing the relationship between personality and motivation, there are problems in using current personality instruments such as conscientiousness to predict motivation. These problems stem from the lack of a strong theoretical linkage between personality and motivation. Although some researchers have offered theories or approaches that connect individual differences in personality and motivation (Heckhausen & Dweck, 1998; Kuhl, 2000), most personality measures are not directly related to the theories of motivation (Austin & Klein, 1996) and prior attempts to link the two lack either the desired conceptual explanation for how motivation relates to personality differences or consistent empirical support for such linkages. In addition, most personality inventories used in selection (e.g. the California Psychological Inventory, NEO-Personality Inventory) were not designed for use as selection tools. For example, FFM has proven robust and generalizable when used across different measures, respondents, and cultures, and it is likely to remain a useful tool for guiding research and accumulating information about the higher-order factor structure of personality (Hough & Oswald, 2000). However, it was not designed to predict motivation or performance. Furthermore, because the higher-order factors of FFM contain facets with both high and low criterion-related validities, the criterion-related validities of the major factors are suboptimal. As for other limitations, personality assessments typically do not result in adverse impact but negative applicant reactions and faking are concerns. While personality assessments have been shown to be objectively fair, their use can produce perceptions of unfairness and other negative reactions (Hogan et al., 1996) what can lead to legal challenges. Because the items in

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most personality instruments appear unrelated to job requirements, it can be difficult for applicants and other constituents to understand how the assessment of personality is relevant. The absence of a clear theoretical linkage between personality and motivation exasperates this lack of face validity as it is often difficult for managers, test administrators, and even many human resource professionals to explain how variance in abstract personality traits relates to effectiveness in a specific position. Negative applicant reactions do not, however, appear to affect the validity of personality tests (Chan, Schmitt, Sacco & DeShon, 1998). A final concern with using personality assessments as selection tools is the issue of item transparency and the subsequent possibility that people will be able to provide false responses to items (Viswesvaran & Ones, 1999). For example, an item designed to assess conscientiousness may ask respondents if they achieve their personal goals or pay attention to detail. Few reasonably observant job applicants would claim that such statements are ‘‘very inaccurate’’ descriptions of themselves. Indeed, it appears that individuals can readily fake their status on major personality factors (Viswesvaran & Ones, 1999), although research suggests that such response distortions do not significantly attenuate the validity of personality constructs in predicting job performance (Barrick & Mount, 1996; Ellingson, Smith & Sackett, 2001; Hogan, 1998; Viswesvaran & Ones, 1999). This conclusion may, however, be dependent on the context of performance and on the particular personality constructs used.

A Measure of Goal Propensity As suggested above, all the current approaches commonly used to assess the motivational tendencies of job applicants have significant limitations. For the interview, validity is the primary concern given the low reliability and suspect precision with which relevant motivational constructs can be assessed through the traditional employment interview. For biodata, motivational tendencies may or may not underlie the demonstrated validity of this tool in predicting performance, making this an indirect and unproven method for assessing motivation. Negative reactions on the part of job applicants are also a concern for biodata as well as for personality assessments, the most promising avenue for assessing individual differences in motivation. The additional problem with personality assessments is that currently available measures are not sufficiently conceptually linked to motivation. While construct valid in terms of the traits are being assessed,

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currently used measures are either too narrow (i.e. deficient) or too broad (i.e. contaminated) to be valid indicators of the dispositional bases of motivation. It is our assertion that the assessment of goal propensity will represent a more direct and theoretically sound approach to evaluating individual differences in motivation. The specification of the particular basic traits that constitute goal propensity and the subsequent refinement of the goal propensity construct obviously need to precede the development of a measure of goal propensity. The necessary steps to do so along with the possible forms a measure of goal propensity could take are discussed in subsequent sections of this chapter. At this point, however, we want to evaluate how a measure of goal propensity would contrast to currently available approaches to the assessment of motivation in terms of desired characteristics of selection tools, namely validity, reliability, cost, applicant reactions, and fairness. Validity To be an effective selection tool, a measure must accurately predict job relevant characteristics. A measure of goal propensity would clearly measure the attribute it is intended to measure, namely individual differences in motivation, and do so more directly and completely than other available approaches or measures. Because goal propensity is defined as a compound trait, a measure of goal propensity will reflect a set of basic personality traits theoretically and empirically related to the criterion it is meant to predict while excluding irrelevant personality dimensions. As such, goal propensity complies with the common recommendation to carefully examine the performance construct and then match the predictor to that criterion (Schneider & Schmitt, 1986). By doing so, a measure of goal propensity should demonstrate higher criterion-related validity than other approaches to assess motivational tendencies. Because goal propensity will be a cleaner measure of individual differences in motivation, the incremental validity over measures assessing job relevant KSAs in predicting job performance should also be greater than for current approaches. Reliability The consistency with which goal propensity can be assessed should be quite high as the construct is defined in terms of component basic traits that reflect self-regulatory tendencies that are not due to situation-specific factors. Assuming established methods for scale development are followed, a measure of goal propensity should be just as reliable as other measures assessing stable personality traits (e.g. the 16PF Select) or other well-developed compound personality traits (e.g. customer service orientation).

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Existing research suggests that internal consistency reliabilities for such scales are around 0.70, while retest reliabilities tend to be around 0.80 (Arbisi, 2003; Hogan & Hogan, 1995). Cost A number of factors contribute to the cost of using a selection tool including the costs of developing or purchasing the instrument and the costs associated with administering the measure. A key factor relating to the development/purchase cost is the generalizability of the instrument across organizations and job categories. The more widely a selection tool can be used, the more widely the development costs can be distributed. Further economies of scale may also be obtained if an instrument can be used by an organization for multiple positions. Because goal pursuit and self-regulation are elements of most positions, goal propensity could potentially be used in selecting applicants across a wide variety of positions and organizations. The development costs and the costs to purchase a measure of goal propensity should therefore be similar to other widely used personality measures and less than the costs of biodata instruments or structured interviews, which are typically developed for specific situations. With regard to the costs of administering the selection tool, a measure of goal propensity can be expected to be similar to a biodata form or other personality assessments and less costly than an employment interview. Applicant Reactions One way to minimize the problem of negative applicant reactions is to provide a post-assessment explanation of the job relevance of the instrument (Hough & Oswald, 2000). That should be easier to do for a measure of goal propensity, because of the clearer conceptual linkage to motivation, than for other personality assessments or biodata instruments. The other common recommendation for ensuring positive applicant reactions is to use questions that will be perceived as relevant by job applicants. Efforts to assess goal propensity using an amalgamation of measures assessing the component basic traits would not be any more ‘‘face valid’’ than current psychological assessments. However, items for subsequently developed direct measures of goal propensity could be written to assess information using contexts and scenarios reflective of work environments. Fairness There is no reason to expect that the distribution of self-regulatory tendencies is different for different groups of applicants. Furthermore, goal

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propensity is a composite of relevant basic personality traits and evidence suggests that personality measures rarely produce any sizable subgroup differences or result in adverse impact (Hough & Oswald, 2000; Ones & Viswesvaran, 1998). As long as items for a direct measure of goal propensity are written in an unbiased manner and the measure is administered in a consistent and appropriate manner there should be a few legal concerns. Based on findings from other personality measures (Hogan et al., 1996; Hough, 1998), the use of goal propensity has the potential to balance the use of other measures to enhance the fairness of the selection process. However, the effectiveness of a composite measure in reducing adverse impact depends on the exact measures used as components of the composite and existing differences in the selection ratio between groups (Sackett & Ellingson, 1997). Furthermore, assuming that a direct measure of goal propensity is developed that is more face valid than most personality or biodata instruments, perceptions of fairness should be greater and the likelihood of rejected applicants pursuing legal action should be reduced. We have outlined the reasons we believe are a measure of goal propensity that would be superior to alternative methods for assessing the motivational tendencies of job applicants. It is also worth noting to believe that such an approach will become even more valuable in the future. As numerous authors have noted, there are dynamic, complex changes occurring in the workplace that are presenting substantial challenges for human resource selection (Borman et al., 2003; Ilgen & Pulakos, 1999). Changes in technology, globalization, and competition result in constantly changing environments in which roles and expectations are continually evolving. In such environments, employees need to regularly upgrade their skills and engage in continuous and lifelong learning. The implications for selection are profound as the focus can no longer be just on assessing current KSAs but the KSAs and motivation that will be needed as the position evolves. These increases in fluidity and autonomy suggest that the assessment of personality in general, and goal propensity in particular, will become increasingly valuable as they are better predictors when roles become less well defined.

Other Practical Implications We have focused on selection in discussing the practical implications of goal propensity because we believe the greatest potential for this construct lies in addressing this key need, namely the prediction of the dispositional

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motivational tendencies of job applicants. The goal propensity construct may also have value, however, for other areas of human resource management. For example, research on mastery orientation has shown personality to influence motivation in learning contexts. Individual differences in goal propensity may similarly help predict motivation to learn and, in turn, learning across a wide range of learning environments. As such, goal propensity could be used for selection into training programs. Environments that involve extensive training across time (e.g. medical school or other professional training) may particularly benefit from including a measure of goal propensity as an element of the selection system. Alternatively, if not used to select individuals for training programs, a measure of goal propensity could be used to identify those individuals who may need more external assistance to engage in the needed self-regulatory mechanisms to succeed in the program. For example, research suggests that some self-regulatory skills can be taught (Kanfer & Ackerman, 1996). Assuming that individuals do approach learning tasks with differential predispositions to engage in self-regulation, knowing those tendencies could help identify those individuals who would benefit most from treatments aimed at enhancing self-regulation during learning. There are also practical implications for the use of goal propensity in ongoing performance management. The assessment of goal propensity could be used to identify those individuals who are less likely to engage in self-regulation on their own. For these individuals, a variety of interventions could potentially help them do so in order to increase their motivation and subsequently their performance. Stated differently, for individuals less predisposed to engage in self-regulation, stronger situational cues could be provided to either engage more self-regulation or the necessary external regulation provided to achieve the desired performance outcomes. Examples of such actions could include scheduling regularly occurring sessions for setting goals and action planning, providing explicit external feedback, and requesting status reports.

FUTURE RESEARCH DIRECTIONS Having made the case for the need for and potential value of goal propensity, we next turn our attention to discuss the sequence of subsequent steps, both conceptual and empirical, needed to realize the above practical implications.

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Specification of the Goal Propensity Construct We have described goal propensity as a compound personality trait in this chapter, but we have not specified the specific set of basic personality traits that should constitute goal propensity. The identification of those component traits is the first step needed to further develop the goal propensity construct. Examples of this type of compound trait development include customer service orientation, which is a combination of agreeableness, adjustment, and conscientiousness (Ones & Viswesvaran, 1996) and social competence, which consists of social insight, social maladjustment, social appropriateness, social openness, social influence, warmth, and extraversion (Schneider, Ackerman & Kanfer, 1996). The basic personality traits that constitute a compound personality trait are chosen based on their ability to predict the specific criterion of interest (Hough & Schneider, 1996). In the case of goal propensity, those criteria are derived from a composite model of self-regulatory theories of work motivation and are represented by the self-regulatory constructs and processes described earlier in this chapter and summarized in Table 1. Using theoretical as well as empirical evidence, basic traits need to be identified based on their ability to relate to and predict one or more aspect of the self-regulatory cycle. For example, some but not all of the facet-level factors underlying FFM traits are likely to be relevant (e.g. achievement striving). Such traits are by themselves too narrow to account for all phases of self-regulation, but in concert with other specific traits, from within or outside of FFM, they may be important components of goal propensity. Once a set of potential component traits has been identified, initial construct validation research is needed to verify the appropriateness of the selected set of component traits. Such research should be conducted by examining the pattern of relationships among those traits as well as relationships, combined and individually, with other constructs within the nomological network surrounding self-regulation. In conducting this initial construct validation research, established measures of each of the component traits should be used. Work along this initial line of research is needed until the most parsimonious set of traits is identified to fully capture the tendency to engage in all aspects of self-regulation and hence represent the goal propensity construct. This initial research should also provide a clear understanding of the structure of that constellation of traits, in terms of the number of factors, specific traits within each factor, and the relationship between each factor and the phases of self-regulation.

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As part of investigating the nomological network around goal propensity, it may be worthwhile to examine the relationship between general cognitive ability and elements of goal propensity. Stankov (2000), for example, found that self-monitoring, defined as the propensity to appraise personal performance, may be closer to the ability domain than to the personality domain. Similarly, there may be constructs that appear to be strong potential components of goal propensity, but in fact are closer to the cognitive domain of individual differences rather than the non-cognitive domain. Any such constructs should be excluded as goal propensity components given the definition of goal propensity as a compound personality trait. The exclusion of such ability-based components will further ensure the incremental validity of goal propensity when used in conjunction with cognitive ability. In addition to examining the nomological network around goal propensity, further explication of the focal criterion for goal propensity, presented in Table 1, is also desirable.

Development of Goal Propensity Measures Once the component traits of goal propensity have been identified and verified, it would be useful to develop a direct, integrative measure of goal propensity. Given that goal propensity is a personality trait, this measure should also be developed and thought of as a personality assessment. It would, however, be a more concise and direct way to assess the construct relative to the combined use of separate measures of each basic-level, component personality trait. Since the goal propensity construct will likely be multidimensional, this direct measure of goal propensity would need to mirror the identified structure of the construct. Items should be integrative, written as to reflect the commonalities among the basic traits within a dimension rather that having different items reflect different basic traits. The development of the core self-evaluation scale, reported by Judge, Erez, Bono and Thoresen (2003), provides an excellent model that could be followed in developing this direct measure of goal propensity. This direct measure would then be evaluated for congruence with the combined measures of the separate traits and validated against self-regulation constructs. There would then be the need for research demonstrating the value of using goal propensity for selection. Those criterion-related validly studies should use indicators of employee motivation as criteria as well as measures of job performance. In addition, studies examining the incremental validity of goal propensity over other established predictors such as cognitive ability and

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conscientiousness would be particularly valuable. Also valuable would be the studies by comparing the predictive validity of goal propensity for different types of jobs, for example comparing positions differing in goal directedness or situational strength, and different dimensions of performance. Alternative measures of goal propensity might also be subsequently developed. For example, given the potential for using goal propensity as a selection tool, an alternative form of the measure could be developed, one in which the items are written specifically for the employment context so that applicants view the assessment as more job relevant than they do for most personality assessments. Alternatively, it might be possible to develop a set of situational interview questions or a computer-based simulation that adequately taps the goal propensity construct, although the interview-based assessment may be inherently less valid. A final possibility might be to use the goal propensity construct as the basis for construct-oriented item generation for the development of a biodata instrument. These alternative measures would need to demonstrate convergence with the other measures of goal propensity.

Additional Research Questions The implications for future research are not limited to validation and scale development issues. A number of assertions have been made in this chapter regarding the nature and role of goal propensity, and these assertions need to be empirically tested. In addition, viewing the propensity to self-regulate as an individual difference can generate a number of other interesting research questions. For example, one avenue of research would be to examine whether a goal propensity state can be induced. If this is possible, then the impact of such states could be examined relative to dispositional goal propensity. That is, can goal propensity have both state and trait qualities similar to a mastery goal orientation? Examples of issues along these lines are examining whether the provision of training on self-regulatory skills or providing strong situational cues to engage in self-regulatory processes can compensate for having a low dispositional goal propensity. In addition, because motivation is a dynamic phenomenon that has both trait and state qualities, longitudinal research designed to investigate the relationships between distal trait-based motivational variables (particularly goal propensity) and proximal, state-based constructs such as specific goals, is necessary. For example, Tisak and Tisak (1996) present several longitudinal models of reliability and validity that could be applied to goal propensity. These models coupled with multilevel modeling methods

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(Bryk & Raudenbush, 1992) may reveal a curvilinear relationship between goal propensity and longitudinal performance. This would be consistent with the findings of Ployhart and Hakel (1998), who found a curvilinear relationship between personality differences and performance over time. Other temporal aspects of goal propensity could also be considered. For example, Fried and Slowik (2004) proposed that people experience time differently because of their personalities, and that this subjective time perspective may have effects on goal-related activities. We expect that the development and use of the goal propensity construct will be a means to investigate these types of relationships between individual differences in time perspective and self-regulation. Another avenue for future research would be to better understand the extent to which different positions are amenable to self-regulation and hence the manifestation of individual differences in goal propensity. The O*NET could potentially be used in this regard, for example, examining differences in mean levels of goal propensity between positions based on experience or occupational requirements (Peterson, Mumford, Borman, Jeanneret & Fleishman, 1999). Also, the person–organization fit paradigm could be used to investigate the relationship between goal propensity and job satisfaction. Specifically, differences in the degree that work environments permit and foster selfregulation could be compared to individual differences in goal propensity. We would expect that persons with higher goal propensity would be attracted to, be more satisfied in, and tend to remain in environments with better opportunities to self-regulate. In contrast, individuals high in goal propensity would likely be frustrated and dissatisfied in environments that constrain selfregulation. Similarly, the proposition that individuals vary on their propensity to be constrained by situations was recently noted by Locke and Latham (2004), who claimed that investigating the effects of ‘‘strong personalities’’ on situations should accompany the study of the moderating effects of ‘‘strong situations’’ on the personality – performance relationship. In this regard, research could examine whether individuals high in goal propensity would be more inclined to change situations to promote more effective self-regulation than individuals low in goal propensity.

CONCLUSION It is widely recognized that there are individual differences in motivation but a major weakness in current treatments of motivation is lack of clarity regarding how individual differences in personality affect motivation.

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Currently there is no theoretically derived and empirically supported individual difference construct adequately reflective of work motivation. To address these weaknesses, we proposed the goal propensity construct to theoretically connect a parsimonious set of critical, non-cognitive individual difference variables to subsequent individual differences in work motivation. Goal propensity would be a unique compound personality trait related to individual differences in the tendency to engage in all phases of the self-regulatory cycle, which is at the core of most current conceptions of work motivation. The development of the goal propensity construct will address the need to systematically incorporate individual differences into current theories of self-regulation. The primary theoretical contribution of articulating the specific basic traits associated with the self-regulatory cycle will be to more clearly and directly define the role of personality in self-regulation models of motivation. The development of a goal propensity construct will advance our understanding of the linkage between individual differences and work motivation (Austin & Klein, 1996) and uniquely contribute to motivation research by providing a theory-based point of integration between personality constructs and motivational processes in all aspects of self-regulation. Specifically, goal propensity will directly link distal motivational influences (the personality traits that constitute goal propensity) to proximal motivational process (self-regulation). The identification of this construct also has the potential to generate numerous research questions. In addition to the validation of the construct, the testing of hypothesized empirical connections between individual differences in goal propensity and all major components of self-regulated behavior as well as other known correlates of those components, substantial research opportunities exist for exploring the value of goal propensity in human resources practices including selection, training, and performance management. This chapter introduced the goal propensity construct, provided the rationale for developing this construct, articulated the desired nature of goal propensity, explored the implications of such a construct for human resource practice, and outlined the subsequent steps necessary to further develop this construct. By developing the goal propensity construct based on a strong theoretical foundation that reflects advances in the conceptualization of personality and motivation, the opportunity exists not only to better understand of the linkage between individual differences in personality and work motivation and ultimately provide tools to reliably and accurately predict individual differences in the tendency to engage in self-regulation and hence motivation.

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‘‘THE ELUSIVE CRITERION OF FIT’’ REVISITED: TOWARD AN INTEGRATIVE THEORY OF MULTIDIMENSIONAL FIT Anthony R. Wheeler, M. Ronald Buckley, Jonathon R. B. Halbesleben, Robyn L. Brouer and Gerald R. Ferris ABSTRACT ‘‘Fit’’ as a human resources decision criterion has emerged as an active body of research in recent years, but its ‘‘elusiveness’’ as a scientific construct, noted more than a decade ago by Judge and Ferris, still remains. To best address this issue, this chapter proposes an integrative theory of multidimensional fit that encompasses five relevant (and distinct) streams of current fit research: Person-Organization Fit, PersonVocation Fit, Person-Job Fit, Person-Preferences for Culture Fit, and Person-Team Fit. It is proposed that these five dimensions of fit relate to an individual’s self-concept; moreover, an individual assesses multidimensional fit utilizing a social cognitive decision-making process called prototype matching. By assessing fit across multiple dimensions, an individual can both gain a social identity and expand the self-concept, which explains the motive to fit. Testable propositions are formulated, and Research in Personnel and Human Resources Management Research in Personnel and Human Resources Management, Volume 24, 265–304 Copyright r 2005 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 0742-7301/doi:10.1016/S0742-7301(05)24007-0

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implications for multidimensional fit across the employment lifecycle are discussed. Furthermore, directions for future fit research are provided.

INTRODUCTION yneeded is research mapping the construct of fit. To date, we only have a nebulous idea of its nature. We need to articulate more precisely the nature of the fit construct as well as intermediate steps which characterize the dynamic process through which it operates (Judge & Ferris, 1992, p. 61). In addition, since there is confusion in the literature about the bases of fit, with personality, values, needs, and goals all identified as bases (Kristof, 1996), we simply asked for an overall fit perceptiony (Mitchell, Holtom, Lee, Sablynski & Erez, 2001, p. 1107).

Over the past two decades, organizational scientists have directed considerable attention toward the construct of fit, and, certainly, that work has helped to address some of Judge and Ferris’s concerns (i.e. in the above quote) regarding the need to develop a better understanding of the construct. However, as the second and more recent quote suggests, an integrative understanding or theory of the construct of fit does not exist to guide research adequately. Although scholars may disagree on the definition of fit (Judge & Ferris, 1992; Kristof, 1996), most organizational science researchers have expressed little doubt that individuals and organizations engage in a mutual process of matching what an individual desires from an organization and what an organization desires in exchange from prospective and current employees (Ferris & Judge, 1991:Wanous, 1992; Wanous & Reichers, 2000). Before the terminology of fit came about, authors were arguing that organizations should ‘‘find employees who desire to work in [the organization’s] world,’’ denoting aspects of the organization from the culture to what the organization can supply the person (Argyris, 1964, p. 260). Although there is agreement on the theoretical importance of fit, most fit studies have provided empirical support to explain how individuals selfselect into or out of organizations, the construct of fit is largely devoid of an integrative theoretical examination. Schneider (1987) provided the seminal framework that has been used to describe this process through his attraction-selection-attrition (ASA) framework, and most scholars now subsume the ASA framework into a larger conceptualization of fit called Person-Organization (P-O) Fit (Chatman, 1989). Holland (1985) described a similar process of fit, Person-Vocation (P-V) Fit, based on personality type, and Caldwell and O’Reilly (1990)

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described a process of fit, Person-Job (P-J) Fit, based on the specific knowledge, skills, and abilities required by any job. Recently, Van Vianen (2000) proposed a fourth type of fit, PersonPreferences for Culture (P-P) Fit, based on an individual’s preference for certain organizational cultures. Additionally, with the rise of team and group-oriented work, researchers have begun exploring the idea of PersonGroup or Team (P-T) Fit (Ferris, Youngblood & Yates, 1985, Hollenbeck et al. 2002). These authors shed light on the importance of fitting the individual to the group or team, utilizing several different aspects of fit, such as personality and abilities. Researchers have examined each conceptualization of fit as a predictor of myriad outcomes such as job satisfaction, organizational commitment, and job involvement (Saks & Ashforth, 1997), as well as intent to turnover (Vancouver & Schmitt, 1991; Chatman, 1991). Recently, researchers have examined the combined effects of at least two of the aforementioned conceptualizations of fit (Saks & Ashforth, 1997, 2002; Kristof-Brown, 2000; Van Vianen, 2000; Cable & DeRue, 2002; Furnham, 2001; Caldwell, Herold & Fedor, 2004). However, a comprehensive theory, which integratively explicates the combined effects of all five of the conceptualizations of fit, is yet to be developed. Such theory building is the objective of this chapter. The purpose of this chapter is to propose an integrative theory of multidimensional fit (MDF), which is presented in Fig. 1. Following the tenets of theory building suggested by Bacharach (1989), we first describe the subdimensions of MDF found in the current disparate fit literatures. Second,

Prototype Matching Process

Self P-O Fit

Prototype P-O Fit

Self P-J Fit

Prototype P-J Fit

Self P-V Fit

Prototype P-V Fit

Self P-P Fit

Prototype P-P Fit

Self P-T Fit

Prototype P-T Fit

SelfExpansion

Social Identity Multidimensional Fit Compartmentalization

Self-Esteem

Fig. 1.

The Process and Outcomes of Multidimensional Fit.

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we discuss how the sub-dimensions of MDF overlap with the self-concept, utilizing social psychological research on self-concept complexity (Linville, 1987), and on social cognitive decision-making, with specific reference to Cantor, Mischel and Schwartz’s (1982) self-to-prototype matching (i.e. referred to throughout as ‘‘prototype matching’’). Third, we develop the motive as to why individuals seek MDF, drawing on Aron and Aron’s (1986) model of self-expansion and on Tajfel and Turner’s (1986) social identity theory. We address when individuals assess MDF throughout the employment lifecycle, tracing MDF through the recruitment, socialization, and re-assessment. Finally, we provide future research directions highlighting three areas of new research: post-socialization fit assessments, misfit, and the phenomenology of fit.

THEORY AND RESEARCH ON FIT Traditionally, researchers have viewed each sub-dimension of fit from either a supplementary or a complementary perspective. Supplementary fit is defined as the perceived match between an individual’s existing characteristics and the existing characteristics found in an environment (Kristof, 1996). As Schneider, Goldstein and Smith (1995) operationalized it, supplementary fit is based ‘‘upon an implicit estimate of the congruence of [an individual’s] own personal characteristics and the attributes of potential work organizations’’ (p. 749), and they suggested that supplementary fit leads to homogeneous organizations where employees relatively possess the same personal characteristics. Complementary fit occurs when an individual’s personal characteristics or attributes add something that is currently not found in the environment (Muchinsky & Monahan, 1987; Kristof, 1996). Most of the fit research has approached the sub-dimensions of MDF from the supplementary perspective; thus, most fit research examines how individuals seek to match their characteristics to the existing environment instead of adding new characteristics to the existing environment (Kristof, 1996). Moreover, researchers typically have studied each sub-dimension of MDF from either the supplementary or complementary perspective, as a mutually exclusive process. We argue that a unified theory of MDF should be able to explain the process of fit through either perspective. In accordance with this view, Cable and Edwards (2004) investigated the combined effects of supplementary and complementary fit on outcomes such as job satisfaction and organizational identification. These authors found

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that supplementary fit, operationalized as value congruence, and complementary fit, operationalized as psychological needs fulfillment, contributed independently to job satisfaction and organizational identification. This provides preliminary support that MDF should be assessed using both supplementary and complementary fit.

Sub-Dimensions of MDF The movement of the fit literature from uni-dimensional to MDF has produced at least five sub-dimensions: P-O Fit, P-V Fit, P-J Fit, P-P Fit, and P-T Fit. Although these conceptualizations of fit generally have evolved in disparate literatures, recent attempts at integration can be found (e.g. Saks & Ashford, 2002; Cable & DeRue, 2002). With the intent of delineating a theory of MDF, we explore these five relevant sub-dimensions. Person-Organization Fit P-O Fit is probably the most examined sub-dimension of MDF in the organizational sciences literature. Kristof (1996) provided the most extensive review of P-O Fit to date; therefore, we do not provide full review of P-O Fit in this chapter. However, we do highlight some aspects of P-O Fit that are pertinent to the development of MDF. Chatman defined P-O Fit as ‘‘the congruence between the norms and values of organizations and the values of persons’’ (1989, p. 339), and researchers typically have examined P-O Fit from a supplementary perspective (Kristof, 1996). Because values, beliefs, and expected norms of behavior drive P-O Fit, researchers commonly associate P-O Fit with organizational culture and the role that culture plays in shaping the behavior of individuals within the organization. To this point, O’Reilly, Chatman and Caldwell (1991) initially referred to P-O Fit as person-culture fit. From this view, the organization establishes behavioral expectations through the culture of the organization (Kristof, 1996). In turn, the individual continually assesses the fit between personal values, beliefs, and norms, and the values, beliefs, and norms espoused by or enacted in an organization’s culture (Schneider et al., 1995). Moreover, research conducted during the selection process suggests that organizations base hiring decisions on the perceived compatibility between the organization’s values, beliefs, and norms and the prospective employees’ values, beliefs, and norms (Bretz, Rynes & Gerhart, 1993; Werbel & Gilliland, 1999; Kristof-Brown, 2000). This suggests that the assessment of P-O Fit involves a dynamic interplay between individuals within an organization.

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Researchers have found many significant relationships between P-O Fit and important organizational and individual outcome variables, such as a positive correlation with job satisfaction (Vancouver & Schmitt, 1991), organizational commitment (Luthans, Baack & Taylor, 1987; Vancouver & Schmitt, 1991), performance through selection based on P-O Fit criterion (Borman, Hanson & Hedge, 1997), and employee retention (O’Reilly et al., 1991; Chatman, 1991). A recent meta-analysis performed by Verquer, Beehr, and Wagner (2003) examined the relationship between various forms of P-O fit and job satisfaction, organizational commitment, and intent to turnover. They examined the relationships using three characterizations of fit (i.e. objective fit, perceived fit, and subjective fit), finding mean correlations for satisfaction and commitment in the high 0.20’s and
0.18 for intent to turnover. However, subjective measures of fit had much higher correlations ranging from 0.61 for job satisfaction to
0.58 for intent to turnover. Further, Erdogan, Kraimer and Liden (2004) examined the possibility of moderators of the P-O Fit-satisfaction relationship, reporting that P-O Fit related positively to both job and career satisfaction when either perceived organizational support (POS) or leader–member exchange (LMX) were low. However, POS and LMX moderated this relationship such that when either POS or LMX was high, there was no relationship between P-O Fit and job and career satisfaction. This brief review of the literature indicates that P-O Fit is a valuable subdimension of MDF. Because P-O Fit focuses on values, beliefs, and norms and has been shown to correlate with many individual and organizational outcomes, we believe that a unified theory of MDF must include P-O Fit as an important sub-dimension of fit. Person-Vocation Fit P-V Fit (Holland, 1985) was originally developed from Super’s (1953) vocational development theory, and researchers typically have viewed P-V Fit from the supplementary perspective. According to P-V Fit, individuals select vocations based upon their personalities or interests (Furnham, 2001), and vocations also have ‘‘personalities’’ that attract individuals (Holland, 1985, 1995). The role of personality has been extensively examined in organizational studies (Barrick & Mount, 1991; Salgado, 1997), and many researchers agree that personality can be an important predictor of organizational outcomes. Furthermore, researchers have noticed that personality traits can be stereotypically associated with vocations, such as ‘‘introverted engineers’’ or ‘‘devious lawyers’’ (Tversky & Kahneman, 1973).

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Recruitment and selection scholars have suggested that organizations recruit and select individuals based on personality type (Tom, 1971; Barrick & Mount, 1991). More recently, researchers have included personality variables as predictors of job search behavior (Kanfer, Wanberg & Kantrowitz, 2001; Boudreau, Boswell, Judge & Bretz, 2001) and voluntary turnover (Mitchell et al., 2001). This research suggests that organizations select and retain employees based on desirable personality traits. Moreover, this research suggests that individuals self-select into or out of organizations based on personality characteristics. Furnham (2001) reported that P-V Fit yielded similar organizational outcomes to P-O Fit, namely, increased satisfaction, increased organizational commitment, and decreased intent to turnover. Although some researchers might include aspects of personality in the discussion of P-O Fit (Kristof-Brown, 2000), P-V Fit fundamentally addresses the role of personality in fit that diverges from the role of values, beliefs, and norms that is emphasized in P-O Fit. To bolster this differentiation, Kristof (1996) explicitly summarized Holland’s (1985, 1995) view of occupational fit as the ‘‘congruence with [an individual’s] self-concept’’ (p. 7). As such, a theory of MDF should include P-V Fit as an important sub-dimension. Person-Job Fit P-J Fit is rooted in the demands-abilities perspective of Person-Environment (P-E) Fit (Edwards, 1991). This perspective ‘‘suggests that fit occurs when an individual has the abilities required to meet organizational demands’’ (Kristof, 1996, p. 3), such as the knowledge, skills, abilities (KSAs) required for a job. Specifically, Edwards (1991) defined P-J Fit as the congruence between an individual’s KSAs and the KSAs required by the job, or the wishes of the individual and the attributes of the job. Recently, research expanded the use of this dimension from the job level to that of the occupation level utilizing the demands-abilities perspective to suggest occupation fit (Converse, Oswald, Gillespie, Field & Bizot, 2004). Specifically, these authors used ability, as measured by the Ball Aptitude Battery Subtests, to match individuals to an occupation through the Occupational Information Network (O*NET). Significant support was found for using a person’s abilities to match them to a fitting occupation. Fit researchers often operationalize P-J Fit as a form of complementary fit (Muchinsky & Monahan, 1987; Kristof, 1996). However, recent literature has suggested that, in the context of recruiting and selection, P-J Fit can be conceptualized as both complementary and supplementary (Kristof-Brown, 2000). Bretz et al. (1993) and Rynes and Gerhart (1990) found that both

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recruiters and applicants perceive P-J Fit in subjective terms, so that an applicant’s KSAs, which are commonly associated with P-J Fit, can be viewed as: (1) adding to the existing environment (complementary) or, (2) matching the characteristics of the existing environment (supplementary). Moreover, P-J Fit has been found to predict important individual and organizational outcomes independent of other sub-dimensions of fit, such as satisfaction (Kristof-Brown, Jansen & Colbert, 2002), organizational commitment (Saks & Ashforth, 2002), and intent to turnover (Edwards & Cooper, 1990; Saks & Ashforth, 2002). This suggests that a unified theory of MDF must include P-J Fit, as perceptions of P-J Fit diverge from perceptions of P-O Fit and P-V Fit.

Person-Preferences for Culture Fit Recently, a fourth stream of fit research has emerged into fit studies called P-P Fit (Van Vianen, 2000). Relying on Schneider’s (1987) ASA framework, Van Vianen described P-P Fit (2000) as the ‘‘match between characteristics of people’’ (p. 117). Whereas P-O Fit measures the congruence between the individual’s values and goals and the organization’s values and goals, P-P Fit measures the overlap between people (i.e. coworkers, subordinates, supervisors). Schneider et al. (1995) found support for their hypothesis that an organization evolves into homogenous group of individuals, and P-P Fit seeks to measure this shared ‘‘personality’’ of the organization. As such, all individuals within an organization should exhibit similar preferences for organizational culture (e.g. pay, benefits, promotion schedules, etc.). Therefore, this sub-dimension of fit is often operationalized as supplementary fit. Where P-O Fit relates to culture, P-P Fit relates to the shared endorsement of culture (Van Vianen, 2000). The distinction between P-P Fit and P-O Fit can be thought of in similar terms as the distinction between organizational climate and organizational culture, where climate represents the surface representations of the deeper-rooted organizational culture (Denison, 1996). Van Vianen (2000) found that perceptions of P-P Fit related to organizational commitment and intent to turnover independent of the relationship between P-O Fit and those outcomes. Furthermore, Christiansen, Villanova and Mikulay’s (1997) study concerning political compatibility supports the notion of P-P Fit. They found that an individual’s preferences for political influence processes, when aligned with the perceived organizational processes, were positively related to numerous work attitudes, such as satisfaction with coworkers and perceptions of procedural fairness.

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Additionally, a recent study explored the P-P Fit phenomenon using a person’s need for autonomy and the supply the organization provided (Simmering, Colquitt, Noe & Porter, 2003). Needs-supplies fit is defined as occurring ‘‘when a person’s job supplies the characteristics that meet his or her needs’’ (p. 956). Essentially this can be interpreted as the fit between characteristics individuals prefer from their organizations and what the organizations provide (e.g. autonomy, benefits, etc.). When a misfit occurred between the needs of the employees and the supply of the organization, conscientious people would seek out career development, which subsequently increased their fit. Although it has received limited empirical support, research to date has indicated that P-P Fit diverges from the other subdimensions. Therefore, we include P-P Fit in our unified theory of MDF. Person-Team Fit Although not much research has been conducted on the P-T Fit subdimension, with the ever-increasing use of teams within organizations, this sub-dimension is of great relevance. The importance of team fit can be found in the literature at least as far back as 1964. Argyris (1964) encouraged organizations selecting personnel to interview prospective employees using individuals from the group in which they would work. He felt these interviews would focus not only on the job requirements but also on the ‘‘uniqueness of that particular subculture’’ of the group (Argyris, 1964, p. 270). Before further review of this sub-dimension, it is necessary to establish the boundaries of P-T Fit. Along with other scholars in team research, we do not distinguish between work teams and work groups, therefore team and P-T Fit refer to both (DeRue & Hollenbeck, in press). We provide a brief overview of P-T Fit, but for a full review of current P-T Fit literature and research directions, please refer to DeRue and Hollenbeck (in press). Ferris et al. (1985, p. 377) introduced the terminology of person-group fit, and operationalized it as ‘‘the congruency between employee personality characteristics and an average profile of successful job incumbents within that occupation.’’ Furthering the definition, recent scholars have come to define P-T Fit as the ‘‘congruence between a combined set of team elements that produces a relatively higher level of team effectiveness, including both individual and team-level outcomes’’ (DeRue & Hollenbeck, in press, p. 10). P-T Fit can be conceptualized as either supplementary (e.g. similar personalities) or complementary fit (e.g. diverse areas of expertise) (DeRue & Hollenbeck, in press). Empirical evidence has demonstrated the importance of team fit concerning several outcomes. For instance, Hollenbeck et al. (2002) argued that

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teams with different organizational structures would require different personnel and be better suited to work in different task environments. Indeed, these authors found that personnel with high cognitive ability fit into teams with a divisional structure. Furthermore, they found that emotional stability was an important factor in individual performance when the divisional structure of the team was misaligned with the environment (i.e. divisional structures being used in predictable rather than unpredictable environments). In both of these conceptualizations of fit, cognitive ability and emotional stability demonstrated significant positive relationships with individual performance. In addition to the other sub-dimensions of MDF, there is value in including P-T Fit, as well, in our integrative conceptualization. For instance, Kritsof-Brown et al. (2002) examined the effects of P-O Fit, P-J Fit, and P-T Fit on work satisfaction. These authors found that each one had an independent, unique relationship with job satisfaction.

RELATIONSHIP BETWEEN FIT AND SELF-CONCEPT Thus far, we have described the basic sub-dimensions of MDF. However, we have yet to offer an explanation as to how these sub-dimensions relate to the individual. After all, each of the sub-dimensions begin with the name ‘‘person.’’ We now outline how each sub-dimension overlaps with a portion of an individual’s self-concept. Humans have the capacity to describe themselves, their self-concept, in many different ways (Donahue, Robins, Roberts & John, 1993). The notion of self-concept complexity is not new to those in the field of social psychology. William James (1890) believed that the self-concept was constructed from multiple components. How humans organize the self-concept, however, has been an ongoing topic of study (Margolin & Niedenthal, 2000). Linville (1987) proposed a model of self-complexity based on the finding that individuals with high self-complexity will have several descriptively distinct aspects of their self-concept. That is, individuals can distinctly describe themselves in terms of social roles (i.e. mother, friend, school teacher), relationships (i.e. colleague, adversary, provider), activities (i.e. playing sports, reading, jogging), traits (i.e. assertive, achievementoriented, friendly), goals (educational success, career/monetary success), and the like (Linville, 1987; Margolin & Niedenthal, 2000). These same aspects of the self-concept have been cited in the voluntary turnover research to explain individuals’ tenure in an organization. Mitchell

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et al. (2001) proposed a construct called job embeddedness that describes how individuals ‘‘can be enmeshed or embedded in many different ways’’ (p. 1104). Individuals can have personal links to coworkers and links to institutions or communities, and individuals can perceive fit between the self and the organization on ‘‘personal values, career goals, and plans for the future’’ (Mitchell et al., 2001, p. 1104). Trevor (2001) and Lee, Mitchell, Holtom, McDaniel and Hill (1999) similarly have suggested that personal attributes, such as cognitive ability, education, and transferable skills, each predict voluntary turnover. The ability to differentiate roles and personal attributes in a complex process such as voluntary turnover should serve a purpose similar to assessing MDF. One of the benefits of self-complexity is that it allows a buffer between the individual and adverse consequences (Linville, 1987; Halberstadt, Niedenthal, & Setterlund, 1996; Margolin & Niedenthal, 2000), so that failure in one domain of the self-concept does not spill over into others. A central feature of this work is that individuals vary in their complexity based on their level of experience with any given social role or trait (Donahue et al., 1993). For instance, college students, who never have held managerial positions, cannot describe themselves as managers. However, as individuals gain experience, they can expand the self-concept to include previously undefined aspects of the self. We have identified five sub-dimensions of MDF, and we propose that each type of fit can be thought of as a potential domain of the self-concept. P-O Fit represents the values, beliefs, and norms that are associated with the self-concept. Zanna and Rempel (1988) supported this contention as they reported that individuals develop a distinct sense of the self-concept through their value system (i.e. ‘‘I value trust’’ or ‘‘I value competition’’). P-V Fit describes that portion of the self-concept that is related to personality. Personality taxonomies, such as ‘‘The Big 5’’ (Costa & McCrae, 1988), were developed from individuals describing their ‘‘self.’’ In fact, individuals typically describe their self-concept by using personality traits (Linville, 1987). P-J Fit is associated with how individuals describe the portion of the self-concept related to KSAs. Kihlstrom and Cantor (1984) demonstrated that an individual constructs a portion of the self-concept based upon KSAs. P-P Fit represents the portion of the self-concept that is related to what the individual prefers or desires from an organization. Finally, the selfconcept is enhanced by the relationships and roles one occupies in various work teams, thus P-T Fit relates to the self-concept. Therefore, we make the following proposition.

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Proposition 1. Five sub-dimensions of fit, P-O, P-V, P-J, P-P, and P-T Fit, describe distinct dimensions of the self-concept and explain how individuals describe the self-concept. Although researchers historically have considered these sub-dimensions of fit as mutually exclusive constructs, recent research has begun to examine some of them as complementary constructs (Werbel & Gilliland, 1999). P-O Fit and P-P Fit share an obvious relationship in that each deals with how individuals experience the organization, via the organizational climateculture continuum (Van Vianen, 2000). Kristof-Brown (2000) and Saks and Ashforth (1997, 2002) conducted a series of studies to examine the combined predictability of P-O Fit and P-J Fit. In each study, the researchers assessed the incremental predictability of viewing P-O Fit and P-J Fit as complementary, as opposed to competing, processes of fit. Kristof-Brown (2000) entered both P-O Fit and P-J Fit into a hierarchical regression analysis and reported a rather large effect size ðDR2 ¼ 0:68Þ: Similarly, Cable and DeRue (2002) found combined effects of P-O Fit and the demands-ability perspective of P-E Fit, which closely resembles P-J Fit. Furnham (2001) suggested that P-O Fit and P-V Fit share a common connection, that of the estimation of congruence. However, these studies do not offer any theoretical explanation for the combined effects of fit. If, as Kristof-Brown (2000) proposed, recruiters and applicants alike can differentiate P-O Fit from P-J Fit and base decisions on this distinction, how do the recruiters have the mental flexibility to consider these dimensions in tandem? If individuals display vocational preference, as Holland (1985) outlined, how can they also assess P-O Fit of an organization within a vocation? How can individuals differentiate between P-O Fit and P-P Fit? Some social psychologists, who explore decision-making processes, have relied upon the process of prototype matching to explain how individuals choose to enter social settings (Niedenthal, Cantor & Kihlstrom, 1985; Kihlstrom & Klein, 1994). Moreover, prototype matching has been used to explain how individuals navigate their multi-layered self-concept in the context of a complex social environment (Setterlund & Niedenthal, 1993).

MDF and Prototype Matching Prototype matching refers to a social cognitive decision-making process in which individuals engage to guide behavior in complex social situations (Cantor et al., 1982; Niedenthal et al., 1985; Kihlstrom & Klein, 1994).

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Research regarding prototype matching seeks to explain how ‘‘the naive perceiver construes, categorizes, and gives meaning to classes of social situations’’ (Cantor et al., 1982, p. 45). That is, in new or novel situations, individuals rely upon a set of features that are associated with the typical person likely to be found in a specific social setting. These sets of features associated with the situation are referred to as prototypes, and prototypes act as frames of reference that guide the expected behavior of individuals (Cantor et al., 1982; Niedenthal et al., 1985; Setterlund & Niedenthal, 1993). When individuals enter a new situation, they immediately attempt to categorize the situation into an existing mental category or schema that closely resembles the new situation. As individuals experience new and distinctly different situations, new categories can be developed to mentally represent and make sense of those situations. Moreover, in social settings, individuals are likely to represent each specific social category or schema in terms of the prototypical person typically found in such settings (Cantor et al., 1982; Niedenthal et al., 1985; Setterlund & Niedenthal, 1993). The use of prototypes enables individuals to develop frames of reference to compare their self-concept, and the individuals can utilize prototypes to maintain consistency in the ‘‘selection of daily contexts to enter’’ (Niedenthal et al., 1985, p. 576). Once individuals access the situation-specific prototype, a comparison process begins. Research in this area has proposed that individuals possess knowledge about their own self-concept (Kihlstrom & Cantor, 1984; Markus & Kunda, 1986; Liville, 1987; Margolin & Niedenthal, 2000). When individuals enter a novel situation, they will access two forms of information (Niedenthal et al., 1985; Setterlund & Niedenthal, 1993). The first is the prototype of the typical person found in that situation, and the second is information about the self-concept. A high degree of overlap between these will likely result in individuals entering the environment (Cantor et al., 1982; Niedenthal et al., 1985; Setterlund & Niedenthal, 1993). Interestingly, direct experience with the social target or environment is not needed to form prototypes or engage in prototype matching (Hassebrauk & Aron, 2001). Individuals can form prototypes through indirect experiences, such as television or a stereotype, and they can assess prototypes on various levels, such as personality, preferences, behaviors, or appearances (Cantor, Markus, Niedenthal & Nurius, 1986). This suggests that prototype matching can explain how individuals can self-select into an environment, even if they never have directly experienced the environment. Moreover, we propose that prototype matching is the process that fit researchers have described

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as the implicit estimation of fit (ASA framework; Schneider et al., 1995), as value-goal congruence (P-O Fit; Kristof, 1996), as the overlap of self-concepts (P-V Fit; Furnham, 2001), as demands-abilities congruence (P-J Fit; Edwards, 1991), as the match between personal preferences (P-P Fit; Van Vianen, 2000), and as the match between themselves and the work team (P-T Fit; DeRue & Hollenbeck, in press). Researchers have studied prototype matching in a number of environments or social settings. Niedenthal et al. (1985) found that individuals have used prototype matching to make decisions on where to live. Prototype matching also has been found to explain preferences for type of clothing to wear (Malafi & Frieze, 1987), whether or not to smoke (Chassin, Presson, Sherman, Corty & Olshavsky, 1981), whether or not to attend graduate school (Burke & Reitzes, 1981), automobile preferences (Setterlund & Niedenthal, 1993), psychiatric diagnoses (Cantor, Smith, French & Mezzich, 1980), alcohol consumption among adolescent boys (Chassin, Tetzloff & Hershey 1985), emotions in close relationships (Fitness & Fletcher, 1993), and satisfaction in close relationships (Hassebrauk & Aron, 2001). In each study, the overlap between the construed prototype and the selfconcept predicted subsequent participation in the environment. Moreover, the studies examined numerous facets of the self-concept, from personality, to values, to preferences, and to behaviors. In terms of organizational studies, Moss and Frieze (1993) and Perry (1994) utilized prototype matching to describe job choice preferences and interviewer evaluation of prospective employees, respectively. Moss and Frieze (1993) assessed the predictive validity of using prototype matching to personality versus Holland’s (1985) model to measure P-V Fit. This between-subjects design (i.e. personality prototype match versus P-V Fit) reported that the expectancy-based P-V Fit model predicted job choice better than did the personality prototype matching (Moss & Frieze, 1993). However, we argue that prototype matching would underlie both types of fit assessed by Moss and Frieze; thus the statistical differences they reported might be a function of using non-commensurate measures of the same construct. Perry (1994) reported that interviewers used prototype matching to rate how closely prospective employees fit a potential job. In both studies, the researchers utilized prototype matching in organizational settings. Prototype matching also can describe how individuals assess MDF with one caveat. Niedenthal et al. (1985), in an examination of prototype matching and housing search, demonstrated that personal goals moderated the use of prototype matching. Individuals who had social goals for the housing search

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(i.e. relationships, status, location, etc.) engaged in prototype matching to decide where to live; however, individuals who had necessity goals for the housing search (i.e. cost of rent, availability, etc.) did not utilize prototype matching to decide where to live. In terms of MDF, this suggests that some will not be concerned with fit. Further, certain individuals may be concerned with fit, but because of their necessity goals, cannot base their decisions on fit. The existing literature on fit seems to assume that every individual has an innate desire to fit, and therefore, that fit is something that is assessed by everyone. However, we argue that although everyone assesses fit, not all use such assessments as the basis for their decisions. Specifically, we would suggest that individuals with social goals do indeed utilize such assessments, but those with necessity goals do not. Proposition 2a. Individuals with social goals will engage in prototype matching to assess fit across multiple dimensions and utilize this assessment in order to make decisions. The degree to which individuals’ multidimensional self-concept overlaps with the multidimensional prototypical concept for any given social setting (i.e. a specific organization) will predict self-selection into that specific social setting. Proposition 2b. Individuals with necessity goals engage in prototype matching to assess fit across multiple dimensions. However, they will not utilize this assessment in order to make decisions.

THE MOTIVE TO FIT Self-complexity and prototype matching explain two important components of a unified theory of MDF; that is, how MDF overlaps with a complex selfconcept and how individuals assess MDF. However, these processes do not entirely explain the motive of MDF. In the major reviews of fit (Kristof, 1996; Tinsley, 2000), conceptualizations of fit appear to be treated as motives in and of themselves. That is, researchers appear to assume that everyone wants to fit, and the reason why everyone wants to fit is that fit leads to desirable outcomes. Erdogan et al. (2004) demonstrated that the relationship between managers and employees diminishes the influence of P-O Fit on these desirable outcomes. Moreover, we outlined the importance of an individual’s goals that could also diminish the relationship between the assessment of fit and those desirable organizational outcomes. While we discussed the role of goals in the assessment of fit, concluding that only individuals with social goals engage in the process of MDF, we have

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yet to establish why individuals are motivated to seek fit. Therefore, the motive to fit is addressed next. In doing so, self-expansion (Aron & Aron, 1986) and social identity (Tajfel & Turner, 1986) theories are used to explain the social motive of MDF.

The Self-Expansion Motive The self-expansion model has five main hypotheses that have received empirical support (see Aron & Aron, 1996 for a review of empirical findings). First, cognition about the self-concept and cognition about others are the endpoints of a continuum. Included in this linear relationship is knowledge about the self, knowledge of close others, and knowledge of unknown or unclose others (e.g. prototypes). Second, all of the components on the continuum result from knowledge about the self. What individuals know about a prototype is based, in part, on knowledge of their own self-concepts. Third, the development of a relationship with another person or entity expands the self-concept. Fourth, individuals seek relationships in order to expand the self-concept. Finally, the individual perceives changes in satisfaction as a function of expanding the self-concept. An individual finds satisfaction in a relationship as long as it allows for or creates an opportunity to expand the self-concept. These hypotheses constitute the core of the ‘‘self-expansion model’’ (Aron & Aron, 1986, 1996), which can explain the positive organizational outcomes typically associated with the fit subdimensions (e.g. satisfaction, commitment, retention, etc.). From a complementary fit perspective (Aron & Aron, 1986, 1996), the selfexpansion model explains individuals’ motive to fit. Entering an organization provides individuals an opportunity to expand the self-concept. By entering new relationships, individuals add to the self-concept (Aron & Aron, 1986, 1996). Individuals desire to become something more than they currently are, and entering a relationship provides that opportunity. From a supplementary fit perspective, where individuals seek to match personal characteristics with the characteristics of the organization, the self-expansion model can explain the motive to fit. As organizations provide myriad opportunities to take on new challenges or meet new people (Schneider, 2001), the self-expansion model hypothesizes that individuals look to close others (i.e. people like them) as the first opportunity to expand the selfconcept (Aron & Aron, 1986). Individuals who share similarities with an organization’s characteristics could view that organization as a gateway to

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self-expansion. Moreover, self-esteem provides individuals with an additional motive to fit.

The Social Identity Motive The social identity theory (Tajfel & Turner, 1986) explains why individuals covet group membership (e.g. membership in an organization). Belonging to a group, especially an attractive group, boosts individual self-esteem (Tajfel & Turner, 1986). Essentially, belonging to a group becomes a part of the core identity for individuals, much like proposed in the self-expansion model (Aron & Aron, 1986). Moreover, Turner and Tajfel (1986) outlined several strategies in which individuals will engage to improve their standing within a group, or to gain access to groups that are more desirable. Cable and Turban (2000) supported these contentions in their research on brand equity theory. They discussed the importance of brand equity in that ‘‘brand names offer signals that consumers [or prospective employees] use to make inferences about the quality of the product [or organization], and consumers [or prospective employees] endeavor to associate themselves with certain brands to improve their self-esteem’’ (p. 4). The second tenet of that proposal provides some insight as to why a prospective employee would be influenced by the name of an organization: it bolsters self-esteem. Keon, Latack and Wanous (1982) provided further evidence for the role of self-esteem in the process of fit. They reported that high self-esteem individuals seek employment with organizations that match their positive self-image, whereas individuals with low self-esteem seek to boost their self-esteem by attempting to gain entry into organizations that have a more positive image than their own self-image. Mitchell et al. (2001) concluded that individuals who belong to an organization invest time, resources, and personal identification in the process of belonging to, or embedding in, that organization. Moreover, the positive affect developed through this process makes it difficult for individuals to leave the organization. In sum, we suggest that individuals need to fit with, and belong to, organizations because it boosts self-esteem. We make the following proposition: Proposition 3. Individuals seek fit between their self-concept and the organization as a means to expand the self-concept; moreover, individuals experience an increase in self-esteem by belonging to and fitting with an organization. Individuals who do not fit will not experience an increase in self-esteem.

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WHEN ASSESSMENT OF MDF OCCURS Up to this point, three of the building blocks of theory development outlined by Bacharach (1989) have been addressed. We have described the theory of MDF, explained how MDF works, and proposed why individuals seek MDF. Now, we turn to the final building block of a unified theory of MDF; that is, when individuals assess MDF. We contend, as Schneider (1987) and Schneider et al. (1995) posited, that the implicit assessment of fit occurs throughout the duration of the employment lifecycle, from recruitment through turnover. Furthermore, at the heart of this continual assessment of fit is the proposition that individuals assess fit as a means of selfselecting into or out of organizations. The existing literature on the five sub-dimensions of fit mostly examines the early stages of individuals’ experiences in organizations, from recruiting through socialization (see Caldwell et al., 2004 for a notable exception). Whereas this same body of literature implies that the lack of fit leads to voluntary employee turnover, none of the literature explicitly has postulated how employees perceive fit through the entire employment lifecycle, nor has it accounted for other possible misfit outcomes. Therefore, we rely upon the existing literature on the five sub-dimensions of fit to address when individuals will assess MDF in the early stages of employment. We utilize the voluntary turnover literature to explain when individuals will assess MDF post-socialization. P-O Fit researchers have provided a substantial body of literature that can be used to explain when individuals will assess MDF. This literature suggests that individuals will assess fit during recruitment (Cable, AimanSmith, Mulvey, & Edwards, 2000; Kristof-Brown, 2000; Dineen, Ash & Noe, 2002), selection (Chatman, 1991), and socialization (Cable & Parsons, 2001). P-J Fit has been examined at these same points in the employment lifecycle (Edwards, 1991; Caldwell & O’Reilly, 1990; Kristof-Brown, 2000). Similarly, most P-V Fit researchers have examined this type of fit during recruitment and selection (Holland, 1985; Furnham, 2001), as have P-P Fit researchers (Van Vianen, 2000). Much of P-T Fit research has been done in a cross-sectional manner with little consideration of when the assessment of fit is occurring (Kristof-Brown et al., 2002). Considering this literature, we propose that individuals will assess MDF, as outlined in this article, starting with the recruitment process. Job applicants form prototypes about organizations from the limited information available, such as through realistic job previews (Dineen et al., 2002), job postings (Wanous & Reichers, 2000), referrals (Rynes & Boudreau, 1986),

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and/or brand identification (Cable & Turban, 2000). As applicants make their way through the selection process, existing prototypes are further shaped and the assessment of MDF continues. After the organization selects the applicant, who in turn accepts the job, the organization continues to shape the prototypes of the new employees through formal and informal socialization. Cable and Parsons (2001) found that organizations influence perceptions of P-O Fit through socialization programs, and they found that the effects of socialization could last well into an employee’s tenure with an organization. We propose that new employees continue to assess MDF through organizational socialization, as their prototypes of the organization become enduring as a result of socialization. However, from this point, the existing literature on any of the five subdimensions of fit does not explain when an incumbent will assess MDF. The voluntary turnover literature provides insight as to when assessment of MDF would occur through the entire employment lifecycle. Most voluntary turnover theories begin to explain the process of staying or leaving after the employee has been hired (Steele, 2002; Mitchell et al., 2001; Trevor, 2001; Lee et al., 1999; Lee & Mitchell, 1994). Some models have suggested that employee dissatisfaction triggers the process of turnover (Trevor, 2001). Personality variables could trigger the dissatisfaction or the desire to simply move on (Boudreau et al., 2001; Kanfer, Wanberg & Kantrowitz, 2001), or an unforeseen event could trigger the dissatisfaction to begin the turnover process (Lee & Mitchell, 1994; Lee et al., 1999). Lee and Mitchell (1994) and Lee et al. (1999) described a turnover process, called the unfolding model of turnover, that includes more variables than satisfaction, prevailing job market conditions, and individual attributes. They proposed that turnover could begin with a shock (e.g. change in job duties, change in management, layoffs, etc.), which leads to a series of outcomes, such as image violation and dissatisfaction. Both the traditional models of turnover and the unfolding model of turnover could be used to describe when individuals assess MDF. Post-socialization, the assessment of MDF would begin with an unplanned shock, with feelings of dissatisfaction, or with large-scale organizational change due to downsizings and restructurings. The implications of this re-assessment of fit are developed further in the directions for future research section of this paper. Therefore, we make the following proposition: Proposition 4. Individuals assess MDF during the recruitment, selection, and socialization phases of the employment lifecycle. Furthermore, individuals will continue to assess MDF post-socialization as a response to decreased satisfaction or some unplanned shock.

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IMPLICATIONS OF MDF We propose an ambitious theory of MDF that spans the employment lifecycle, and several implications of MDF theory are notable. First, goals play a vital role in MDF. One consistent finding in the prototype-matching literature is the role of goals. As previously stated, goals moderate the relationship between the self-concept and prototype matching (Setterlund & Niedenthal, 1993). We suggest that although all individuals assess fit, only those with social goals use such assessments in decision making. We know of no fit literature that proposes the interplay between goals and assessment of fit, and we believe that this in an important contribution to the existing literature. A second implication is that, in terms of the sub-dimensions of fit, saliency should play an important role in MDF. Because MDF operates as a function of the multidimensional self, individuals’ immediate assessment of fit depends on what sub-dimension of the self-concept is active. Markus and Kunda (1986) outlined the self-concept as a collection of self-conceptions that are activated by cues in the environment. Individuals will describe the most salient characteristics of the self based on the demands of the environment. Some aspects of the self-concept are consistently more activated or more accessible than are other aspects of the self due to the importance individuals places on that aspect (Higgins, King & Mavin, 1982). Additionally, this likely is a function of the considerable attention, investment, salience, and concern placed on these core aspects of the self-concept (Markus, 1977). However, some aspects of the self-concept become more or less accessible due to an individual’s motivation or emotional state, or as a function of the demands of the environment (Markus & Kunda, 1986). In terms of MDF, this suggests that individuals consistently might express a preference for one of the sub-dimensions of fit because it is core to the self-concept. Nevertheless, the environment or social pressure may cause other subdimensions to become more salient. The fluidity of the self-concept, and therefore the fluidity of MDF, leads to the issue of conflict among the subdimensions of MDF. Although individuals might express a preference toward one sub-dimension of fit, the demands of the environment may cause other sub-dimensions to become more salient. Linville (1987) and Donahue et al. (1993) found that having many distinct aspects of the self-concept leads to a greater possibility of conflict between the aspects. In terms of MDF, five distinct sub-dimensions of fit could be used to describe how individuals assess fit

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with any given organizational setting. Individuals could experience conflict between a core aspect of the self-concept (e.g. the value-driven P-O Fit) and another aspect of the self-concept that has been activated by environmental or social cues (e.g. the personality-driven P-V Fit). This conflict could be described as cognitive dissonance (Festinger, 1957). Traditionally, researchers have defined cognitive dissonance as ‘‘the emotional-motivational state evoked by cognitive discrepancy’’ between two thoughts (Harmon-Jones, 2000, p. 121). When confronted with two thoughts about the self-concept, individuals seek to alleviate the discomfort associated with the discrepant thoughts. Cognitive dissonance researchers have proposed that individuals can choose several avenues to alleviate the dissonance. They could choose to act in a behaviorally consistent manner with one of the discrepant thoughts (Aronson, 1999), which could appear as a change in the individual. They could maintain a behavior that is consistent with the most enduring of the discrepant thoughts, which would appear to maintain or affirm an existing self-ascribed image (Steele, 1988). In terms of MDF, this suggests that individuals who feel dissonance between sub-dimensions of fit could either ascribe to a self-conception that was heretofore not salient to them, or could choose to affirm or maintain an image associated with the most activated or core sub-dimension. Although these two possibilities make sense from a dissonance standpoint, selfcomplexity researchers posit a different outcome of conflict between aspects of the self-concept. The self-expansion model postulates that individuals seek to expand the self-concept (Aron & Aron, 1996). In the process of expanding the selfconcept, it is inevitable that aspects of the self-concept will come into conflict. At the point of conflict, individuals receive several benefits of possessing a multidimensional self-concept. Experiencing a negative event, such as a change in management, most likely will activate only one of the aspects of the self-concept (Linville, 1987), and the other aspects of the self-concept will not experience the negative event. In essence, only one part of the selfconcept experiences the negative effects. To support this notion, Linville (1987) also found that individuals with high self-complexity experience fewer emotional swings than do individuals with low self-complexity. Showers (1992) called this process of walling-off negative events in life to limit the impact on the self-concept, compartmentalization. Further, Donahue et al. (1993) and Margolin and Niedenthal (2000) found that individuals also could collapse the self-concept to avoid possible conflict. The environment, or the salient social cues, determine the

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degree of self-complexity. When individuals expect negative information, the self-concept expands, but the expectation of positive feedback causes the self-concept to collapse to a few core aspects of the self-concept (Margolin & Niedenthal, 2000). Thus, we make the following proposition: Proposition 5. Assessing fit across the five sub-dimensions allows individuals to expand the self-concept; thus, individuals can compartmentalize any dissonance caused by the conflict between any of sub-dimensions of fit. Compartmentalization of the sub-dimensions of fit can alleviate any dissonance experienced when sub-dimensions of fit come into conflict. In terms of MDF, this suggests that individuals perceive cues from the environment that could cause the self-concept, in terms of the subdimensions of fit, to either expand or collapse. Individuals could hold one sub-dimension of fit as being core to the self-concept, and could assess fit on this one sub-dimension. However, a collapsed self-concept leaves individuals vulnerable to dissonance or other negative events. Although individuals could prefer one of the sub-dimensions as being primary, assessing fit across all of the sub-dimensions allows for compartmentalization of the self-concept. On the job, individuals could experience a negative event that relates to one of the sub-dimensions (e.g. a change in the job). Instead of voluntarily terminating the employment contract, individuals could compartmentalize the dissonant feelings by falling back on the other sub-dimensions (e.g. working for a company that shares similar values). Mitchell et al. (2001) supported this contention in their concept of job-embeddedness. Because individuals can be highly embedded in an organization, it allows for negative events to not necessarily lead to turnover. Compartmentalization mitigates the effects of negative events on the job.

DIRECTIONS FOR FUTURE RESEARCH Although research on fit has evolved greatly, there are many directions for future research. Much of the literature to date has conceptualized fit as a static construct that cannot be manipulated by either the person or the organization. As described earlier, although the literature implies that fit is assessed continuously throughout one’s career, almost all of the research on fit has centered on the recruitment, selection, and socialization phases of the career. Moreover, if employees continuously assess post-socialization, the process of assessing fit, as it is a social cognitive decision-making process,

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Fit

Dissatisfaction or Unplanned Shock

Re-assess Fit via Prototype Matching

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Y Misfit

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Fig. 2.

Voice

Acceptable Outside Alternatives In-Action

Impression Management

The Outcomes of Misfit.

would become a cognitive burden to employees and diminish available cognitive resources to perform other job-related functions. Cognitive and information overloading has been found to increase employee burnout and withdrawal behaviors and decrease job satisfaction (Rader, 1981; Seitz & Miner, 2002); thus we posited that employees re-assess fit in response to specific incidences. Therefore, our utilization of the voluntary turnover literature has provided support for the re-assessment of fit further in one’s career. That is, an unplanned shock, such as an organizational change or a major life change, might provoke assessments of MDF. Furthermore, much of the MDF literature has centered on Schneider and colleagues’ ASA framework. They would argue that assessments of fit occur continuously, and if a lack of fit should arise between the person and the organization on any of the dimensions of fit, then the person will leave the organization either voluntarily or involuntarily (Schneider et al., 1995). We believe that this is a narrow view of the outcomes of misfit. Therefore, the purpose of the following section is to outline five possible reactions to misfit. To facilitate this undertaking, Fig. 2 was developed as a guide.

Misfit Organizations often operate in rapidly changing environments. In the last several decades, organizations increasingly have adopted strategies of

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restructuring, redesign, mergers, and acquisitions to cut costs and increase effectiveness. These strategies bring about vast changes in the internal environments of organizations (Daft & Lewin, 1993). Such changes in organizational environments may lead to a misfit between individuals and the new environmental surroundings, as suggested previously, and these unplanned shocks can trigger the re-assessment of fit. Misfit can be thought of as an incongruence occurring on any or all of the five dimensions of MDF. This misfit does not have to occur only in a changing environment, such as a merger or being assigned a new supervisor; it can also occur when starting a new role in the organization. Chao, O’Leary-Kelly, Wolf, Klein and Gardner (1994, p. 731) suggested that not only changes in the organization, but also subtle life changes, can ‘‘redefine life priorities and the meaning of success’’ for individuals, indicating that a misfit could occur at any point in one’s career. In support of this notion, one recent study examined the impact of organizational changes on employees’ fit. Indeed, Caldwell et al. (2004) found that when faced with organizational change, employees may perceive a shift in their P-E Fit. Although the direct relationship between organizational change and P-E Fit change was not supported, the authors did find that managerial support during the change was related to perceived change in P-J Fit. Additionally, the fairness of the change process was related to perceived change in P-O Fit. The different interactions suggest a paramount need for further research with regard to organizational change and fit change. With the ever-changing organizational landscape, it is necessary to examine the outcomes of the possible misfit that can occur alongside this change. However, with a few notable exceptions (e.g. Chatman & Barsade, 1995), there has been little research regarding the outcomes of misfit with the exception of exit. As with the voluntary turnover literature, we believe an unplanned shock or the accumulation of dissatisfaction can trigger the assessment of MDF. This assessment can lead to either perceptions of fit or misfit. If the perception is that of fit, then the person is likely to stay with the organization. However, if the perception is misfit, we believe that individuals will engage in a decision-making process similar to that shown in Fig. 2. First, individuals will assess whether they are willing to adapt to realign their fit with the organization. If adaptation is acceptable, they will adapt. If adaptation is not acceptable, the individuals will begin to assess the available outside alternatives. If the outside alternatives are desirable, they will exit the organization. However, if the outside alternatives are not desirable, the individuals will deal with the misfit in one of three ways: (1) in-action, (2)

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voice, or (3) impression management. The following sections outline this process in more detail. Adaptation After a misfit is perceived, it is proposed that the individuals begin the adaptation process. In an attempt to deal with misfit, individuals may take actions to increase their fit with the environment. This notion is generally labeled adaptation (Ashford & Taylor, 1990). Unfortunately, the fit literature largely has overlooked the ability of individuals to use processes of adaptation in coping with situations of misfit. However, the notion of adaptation has been used in relation with such issues as work transitions and managerial effectiveness (Ashford & Taylor, 1990; Tsui & Ashford, 1994). Adaptation incorporates a number of processes that individuals use to understand and negotiate environmental demands in order to determine appropriate responses (Ashford & Taylor, 1990). Ashford and Taylor suggested that adaptation is a function of four tasks. The first is the learning/ sense-making task, during which time individuals see the need to adapt, assess the environment for demands, constraints, and opportunities, and gain an understanding of how to assess the adaptation progress. The second task is the decision-making/negotiation task, whereby individuals must decide if they are willing to adapt and what changes must be made in order for them to adapt. The third task is the action-regulation task, which is the actual adaptation, in which the individual adapts and must maintain and regulate the new behaviors adapted. Stress-management is the fourth task (Ashford & Taylor, 1990). According to this model of adaptation, an unplanned shock or dissatisfaction and ensuing misfit should stimulate individuals to engage in the learning/sense-making task. During this process, the individuals assess the new environment and try to understand how they should change. This flows into the second task, decision-making/negotiation. During this task, individuals decide if they are willing to make the changes necessary in order to adapt to the new environment. If not, they will choose another option in dealing with the misfit. If they are willing to engage in the adaptation process, they will move to the third task, the action-regulation task. During this task, individuals make the necessary changes in order to regain their fit and formulate ways to regulate and maintain this fit. Lastly, the individuals cope with the stress sustained during the adaptation process. If there is a misfit with any of the five dimensions of MDF, a person could adapt in order to increase fit. For instance, Simmering et al. (2003) found that conscientious individuals in an organizational environment that did not

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meet their preferences for autonomy would seek to quell this misfit through career development activities such as course work. Additionally, Chatman and Barsade (1995) found that cooperative individuals will adapt their behaviors in an individualistic work environment in order to increase their fit. It seems that these individuals are willing to adapt to the culture and preferences of the organization. Preliminary evidence suggests that individuals do utilize adaptation to enhance fit with the work environment. However, these studies indicate a need for further explanation. It seems that the willingness and ability to adapt are affected largely by individual characteristics. In the same study, Chatman and Barsade (1995) found that individualistic people did not alter their behaviors to fit with environments that are more cooperative, as cooperative people did in individualistic environments. Therefore, it is necessary to examine other individual difference variables and the adaptation process of fit. For instance, self-monitoring might affect one’s ability to adapt. High self-monitors are attuned to the situation around them and what is expected of them in their various roles. Low self-monitors, however, are not attuned to the appropriate behaviors concerning various social interactions (Snyder & Copeland, 1989). This seems to indicate that high self-monitors might be more able to adapt than low self-monitors. Moreover, because certain dimensions of fit are more salient than others, it might not be feasible for individuals to adapt in every circumstance. For instance, adapting when the misfit occurs on the dimension of P-P Fit might be easier and more desirable than adapting on the dimension of P-O Fit. P-P Fit involves a fit between the characteristics of the job and the preferences of the individual (Van Vianen, 2000). Simmering et al. (2003) found that when the organization did not supply the preferred characteristics (i.e. autonomy), conscientious individuals would seek to remedy this misfit through development. However, P-O Fit concerns the fit between the norms and values of the organization and the values of the individual (Kristof, 1996). Values are enduring, central to a person’s identity, and rooted in his/her upbringing, early life influences, socioeconomic status, and cultural background, and they are considered stable across time and work situations (Lachman, 1988; Rokeach, 1973). This implies that simply changing one’s values to fit the organization may not be possible or desirable for most individuals. Finally, the issue of adaptability in the assessment of MDF raises the question of attribution. When an employee assesses misfit, to whom does the employee attribute this misfit, to internal or external causes? That is, does a

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poor fitting employee perceive that the lack of fit is due to some missing personal attribute, whether it be a skill, value, or personality trait? Or is the misfit attributed to the organization lacking a desired attribute? The process of attribution is a growing area of organizational research, especially in human resource management (Beehr & Gilmore, 1982; Dedrick & Dobbins, 1991; Landis & Scalet, 1994, Schaffer, 2001; Silvester, AndersonGouch, Anderson & Mohamed, 2002). Given that Verquer et al. (2004) found that subjective measures of P-O Fit (e.g. ‘‘I fit with my organization’s values and goals’’) best predicted positive organizational outcomes compared to objective and profile comparison measures of P-O Fit, it seems prudent to examine the attribution process of employees when assessing fit. Further research is needed to distinguish the limits of adaptability, attribution, and fit. Exit If individuals decide, in the second task of adaptation, that they are unwilling to engage in the adaptation processes, they might start the process of voluntary turnover, which is the most commonly studied outcome of misfit. Given a good job market and a set of marketable skills, individuals will engage in job search via ‘‘passive scanning’’ (Steele, 2002). This entails attending to the job market, and beginning to notice other desirable organizations in which they would like to work. As the individuals continue to contemplate turnover, they begin a more ‘‘focused search’’ (Steele, 2002). Individuals search for job openings and examine promising leads. If promising outside alternatives are found during the job search, individuals will leave the organization. Hirschman (1970) described exit as organization members leaving the organization. However, due to external pressures, exit may not be feasible. Poor outside alternatives often hinder an employee’s ability and willingness to leave the organization (Farrell & Petersen, 1982). There are many external pressures that could affect an employee’s willingness to leave a job. The economy is a good example. In a poor economy, jobs are likely to be scarce. Employee turnover negatively relates to unemployment rates, suggesting that when unemployment rates are high, employees are less likely to leave their employers (Cotton & Tuttle, 1986). This, coupled with monetary obligations, could very well influence an employee to stay with a company even in light of misfit on any or all sub-dimensions of fit (which is similar to a job searcher with a necessity goal who chooses to ignore pre-hire assessment of misfit in order to gain employment). Because individuals might be unwilling to adapt and the outside alternatives prove to be unacceptable, the

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individuals are left with three alternatives: (1) in-action, (2) voice, or (3) impression management. In-Action In-action occurs when individuals choose not to express the new organizationally sanctioned values or perform newly required procedures or duties. This in-action relates to neglect, a reaction studied with regard to dissatisfaction. Neglect refers to employees who are passive in response to dissatisfaction, losing interest in their jobs and decreasing their effort (Rusbult, Farrell, Rogers & Mainous III, 1988). In this situation, the employee just ignores the misfit altogether. There are several reasons why individuals may not chose in-action as a response to misfit. First, social desirability creates ‘‘strong pressures to publicly express and validate values whether or not they are held internally’’ (Meglino & Ravlin, 1998, p. 356). Bell (1990) determined that black women professionals felt strong pressure to conform to organizational standards and culture (i.e. values). Second, ignoring organizational standards, procedures, and culture may lead to decreased performance, which could result in negative consequences such as reduced pay raises or negative performance appraisals. Further research is needed on this reaction to misfit. For example, a number of interesting thoughts and questions come to mind regarding this option. Perhaps in cases in which the misfit is less salient, this reaction would be more likely. Additionally, are certain individuals, such as those with low agreeability, more likely to choose this option? What are the consequences of inaction and neglect? We mentioned a few consequences, but others could be possible, such as being shunned by co-workers. Voice Voice is another option available to individuals experiencing misfit. Whereas there has been little direct mention of voice in the fit literature, it is a wellstudied reaction to dissatisfaction within organizations. Hirschman (1970, p. 4) defined voice as an organization’s members expression of ‘‘their dissatisfaction directly to management or to some other authority to which management is subordinate or through general protest addressed to anyone who cares to listen.’’ In a situation of MDF misfit, employees could voice concerns about their misfit and attempt changes in their job or the company to allow for the misfit. For instance, if the misfit should occur with P-O Fit, P-P Fit, P-T Fit or all, individuals could use voice to become what Meyerson and Scully (1995) term ‘‘tempered radicals.’’ These individuals have values that vary from the

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organization, but utilize voice to seek to balance the organization with regard to their personal values, preferences, attitudes, and so forth. They challenge the status quo. In doing so, these employees can become a major catalyst for change in the organization, by providing various insights into the organization, which others with the same viewpoints may not have seen (Meyerson & Scully, 1995). There is a great deal of research needed in conjunction with this option of misfit. Specifically, what types of companies or company policies encourage individuals to be themselves despite misfits on various dimensions? What are the consequences of using voice successfully for the individual and the company? What are the consequences of using voice unsuccessfully for the individual and the company? Are individuals more likely to use voice with regard to certain sub-dimensions and less likely to use it with others? It seems reasonable that a person might utilize voice as a way of dealing with misfit with P-P Fit. It should be relatively easy to ask a manager to provide a desired working condition. However, asking a manger to change the culture of the company (P-O Fit) or the personality of the co-workers (P-V Fit/P-T Fit) might prove more challenging, if not impossible. Although voice as a reaction to misfit seems promising for the organization and the individuals in it, this may not always represent the best action. Expressing misfit can result in such things as delayed or non-access into desired relationships (Dulebohn, 1997). Therefore, this option may not represent the preferred path for some individuals and, as shown in the example above, this option may be suited better to handle certain aspects of misfit rather than all five sub-dimensions. Impression Management One final way in which individuals could cope with MDF misfit is by way of impression management (IM). Individuals use IM with the aim of maintaining their own identities while projecting a different identity to various significant others (Wayne & Liden, 1995). Ferris, King, Judge and Kacmar (1991) suggested that subordinates might use IM to communicate organizational value statements. This reaction to misfit involves the individual using IM to project fit. Kowalski and Leary (1990) considered IM to be a function of five factors. First is one’s self-concept, which is how one views him/herself. The second factor is the individual’s desired identity image, which is how the person would like to be viewed. Role constraints represent the third factor, which are defined as the expectations associated with the various social roles in the person’s life. The fourth factor is the target’s values, which can be seen as

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the beliefs and preferences of others. The fifth factor is the current and potential social image, which is the perception of how the person is currently regarded or would like to be regarded by others. These five factors come together to influence an individual’s use of IM. In the case of a misfit, individuals realize via prototype matching during the first task of adaptation, that their self-concept and social image are no longer appropriate in the face of changing target values (e.g. a change in the organization) or changing self-concept (e.g. the birth of a child or the completion of a graduate degree). For individuals that choose the IM option, either their new self-concept is valued too highly to adapt to increase their fit, or they are unwilling to adapt their self-concept to match the new target values. Rather, these individuals will use IM to portray a social image that is congruent with that of the post-change organization. For instance, individuals with P-O misfit could use IM to portray the correct values while in the organizational setting without changing their personal values. In effect, individuals would use IM to conceal their true values and display the expected organizational values. Some evidence of using IM in order to display fit has been found in the recruitment literature. Kristof-Brown, Barrick and Franke (2002) found that the IM tactic of self-promotion used by interviewees was related to the interviewers’ perceptions of P-J Fit. This study also found that the use of nonverbal IM increased perceived similarity. Given these findings, it is paramount to investigate the use of IM as a response to MDF misfit. As with the other options, it may be that this option is used more frequently for certain types of misfit. For example, P-O misfit, as shown in the above example, could probably be hidden by the use of IM. However, P-J misfit might be harder to hide using IM. Additionally, what are the consequences of using IM to hide misfit? Hewlin (2003) recently has proposed that individuals utilize facades of conformity to display organizationally sanctioned values. These facades of conformity, which consist of emotional displays, behaviors, gestures, verbal statements, and any other way an employee indicates the embrace of organizational values, cause an inconsistency between displayed behavior and internal values. Hewlin argued that individuals creating a facade will experience emotional distress due to this inconsistency. In effect, using IM to mask misfit is creating a facade of conformity. Therefore, one possible consequence of using IM to hide misfit, is emotional distress or even experienced work stress. Furthermore, the use of IM to address misfit can prove problematic as a function of the expected duration of the interpersonal influence. That is,

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most research on IM in organizations has examined a short-term, crosssectional examination of the effects of some tactic, displayed within a relatively short period of time, on target (e.g. supervisor) reactions. In order to use IM to address misfit, it seems that the individual would need to put up this facade on a continual basis over time, which might not only be very difficult to pull off convincingly, but also lead to the emotional labor and internal distress mentioned above. Certainly, more research is needed in this area. Summary This section described decision-making processes in which individuals engage once a misfit is perceived. Five different ways in which misfit might be dealt with at the individual level were described: adaptation, exit, inaction, voice, and impression management. Individual differences and environmental contexts are likely to affect which option a person will chose. Furthermore, there might be different reactions with different subdimensions of misfit. As noted earlier, some sub-dimensions of MDF might be more salient than others, garnering different responses to misfit. Interactions between several sub-dimensions of fit may result in different outcomes than misfit on any one sub-dimension. Therefore, much research is needed addressing misfit in accordance with the MDF framework provided in this chapter.

Phenomenology of Fit Another important area for future conceptualization and perhaps qualitative research concerns the phenomenology of fit, or what are the subjective psychological feelings associated with both fit and misfit. The MDF paradigm suggests that attitudes and behaviors are the result of fit, therefore implying that there is a psychological reaction to fit (Schneider, 1987). Further research is needed on not only the specific attitudes and behaviors created by fit and misfit for an individual, but also the psychological reactions of fit and misfit. For instance, it has been argued that being similar to others (i.e. fit) facilitates more effective interpersonal interactions (Chatman, Polzer, Barsade, & Neale, 1998). Additionally, James (2000) found that similarity enhanced the accumulation of social capital, in turn, predicting career advancement. Furthermore, the socialization literature has suggested that expressing fitting values and attitudes in an organization will help

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individuals gain entry and maintain preferred relationships (Dulebohn, 1997). This implies that fit creates feelings of belonging, whereas misfit might engender feelings of social isolation. By experiencing fit, individuals may reduce the stress felt by the strong pressures for conformity (Meglino & Ravlin, 1998). For instance, Bell (1990) referred to incongruence as being ‘‘psychologically distressing,’’ and a source of conflict in one’s life. Therefore, individuals who fit on all or various dimensions may experience less stress than individuals experiencing misfit. However, further research is needed in this area to determine what are the exact benefits and consequences of fit and misfit at the individual level. Specifically, we need to know much more about misfits, and how they behave. Some misfits, it seems, might not only not want to adapt and fit, but are proud of their ‘‘maverick’’ misfit status, fashioning themselves as crusaders for change, and the ultimate ‘‘devil’s advocates.’’ Finally, are certain individuals more sensitive to fit issues than others? Fitsensitive individuals might be more sensitive to environmental cues about their fit or misfit within organizations. Therefore, they may require a higher degree of fit or close to perfect fit on all five sub-dimensions of MDF. They may perceive even the slightest misfit as unacceptable. Further research is necessary in order to understand if some individuals are more fit sensitive than others.

CONCLUSION MDF proposes that individuals assess fit in multidimensional terms. Because of the complexity of the self-concept, fit researchers should consider each sub-dimension of fit as describing a portion of the self-concept. Currently, many organizational fit researchers study each sub-dimension of fit as a mutually exclusive construct. Prototype matching explains how individuals can assess fit across multiple sub-dimensions in relation to the self-concept. Finally, MDF suggests a motive to assess fit across multiple sub-dimensions. In order for further progress to be made in fit research, it is necessary to consider the multidimensional nature of the fit construct, and the resulting implications for employee attitudes and behavior in organizations. Furthermore, we offer some practical implications for organizations to consider in relation to MDF. Organizations can influence MDF through the use of realistic job previews (RJPs), by developing a specific multidimensional prototype to transmit to perspective employees. Because RJPs have

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been found to influence applicant perceptions of P-O Fit (Dineen et al., 2002), we should expect RJPs with multidimensional content to influence applicant perceptions of MDF. The process of shaping multidimensional prototypes would continue through organizational socialization, as was found to occur with perceptions of P-O Fit during organizational socialization (Cable & Parsons, 2001). The disparate literatures on recruitment fit (Dineen et al., 2002), selection fit (Werbel & Gilliland, 1999), organizational socialization fit (Cable & Parsons, 2001), and re-assessment of fit (Mitchell et al., 2001) all indicate that organizations become homogenous as time progresses. We have proposed an integrative theory of MDF that attempts to plausibly explain how this process occurs. Finally, we addressed some voids in the MDF literature as a whole, and suggested that future research not only include multiple dimensions of fit but also the effects of misfit and the phenomenology of fit. Thirteen years ago, Judge and Ferris (1992) characterized a relatively new, but increasing area of research activity in the organizational sciences as focusing on ‘‘the elusion criterion of fit.’’ Their concerns focused on the realization that human resources decisions increasingly made on the basis of ‘‘fit,’’ yet we did not seem to share a common notion or definition of what fit is and what would be the implications of such decisions. This area of research has come a long way since Judge and Ferris expressed those concerns, and we certainly know much more than we did in 1992 about the nature and essence of fit. However, for continued progress to be made on fit in organizations, more integrative theoretical work is needed to better articulate the dynamics of fit and how it affects organizations and individuals. This chapter was one effort to address this need, and hopefully it will stimulate further thought and research in this important area of behavior in organizations.

ACKNOWLEDGMENTS This article is based on the dissertation of the first author, which was completed under the guidance of M. Ronald Buckley at the University of Oklahoma. We would like to thank the other members of the dissertation committee, Robert Terry, Jorge Mendoza, Ryan Brown, and Claudia Cogliser. We would also like to thank Jim Austin and Jim Dulebohn for their helpful comments on an earlier draft of this article.

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ABOUT THE AUTHORS M. Ronald Buckley is a Professor of Management and a Professor of Psychology and the holder of the JC Penney Company Business Leadership Chair in the Michael F. Price College of Business at the University of Oklahoma. He received his Ph.D. in Industrial/Organizational Psychology from Auburn University. His research interests are diverse and include decision making in the employment interview, performance evaluation, organizational entry processes, and the issues surrounding unethical behavior in organizations. He has published over 70 refereed journal articles in, among others, the Academy of Management Review, Journal of Applied Psychology, Applied Psychological Measurement, Journal of Management, and Organizational Behavior and Human Decision Processes on topics related to human resource management issues. Robyn L. Brouer is a Ph.D. student in Management at Florida State University with a focus in Organizational Behavior and Human Resources Management. She received an M.S. in Management from the University of Central Florida with an emphasis in Human Resources Management, and a B.S. degree in Psychology and Sociology from the University of Georgia. She has research interests in the areas of the multi-dimensional aspects of person–environment fit, social influence and effectiveness processes in organizations, leader-member exchange, and work stress. Jason A. Colquitt is an Associate Professor in the Management department at the University of Florida’s Warrington College of Business. He received his Ph.D. from Michigan State University’s Eli Broad Graduate School of Management, and earned his B.S. in Psychology from Indiana University. His research interests include organizational justice, trust, team effectiveness, and personality influences on task and learning performance. He has published more than 20 articles on these and other topics, and is a coauthor of the forthcoming Handbook of Organizational Justice (with Jerald Greenberg). He is currently serving on editorial boards for the Academy of Management Journal, Journal of Applied Psychology, Organizational Behavior and Human Decision Processes, Personnel Psychology, and Journal 305

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of Management, and is a recipient of the Society for Industrial and Organizational Psychology’s Distinguished Early Career Contributions Award. Rene´e E. DeRouin is a doctoral student in the industrial and organizational psychology program at the University of Central Florida and the recipient of the Society for Industrial and Organizational Psychology’s Robert J. Wherry Award for 2004. Her research interests include training, distance learning, learner control, and stereotype threat, and her work will be appearing in the Journal of Management, Human Resource Management Journal, Research in Personnel and Human Resources Management, Advances in Human Performance and Cognitive Engineering Research, and the Handbook of Human Factors and Ergonomics Methods. James H. Dulebohn is an Associate Professor of Human Resource Management at Michigan State University. He earned a Ph.D. in Human Resource Management from the University of Illinois at Urbana-Champaign’s (UIUC) Institute of Labor and Industrial Relations in October 1995. His research interests include human resource information systems, decision making, compensation and benefits, and organizational justice and politics in human resource management systems. His articles have appeared in journals including the Academy of Management Journal, Personnel Psychology, Journal of Management, Journal of Risk and Insurance, Journal of Organizational Behavior and others. He has also written chapters that have appeared in Research in Human Performance and Cognitive Engineering, Research in Personnel and Human Resources Management, Research in the Sociology of Organizations, and The Handbook of Human Resource Management. Dr. Dulebohn is currently the Associate Editor of Human Resource Management for International Journal of Organizational Analysis. Erich C. Fein is a doctoral candidate in Industrial-Organizational Psychology at the Ohio State University. He received his B.S. degree in History from the U.S. Air Force Academy and served 4 years as an Intelligence Officer in the U.S. Air Force before receiving his M.A. degree in IndustrialOrganizational Psychology from the Ohio State University in 2001. His primary research interests include the impact of personality traits on selfregulation and the relationships between mentoring processes and the emergence of leaders in organizations. His work has appeared in Personality and Individual Differences.

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Gerald R. Ferris is the Francis Eppes Professor of Management and Professor of Psychology at Florida State University. He received a Ph.D. in Business Administration from the University of Illinois at Urbana-Champaign. He has research interests in the areas of social influence in organizations, performance evaluation, and reputation in organizational contexts. He has authored numerous articles published in such scholarly journals as the Journal of Applied Psychology, Organizational Behavior and Human Decision Processes, Personnel Psychology, Academy of Management Journal, and Academy of Management Review, and such applied journals as the Academy of Management Executive, Human Resource Management, Human Resource Planning, and Organizational Dynamics. He served as an editor of the annual series, Research in Personnel and Human Resources Management, from 1981 to 2003. He has authored or edited a number of books including Political Skill at Work, Handbook of Human Resource Management, Strategy and Human Resources Management, and Method & Analysis in Organizational Research. He has consulted on a variety of human resources topics with companies including ARCO, Borg-Warner, Eli Lilly, Motorola, and PPG, and he has taught in management development programs and lectured in Austria, Greece, Hong Kong, Japan, Singapore, and Taiwan, in addition to various U.S. universities. Barbara A. Fritzsche is an Associate Professor and Director of the Ph.D. program in industrial and organizational psychology at the University of Central Florida. Her applied background includes validating psychological tests, developing surveys, and conducting job analyses. Dr. Fritzsche’s research interests include learner control in training, decision making in job selection, personality predictors of job performance, diversity in the workplace, and prosocial personality in the workplace. She has published papers in journals, such as the Journal of Personality and Social Psychology, Journal of Occupational and Organizational Psychology, Journal of Vocational Behavior, Personality and Individual Differences, and Journal of Applied Social Psychology. Stanley M. Gully is an Associate Professor of Human Resource Management at the School of Management and Labor Relations, Rutgers University. He received his Ph.D. in Industrial and Organizational Psychology from the Michigan State University in 1997. His research interests include identification of key factors that influence leadership and team performance, organizational learning and training effectiveness, and novel applications of

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research methodologies to the investigation of multi-level phenomena. His work has appeared in a variety of outlets including the Journal of Applied Psychology, Organizational Behavior and Human Decision Processes, Organizational Research Methods, and Research in Personnel and Human Resources Management. He has served or is currently serving on the editorial boards of the Academy of Management Journal, Journal of Applied Psychology, Journal of Organizational Behavior, and Journal of Management. Jonathon R. B. Halbesleben is a Visiting Assistant Professor of Management in the Michael F. Price College of Business at the University of Oklahoma. He received his Ph.D. in Industrial/Organizational Psychology from the University of Oklahoma, Norman. His current research interests include stress and burnout, the utilization of nontraditional human resources, and social comparison. He has published his research in the Journal of Management, Journal of Business Ethics, Organization Dynamics, Leadership Quarterly, and Personnel Review. He has also completed human resources consulting for the States of Arkansas and Oklahoma and for the United States government. Howard J. Klein is an Associate Professor of Management and Human Resources at the Ohio State University. He received his B.A. degree from the University of Minnesota in psychology, his M.B.A. from Michigan State University in human resource management, and his Ph.D. degree in organizational behavior and human resource management from Michigan State University. His research interests center on improving individual and team performance through the use of selection, socialization, commitment, goal setting, performance management, and training. His articles have been published in outlets including the Academy of Management Review, Academy of Management Journal, Industrial Relations, Journal of Applied Psychology, Organizational Behavior and Human Decision Processes, and Personnel Psychology. Janet H. Marler is an Assistant Professor of Management at the School of Business at the University at Albany–State University of New York. She earned her Ph.D. in Industrial and Labor Relations from Cornell University. Prior to earning her doctorate, she held executive and professional positions in the financial services industry and was a CPA. Her research

About the Authors

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has been published in several journals and books including the Journal of Organizational Behavior, Journal of Quality Management, Academy of Management Best Paper Proceedings, IHRIM Journal, and It’s All About Time: Couples and Careers. Her research centers on the strategic use of human resource information systems, employee and managerial self-service, compensation and benefits strategy, and work and family initiatives. Marcia P. Miceli is Professor of Management at the McDonough School of Business at Georgetown University. One stream of her research focuses on whistle-blowing in organizations; together with Dr. Janet P. Near at Indiana University, she has published many articles in leading refereed journals, chapters, and a book, on the topic of whistle-blowing. A second stream of her published work focuses on organizational compensation systems. Dr. Miceli has presented many papers for academic and practitioner audiences. Dr. Near and Dr. Miceli were co-principal investigators on research sponsored by the Institute for Internal Auditors, and both have held a variety of administrative positions. Janet P. Near holds the Coleman Chair of Management at the Kelley School of Business at Indiana University. She has collaborated with Dr. Miceli on numerous studies of whistle-blowing, leading to articles in several journals. Her second stream of research focuses on the relationship between work and nonwork domains of life, especially predictors of job satisfaction, life satisfaction, and their interrelationship. Jean M. Phillips is Associate Professor of Human Resource Management in the School of Management and Labor Relations at Rutgers University. She received her Ph.D. in 1997 from Michigan State University in Business Management and Organizational Behavior. Her current research interests focus on recruitment and staffing, leadership and team effectiveness, and issues related to learning organizations. Her work has appeared in the Academy of Management Journal, Journal of Applied Psychology, Organizational Behavior and Human Decision Processes, Personnel Psychology, Small Group Research, and International Journal of Human Resource Management. Jean was among the top 5% of published authors in the Journal of Applied Psychology and Personnel Psychology during the 1990s and received the 2004 Cummings Scholar Award from the Organizational Behavior Division of the Academy of Management. She has served or is currently

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serving on the Editorial Boards of the Journal of Applied Psychology, Journal of Management, and Personnel Psychology. Quinetta M. Roberson is an Assistant Professor of Human Resource Management in the School of Industrial and Labor Relations at Cornell University. She received her Ph.D. in Organizational Behavior from the University of Maryland. In addition, she holds a B.S. from the University of Delaware in Finance and Accounting and an M.B.A. from the University of Pittsburgh in Finance and Strategic Planning. Her research focuses on contextual examinations of justice – particularly justice at higher levels of analysis – to consider how our current thinking about organizational justice might change in different work contexts. In addition, she does research in the area of strategic diversity management – specifically, building climates for diversity and inclusion in organizations and linking diversity management initiatives to bottom-line outcomes. Her research has appeared in various journals, including the Academy of Management Review, Journal of Applied Psychology, Personnel Psychology and Group and Organization Management. Eduardo Salas is Trustee Chair and Professor of Psychology at the University of Central Florida and Program Director of the Human Systems Integration Department at the Institute for Simulation and Training (IST). He has authored over 300 journal articles and book chapters and co-edited 13 books. Dr. Salas is Editor of Human Factors and is on the editorial boards of the Journal of Applied Psychology, Military Psychology, Group Dynamics, and Journal of Organizational Behavior. He is a Fellow of the Society for Industrial and Organizational Psychology and his research interests include team training, distributed training, learning principles, and training evaluation. He consults extensively on how to design and deliver effective training programs. Anthony R. Wheeler is an Assistant Professor of Human Resources Management at California State University, Sacramento. He received his Ph.D. from the University of Oklahoma, Norman in Industrial/Organizational Psychology. Prior to returning to graduate school, he worked in the field of change management consulting for Management Analysis, Inc. and KPMG (now BearingPoint). He has also consulted for the States of Arkansas and Oklahoma to improve each state’s compensation and performance

About the Authors

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evaluation systems, respectively. He has research interests in areas of fit, recruitment and job search, and contingent employment, and he has published research in the International Journal of Selection and Assessment, Journal of Business Ethics, Journal of Managerial Psychology, Management Decision, and Journal of Managerial Issues. Cindy P. Zapata-Phelan is a doctoral student in the Management department at the University of Florida’s Warrington College of Business. She earned her B.S. in Psychology from the University of Florida. Her research interests include organizational justice, team role development and composition, and motivation. She has presented her work at the Society for Industrial and Organizational Psychology’s annual meeting.