Knowledge and Information Technology Management: Human and Social Perspectives Angappa Gunasekaran and Omar Khalil University of Massachusetts, USA Syed Mahbubur Rahman Minnesota State University, Mankato, USA
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Knowledge and Information Technology Management: Human and Social Perspectives Table of Contents
Preface ........................................................................................................................... i Section I: Introduction to Knowledge and Information Technology Management Chapter I ....................................................................................................................... 1 Developments in Managing Innovation, Knowledge and E-Business Benn Lawson, The University of Melbourne, Australia Danny Samson, The University of Melbourne, Australia Chapter II ....................................................................................................................14 Sources of Knowledge Acquisition by U.S. Managers: An Empirical Analysis Jaideep Motwani, Grand Valley State University, USA Pradeep Gopalakrishna, Pace University, USA Ram Subramanian, Grand Valley State University, USA Section II: Integration of Business and Knowledge/ Information Technology Management Chapter III ..................................................................................................................30 Information Systems and Business Strategy: A Concurrent Planning Model Antonio Torres-Perez, University of Valencia, Spain Isidre March-Chorda, University of Valencia, Spain Chapter IV ..................................................................................................................51 Integrated QFD and Knowledge Management System for the Development of Common Product Platform Walter W. C. Chung, The Hong Kong Polytechnic University, Hong Kong Colin K. S. Tam, The Hong Kong Polytechnic University, Hong Kong Michael F. S. Chan, The Hong Kong Polytechnic University, Hong Kong Chapter V ...................................................................................................................72 An Expanded Model of the Effects of Organizational Culture Upon the Acceptance of Knowledge Management Nancy C. Shaw, George Mason University, USA Francis D. Tuggle, American University, USA
Chapter VI ..................................................................................................................89 Information-Based Integration for Complex Systems E. Szczerbicki, The University of Newcastle, NSW, Australia Chapter VII .............................................................................................................. 104 An Experimental Analysis of the Effectiveness and Efficiency of Teams with Partial Problem Domain Knowledge Dinesh A. Mirchandani, Grand Valley State University, USA Jaideep Motwani, Grand Valley State University, USA Chapter VIII ............................................................................................................ 115 Collaboration in the Large: Using Videoconferencing to Facilitate Large-Group Interaction Diane H. Sonnenwald, University of North Carolina at Chapel Hill, USA Paul Solomon, University of North Carolina at Chapel Hill, USA Noriko Hara, Indiana University, USA Reto Bolliger, National Science Foundation STC-ERSP, USA Thomas H. Cox, University of North Carolina at Chapel Hill, USA Section III: Knowledge and Information Technology Management in Virtual Enterprises Chapter IX ............................................................................................................... 138 A Dynamic Perspective on Knowledge Creation in Virtual Teams—In a Search for New Insights Violina Ratcheva, The University of Nottingham, UK Chapter X ................................................................................................................ 153 The Impact of Trust in Virtual Enterprises T. T. Wong, The Hong Kong Polytechnic University, Hong Kong Henry C. W. Lau, The Hong Kong Polytechnic University, Hong Kong Chapter XI ............................................................................................................... 169 Market of Resources as an Environment for Agile / Virtual Enterprise Dynamic Integration and for Business Alignment Maria Manuela Cunha, Instituto Politécnico do Cávado e do Ave, Portugal Goran D. Putnik, Universidade do Minho, Portugal A. Gunasekaran, University of Massachusetts, USA Section IV: Knowledge Management in E-Commerce Environment Chapter XII .............................................................................................................. 192 Managing Business-Consumer Interactions in the E-World Sushil K. Sharma, Ball State University, USA Jatinder N. D. Gupta, The University of Alabama in Huntsville, USA
Chapter XIII ............................................................................................................ 214 Electronic Money and Payment Systems Santosh K. Misra, Cleveland State University, USA Jayavel Sounderpandian, University of Wisconsin–Parkside, USA Chapter XIV ............................................................................................................ 251 A Managerial Perspective on E-Commerce: Adoption, Diffusion and Cultural Issues Thuong T. Le, The University of Toledo, USA S. Subba Rao, The University of Toledo, USA Dothang Truong, The University of Toledo, USA Section V: Human and Social Aspects of Knowledge and Information Technology Management Chapter XV .............................................................................................................. 268 Human and Social Perspectives in Information Technology: An Examination of Fraud on the Internet C. Richard Baker, University of Massachusetts, USA Chapter XVI ............................................................................................................ 283 The Role of Trust in Information Technology Management István Mezgár, Computer and Automation Research Institute, Hungary Zoltán Kincses, Eötvös Loránd University of Sciences, Hungary Chapter XVII ........................................................................................................... 305 Inexperienced Software Team and Global Software Team Kim Man Lui, The Hong Kong Polytechnic University, Hong Kong Keith C. C. Chan, The Hong Kong Polytechnic University, Hong Kong Chapter XVIII .......................................................................................................... 324 The Knowledge Edge: Knowledge Management and Social Learning in Military Settings Leoni Warne, Defence Science and Technology Organisation, Australia Katerina Agostino, Defence Science and Technology Organisation, Australia Irena Ali, Defence Science and Technology Organisation, Australia Celina Pascoe, University of Canberra, Australia Derek Bopping, Defence Science and Technology Organisation, Australia About the Authors ................................................................................................... 354 Index ......................................................................................................................... 365
i
Preface Success in an increasingly competitive market depends on the quality of knowledge which organisations apply to their major business processes. For example, a supply chain depends on knowledge of diverse areas, including raw materials, planning, manufacturing, and distribution. Likewise, product development requires knowledge of consumer requirements, new science, new technology, and marketing. Knowledge is broadly defined as credible information that is of potential value to an organisation. Knowledge management (KM) is a function of generation and dissemination of information, developing a shared understanding of information, filtering shared understandings into degrees of potential value, and storing valuable knowledge within the confines of an accessible organisational mechanism. Since 1990, organisations are increasingly focusing on learning and knowledge creation. This indicates that an organisation should utilize its intellectual capacity and improve knowledge flows among its members to achieve a competitive advantage. The influence of global competitiveness and development in information technology has led to the recognition that knowledge and the capacity to develop knowledge are the resources that have tremendous impact on achieving a sustainable competitive advantage. The learning culture is the integrated system of ideologies, values, and beliefs that provides behavioral norms for knowledge management in the organisation. The learning climate is the way that organisations operationalize knowledge management cultures. Thus, the learning climate is the behavioral manifestation of the learning culture. Essential to knowledge development is the understanding of the elements of an organisation’s culture and climate that facilitate the development and maintenance of knowledge management initiatives. Critical to the flow of information and knowledge is knowing the ‘how’ (tacit knowledge) and knowing the ‘about’ (explicit knowledge) distinction of knowledge transferability. Knowledge building involves generation and dissemination of information, followed by shared interpretation of processed information into “knowledge.” Knowledge building depends not only on information processing but also on shared interpretation of information and filtering of knowledge into degrees of importance. Knowledge development includes mechanisms for evaluating the quality and usefulness of processed information, developing a shared understanding of the information, and filtering knowledge to be kept in accessible organisational memory. Knowledge management initiatives are undertaken for the purpose of achieving better organisational efficiency and effectiveness, with the goal being able to
ii achieve superior performance. In this respect, spending resources on developing knowledge without a plan to use the knowledge to achieve success is of limited value to organisations. Knowledge management has performance implications at various levels of an organisation: individual process-level (such as supply chain cycle time, product development initiatives, and globalization efforts), functional level (such as performance of various organisational domestic and international units), and the overall performance of the organisation (such as ROI and sales growth). Knowledge management helps an organisation to gain insight and understanding of its own experience. Specific knowledge management activities help focus an organisation on acquiring, problem solving, dynamic learning, strategic planning, and decision making. Computers and communications systems are good at capturing, transforming, and distributing highly structured knowledge that changes rapidly. The KM process involves four major steps: knowledge creation/acquisition, knowledge storage/organisation, knowledge distribution, and application. However, the socio-cultural factors and information technology resources available influence the way the knowledge management process is used for improving organisational competitiveness. There are few books covering aspects of organisational knowledge and information technology management mainly from the viewpoint of technology. We did not come across any book that has included in-depth focus on human and social dimensions of knowledge and information technology management. The objective of this book is to discuss the human and social aspects of knowledge and information technology management. The book emphasizes the human and social aspects of IT-based knowledge creation and sharing, and management systems and techniques. The book contains 18 chapters from professionals, researchers, and the business community that discuss many of the issues highlighted above in knowledge and information technology management. The chapters have been grouped into five interrelated sections. • • • • •
Introduction to knowledge and information technology management Integration of business and knowledge/information technology management Knowledge and information technology management in virtual enterprises Knowledge management in e-commerce environment Human and social aspects of knowledge and information technology management
The chapters in Section I discuss the importance of managing innovation, knowledge, and e-business, and possible sources of knowledge acquisition. Chapter 1 explains how leading innovators leverage e-business tools to harness knowledge residing in all areas of their value chain, including suppliers and customers. Also, a
iii case study of Cisco Systems, Inc. is used to illustrate this new operating model. Chapter 2 in this section presents a study that specifically examines the relationship among perceived accessibility, perceived task complexity, and the information-gathering behaviour of U.S. managers. One of the major conclusions resulting from this study is that the accessibility of an information source, and not the perceived complexity of the task at hand, influences the choice of source used. Section II focuses on the integration issues between business and knowledge/ information technology management. Chapter 3 examines the importance attached to information systems for the formulation of corporate strategy. It discusses the integration between information and decision-making processes. Chapter 4 presents an integrated QFD and knowledge management system for the development of common product platform. Chapter 5 reviews the literature on knowledge management and organisational culture, and identifies 13 factors that are poised to affect the adoption of technological change, KM in particular. It integrates these factors into a two-layer model of the effects of organisational culture upon the knowledge workers in the organisation, and uses four case studies to test the efficacy of the model. Chapter 6 shows how the information resource can be used in integration issues by introducing the problem of information-based integration, proposing a solution, and illustrating the solution with an example. Chapter 7 deals with an experimental analysis of the effectiveness and efficiency of teams with partial problem domain knowledge. Teams that had partial problem domain knowledge did not perform better than teams that had no specific problem domain knowledge. Chapter 8 describes the role of videoconferencing in facilitating large-group collaboration. Both social and technical infrastructures are required to enable and empower collaboration. Section III discusses issues related to knowledge management in the virtual enterprise. Chapter 9 contributes to the ongoing debate on the nature of knowledge creation and sharing in a distributed organisational environment, and current understanding of the sources of creative potential of virtual teams. The author adopts the view that an in-depth understanding of new knowledge creation depends on considering knowledge as socially constructed, or more simply stated as embedded in the organising practices of human activities. Chapter 10 proposes a decision support system based on neural network and data mining technologies, and uses a case example to illustrate the feasibility of incorporating inter-firm trust in real industrial situations. Chapter 11 presents the role of the Market of Resources as an enabler of the process of dynamic Agile/Virtual Enterprise Integration. The authors also specify the market resources’ structure, creation, and operational framework. Section IV is dedicated to knowledge management in e-commerce environments. Chapter 12 shows how the emerging knowledge management concepts can be used to create an appropriate framework for managing business-consumer relationships for understanding and retaining customers. Chapter 13 discusses the basic
iv requirements of electronic money as a medium of exchange in e-commerce transactions. The authors review the basic requirements of a transaction—atomicity, anonymity, durability, and non-reputability—and discuss a payment system that is needed in order to satisfy these requirements. Chapter 14 describes and discusses the issues of adoption and diffusion of e-commerce from a macro perspective, as well as the impact of cultural issues on e-commerce. Section V addresses human and social perspectives of knowledge and information technology management. Chapter 15 of this section focuses on one aspect of this question, namely the existence and extent of fraud perpetuated through the Internet. The chapter further discusses whether fraud using the Internet constitutes a new category of fraud or just a classic form of fraud committed through other means. Chapter 16 presents an overview of the possible attacks against information systems, and introduces tools and technologies that are appropriate to increase the level of trust of the users. Chapter 17 addresses some interesting issues on inexperienced global software teams and virtual software teams. It presents a new managerial, technical, and social approach to develop an effective global virtual software team. The last chapter, Chapter 18, sheds light on important experiences with the knowledge management and social learning in military settings. The book will be useful for professionals, researchers, and practitioners from the business community seeking an understanding of the current and emerging human resources and social issues in knowledge and IT management in the global knowledge economy. It will enable such business professionals to approach the increasingly adoptable and autonomous technologies with increased confidence. The book will also provide information technology researchers and professionals with a better understanding of how to apply information technologies to knowledge management in global e-marketing. It should also be useful to students as a text for courses in the areas of knowledge, information and IT management, as well as in human resource and social development in e-commerce environment. Readers should be able to understand the challenges arising from the new technologies and the growing demand for the development of human resources and social implications, and performance measurement in such an environment. Angappa Gunasekaran, PhD University of Massachusetts, Dartmouth, Massachusetts, USA Omar Khalil, PhD University of Massachusetts, Dartmouth, Massachusetts, USA Syed Mahbubur Rahman, PhD Minnesota State University, Mankato, Minnesota, USA
v
Acknowledgments The editors would like to acknowledge the help of all involved in the collation and review process of the book, without whose support the project could not have been successfully completed. A further special note of thanks goes also to all the staff at Idea Group Publishing, whose contributions throughout the whole process from inception of the initial idea to final publication have been invaluable. In particular to Michele Rossi, who continuously prodded via e-mail for keeping the project on schedule and to Mehdi Khosrowpour, whose enthusiasm motivated us to initially accept his invitation for taking on this project. We wish to thank all of the authors for their insights and excellent contributions to this book. Most of the authors of chapters included in this edited book also served as referees for articles written by other authors. Thanks go to all those who provided constructive and comprehensive reviews. In closing, we are grateful to our parents who by their unconditional love have steered us to this point, and to our wives, sons and daughters who have steadfastly supported us throughout this project. Special thanks go to Ms. Latha Gunasekaran for her diligent work in assisting the review process and compilation of all the chapters, helping to maintain all the documents related to this book over a period of two years. Angappa Gunasekaran, PhD University of Massachusetts, Dartmouth, Massachusetts, USA Omar Khalil, PhD University of Massachusetts, Dartmouth, Massachusetts, USA Syed Mahbubur Rahman, PhD Minnesota State University, Mankato, Minnesota, USA
Section I Introduction to Knowledge and Information Technology Management
Developments in Managing Innovation, Knowledge and E-Business 1
Chapter I
Developments in Managing Innovation, Knowledge and E-Business Benn Lawson and Danny Samson The University of Melbourne, Australia
ABSTRACT Successful innovation is seen as a top priority within many organisations (Porter, Stern, & Council on Competitiveness, 1999). Innovation is the mechanism by which organisations produce the new products, processes and systems required for adapting to changing markets, technologies and modes of competition (D’Aveni, 1994; Dougherty & Hardy, 1996; Utterback, 1994). This process requires the application of knowledge in some new or novel way. However, the explosion in knowledge and increasingly specialized technologies and markets has meant that a single firm alone is unlikely to possess all the relevant knowledge required to innovate. Consequently, organisations have been searching for new ways of overcoming this difficulty. One such mechanism is harnessing information technology to facilitate new organisational structures suited to managing innovation and knowledge into the 21st century. Information technology has expanded the opportunities (and challenges) of undertaking innovation. Twenty-four-hour product development processes are now a reality as multi-nationals race new products to the market. This chapter shows how leading innovators leverage e-business tools to harness knowledge residing in all areas of their value chain, including suppliers and customers. A case study of Cisco Systems, Inc. is used to illustrate this new operating model. Copyright © 2003, Idea Group, Inc.
2 Lawson & Samson
BACKGROUND Broadly viewed, innovation and particularly technological progress have been key drivers in the development of human civilization and the increasing standard of living and productivity witnessed through the 20th century. Entire new industries and fields of knowledge, like biotechnology, communications and information technologies, have developed and subsequently revolutionized everyday life. Innovation as a concept has vast scope and depth. This chapter focuses particularly on the management of innovation at the organisational level. Within an organisation, innovation may range from the highest levels of capital investment producing radically new products through to the most basic changes in product design, manufacture, supply or packaging. An innovation might even involve the use of marketing to modify the perceptions of an organisation’s customers. Some innovations create new industries and destroy others (Utterback, 1994). Innovation is not just about the commercialization of new technologies, and is thus broader than a research and development function. It includes the myriad of small and large changes constantly made to products, processes, administration, systems and markets. In an environment where control over quality, cost and flexibility is becoming commonplace, innovation is increasingly recognised as an important strategic dimension. In fact, innovation may ultimately become the only sustainable advantage available to an organisation. This is due to innovation being fundamentally a dynamic concept with advantage coming from innovating faster and more effectively than competitors (Collins & Smith, 1999). This target is becoming more difficult as organisations learn and improve their innovation processes. Organisations today require an increasing commitment to innovation simply to stay in the same position, much less improve (Porter et al., 1999). In the search to increase the effectiveness of their innovation processes, leading innovators are looking for ways to harness the knowledge residing in their organisation and externally within their network of suppliers, customers and other institutions. Businesses no longer compete based on the assets they possess, but rather on their ability to harness and diffuse knowledge throughout their operations. Drucker (1993) observed that productivity improvements in the 21st century arise from the application of knowledge to knowledge. This is in contrast to the industrial economy of the 20th century where productivity improvements were achieved through the application of knowledge to natural resources, labor and machines. Knowledge has thus driven increases in productivity and competitiveness. At a macro-level, innovation is the means by which knowledge is incorporated into economic activity. Knowledge lies at the heart of the innovation process. It relies heavily on the creation, utilization and diffusion of knowledge within companies and new product development processes (Cohen & Levinthal, 1990; Fiol, 1996; Teece, Pisano & Shuen, 1997). Knowledge is viewed as an important means of linking and leveraging the different areas of the business (Cohen & Levinthal, 1990). Nonaka and Takeuchi (1995) summarize the link between knowledge and innovation stating that “successful companies can create knowledge, disseminate it through the
Developments in Managing Innovation, Knowledge and E-Business 3
organisation and embody it in new technology and products. These activities define a knowledge-creating company, whose sole business is continuous innovation.” The ability of the firm to absorb knowledge from internal and external sources and incorporate it into products and services is termed their absorptive capacity (Cohen & Levinthal, 1990). This concept recognises that knowledge about resources, capabilities, markets, technologies and opportunities is generated not only from internal sources, like research and development, but also from external sources such as customers and suppliers. Successful innovators having high absorptive capacity are able to incorporate knowledge effectively into their products and services. Hargadon and Sutton (2000) describe the process of innovation within a firm possessing this high absorptive capacity. Their study examined the knowledgebrokering cycle used by IDEO, a leading product design firm, to capture good ideas, keep them alive, to imagine new uses for old ideas and to put promising concepts to the test. Innovation in this case occurs through information and knowledge sharing, supported by sourcing ideas externally or internally. Other examples of companies who have learned to transfer knowledge and technology from one product platform to another include Motorola, who built on its portable pager business to develop cellular phones, and Corning, who used their expertise in glass technology to become a world-leader in optic fibre production. The positive relationship between the possession of knowledge and innovation has been well documented. However, many organisations, particularly those with decentralized structures, require a means of facilitating the transfer and diffusion of this knowledge to enable innovation. This is achieved by harnessing information technologies. Information technology helps knowledge become visible at all levels of the organisations. Boynton (1993) calls information technologies ‘systems of scope’ in the sense that they help in the sharing of global knowledge in the firm. The application of information technologies provides immense scope for innovation to discard old processes, diffuse local innovations globally, remove constraints to innovation and create entirely new innovative practices and models (Metz, 1999). Indeed if we imagine the business as a human body, e-business is the central nervous system linking all the limbs and muscles together and transferring information instantaneously throughout the body. The use of information technologies in such a manner is often cited as one of the key drivers of increased productivity worldwide. These systems include knowledge management systems (KMS), supply chain management (SCM) and customer relationship management (CRM). Much research has focused on the positive benefits of information technologies for the identification, codification, diffusion and application of knowledge residing throughout an organisation. However, relatively little attention and research has focused on the relationship between information technology, knowledge management and innovation. The remainder of the chapter looks at this relationship through an examination of the evolution in organisational structures for innovation. A new innovation-oriented structure termed “network innovation” is used as an example of the effective application of information technology, facilitating knowledge and enabling superior innovation outcomes.
4 Lawson & Samson
The most innovative companies have recognised this relationship. They view information technology not merely as a means of achieving short-term cost reductions and efficiencies, but rather, as a longer-term mechanism for gathering, processing and diffusing knowledge throughout the organisation and its external partners. The application of this knowledge increases the potential for successful innovation. Innovation is innately a process of human thought and creativity enhanced by information technologies aimed at enabling people within the company and network to communicate effectively and improve innovation outcomes.
EVOLVING FORMS OF ORGANISING FOR INNOVATION Tremendous growth in knowledge and technology has underpinned the many innovations that revolutionized society. The techniques pursued for managing innovation have also had to evolve concomitantly with this growth in knowledge and technology. These stages of evolution in the nature of innovation are discussed below.
The Lone Innovator In the early 19th century, innovation was driven predominantly by immensely talented, creative and visionary individuals. Inventors such as Thomas Edison, Alexander Bell and Henry Ford formed their own laboratories and factories to commercialize their product ideas. Like no other time in history, a single individual could not only possess the knowledge, but also the ability to create (and destroy) entire industries. The foundation of many of the great companies of today, like General Electric and AT&T, was built during this period. However, aside from the original product idea, innovation did not feature heavily in most companies’ strategies or operations. These companies were typically vertically integrated and heavily involved in all aspects of their value chain. The ability to produce for the mass market at low cost was paramount and the management of knowledge not systematically considered.
The Organisation as the Innovator As the 20th century progressed, the breadth and depth of knowledge and resources required to innovate increased considerably. No longer could a single individual maintain competence in increasingly specialized markets and technologies. The focus of innovation had to shift from individual pursuits and toward collective effort housed within an organisational structure. In many cases only large organisations have the financial resources and ability to coordinate the myriad of skills required to bring an innovation successfully to the market. This geographic reach and scale enabled by global corporations has been critical to the success of many innovations.
Developments in Managing Innovation, Knowledge and E-Business 5
Highly innovative companies view innovation as a mechanism for creating new knowledge. Innovation is not seen as a user of scarce resources with uncertain paybacks. These companies recognise that the business units producing profits today may not be the best opportunities for business tomorrow. Disney, Sony, 3M, HewlettPackard, ABB and Intel are seen as prime examples of such organisations. These companies possess a broad competence base, enabling them to produce new products and services more quickly and at lower cost than their competitors. They incorporate explicit processes and strategies for systematic, continuous innovation. Successful innovators have been able to redefine industries and product portfolios time and time gain, consistently outperforming their competitors. Knowledge management enabled by information technologies has come to the fore in this operating model. Innovative companies have generally adopted a personalization strategy to managing knowledge (Hansen, Nohria & Tierney, 1999). They invest in information technology for the purpose of facilitating conversations and exchange of tacit knowledge. To make these relationships work, they invest in building networks of people. Knowledge is shared face-to-face, but also over the telephone, email and videoconferencing. Although other strategies like codification may work for other purposes, innovation requires original thought and iterative problem solving. Companies following an innovation strategy must have technical knowledge transferred to product development teams. These knowledge management strategies are distinct from the operational information systems required to support manufacturing and value chain operations. The vast majority of research into managing innovation and knowledge has been focused on this operational level. This view treats firms as autonomous entities searching for competitive advantage from external sources or their own internal resources and capabilities (Gulati, Nohria & Zaheer, 2000). Research and development, internal investments and organisational systems have all supported internal innovations. More recently, the importance of developing effective exchange relationships with units and individuals beyond the boundaries of the firm has been investigated. This has been in response to recognition that firms are increasingly competing as a network innovator, rather than a single organisation.
THE NETWORK AS THE INNOVATOR In today’s world of rapid change, uncertainty and hyper-competition, a new breed of innovative company is beginning to emerge. The continued and unrelenting trend of knowledge creation has meant that in many industries it is no longer possible for a single organisation to possess all of the required information, knowledge and skills to innovate effectively. As a consequence, this new breed of organisation is extending their sphere of innovation to incorporate their network of suppliers, customers and institutions. These network innovators have split their activities internally and in effect outsourced part of their innovation processes. Each node of the network becomes
6 Lawson & Samson
a source of unique knowledge and skills knowledge managed via electronic systems. This structure allows the organisation to ‘capability shop’ leveraging external sources of knowledge and expertise into their new product development and innovation processes. Quinn (2000) describes the process of outsourcing innovation and the options available to firms in innovating along the various stages of their value chain. This model of innovation capability is consistent with Rothwell’s (1992) conceptualization of a fifth-generation innovation process reflecting much more intensive use of information technologies to create and reinforce internal and external linkages. This new and dispersed organisational form presents many new challenges and opportunities for innovation. Many of the techniques for managing innovation in contemporary organisations cannot be easily applied, particularly where each company may bring different history, norms and understandings. A mechanism is required to bring these elements together. This is through the application of information technologies. Networked innovators use information technology as the glue to bind the various nodes of the network together. Close relationships and alliances with customers and suppliers are linked and managed via e-business tools, like Customer Relationship Management (CRM) and Supply Chain Management (SCM). This has resulted in reduced product development cycle times, lower costs and stronger products that better meet market demands.
Management of the Network At the level of competitive advantage, this approach to innovation networking represents a core capability of the firm. Networks constructed by organisations are idiosyncratic and path dependent (Gulati & Gargiulo, 1999), making them difficult for competitors to imitate or substitute. This is particularly so for innovation networks. Similarly, the resources accessed within the networks are idiosyncratic, arising from the combination of unique nodes of the network available to the firm. Thus, competitive advantage can be more broadly viewed as arising from a firm’s networks and the resources they can access. Additionally, the management of these networks is a new capability that these innovators must understand. The success of the network itself is hugely dependent on the implicit relationships and personal contacts among top managers, alliance managers and project champions. The need to successfully manage these relationships should be continually communicated to all parties to prevent politics, minor difficulties and relationships problems from escalating. Effective network management facilitates the process of transferring information and knowledge between parties through personal contact and information systems. The use of information technologies improves the company’s capabilities and ability to innovate by interacting with best-in-world knowledge sources throughout their network. These organisations lever their internal innovation capabilities many times over through effective IT, and personal and motivational links with outside sources (Quinn, Zien & Baruch, 1997). Information systems capture data and ideas
Developments in Managing Innovation, Knowledge and E-Business 7
from all sources in the network, giving the company an innovative edge beyond its own capabilities. Some suggest these benefits are in the order of 10 to 100 times. These companies use information technologies not just to draw on knowledge, but also to improve and apply to new technological breakthroughs, pushing the firm into new markets and creation of new industries. Utilizing the linkages with external parties via information technology, and enhanced monitoring offered by new systems, organisations like Cisco and Dell can decrease innovation cycle times and costs, and decrease investments and risks by equal amounts, enhancing the value of their innovations to customers and shareholders. Ultimately, just as firms no longer have the need to be vertically integrated in a production sense (i.e., outsourcing parts of their supply and production), so it is for innovation itself. The great potential for at least partially outsourcing innovation lies in combining the depth of knowledge and systems that suppliers and other partners can bring, with the deep understanding of customer needs and market insights residing within the organisation. Innovation outcomes and the business performance that results will measure the proof of the value of innovation networks.
Harnessing Suppliers’ Capabilities Outsourcing innovation allows selected suppliers to innovate freely within the organisation’s product and process development models. Even large manufacturers across many industries have used their network of suppliers and complementary associations to innovate more successfully. Examples include aerospace (Boeing/ Airbus), transportation (Ford/Chrysler), communications (AT&T) and energy (Mobil). These companies have substantially outsourced product development to their suppliers, retaining control only over their core capabilities. Moreover, Internet software companies routinely release beta versions of their products, encouraging thousands of external parties to use their own resources to error-check, problem solve and develop new applications which add further value to their product. Examples include Netscape with their Navigator browser, Apple with OSX and Microsoft with Windows. This interactive development process allows the company to improve development times, reduce market risk, incorporate customer insights, achieve customer commitment and thereby enhance the chances of a successful innovation.
Understanding Customer Needs There is clearly a difference between those firms that do achieve an essentially minimal level of measuring customer satisfaction and those that strive to be at the leading edge of driving the creation of new requirements. Sony and 3M are well known as companies that do not wait and follow, but rather work with lead customers to ‘stay ahead of the curve.’ Indeed, each of these companies has been known to proactively create the next curve, well ahead of customers consciously knowing their requirements. Almost without exception, successful innovators bring the ‘voice of the customer’ pervasively into their innovation and product design processes. They
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work with lead customers to create and specify new requirements, then match product/service designs and production capabilities to these.
Enabling Social Networks: Human Resource Aspects The application of information technology has built up the social capital critical to innovation by linking employees globally in a local manner. For example, Boeing broke through in their use of the state-of-the-art, digital design computer system called CATIA for the development of their new 777 commercial jet family. In addition to the many technical benefits which dramatically reduced new product development cycle time and errors, the system also linked Boeing engineers worldwide for the first time. CATIA contained built-in flags and alerts enabling engineers to link worldwide and form a personal innovation network within the company where none had previously existed. These engineers were now able to exchange thoughts and ideas at a personal level through information technology. Suppliers were also brought into this network. In such environments, people work collaboratively with their colleagues both within and outside their firms. Firms such as ABB, renowned particularly in Europe for their excellent leadership, encourage their staff to take risks, such that mistakes are acceptable so long as their ‘batting average is high’ in decision making. Such firms encourage creativity in their staff and find ways to keep their technical staff challenged and satisfied in a technical stream of work. Such companies adjust their human resource strategies of reward, recognition, promotion and performance measurement to build in the collaborative culture and creativity aspects of innovation that are required to achieve effective inter-firm as well as intra-firm innovation. One of the significant success factors is achieving the right balance between technical excellence and business focus and excellence. Further it is not enough to achieve these two things separately, but in truly great innovator companies, technical prowess and business focus are closely integrated. While systems and formal processes can help with the achievement of this integration, the importance of human resource policy settings cannot be underestimated. The attitudes and behaviors of people, and the incentive systems within which they work, are key to the achievement of successful innovation that includes the commercial aspects as well as the technical success of new products and processes.
Risks of Networking and Outsourcing Innovation Quinn et al. (1997) reports on three practices that most prevent the benefits from innovation being realized within such networks. These are the insistence on the use of standardized practices, unwillingness to move from hourly charge rates to value pricing and finally, attempting to manage the ‘how’ and not ‘what’ of innovation. Firms are also concerned about the risks involved in outsourcing their development operations to suppliers. If not properly managed this may mean they lose advantages from outsourcing innovation in terms of integrated systems, higher quality, lower cost, improved flexibility and minimal front-end investment (Quinn et al., 1997). However, many firms also don’t take into account the risks and costs they
Developments in Managing Innovation, Knowledge and E-Business 9
regularly incur by not incorporating external parties, like lost innovation, delays, poor design and inability to access ‘world’s best’ knowledge. Similarly, the commitment of management time, effort and investments can be underestimated.
CASE STUDY – CISCO SYSTEMS, INC. Cisco Systems is illustrative of a new breed of innovative organisations that have e-business at the heart of its operating model. Cisco uses its substantial investment in innovation capability and information technology infrastructure as its primary engine of wealth creation. The company is not focused on building up physical assets whose use is limited. Instead, it uses knowledge, one of the rare commodities whose value grows the more it is applied, to innovate. This has enabled Cisco, Inc. to become one of the most successful companies in history, with its stock rising on average 80% annually to become a world leader in network routers. The very rapid growth rate experienced by Cisco over the past decade would stress most organisations beyond breaking point. The implementation of information technology and e-business tools has enabled Cisco to cope with the ramp up of operating a large business while remaining flexible in the dynamic world of network technologies. The company has evolved to become what we term an ‘innovation engine,’ outsourcing approximately 85% of its manufacturing and focusing its attention on managing the strategic technologies and uncertainties of its business. In fact, Cisco competes as a flexible network of companies with central direction and using information technology to link the different parts. This is illustrated in Figure 1. Cisco Systems links its manufacturing sub-contractors to an Enterprise Resource Planning (ERP) system providing real-time production and forecast sales data. Its subcontracted manufacturers possess the same information as Cisco, enabling the company to manage production far more effectively, and freeing up Cisco management to concentrate on key strategic issues and uncertainties. It is estimated that only 20% of Cisco products pass through a Cisco warehouse. Figure 1: Conceptualisation of the Cisco Systems Innovation Network Acquisitions Technology & Intellectual Property
Joint Ventures/ Alliances
Resellers Cisco Systems, Inc. Manufacturing SubContractors
• •
Internal Innovation Management of Their Innovation Network
Customers
Configurator
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Cisco also uses its suppliers to promote the development of new innovative components, hardware and software innovation. For example, Cisco has formed the Cisco Hosting Applications Initiative (CHAI) made up of around 30 vendors and service providers with the aim of developing new router technology and optimizing performance on current products. In addition, Cisco manages most of its customer relationships via the Internet. The product delivered to the customer is also managed through a program called the Cisco Configurator, which translates customer needs into hardware specifications. The Cisco website also links customers and their problems in a discussion group format, in effect outsourcing much of the customer sales support function. If major customer problems aren’t resolved within an hour, Cisco’s engineering managers receive an email and follow up with personal phone calls to the customer. The company also maintains contact with leading customers through its network of resellers, which is carefully managed. This greater depth of knowledge and numerous inputs of customer problems, issues and thoughts through the Configurator and website, have enabled Cisco to become more innovative rather than simply remaining internally focused and R&D oriented. Cisco concentrates on developing a deep understanding of customers and customer support needs. A shared information system enables efficient and enhanced relationships with upstream suppliers of components and products. The common language enforced through information systems improves human communication, and enables knowledge from all areas of the network to be captured and shared with speed and accuracy that paper-based knowledge systems cannot match. Like many high-technology companies operating in markets with rapidly advancing technologies, Cisco Systems, Inc. cannot do everything itself, and the company is well aware of this. The company grows by buying the products, technologies and intellectual property it can’t or doesn’t want to develop. Research and Development efforts (around 13% of revenues) are largely focused on integrating these acquired technologies. In fact, Cisco has acquired more than 51 companies in the past 6.5 years–21 of them in the last 12 months. Cisco has also entered into strategic alliances with many other companies, including EDS, IBM, Hewlett-Packard, Microsoft and Motorola, to access additional market and technological knowledge. Notably Cisco has invested where it can add value to their customers–namely access to new technologies. Cisco generally does not favor one technology over another, but instead listens to customers, offers multiple technology alternatives and provides customers with the flexibility to choose. In the highly uncertain environment of network routers, Cisco has avoided the commitments in expensive infrastructure and inventory, preferring to outsource many of these functions to suppliers who possess core capabilities in these areas. Much of the meteoric rise of Cisco Systems can be attributed to use of information technology to manage knowledge. The company has avoided the trap many companies fall into when implementing information technology of ignoring the people perspective. Cisco recognises when personal contact needs to overrule the use of information technology. This is particularly so when dealing with new
Developments in Managing Innovation, Knowledge and E-Business 11
acquisitions and innovation. By doing so, the company retains a flexibility and growth rate rare in the industry. The structure of Cisco and its network of suppliers enables the company to support innovations across multiple technologies and customer demands that Cisco alone could not achieve. A framework illustrating the relationship between knowledge, e-business and innovation within Cisco Systems is shown in Figure 2. Cisco Systems integrates its innovation network by harnessing e-business tools and techniques, including Supply Chain Management and Customer Relationship Management, to enable successful knowledge transfer throughout its network of customers, suppliers and other partners. This has led to superior innovation outcomes and excellent business performance. These tools and techniques, however, do not represent Cisco’s strategy. Rather, they enable Cisco to operate a business model structure that provides competitive advantage in their industry.
FUTURE DIRECTIONS FOR RESEARCH Over the past century, high-performing innovators have evolved their operating structures to facilitate creativity and innovation. Much of this evolution in structure has attempted to identify, manage and apply the world’s growing knowledge base. This trend shows no signs of slowing. As the leading innovators illustrate, there are immense benefits from outsourcing innovation to suppliers, customers and other institutions. However, just as networks increase the potential for innovation, so do the challenges also increase. These companies are learning new skills to manage this process. Innovation for competitive advantage is inherently a dynamic game. We can expect to see more expansive and tight linkages for innovation in all aspects of the value chain. As the next decade unfolds, we will see more attention from managers and governments on how best to manage innovation within and particularly across firms Figure 2: Relationship of knowledge, e-business and innovation within Cisco Systems The Cisco Innovation Network E-Business enabling effective knowledge transfers
Cisco Systems, Inc.
Superior Innovation Performance
Business Performance
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and industries. Additional research is required to investigate how leading innovators manage their network structures for maximum benefit. This research should extend beyond the improvement achieved in financial and non-financial aspects of firm performance through information technology, but also to the human and socialization aspects. The importance of face-to-face contact in managing the network and unlocking the knowledge residing within has already been shown. The literature shows the positive relationships between knowledge and innovation, information technology and knowledge. Future research should focus on the interactions among these variables. While the opportunities have developed first in rapidly growing industries such as information technology, telecommunications and biotechnology, research is needed to test the value and validity of innovation networks in segments that are not growing so quickly.
CONCLUSIONS The challenges of innovating in today’s environment are immense. The knowledge required to innovate has become increasingly specialized in most markets and technologies. Organisations have had to evolve their structures and processes to best use this distributed knowledge and achieve superior innovation outcomes. The modern breed of innovative company pursues a network approach to innovation, linking the nodes together via information technology. Although relying heavily on information technology for knowledge management and control over operational processes, these companies recognise that innovation is a human process requiring individual and group thought and interaction. Consequently, information technology is used as a means of enabling social interactions and free exchange of ideas and knowledge, rather than being solely a tool for cost reduction and efficiency.
REFERENCES Boynton, A. C. (1993). Achieving dynamic stability through information technology. California Management Review, 35(2), 58-77. Cohen, J. and Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. ASQ, 35(1), 554-571. Collins, J. and Smith, D. (1999). Innovation metrics: A framework to accelerate growth. Prism, First Quarter, 33-47. D’Aveni, R. A. (1994). Hypercompetition: Managing the Dynamics of Strategic Maneuvering. New York: The Free Press. Dougherty, D. and Hardy, C. (1996). Sustained production innovation in large, mature organisations: Overcoming innovation-to-organisation problems. Academy of Management Journal, 39(5), 1120-1153. Drucker, P. F. (1993). The New Society: The Anatomy of Industrial Order. New Brunswick, CT: Transaction Publishers.
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Fiol, C. M. (1996). Squeezing harder doesn’t always work: Continuing the search for consistency in innovation research. Academy of Management Review, 21(4), 1012-1021. Gulati, R. and Gargiulo, M. (1999). Where do interorganisational networks come from? American Journal of Sociology, 104(5), 1439-1493. Gulati, R., Nohria, N. and Zaheer, A. (2000). Strategic networks. Strategic Management Journal, 21, 203-215. Hansen, M. T., Nohria, N. and Tierney, T. (1999). What’s your strategy for managing knowledge? Harvard Business Review, March-April, 106-116. Hargadon, A. and Sutton, R. I. (2000). Building an innovation factory. Harvard Business Review, 157-166. Metz, P. (1999). Innovation in a wired world. PRISM: The Journal of Arthur D. Little, First Quarter. Nonaka, I. and Takeuchi, H. (1995). The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. New York: Oxford University Press. Porter, M. E., Stern, S. and Council on Competitiveness. (1999). The New Challenge to America’s Prosperity: Findings from the Innovation Index. Washington DC: Council on Competitiveness. Quinn, J. B. (2000). Outsourcing innovation: The new engine of growth. Sloan Management Review, Summer, 13-28. Quinn, J. B., Zien, K. A. and Baruch, J. J. (1997). Innovation Explosion: Using Intellect and Software to Revolutionise Growth Strategies. New York: The Free Press. Rothwell, R. (1992). Successful industrial innovation: Critical factors for the 1990s. R&D Management, 22(3), 221-240. Teece, D. J., Pisano, G. and Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509-533. Utterback, J. M. (1994). Mastering the Dynamics of Innovation: How Companies Can Seize Opportunities in the Face of Technological Change. Boston, MA: Harvard Business School Press.
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Chapter II
Sources of Knowledge Acquisition by U.S. Managers: An Empirical Analysis Jaideep Motwani Grand Valley State University, USA Pradeep Gopalakrishna Pace University, USA Ram Subramanian Grand Valley State University, USA
ABSTRACT Questionnaire surveys of 156 U.S. managers are used to study knowledge acquisition behaviors. The study specifically examined the relationship among perceived accessibility, perceived task complexity, and the information-gathering behavior of U.S. managers. One of the major conclusions resulting from this study is that the accessibility of an information source, and not the perceived complexity of the task at hand, influences the choice of source used. Other study results are discussed and implications are offered for practicing managers. In addition, a knowledge management framework based on perspectives of the various management disciplines is also presented.
INTRODUCTION Knowledge management (KM) has become the latest strategy in increasing organisational competitiveness. It is the most innovative, creative, and important Copyright © 2003, Idea Group, Inc.
Sources of Knowledge Acquisition by U.S. Managers 15
management concept to come along in the last 25 years. It doesn’t imply downsizing, restructuring, or reorganising; but rather, KM reflects a point made by Lew Platt, former CEO of Hewlett-Packard (HP): “If HP knew what HP knows, we would be three times as profitable” (Coates 2001). Researchers are calling it the only solution for competitive advantage in the new century (Evans, 1997; Hedlund, 1994; Hibbard, 1997; Martinez, 1998; Trussler, 1998). According to Robert H. Buckman, CEO of Buckman Labs, the purpose of the KM and sharing system at his corporation is to “facilitate communication across all of the organisation’s boundaries, so that the entire company works together to help everyone to be the best they can be” (Buckman, 1998, p. 11). Many forward-thinking companies are realizing the value in systematically capturing, analyzing, archiving, and distributing knowledge. From Motorola’s Six Sigma program to the integrated KM systems of today, firms have derived substantial value from effectively managing their knowledge assets. A recent survey by Ernst & Young’s Center for Business Innovation and Business Intelligence reports 94% of the respondents admit they could better use the knowledge in their companies through more effective management, 40% have KM systems up and running or in development, and 25% have plans to develop KM strategies in the next year (Hibbard, 1997, p. 2; Evans, 1997, p. 2). A survey by the Delphi Consulting Group in Boston reports even stronger results, with 70% of the companies it surveyed saying they plan to make their first investments in KM in the next one to three years (Hibbard & Carrillo, 1998). According to a recent study by Ipsos-Reid and Microsoft Canada Co., a majority of Canadian business leaders indicate that KM practices have created value by improving organisational effectiveness, delivering customer value, and improving product innovation and delivery (Anonymous, 2001). The study reveals that 65% of Canadian companies practicing KM believe it has given their organisation a competitive advantage. The attractiveness of the term KM appears to have been prompted by three major forces, according to the Knowledge Management Resource Center (www.kmresource.com): increasing dominance of knowledge as a basis for organisational effectiveness; the failure of financial models to represent the dynamics of knowledge; and the failure of information technology by itself to achieve substantial benefits for organisations. Knowledge can be characterized in many ways. Popular taxonomies distinguish between tacit and explicit knowledge, general and situated context-specific knowledge, and individual and collective knowledge (Spender, 1996). Knowledge sources may lie within or outside the firm. Internal knowledge may reside within peoples’ heads; embedded in behaviors, procedures, software, and equipment; recorded in various documents; or stored in databases and online repositories. Common sources of external knowledge include publications, universities, government agencies, consultants, and knowledge brokers, among others (Zack, 1999). There are two prominent themes dominating the field of KM: knowledge creation and knowledge use. The latter provided the initial spur for the field and still dominates academic and practical discourse. Interest in knowledge creation,
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however, is increasing noticeably. According to von Krogh (1998), “Knowledge creation is the key source of innovation in any company. ” How organisations obtain relevant information is crucial to the development of an empirical theory of organisations. Specifically, this chapter focuses on the process and tools whereby information can be captured, communicated, and analyzed into useful knowledge. In this study, we look at how top mangers of U.S. companies acquire knowledge about the external business environment. We identify the variables that influence an individual manager’s decision to use a particular information source for acquiring external information. The literature is equivocal on whether accessibility of information or the complexity of the task at hand is the key determinant of the source used (Culnan, 1983). This has particular importance to the field of KM because information collection is the first step in the process, and the source of information may very well impact the quality of information collected and hence, the knowledge created by the organisation. The chapter is organised into the following parts: the next section describes the KM literature with particular reference to the process of collecting information. The subsequent section focuses on the study methodology and results. The final section discusses the study’s findings.
THEORETICAL FRAMEWORK ON KM The existing literature on KM, especially the knowledge creation literature, can be classified based on the perspectives of the various management disciplines (strategic management, organisational behavior, production management, and information management). Below is an overview of how these disciplines view KM.
Strategic Management Perspective Winter (1993) argues that organisational knowledge and competence are forms of strategic asset that appropriately deployed; enhance the firm’s long-run adaptation in the face of competitive and other environmental contingencies. The strategic management perspective of KM creation literature centers on the resource-based theory of the firm (Wong, 2000). The resource-based strategy paradigm emphasizes distinctive, firm-specific, and thus hard-to-copy assets, skills, and knowledge. They are referred to generically as core competencies or distinctive capabilities that confer competitive advantage on the firm that possesses them. Resource-based thinking about the firm (Pitt and Clarke, 1999) equates capability with the firm’s exploitation of its tangible and intangible value-generating assets and resources. Proponents of resource-based theory suggest that knowledge-based advantages are difficult to imitate when the reasons for superior performance cannot be identified or controlled (Dierickx and Cool, 1989; Gulati et al., 2000; Lippman and Rumelt, 1982). Advocates of the theory maintain that resources that are well protected from imitation can be a durable source of advantage, and several authors
Sources of Knowledge Acquisition by U.S. Managers 17
have discussed numerous mechanisms that increase the cost of replication (Barney 1991; Ghemawat, 1986; Mahoney and Pandian, 1992), and classes of resources that are inherently tough to copy (Barney and Hansen, 1994; Castanias and Helfat, 1991). Causal ambiguities (Teece, 1998; Winter, 1987), concepts of knowledge base and intellectual capital (Grant, 1996; Tsoukas, 1996, Stewart, 1997; Teece, 1998, Sullivan, 1999), and the occurrence of knowledge creation in strategic alliances (Contractor and Lorange, 1998; Kogut, 1988; Phan and Peridis, 2000) have also been a focus in studies of knowledge resources in the strategic management field.
Organisational Behavior Perspective The field of organisational behavior views knowledge creation from the perspective of organisational learning (March, 1991; Nelson and Winter, 1982; Nonaka, 1994; Spender, 1996). Organisational learning is about how organisations can gain a better action repertoire in increasingly complex and dynamic environments by expanding their knowledge base (De Geus, 1988; Fiol and Lyles, 1985; Nonaka and Takeuchi, 1995). For these environments it is not the knowledge itself, but the learning capabilities that determine effectiveness (Grant, 1996). Although many authors on organisational learning show the importance of organisational learning, surprisingly the learning needs concept has not been explicitly defined. Four approaches to learning needs are recognized here: (1) knowledge gap analysis for identifying strategic knowledge needs (Helleloid and Simonin, 1994); (2) classification of problems to select operationally required knowledge and skills (Tampoe, 1994); (3) coping with organisational tremors and jolts by anticipation, response, and adjustments of behavioral repertoires (Meyer, 1982); and (4) decisional uncertainty (contingency) measurement (Duncan and Weiss, 1979). While organisational knowledge can take several forms, knowledge is generally referred to as either explicit or tacit (Buckman, 1998; Hedlund, 1994; Hibbard, 1997). Explicit knowledge is that which is already documented: located in files, manuals, databases, etc. Tacit knowledge, called by some “the greatest knowledge base in any company,” is that which is tucked away in employees’ heads (Buckman, 1998, p.12). By accessing, sharing, and implementing both explicit and tacit knowledge, organisations can influence behavior and achieve improved performance both individually and organisationally, and “the more effective organisations are at learning, the more likely they will be at being innovative” (Argyris, 1992).
Production Management Perspective Knowledge acquired by an organisation over long periods of time is a valuable asset of the organisation concerned. In the world of manufacturing, design knowledge of the products is vital for the manufacturers in maintaining its competitive advantage and the commercial success of the enterprises. Leveraging the design knowledge associated with their products is especially critical for SMEs who operate under difficult conditions.
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Various research scholars interested in the process of technological innovation have also initiated research pertaining to the process of knowledge creation in the production management field. According to Wong (2000), “the process of knowledge creation is intimately linked to the process for its use and transformation into products and services through the concept of innovation.” Innovation research demonstrates the need for firms to have complementary assets or other receptive technical capacity in order to translate new technology into commercial success (Thorburn, 2000). These assets are both formal and informal, or tacit, and need to be embedded in an organisation if it is to build its core competencies (Lei et al., 1997). Also, the success of formal technology licensing can be increased when tacit knowledge is transferred at the same time (Wong, 2000).
Information Systems Perspective Advances in information technology have propelled much of the excitement around KM. Information technology has provided new tools to better perform the activity of building knowledge capital. Two important areas in particular have contributed to the birth of modern KM systems: communication (or network technologies) and relational databases (Sarvary, 1999). When these tools are employed, people start thinking explicitly about the underlying business processes. Where does information originate? What parts of the process can be or should be automated? Is the process as it stands today worth automating, or should a new process be built? Essentially, information technology has a critical role in raising this consciousness because its use requires the firm to re-evaluate the entire KM process and its role within the firm. The combination of information technology and co-aligned organisational processes can significantly enhance learning and competitive advantage. Knowing how to create, select, interpret, and integrate information into a usable body of knowledge is the focus of this discipline (Borghoff and Pareschi, 1998; Liebowitz, 1999; Liebowitz and Wilcox, 1997; Slater and Narver, 1997). In addition, the conversion of tacit to codified or explicit knowledge assists in knowledge transfer and sharing, thereby helping to make the firm more innovative and productive (Davenport et al., 1998; Mansfield, 1985; Teece, 2000). According to Teece (2000), there are three broad objectives advanced by information system scholars pertaining to KM. These are: (1) The creation of knowledge repositories (data warehouses) for external information, particularly competitive intelligence; internal information, such as internal research reports; and informal internal knowledge, like discussion databases. (2) The delivery of improved knowledge access and hence reuses through the development of userfriendly analytical tools. (3) The enhancement of the organisation’s knowledge environment, including the willingness of individuals to freely share their knowledge and experiences. Data warehousing can be defined as a process that extracts data captured by multiple business applications and organises it in a way that is meaningful to
Sources of Knowledge Acquisition by U.S. Managers 19
the business, supporting the need to inform decision makers. Two types of data warehousing software are used extensively in support of KM initiatives (Sarvary, 1999): • software that supports the transfer of operational data to the warehouse (i.e., data extraction, cleansing, transformation, loading, and administration); • warehouse management (e.g., software that supports ongoing data management through the use of multi-user database server software). In conclusion, the extant literature on KM reiterates the importance of organisational variables that impact the collection of information that is subsequently transformed into knowledge. Figure 1 provides a knowledge management framework based on the perspectives of the four management disciplines discussed above. In the first part of the framework, knowledge is obtained and created from data/ information obtained from the four disciplines. Practitioners and researchers to build and/or test theory then use this knowledge. Lastly, the theories developed and/or tested result in specific knowledge use/applications in each of the four disciplines. This process keeps on evolving leading to better models and applications being developed.
METHODOLOGY Sample Data was collected by means of a questionnaire which is described in detail in the next sub-section. The questionnaires were administered to managers who were enrolled in the part-time MBA program of a university in the mid-west. Multiple Figure 1: Knowledge management framework based on the perspectives of the various management disciplines Strategic Management Discipline
Organization Behavior Discipline
Strategic Management Discipline
Organization Behavior Discipline
Theory Building
Knowledge Creation
Leads to
Leads to
Knowledge Use/ Applications
Theory Testing
Production Management Discipline
Information Management Discipline
Production Management Discipline
Feedback
Information Management Discipline
20 Motwani, Gopalakrishna & Subramanian
sections of employed students formed the population. One-hundred-and-fifty-six (156) completed questionnaires formed the data set. The authors tested for response bias, comparing the results of early respondents with those of late respondents (who are akin to non-responders, because they responded only after repeated reminders) using chi-square tests of independence (Armstrong and Overton, 1977). The comparisons were made using a few demographic variables. No significant differences were found.
Survey Measures Used in the Study The questionnaire titled “Managerial Information Acquisition Behavior Survey” was divided into five primary sections. Section 1 focuses on the “source of information” used by managers to acquire industry information. Nine separate items were used to capture this construct. Five-point Likert scales were used to measure the frequency of use of various “sources of information.” A score of 1 indicated the “source of information” was never used, while 5 indicated the source was used once a week. Section 2 of the survey queries respondents about the degree of accessibility of various sources of information. Section 3 addresses the uncertainty faced by managers with reference to multiple publics like customers, suppliers, and competitors, in addition to several environmental factors. In the next section of the survey, managers are asked to report about their “information sharing habits,” with peers and significant others in the workplace. Finally, demographic questions focus on the following: length of employment, level of highest education attained, age of manager, line versus staff responsibilities, and type of organisation (service or manufacturing) that the respondent works for. Service firms formed 53.2 percent of the sample in comparison to 46.8 percent for manufacturing firms. The questions on the survey instrument were drawn from Culnan’s survey (1983), updated to reflect recent changes in sources of information. The data set was analyzed using t-tests, correlation analysis, and regression analysis. We used t-tests to test for differences between the two groups, manufacturing and service, since our interest was in comparing the means of two distinct populations. To test for association between “information source use” and “accessibility,” we used the common statistical measure of association, viz., coefficient of correlation. Finally, to test the linear relationship between the independent and dependent variables, we used regression analysis.
RESULTS Table 1 presents the mean frequency and corresponding ranks of “use of information sources” by managers in the manufacturing and service industries. T-test results comparing the two industries are also presented here. The mean ranking of information sources used in the manufacturing industry is as follows. Superiors were ranked as the most important source of information, followed by personal subscriptions and then peers. In the service industry, managers ranked
Sources of Knowledge Acquisition by U.S. Managers 21
Table 1: Mean frequencies of use for nine information sourcesa
Manufacturing Source
Service Mean Rank S.D. Mean Rank S.D. t-value 3.89 2 1.25 3.88 4 1.19 .070
Personal Subscriptions Company Library 2.75 7 1.26 2.81 7 1.49 -.245 Databases 3.16 6 1.51 3.31 6 1.22 -.672 Superiors 4.01 1 1.03 4.06 3 1.14 -.268 Subordinates 3.84 5 1.19 3.73 5 1.22 .536 Peers 3.88 3 1.05 4.66 1 .90 -1.79* Internal Documents 3.85 4 1.08 4.23 2 5.33 -.598 Consultants 2.21 9 0.90 2.01 9 .89 1.350 Other Outsiders 2.55 8 1.04 2.63 8 1.08 -.504 a Scale:1=never, 2=1-2times a year, 3=4-5 times a year, 4=once a month, 5=once a week *p≤.10
“peers” first followed by “internal documents and next “superiors” as information sources. In the same table, t-test results reveal differences between managers’ use of information sources in the manufacturing and service sectors. Among the nine information sources, the use of “peers” as sources of information revealed somewhat weak differences between the two industries (p<.10). In Table 2 the correlations between “information source use” and “accessibility” take into consideration the relationship between these variables. Also included in this table are the correlations between source of information use and complexity. As can be seen in Table 2, the correlations between “source used” and “accessibility to information” in the service sector exhibit “very weak (.225)” to moderately strong (.639) associations. All of the associations are statistically significant at p<.001 level. In the manufacturing industry correlations ranging from a low of .241 (very weak association) to a high of .747 (strong association) were detected. Also, the correlations are statistically significant at p<.001. However, in the case of correlations between source of information used and complexity, none of the nine items under sources exhibit statistically significant correlations. The correlations exhibited range from “none” to “very weak” at best. This is true for both the industries. Table 3 shows two separate sets of regression analyses, run with “accessibility” and “complexity” as independent variables, while the nine items under sources of information use were specified as dependent variables. First, Table 3a takes a look at the relationships in the service sector and Table 3b pertains to the manufacturing industry. A total of nine regression equations are shown in each table. Table 3a reveals F is significant (.001
22 Motwani, Gopalakrishna & Subramanian
Table 2: Correlations between information source use and accessibility and complexity Source Personal Subscriptions Internal Impersonal - Library -Databases Internal Personal -Superiors -Subordinates -Peers Internal Documents External -Consultants Other Outsiders
Accessibility Complexity Service Manufacturer Service Manufacturer .305** .747** .049 -.088 .626** .509**
.694** .597**
.039 .018
.154 -.019
.639** .431** .428** .225**
.241 * .331** .506** .534**
-.130 .047 .184 .036
-.027 .154 .208 .089
.540** .597**
.470** .345**
.092 .069
-.043 .199
**p<.001
Table 3a: Regression results to describe source use based on accessibility and task complexity Dependent Var.
Service (d.f.=2,79) Accessibilitya Complexitya Personal Subscriptions .305*** -.048 Internal Impersonal Library .626*** -.001 Databases .509*** -.002 Internal Personal Superiors .639*** -.049 Subordinates 431*** .01 Peers .428*** .083 Internal Documents .225** -.018 External Consultants .540*** .022 Other Outsiders .597*** -.07
Adj. R2 .082
F 8.112**
.385 .259
51.636*** 28.33***
.40 .176 .173 .039
55.1*** 18.25*** 18.163*** 4.34**
.282 .348
32.861*** 42.68***
____________________________________________________________________________________________________________ a
Entries are standardized regression coefficients ***p≤.001 ** p≤.05
are statistically significant. The adjusted R2 ranges from .039 to .40. In Table 3b the adjusted R2 and significant F-value for all nine regression equations signify that the null hypothesis is rejected. The adjusted R2 ranges from .044 to .589. Again, all the computed betas of accessibility are significant at p<.001 levels. With the exception of personal subscriptions, all the other eight “sources of information use” are not significant.
Sources of Knowledge Acquisition by U.S. Managers 23
Table 3b: Regression results to describe source use based on accessibility and task complexity Dependent Var.
Personal Subscriptions Internal Impersonal Library Databases Internal Personal Superiors Subordinates Peers Internal Documents External Consultants Other Outsiders
Manufacturing (d.f.=2,71) Accessibilitya Complexitya
Adj. R2
F
.779***
-.209***
.589
52.663***
.694*** .603***
.027 -.095
.474 .355
65.991*** 40.083***
.241*** .331*** .506 .534***
.024 .109 .167 .032
.044 .097 .246 .275
4.26** 8.745 24.48*** 28.296***
.470*** .345***
-.091 .124
.210 .106
20.182*** 9.46***
____________________________________________________________________________________________________________ a
Entries are standardized regression coefficients ***p≤.001 **p≤.05
Tables 4 and 5 contain the t-test results for use of information sources in both manufacturing and service organisations. In the case of manufacturing organisations (see Table 4), among the group of nine items, only one was found to be statistically significant across the groups of “line” versus “staff” employees, viz., databases as sources of information. In Table 5, among the nine items only “library” as sources of information used was found to be somewhat statistically different (p<.10) between line and staff employees in service organisations.
DISCUSSION This study examined the relationship among perceived accessibility, perceived task complexity, and the information-gathering behavior of managers in manufacturing and service organisations. Since information gathering is an integral part of the knowledge-creation process, the findings of the study contribute to the field of knowledge management. A main contribution of this study is the finding that it is the accessibility of an information source and not the perceived complexity of the task at hand that influences the choice of source used. This is consistent with early research in the field, but contradicts Culnan’s (1983) assertion. A possible reason for this finding is that accessibility is paramount. If particular information is inaccessible or difficult to access, then regardless of the complexity of the task at hand, it is unlikely to be used.
24 Motwani, Gopalakrishna & Subramanian
Table 4: Mean differences between line and staff employees for use of information sources in manufacturing organisations
Source t-value sig. (2-tailed) _____________________________________________________________ Personal Subscriptions -1.718 .090* Internal Impersonal Library -.742 .461 Databases -2.205 .031** Internal Personal Superiors .317 .752 Subordinates -.770 .444 Peers -.954 .343 Internal Documents -1.182 .241 External Consultants -1.1025 .309 Other Outsiders -.947 .347 __________________________________________________________ **p≤.05,*p≤.10
Table 5: Mean differences between line and staff employees for use of information sources in service organisations
Source t-value sig. (2-tailed) _____________________________________________________________ Personal Subscriptions -.165 .869 Internal Impersonal Library -1.965 .053 Databases .086 .931* Internal Personal Superiors -1.543 .135 Subordinates -.017 .987 Peers -.502 .617 Internal Documents .906 .377 External Consultants -.595 .554 Other Outsiders 1.526 .131 **p≤.05, * p≤.10
This underscores the relative importance of task complexity and brings into sharp focus the accessibility of an information source. This has important implications both for users of as well as providers of information.
Sources of Knowledge Acquisition by U.S. Managers 25
Personal sources appear to be more popular than impersonal sources. This is brought out in Table 1, where, for manufacturing industry managers “superiors” ranked first and “peers” for service industry managers. Again, it is likely that because of the ease of access of these personal sources, they are preferred over impersonal sources such as publications and databases. Managers tend to want to talk to their superiors or peers to collect information simply because it is easier to do that than to seek published information. This is indeed surprising given that, in this day and age, electronic databases are produced with user ease of use in mind. Apparently, there is a mental block that makes users less inclined to use these sources and more inclined to seek personal information sources. “Staff” employees in organisations perform a “boundary-spanning” role. They perform a gatekeeping function by acquiring information from outside the organisation and disseminating this information to others in the organisation. This is in contrast to “line” employees who are typically more insulated from the external environment. A priori it would appear that staff employees would tend to use outside sources of information more than line employees. This is only partially supported by the results of this study. Of all the information sources examined in the study, only “databases” appear to be used more by staff than by line. This is consistent with extant theory because databases typically emanate from outside an organisation. However, there were no significant differences between line and staff employees on other external information sources such as library and publications. It is likely that organisations no longer want to insulate line employees from the outside world. By forcing line employees to interact with the external environment, organisations may become more competitive by exhibiting a higher degree of market orientation. The current study extends prior work on information use by managers. Replications and extensions would allow us to better understand the factors that influence the choice of various sources of information. This is important because information collection puts the organisation on the path to knowledge management.
FUTURE RESEARCH DIRECTIONS While this study adds to the growing body of literature on knowledge management, subsequent research should contribute to a more complete understanding of the entire process of knowledge acquisition and use. For example, it is possible that there are certain factors that moderate the knowledge acquisition process, such as organisational resources, industry type, and competitive intensity. The impact of these factors has to be empirically established. Similarly, certain factors may mediate the knowledge acquisition process. These factors may be size of the organisation, age, and technological intensity of the industry. Practical implications of these mediating factors would help organisations develop a plan for knowledge management.
26 Motwani, Gopalakrishna & Subramanian
REFERENCES Anonymous. (2001). Knowledge management’s early adopters. Computing Canada, 27(8), 15. Argyris, C. (1992). On Organisational Learning. Cambridge, MA: Blackwell. Armstrong, J. S. and Overton, T. S. (1977). Estimating non-response bias in mail survey. Journal of Marketing Research, 14(3), 396-402. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17, 99-120. Barney, J. and Hansen, M. (1994). Trustworthiness as a source of competitive advantage. Strategic Management Journal, 15 (Winter Special Issue), 175190. Borghoff, U. M. and Pareschi, R. (Eds). (1998). Information Technology for Knowledge Management. New York: Springer-Verlag. Buckman, R. H. (1998). Knowledge sharing at Buckman Labs. Journal of Business Strategy, (19), 1-15. Castanias, R. P. and Helfat, C. (1991). Managerial resources and rents. Journal of Management, 17, 155, 171. Coates, J. (2001). Knowledge management is a person-to-person enterprise. Research Technology Management, 44(3), 9-13. Contractor, F. and Lorange, P. (1998). Cooperative Strategies in International Business. Lexington, MA: Lexington Books. Culnan, M. J. (1983). Environmental scanning: The effects of task complexity and source accessibility on information gathering behavior. Decision Sciences, 194-206. Davenport, T., DeLong D. and Beers, M. (1998). Successful management projects. Sloan Management Review, Winter. De Geus, A. P. (1988). Planning as learning. Harvard Business Review, 66(2), 7074. Dierickx, I. and Cool, K. (1989). Asset stock accumulation and sustainability of competitive advantage. Management Science, 35, 1504-1514. Duncan, R. and Weiss, A. (1979). Organisational learning: Implications for organisational design. Research in Organisational Behaviour, (1), 75-123. Evans, B. (1997). Knowledge management–Fuel for innovation. InformationWeek Online, October 20, http: //www.informationweek.com. Fiol, C. M. and Lyles, M. A. (1985). Organisational learning. Academy of Management Review, (10), 803-813. Ghemawat, P. (1986). Sustainable advantage. Harvard Business Review, 64(5), 53-57. Grant, R. M. (1996). Toward a knowledge-based theory for a firm. Strategic Management Journal, 17, 109-122. Gulati, R. Nohria, N. and Zaheer, A. (2000). Strategic networks. Strategic Management Journal, 21, 203-215.
Sources of Knowledge Acquisition by U.S. Managers 27
Hedlund, G. (1994). A model of knowledge management and the nform corporation. Strategic Management Journal, (15), 73-90. Helleloid, D. and Simonin, B. (1994). Organisational learning and a firm’s core competence. In Hamel, G. and Heene, A. (Eds), Competence-Based Competition, 213-40. Chichester: John Wiley & Sons. Hibbard, J. (1997). Knowledge management: Knowing what we know. InformationWeek Online, October 20, http://www.informationweek.com. Hibbard, J. and Carillo, K. (1998). Knowledge revolution. Information Week Online, January 5, http: //www.informationweek.com. Kogut, B. (1988). Joint ventures: Theoretical and empirical perspectives. Strategic Management Journal, (9), 319-332. Lei, D., Slocum, J. and Pitts, R. (1997). Building cooperative advantage: Managing strategic alliances to promote organisational learning. Journal of World Business, 32(3), 203-224. Liebowitz, J. (Ed). (1999). Knowledge Management Handbook. Boca Raton, FL: CRC Press. Liebowitz, J. and Wilcox, L. C. (Eds). (1997). Knowledge Management and its Integrative Elements. Boca Raton, FL: CRC Press. Lippman, S., and Rumelt, R. (1982). Uncertain irritability: An analysis of interfirm differences in efficiency under competition. Bell Journal of Economics, 13, 418-438. Mahoney, J. and Pandian, J. (1992). The resource-based view within the conversation of strategic management. Strategic Management Journal, 13, 363-380. Mansfield, E. (1985). How rapidly does new industry technology leak out? The Journal of Industrial Economics, 34(2), 217-223. March, J. G. (1991). Exploration and exploitation in organisational learning. Organisational Science, 2(1), 71-87. Martinez, M. N. (1998). The collective power of employee knowledge. HR Magazine, 43(February), 88-94. Meyer, A. D. (1982). Adapting to environmental jolts. Administrative Science Quarterly, 27, 515-37. Nelson, R. and Winter, S. (1982). An Evolutionary Theory of Economic Change. Cambridge, MA: Harvard University Press. Nonaka, I. (1994). A dynamic theory of organisational knowledge creation. Organisation Science, 5(1), 14-37. Nonaka, I. and Takeuchi, H. (1995). The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. London: Oxford University Press. Pan, S. and Scarbrough, H. (1999). Knowledge management in practice: An exploratory case study. Technology Analysis & Strategic Management, 11(3), 359-374. Phan, P. and Peridis, T. (2000). Knowledge creation in strategic alliances: Another look at organisational learning. Asia Pacific Journal of Management, 17, 201-222.
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Pitt, M. and Clarke, K. (1999). Competing on competence: A knowledge perspective on the management of strategic innovation. Technology Analysis & Strategic Management, 11(3), 301-316. Sarvary, M. (1999). Knowledge management and competition in the consulting industry. California Management Review, 41(2), 95-107. Slater, S. and Narver, J. (1997). Information search style and business performance in dynamic and stable environments: An exploratory study. Report No. 97104. Cambridge, MA: Marketing Science Institute. Spender, J. (1996). Making knowledge the basis of a dynamic theory of a firm. Strategic Management Journal, 17 (Winter Special Issue), 45-62. Stewart, T. (1997). Intellectual Capital: The New Wealth of Organisations. New York: Doubleday. Sullivan, P. H. (1999). Profiting from intellectual capital. Journal of Knowledge Management, 3(2), 132-142. Tampoe, M. (1994). Exploiting the core competences of your organisation. Long Range Planning, 27(4), 66-77. Teece, D. J. (1998). Capturing value from knowledge assets: The new economy, markets for know-how, and intangible assets. California Management Review, 40(3), 55-79. Teece, D. J. (2000). Strategies for managing knowledge assets: The role of firm structure and industrial context. Long Range Planning, 33, 35-54. Thorburn, L. (2000). Knowledge management, research spinoffs and commercialization of R&D in Australia. Asia Pacific Journal of Management, 17, 257275. Trussler, S. (1998). The rules of the game. Journal of Business Strategy, January/ February, (19), 16-19. Tsoukas, H.(1996). The firm as a distributed knowledge system: A constructionist approach. Strategic Management Journal, 17 (Winter Special Issue), 11-25. von Krogh, G. (1998). Care in knowledge creation. California Management Review, 40(3), 133-153. Winter, S. (1987). Knowledge and competence as strategic assets. In Teece, D. J. (Ed.), The Competitive Challenge: Strategies for Industrial Innovation and Renewal: 159184. Cambridge, MA: Blackwell. Winter, D. (1993). Hometown team says U.S. has closed quality gap with Japanese. Ward’s Auto World, (29), 37-39. Wong, P.-K. (2000). Knowledge creation management: Issues and challenges. Strategic Management Journal, 17 (Winter Special Issue), 193-200. Zack, M. (1999). Developing a knowledge strategy. California Management Review, 41(3), 125-146.
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Section II Integration of Business and Knowledge/Information Technology Management
30 Torres-Perez & March-Chorda
Chapter III
Information Systems and Business Strategy: A Concurrent Planning Model Antonio Torres-Perez and Isidre March-Chorda University of Valencia, Spain
ABSTRACT The main purpose of this chapter is to deep into the importance attached to the Information Systems for the proper formulation of the corporate strategy. After introducing the basis of the strategic planning tradition model, which suggests the subordination of the Information Systems (IS) to the Business Strategy, we propose a new model that views the IS as an strategic instrument suited for the strategy formulation stage, rather than operational tools for the strategy control phase. Success in the application of this model we call “Concurrent Business/IS Strategic Planning Model” will closely depend on the consistency, coherence and soundness of the IS, both internal and external. In order to contrast the level of application of this model, an empirical case-study fieldwork was undertaken to rise up some empirical evidence on the degree of alignment between the entrepreneurial practice and the theoretical model proposed. This last section contains the main results arising from the empirical analysis.
INTRODUCTION The main purpose of this chapter is to examine the importance attached to information systems for the proper formulation of corporate strategy. Before starting with the core content of the chapter, we should clearly state the position and value of information within organisations, by analysing the role assigned to information resources by different organisational theories. Copyright © 2003, Idea Group, Inc.
Information Systems and Business Strategy
31
Information has become a key resource for most organisations, along with human resources or technological assets. A proper use of these resources underlies the competitiveness prospects of most organisations. Having stated the key character of information as an input to be considered in a company’s decision-making process, we should remember that not all information is equally important, nor should it all be treated in the same way. Several typologies of information exist, such as the one proposed by Cornellà (1994): 1. According to the capacity of synthesis and the types of decisions to be made on the basis of it: Operational information / Tactical information / Strategic information 2. According to the place where the information is generated: External information / Internal information 3. According to the degree of specificity of the information: Guidance information / End user information 4. According to the degree of immediacy that their treatment requires: Active information / Inactive information
INFORMATION SYSTEMS Information systems (IS) are usually extremely complex, which makes it difficult to provide an exact definition of them. As a starting point we use the one proposed by Andreu, Ricart and Valor (1996: 13): “An integrated group of processes, primarily of a formal nature, developed in a user-computer environment, which operate on a quantity of structured data about an organisation, whose function is to gather, process and distribute selectively all the information needed for the management and the proper functioning of the organisation.” Some nuances can be added to this initial definition: IS refers basically to formalised processes. However, IS should not neglect informal processes, of growing concern and presence in current organisations. IS makes use of computers, although conceptually an IS can still exist without computers. However, nowadays, the use of computers is almost compulsory to ensure a minimum of productivity, profitability and scope of the information gathered. Having defined what an information system means, we now turn our interest to the main purpose of these systems, i.e., to integrate information in support of the decision-making process. From the vast array of applications of IS in the management of companies, three basic information systems can be identified: MIS (Management Information System), DDS (Decision Support System) and EIS (Executive Information System). The MIS is not a new concept. It was defined in the ’60s by Ackoff (1967) as a system that furnishes the managers–across the organisation–with detailed and summarised information from company databases on operations and performance. MIS encompasses both databases and a series of routines for data treatment. The system is structured around a set of previously determined rules for decision
32 Torres-Perez & March-Chorda
making. The MIS gathers mainly operational information and reveals its usefulness by providing solutions to problems of an operational nature; its main limitation is that it can be used only when the problem to be solved has been previously programmed. This implies the existence of a rule for problem-solving . Also, its level of aggregation is low (Antill and Clare, 1991). DDSs on the other hand go a step further in the level of aggregation. They are fed by tactical information and are orientated to satisfying information demands from middle managers and staff. The main divergence from MIS lies in the capacity to deal not only with previously structured decisions but also with poorly structured or completely unstructured ones (Keen and Morton, 1978; Sprague, 1980). Finally, EIS are defined as information systems with a broad level of aggregation, which incorporate information of a strategic nature, both from the firm and the Figure 1: Typologies of information (author compilation)
EIS Strategic
DSS
Tactical
Operational Management Software Financing Internal / external logistics Commercial Purchases
Operational
Human Resources Production ...
Information Systems
Types of decisions
Figure 2: IS as an integrated group of processes (adapted from Andreu, Ricart and Valor, 1996) IS
COMPANY INFRASTRUCTURE HUMAN RESOURCES MANAGEMENT
MARGIN
TECHNOLOGY DEVELOPMENT PURCHASES
SERVICES INPUT LOGISTIC
PRODUCTION
:
OUTPUT LOGSTIC
,
COMMERCIALIZING AND SALES
MARGIN
Information Systems and Business Strategy
33
environment. They provide a broad volume of information and a high level of flexibility, enabling them to be used as a tool for analysis and action by top managers (Rockart and Treacy, 1982).
THE INFORMATION SYSTEMS IN THE ORGANISATION The IS can be compiled within the set of subsystems that form any organisation. The IS must be consistent with the other subsystems conforming the organisation, with the mission of fostering their coordination. The value chain proposed by Porter (1985) is a valuable tool for placing the IS in this frame of subsystems. The IS belongs to the infrastructure of the company, with the responsibility of obtaining and providing information from/to any area of the organisation. The tendency to decentralize the typical IS functions may lead to the emergence of functional IS. Another approach to placing the IS within the organisation is the one provided by Hax and Majluf (1984). From a systemic perspective, this model integrates the information/communication system with the rest of the systems conforming the organisation. For these authors, the IS are just one element forming the infrastructure of the company, in close interdependence with the other systems, as represented by Figure 3. The interdependence between the different systems revealed by this model goes further, to include the consistency between the systems and the mission and objectives of the organisation. This correspondence intra-systems and extrasystems holds several implications, the basic one being the need for balance among all the systems, in such a way that any change in one of them will have implications for the rest (will tend to unbalance the rest). Here, the need to ensure the fit or balance between the information system and all the others arises as a basic requirement of this approach. Hence, any modification in the IS should take into account the probable impact on the other systems of the organisation. Figure 3: IS as one element forming the infrastructure of the company (Hax and Majluf, 1984) PLANNING SYSTEM
CONTROL SYSTEM
ORGANIZATIONAL STRUCTURE
INFORMATION / COMUNICATION SYSTEM
COMPENSATION AND INCENTIVES SYSTEM STRATEGIC AND OPERATIONAL LEVELS
34 Torres-Perez & March-Chorda
INFORMATION SYSTEMS AND CORPORATE STRATEGY From the previous section we can derive that all functions related to the IS (design, planning, use,...) also have a close relationship with the competitive strategy of the firm. On the one hand, information systems are tools to support strategy, both in the formulation and the implementation and control phases. On the other hand, ISs are able, on their own, to generate competitive advantages for the company. Creating competitive advantage implies facing the challenges of the competitive environment. The strategic process starts with the phase where the mission is internalized by all the members of the organisation, which we call vision. Business strategies determine the way of achieving the general objectives attached to the organisation´s mission. Tactics act in the short run and focus on the measurable objectives which the general objectives can be divided into. Finally, business plans identify the resources to be allocated by the firm to fulfill those objectives, taking the form of budgets and operational plans. These four essential factors condition the use of IS. Also, five elements identify the IS as a supportive tool: • Competitive options: IS represents a model for assessment of competititive options. • Role and relationships: The role played by CEOs who consider the information system as a competitive resource and the importance of the top managers in the design of IS. • Definition and re-definition: Indicate how the IS can change or clarity value for customers, by changing the business, products or services, or the organisation’s processes. • Telecommunications: As an information vehicle for linkage with customers, suppliers and partners. • Success factors: The competitive position obtained by those firms that have used their information systems efficiently.
INFORMATION SYSTEMS AND STRATEGY Given the structural configuration of the economy, the challenges of the present day require the use of strategic planning to properly communicate the company´s mission to all the organisation, as well as to fulfill that mission through the formulation of the best possible strategies and tactics. Technology change and globalisation are reshaping the environment at an accelerated rate. In this framework, a competitive model explaining the factors which underlie the positioning of firms is needed. Figure 4 shows a simple model with the four most essential factors for attaining a competitive position. Undoubtedly, the market, the use of technology, the regulatory legislation and the working environment and values shared by the employees, play
Information Systems and Business Strategy
35
Figure 4: Essential factors for attaining a competitive position (Callon, 1996) Market
Tecnology
Regulations
Employees
ORGANIZATION
Business Processes
a primary role in indicating how the business and the organisational processes should be developed. Companies attempt to position themselves in terms of these four factors in a way that leads them to an advantageous position. That may be possible if the firm finds the best way to compete by reducing the life cycles of its products, enhancing its organisational flexibility and improving its responsiveness to customers. One of the keys to achieving all this is to transmitting the right message to the right person at the most suitable moment, as well as furnishing the resources needed to give support to the strategies and tactics mentioned in the previous section The basic task of strategic planning is to ensure the correct understanding of the vision, the strategies and the tactics of the firm. Strategic planning incorporates a systematic exploration of the opportunities and threats to be encountered in the competitive environment, in order subsequently to make the best decisions. Figure 5 shows the traditional approach to business planning. In this model, the planning task of the information system must be done parallel to the rest and duly integrated within the whole process. This model suggests a top-down view, which many organisations are starting to fight against. But in reality, ISs are often developed following a bottom-up approach. It is easy to see the benefits that can be derived from efficient integration between strategic planning and the information system. Figure 6 shows how IS planning should be integrated within strategic planning. The process starts with the preparation of a Strategic Business Plan. The IS Plan is based on the core elements of the Business Plan. Properly designing the architecture of the information system is of critical importance in a context of rapid development of computing and telecommunications supply. In this first stage, the future paths of development of the information systems and the telecommunications networks should be projected. The next step in the design of the IS rests on planning at the tactical level, including elements such as timing, costs, skills requirements, distribution of respon-
36 Torres-Perez & March-Chorda
Figure 5: Traditional approach to business planning (author compilation) Vision
Strategy
Tactics
IS Traditional Role
Figure 6: Planning goal (Callon, 1986) PLANNING GOAL
Corporate Strategic Planning
Corporate Strategy
Information Strategic Planning
Information Strategy
Architectural Planning
Tactical Planning
Implementatio Planning Implementation
Architecture
Short run objectives
Project plans
sibilities and so on. Finally, the IS planning process ends with the implementation of the plan, taking the form of different projects. Adjustment or alignment between the use of the IS and the Business Strategies can be observed on different dimensions. Two basic factors can strongly condition from the outset the success expectations of any IS: the first is the perception by the top managers (CEOs: Chief Executive Officers) of the mission and role to be attributed to the information systems. The second refers to the skills and knowledge held by the top manager in charge of the information system (CIOs: Chief Information Officers).
Information Systems and Business Strategy
37
Perception is the key word in this search for the fit between IS and Strategy. In the present world of business, ISs tend to suffer from a certain lack of credibility, in part due to the shortage of skills in CIOs, to the meagre resources assigned to this area, to the lack of convincing leadership exerted by these managers, or maybe due to lack of commitment and skills on the part of CEOs. Some empirical evidence reveals the persistence of problems of communication and coordination between managers responsible for IS and the top managers or CEOs, the reasons for this lack of mutual understanding not being clear. If the fault lies on the side of the top business managers, the reasons probably include: • Top managers tend to act in a reactive and defensive way to problems arising, neglecting the long-term perspective. • Lack of motivation to become involved with IS strategies, due either to feeling uncomfortable in this field or to knowing nothing about it. • It seems difficult to determine distinctively the role to be played by the IS in business strategy. • Tendency to underestimate the value of the IS as a competitive weapon. • Sometimes the introduction of IS is perceived as a mechanism for inciting changes in the company´s mission or in the business processes, which might cause a sort of rejection or negative perception by the final users who are unwilling to incorporate these changes. • The cost of introducing IS is usually relatively high and easily measurable, whereas the benefits derived from their use are not so clear in quantitative terms beforehand. • Fears about the impact the IS might have on the tasks fulfilled by an important group of future users. On the side of the CIOs, the factors hampering the balance between IS and strategy might include: • Often the CIOs fail to understand the challenges and goals of the business, so they are not in a position to decide the role to be played by the information systems. • In organisations with a small or non-existent computing system, the efforts needed to implant one IS would require an investment in time and resources the company cannot afford. • Some ISs fail to succeed due to the CIOs’ tendency to underestimate the skills and competencies the organisation needs to acquire for the proper implementation of these systems. • Some CIOs prefer to keep their distance from the managers responsible for drawing up the strategies of the company, and therefore, do not get sufficiently informed about business goals and strategies. • The tendency to judge the investment in IS as an opportunity cost, leads to assessing these systems by their measurable efficiency rather than by their support to the firm’s strategic actions.
38 Torres-Perez & March-Chorda
•
The high level of dispersion usually attached to information technologies, resulting from the lack of definition of the architectural hardware supporting the information system, causes new problems that are difficult to solve.
Models of Strategic Planning with Information Systems The traditional strategic planning model suggests the subordination of the IS to Business Strategy. The final architecture and configuration of these systems will be determined by the strategic lines drawn in a previous stage. As shown in Figure 7, the Business Strategy guides and leads the IS strategy. In this situation, ISs become supportive tools for the implementation and control of business strategy, but on the other hand, due to their subordination, fall short as instruments for the formulation of strategies. In this model, information technologies are treated as an endogenous variable. The question is posed in the following way: Given the strategic guidelines, which is the most suitable technology to provide support to the strategy? This approach seems valid and acceptable at the conceptual level, but reveals some weaknesses when determining the objectives to be attained by the IS. As suggested by Callon (1996), this traditional approach could be enriched by introducing a new dimension that suggests combining the corporate vision with the technological environment in order to come across with new business opportunities sources. This model seeks to determine how information technologies can create new strategic business capacities and how the technology can incite changes in the business. The model represented by Figure 8 goes beyond the previous one, by considering technology as an exogenous variable, with capacity to stimulate strategic capacities and skills. In this model, IS plays a proactive and broader role, becoming the technological core for corporate businesses and a key ingredient in the strategic planning process. As one condition required to make this model work properly, the CIOs must be determined to dedicate part of their time to strategic planning activities, Figure 7: Business strategic planning and IS (author compilation) Business Strategy
Dictates
Supports
IS Strategy
Determines
Information Technologies in the firm
Information Systems and Business Strategy
39
Figure 8: Business strategic planning and IS (author compilation) Vision
Technological Environment
Opportunities
Dictates
Business Strategy
IS Strategy
Supports
Determines
Information Technologies in the firm
Figure 9: A concurrent business/IS strategic planning model (I) (author compilation)
Information Technologies Field
Business Field
Impact Strategic Plan
Opportunities
Organization
Organization and Business Processes
IT Opportunities
Alignment
IS Architecture and Organization
to the detriment of the daily IS activities. Accordingly, the cost of maintaining this department will rise, and so broader skills will be needed in the CIOs to enable them to participate in the process of strategy formulation. The broadest and most comprehensive model we propose in this section takes as a starting point the one developed by Parker, Trainor and Benson (1989), completed with contributions by other authors like Hax and Majluf (1991), Andreu, Ricart and Valor (1996, 1997), Bloch and Pigneur (1998), Orellana and Torres (1997), among others.
40 Torres-Perez & March-Chorda
This model endows information systems with a proactive role through the whole process of strategic planning, rather than seeing it as a supportive instrument for the strategy implementation and control phases. Under this model, the organisation’s strategic guidelines duly incorporate all the IS components that might contribute to obtaining competitive advantages. The model distinctively identifies the relationships between business planning and information technology planning. The proper combination of strategic plan, business processes and structure, together with information systems, brings new opportunities for information technologies to become a key competitive factor, as shown in Figure 9. To better interpret Figure 9, let us clarify some concepts, especially those referring to information technologies: Strategic Planning: Organisational vision and guiding force in the process of decision-making, rooted in the competitive environment in which the company operates. It reflects the role and tasks to be performed by managers, and the product portfolio and market on which the company wishes to compete. Business Processes and Organisational Structure: Directly obtained from the Strategic Plan, these refer to issues of entrepreneurial culture, systems for measuring productivity, human capital empowerment, adjusting the organisational structure to the business strategies, etc. Architecture and Structure of Information Systems: This is the component to be aligned with the Business Processes in such a way as to guarantee the expected synergies between them. Several dimensions must be monitored: • Information systems existing in the firm. To what extent these systems are critical for the daily functioning of the firm. • Access to and availability of information for all the workers that request it. • Procedures to help the final user of the IS. • Integration in networks of both producers and receivers of information. This means extending the IS with expanded functions to the industry value chain, to reach customers and suppliers. • IS management: The information systems must be judged as one business within the company’s business, having the rest of the company´s functional subsystems as customers. • Information technology planning: includes the IS requirements in terms of hardware and software, to properly support the business strategies, from formulation to control phases. • User training plan: to ensure the organisation’s members take advantage of the benefits associated with using IS. Opportunities in information technology (IT): their extent and scope will be determined by: • Level of dependence on information technology for succeess in the market. • Competitors’ intensity in IS as a mechanism for differentiating their products and services.
Information Systems and Business Strategy
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•
Information technology culture: managers’ awareness of the role played by IT in producing competitive advantage. • Customers’ expectations of the value added to the products and services they purchase by the use of information technologies by the supplier. How much extra are they prepared to pay for the addition of that value? • Detection of opportunity windows opened up by the supply of products or services (either new or existing) based on new information systems. • Joint work with information technology suppliers, either to develop new computing solutions or to take better advantage of the potential of existing equipment. In conclusion, this model views information systems as strategic instruments suited to the strategy formulation stage, rather than operational tools for the strategy control phase. On the other hand, this is an open model, which in contrast to the traditional approach, recognises the capacity of information technologies to stimulate opportunities for the firm. Success in the application of this model will depend heavily on the consistency, coherence and soundness of the IS, both internal and external. As reflected in Figure 10, the external consistency refers to the necessary alignment between the IS and the structure of the sector, as well as the fit with the internal structure of the company and with the competitive strategy chosen to position it within the sector. Internal consistency has already been explained in Section 1 and refers to the necessary balance between the information system and the other systems in the firm, being aware of the strong interdependence among systems, and the role IS plays in integrating the systems forming the company. To end this second section, we provide in Figure 11 a summary of the interrelationships existing in our proposed concurrent Business/IS Strategic Planning Model.
EMPIRICAL ANALYSIS Scope and Objectives Our purpose in this section is to present some empirical evidence on the degree of alignment between entrepreneurial practice and the theoretical model proposed Figure 10: The external consistency (Andreu, Ricart and Valor, 1996) STRUCTURE OF THE SECTOR
ORGANIZATIONAL STRUCTURE IT / IS ROLE
COMPETITIVE STRATEGY
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Figure 11: Theoretical model (author compilation) Information Technologies Field
Business Field Managers role
Innovation
IT dependency on
Vision
IT use by referents
IT culture
the sector
Customers’ expectations
Reaction Capacity flexibility Impact
Opportunity windows
IT Opportunities
Strategic Plan ( product, market)
Cooperation with suppliers
Opportunities
Quality Organisation Entrepreneurial Culture
Critical role by IS
Productivity Alignment
Organisation and Business Processes
Access/availability of information
Architecture and Organisation of IS
Objectives Supportive procedures Measuring systems
Integration in network
Empowering
Structure – Strategy Fit
Training plans for users
IS functional extension IT Plan
IS Management
in Figure 11. For this purpose we have explored the relationships between IS and the strategic planning tasks developed by a sample of Spanish firms. The method for obtaining the information consisted of personal interviews with the CEOs and the CIOs of four companies, on a case-study basis. These four companies are located in the Spanish region of Valencia and belong to different sectors: furniture, metal-mechanics, plastics and services. Two of them exhibit a strongly innovative character, having received national awards in recognition of their innovative capacity. All of them fall into the category of medium-sized firms, with turnovers ranging from a minimum of $5 million to a maximum of $25 million, over 50 employees and presence in international markets. One of the key selection requirements was to have recently implanted information systems, for the managers to be able to make a comparative assessment of the situations before and after the investment in IS processes.
Companies’ Background Table 1: Key data Sector Activity
Firm A Furniture Home furniture
Firm B Firm C Metal-mechanics Services Lighting Civil engineering projects
1976 Average
1970 Leader
1984 Average
Firm D Plastics Plastic components for the automobile industry 1963 Average
Activity started in Competitive position Turnover (2000) thousand Euros Employees Main markets
7.813
27.046
4.886
12.621
63 Spain, EU
180 Spain, EU, Asia
117 Spain, France
Nº Product lines
12
6
53 Spain, EU, Near East 2
4
Information Systems and Business Strategy
43
To better illustrate the key traits of the companies under analysis, Table 1 displays some key data on the profiles of these companies: These firms show marked heterogeneity, a medium size in the Spanish industrial context and strong orientation towards foreign markets. Table 2 reveals some features characterising the CEOs and CIOs of these four companies. An outstanding trait is the medium experience accrued by CEOs in information systems management, basically limited to an average users level. Concerning the CIOs, their qualification is generally technical but not managerial. This orientation probably leads them to underestimate the strategic role the IS can play in their firms. As expressed in Table 3 the usual information technologies are notably installed in the four companies. At the hardware level, the proportion of computers per employee is satisfactory. Not so positive is the situation concerning software programmes. The high rate of use of office software contrasts with the low presence in the operations and production systems. Penetration of Internet in the firms under study is significant and email is widely employed.
Methodology of Analysis The methodology employed to obtain the data included a series of personal interviews with the CEOs and CIOs of these four firms with a structured questionnaire. Table 2: Features characterising the CEOs and CIOs CEO Owner Age Qualification Years in the post Experience in information technology Experience in Information Systems CIO Age Qualification Years in the post Experience in information technology Experience in information systems
Firm A
Firm B
Firm C
Firm D
Yes 45 Economics 16 High
Yes 65 Economics 22 Medium
No 38 Engineering 6 High
No 47 Engineering 12 High
Medium
Medium
High
Medium
31 Computer Eng. 3 High
52 Engineering 12 High
33 Computer Eng. 2 High
45 Engineering 10 High
Medium
Medium
High
High
44 Torres-Perez & March-Chorda
Table 3: The usual information technologies are notably installed Number of central computers Numbers of workstations Computers per worker Departments with computers: . Top management . Administration / Financing . Supplies . Production . Operations . Human Resources . Commercial / Marketing Frequently used software: . Ofimatics . Accounting / Financing . Logistic and Operations . Production control . Calculations . Design . Human Resources Management Employees using Internet ? Use of email: . Common accounts of ISP . Internal corporate . Internal and external
Firm A 2 15 0.24
Firm B 4 45 0.25
Firm C 3 34 0.64
Firm D 2 51 0.44
yes yes yes yes not not yes
yes yes yes yes yes yes yes
yes yes yes yes yes yes
yes yes yes yes yes yes yes
yes yes no no no no no no
yes yes yes no no yes no yes
yes yes yes no yes yes yes yes
yes yes yes yes no yes no yes
no no yes
no no yes
no no yes
no no yes
The first interview with the CEO attempted to ascertain some basic characteristics of the firm, regarding processes, organisational structure and the competitive environment in which it operates. The second interview, again with the CEO, was structured in the following parts: Part I: Description of the firm’s strategic process before implanting the current IS: Members participating in the process Methods employed to undertake the strategic analysis Process of strategy formulation Systems of strategy implementation Control mechanisms Role played by the previous information systems, if any, in the strategic process Part II: Analysis of the information systems of the company: Situation of the company before the IS was implanted Analysis of the IS effectively implanted Part III: Analysis of the current strategic activity: Description of the strategic process after implanting the IS Method for strategic planning of the information systems
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The theoretical model proposed was shown to the CEO, to afterwards establish similarities and divergences with the IS effectively installed in his firm. Finally, the third interview took place with the managers responsible for the information systems in the firm (CIO), with the purpose of contrasting their opinions with those of the CEOs, as follows: Part I: Description of the role played by the CIO and his department in the firm Part II: Description of the information systems of the company
Empirical Results All the information gathered from these three interviews in each company was aggregated to come up with a set of empirical results. Given the small size of the sample–only four companies–the purpose of this empirical analysis was never to draw a comprehensive and precise picture of the information systems operating in Spanish medium-sized firms, but is much more modest. This pilot empirical study tries to approximate the role ISs seem to play in a few companies, and especially to test out the degree of alignment or fit between these systems and the proposed theoretical model of concurrency between business planning and IS. This section contains the main results arising from the empirical analysis, divided into three parts. Part I refers to the strategic planning processes followed by the company before the introduction of the current IS. Part II gives the most common traits of the IS recently introduced. Part III highlights changes brought about by the IS in the strategic planning process. This final part attempts to describe the degree of fit between the proposed model and the model effectively used by the company.
Part I: Before the New IS The four companies under analysis display an average level of application of information technologies, representative of Spanish medium-sized firms. In particular, they have nowadays their own Computing Services Department, devoted to technical maintenance of the computing systems installed in the company. Despite being well-endowed, with one computer per person, computers are merely used as office tools, without any interconnectivity role. All the companies had maintained so far a sort of functional IS covering fields whose information was perceived as strategic for the company, particularly accounting and commercialization. Usually conceived as control instruments, the ISs were subordinated to the different functional subsystems of the firm, and did not have an influence on the existing strategic activity procedures. This subordinate role is consistent with the position held by the IS in the organisational chart, usually integrated within the General Administration Department. The CIOs’ tasks were usually supervised by the head of this department, the one in touch with the top managers. In this framework, the role the CIOs could play in strategic activity was practically non-existent. Even strategies directly concerning information systems were usually formulated by the general managers, once the strategic business plan had been defined.
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However, this situation was not considered optimal by the CEOs. They already recognised the need to obtain aggregated information of quality in time for the proper decision-making process. For all the CEOs, this need was the main reason justifying the introduction of new multifunctional ISs. In conclusion, the situation portrayed by the four firms, before the new IS was implanted, corresponds to a traditional approach to business planning /IS, in which the IS planning is completely subordinated to strategic business planning, with a role of simple operational control instruments solely applicable in the phase of strategy implementation.
Part II: Characteristics of the New IS Recently introduced The interviews with the CEOs confirmed that the ultimate justification for the introduction of a new IS was the growing concern regarding information as a supportive basis for the strategy planning process. Problems of dispersion and incompatibility among different kinds of information had so far hindered their aggregation to further support strategic activity in the firm. The main reasons given for introducing a new and updated IS were: • Need to homogenize the information produced by all departments • Need to reduce costs associated with the development of IS at the department level and the costs of integrating different functional IS • Need to reduce costs of coordination among departments and with customers and suppliers • To enable the existence of tools dynamic enough to give support to the processes of strategic decision making • To facilitate organisational learning All these reasons given by the CEOs are in line with the most up-to-date guidelines for the Strategic Management of Information Systems. However, they may be no more than declarations of intent. What really matters is the actual use the managers make of the IS effectively introduced. The four companies have introduced IS developed by outside specialist firms, following the classical introduction procedures of adapting the new IS software to the business processes existing in the firms. The new systems have never been introduced with the purpose of promoting the re-engineering of processes leading to a better fit between the internal capacities of the system and the structure of the firm. Therefore, these four companies, and probably the vast majority of firms installing new IS, by focusing on the existing business processes, fail to profit from the IS’s capacity to stimulate authentic reengineering processes, one of the most outstanding and powerful areas in which the new IS can have a significant impact. None of the ISs installed cover the whole range of areas of the firms, but just few of them, particularly: • Financing • Commercial • Internal and external logistics
Information Systems and Business Strategy
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• •
Purchases Production (MRP II) just in one company All the CEOs and CIOs interviewed agree the new IS should be introduced gradually, first covering basic or strategic issues to progressively enlarge their competencies to new basic modules as they consolidate. This view, broadly shared, reflects a sort of conservative attitude in the managers, as they do not seem to believe in the theoretical benefits associated with a complete introduction of IS on a larger scale. New IS have always been supported by external consulting firms. In the introduction process, the CIOs have usually played a liaison role between the consulting companies and the CEOs firmly involved in this process. In the CEOs’ view, the work of external consulting firms has not been entirely satisfactory, as the process of IS installation has usually taken longer and has incurred higher costs than expected. Furthermore, once implanted, these systems did not completely fit with the specific characteristics defining the host firms. CEOs believe the origin of this mismatch or lack of fit between IS and the firm lies in the consultancy company’s difficulties in obtaining a broad and precise knowledge of specific key issues concerning the objectives, structure and business processes of the client firm. In our view, the reasons for this mismatch go further. On the one hand, it is part of the CEO’s responsibility to make sure the consultants have the necessary information about the strategy and processes of the firm, a responsibility that should not be neglected. On the other hand, the lack of experience and qualifications of IS professionals in in Spain, due to the short span of time doing these tasks, is also a factor. As soon as the introduction of new IS becomes more generalised, and the competition among IS software firms increases, we believe these obstacles and problems of internal consistency will tend to disappear.
Part III: After the Introduction of the New IS After a few years in operation, an overall assessment of the new IS is fairly positive, according to the CEOs. However, there is room for improvement on several issues. First, the fit between the capacities inherent in the IS and the information needs stated by the firm is far from optimal. CEOs argue that it is difficult to make the right choice of one specific IS among the wide variety available in the market, since the actual capacity and properties of each system is unknown for the future user. They also demand greater flexibility in ISs in order to make them more adaptable to the specific characteristics of the firm. In the CEOs’ view, the IS available in the market tend to be too wide-ranging and general, and lack the versatility to adapt to particular requirements and situations.
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As already stated, the four companies under analysis, and probably the vast majority of medium-sized firms in Spain, do not usually follow a strategic planning process like the one presented earlier, when deciding to install new IS and undertake new lines of action orientated by the IS. The selection process is rather precipitate and fails to incorporate a sound strategic analysis. A proper selection process would start with a deep analysis to identify the needs motivating the purchase of the system, as well as the procedures to adjust it to the specific strategic process followed by the firm, and the reengineering processes needed prior to installation and full operation. On the benefit side, CEOs recognise that the quality, level of aggregation and the timely availability of information, the main operational goals of the new IS, have improved significantly. On the other hand, however, the broader goals, associated with the capacity to intervene in the process of strategy formulation, remain unachieved with the new IS. All the CEOs interviewed agree with the principles of the theoretical model of concurrency between strategy and IS presented in this chapter, and recognise its positive implications. They even declare their intention of gradually progressing in the direction indicated by this model.
CONCLUSIONS This section reviews the main conclusions drawn from the study. First, it is worth recalling the key value that different business administration theories ascribe to information, a critical input in the strategic process with capacity to generate competitive advantages, under certain conditions. Accordingly, the systems responsible for gathering, storing, treating and diffusing information to the potential users within the organisation are also destined to play a key role in most business organisations. These systems are known as information systems. The existing literature places the planning of IS and information technology as one basic task in the management of firms, with capacity to create competitive benefits, as has traditionally occurred in some large firms. However, the capacity to generate significant benefits from the IS seems lower in smaller firms. Practice proves that introducing a more advanced IS is a difficult and risky task. Some problems usually emerge beforehand, including a wrong definition of the technologies involved in the systems, mistakes in the selection of information or an inappropriate position of the IS within the organisational structure of the firm. After the introduction of the IS, problems arise from the different perceptions held by the three basic actors involved in the success of the IS: CEOs, CIOs and final users. Several models or approaches try to explain the linkages between business strategic planning and the IS. The generation of sustainable competitive advantages will depend at least partly on the selection of the most suitable model. Two approaches are the most widespread. The traditional model proposes the subordina-
Information Systems and Business Strategy
49
tion of information systems to business strategy, the IS becoming an operational tool that gives support to the process of strategic decision making. As stated in the empirical analysis section, this model involves little risk for the company, but also a low strategic profitability. Most CEOs consider this model to be a starting point for properly integrating the IS within the organisational structure. The second model, called the Concurrent Business/IS Strategic Planning Model, defends the development in parallel of the information system and the business strategy, so that both fields are mutually reinforced. This is a rather complex model which tries to match two fields, first the business, which includes the strategic plan, dependent on the vision of the firm, and the role played by key issues such as innovation, quality, flexibility and market segment. The business also entails the organisation and business processes concerning, among others, productivity, entrepreneurial culture and the structure-strategy fit. The second field refers to information technologies, and captures the opportunities opened by IT in the present competitive environment, as well as the architecture and organisation of information systems devoted to spotting those opportunities and turning them into competitive advantages. From the empirical study undertaken in four Spanish medium-sized firms, we can derive several conclusions. Most top managers tend to consider the different models of strategic business planning/IS as sequential stages in the introduction of IS in the company, which need to be covered one after another in order to ensure the correct fit and avoid negative impacts on the structure and the human resources of the firm. The empirical evidence found in the firms reveals some deficiencies, including the coexistence of several insufficiently interrelated ISs, communication problems, mismatch of the IS with the organisational structure, and disconnection between the business strategic planning and the IS planning. In this framework, the empirical study shows that the theoretical model proposed is far from being implanted in the firms, despite revealing a broad capacity to stimulate competitive advantages through the use of IS, already recognised by the CEOs. To better test out these initial results, the pilot empirical study presented here should be revised in a few years, to examine the evolution of the recently implanted IS, and to check if the path proposed by the theoretical model is being followed, or if on the other hand, the IS is behaving according to the traditional model. In the four companies under analysis, the new IS have been running for too short a time to allow definitive conclusions to be drawn. A second assessment in a few years’ time would render sounder and more definitive results on the real possibilities and benefits derived from IS in supporting the processes of strategy formulation.
REFERENCES Andreu, R., Ricart, J. R. and Valor, J. (1996). Estrategia y Sistemas de Información. Madrid: McGraw-Hill.
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Andréu, R., Ricart, J. R. and Valor, J. (1997). La Organisación en la Era de la Información. Madrid: McGraw-Hill. Antill, L. and Clare, C. (1991). Office Information Systems. London: Blackwell. Bloch, M. and Pigneur, I. (1998). The extended enterprise: A descriptive framework, some enabling technologies and case studies in the Lotus Notes environment. Journal for Strategic Information Systems. Callon, J. (1996). Competitive Advantage Through Information Technology. New York: McGraw-Hill. Cash, J., McFarlan, F. W. and McKenney, J. (1990). Gestión de los Sistemas de Información de la Empresa. Los Problemas que Afronta la Alta Dirección. Madrid: Alianza Editorial. Cornellá, A. (1994). Los Recursos de la Información. Madrid: McGraw-Hill. Cuervo, A. et al. (1996). Introducción a la Administración de Empresas. Madrid: Civitas. Emery, J. C. (1990). Sistemas de Información Para la Dirección: El Recurso Estratégico Crítico. Madrid: Diaz de Santos. Hax, A. and Majluf, N. S. (1991). The Strategy: Concept and Process. New York: Prentice-Hall. Keen, P. and Scott-Morton, M. (1978). Decision Support Systems: An Organisational Perspective. Reading, MA: Addison-Wesley. Mélèse, J. (1979). Approches Systemiques des Organisations. Paris: Hommes et Techniques. Orellana, W. and Torres, A. (1997). La información en la alta dirección. Una actualización de los EIS a las nuevas oportunidades de las tecnologías de la información. Paper for XI AEDEM Congress, Lleida. Parker, M., Trainor, C. and Benson, R. (1989). Information Strategy and Economics. Englewood Cliffs, NJ: Prentice-Hall. Porter, M. (1985). Competitive Advantage: Creating and Sustaining Superviser Performances. New York: The Free Press. Rockart, J. F. and Treacy, M. E. (1982). The CEOs goes online. Harvard Business Review, January-February. Sprague, R. H. (1980). A framework for the development of decision support systems. IS Planning for Decision Making. Van Nostrand Reinhold.
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Chapter IV
Integrated QFD and Knowledge Management System for the Development of Common Product Platform Walter W. C. Chung, Colin K. S. Tam and Michael F. S. Chan The Hong Kong Polytechnic University, Hong Kong
ABSTRACT This paper provides a framework to implement quality function deployment (QFD) with knowledge management (KM) in the form of an integrated quality and knowledge management system (IQKS). This inter-organizational information system enables the sharing of information among customers, manufacturers and suppliers in the new product development process. It links up various strategically independent and autonomous business entities together in a common product platform (CPP). Up to now little has been published academically on sharing vital information involving the give-away of a firm’s bargaining position about customers, product specification and process requirement. The common product platform facilitates an innovative organization to persuade like-minds in coming together and opening up useful and relevant information to all parties interested in creating a new level of competitive advantage in assessing the dynamics of market realities. They share knowledge and learn to support new organizational capabilities to leverage information technology that incorporate market knowledge, design knowledge, process knowledge and production knowledge. Copyright © 2003, Idea Group, Inc.
52 Chung, Tam & Chan
INTRODUCTION In the new economy, customers, manufacturers and suppliers dispersed across various geographical locations use information technology to talk to each other to coordinate engineering design and the flow of materials through different manufacturing operations. Hence, a product can have a choice to be made in various processes across organizations, at different geographical locations and utilizing different logistics for delivery. This enables the entire operational process to be linked up across organizations for customization by the participating firms. A new level of competitive advantage may be achieved when the operational processes of different firms can be adapted with the dynamic market environment. Such advantage can be crucial to the development of new products. A product is manufactured through subassemblies and parts that can be put together. Each subassembly is represented by bills-of-materials (BOM). They can be deployed in a common product platform (inter-organizational information system) by partnering firms linked together with an information technology network. The coordination, marketing, design, engineering and production control can be more effective. This chapter is divided into four sections. The first section addresses the view on contract manufacturing. It suggests how a manufacturing organization can leverage IT to compete in the information age. In the second section, relevant literature is reviewed on the area of extended enterprise. It portrays that knowledge has become an important consideration in developing extended enterprise architecture. For the third section, a conceptual framework of extended enterprise architecture is derived from observation on contract manufacturing. It reflects the necessary components and the linkage of quality function deployment (QFD) and knowledge management system (KMS) to form a framework for developing an integrated interorganization information system. A case study is used to illustrate how the conceptual framework can be operationalized in the form of a common product platform making use of the bill-of-material (BOM) modules.
LITERATURE REVIEW Contract Manufacturing Contract manufacturing is more than a firm sub-contracting out its manufacturing process and is about a strategy on positioning the firm in the future of providing a service of value. The firm operating with a contract manufacturing strategy is adopting a management paradigm that has far-reaching implications in terms of ownership, management succession planning and resources deployment. Contract manufacturing is a phenomenon occurring in various manufacturing industries. It extensively aligns partners for collaboration in serving the customers. The firm in focus will only keep those processes that it is strong in and outsource the rest of the processes to suppliers or competitors. Without a good understanding of its implication on the nature of business and investments in appropriate technology, knowledge and research, manufacturing firms are likely to miss business opportuni-
Integrated QFD and Knowledge Management System 53
ties in the ventures that create wealth and create customers for growth and profits. Currently, views on contract manufacturing are diversified. In here we summarize a few points that are purported to be the key enablers of contract manufacturing. They are: 1. Changed view of international production strategy from “stand alone” operations to “deep integration” of operations with partners in a supply chain. 2. The increasing separation between the supply side and demand side of business: e.g., supply-side consolidation for economies of scale; demand side fragmentation enabled by e-commerce. 3. Knowledge separation enabled by digital factory concept. Manufacturing knowledge can be separated from design knowledge; or marketing knowledge can be separated from design knowledge. 4. The transition from a “command and control” style of management to a “onestop shopping” self-adaptive supply chain style of management to enable customization of value.
The Change of International Production Strategy A “stand-alone” operation for control is leveraging on a level of technology investment that encourages the moving of production facilities to a low-wage manufacturing environment for competitiveness. (Most Hong Kong manufacturing companies have relocated their production bases to Mainland China after China announced its open door policy in 1978). However, a “deep integration” operation is about the strategy of developing an integrated production system to support disserting the different steps of a production process and have different parts undertaken by different countries according to their relative cost and logistical advantages. (UNCATDA, 1993, 1999).
Increasing Separation Between Supply and Demand Side The advance in information technology and networking economy, the development of electronic commerce and the proliferation of B2B online exchanges further separate the supply and demand sides of many industries. On the demand side, there is a rise of customer expectation. Customer relationships or learning relationships are being developed to retain future business value and growth (Peppers & Rogers, 1997). On the supply side the pressure of getting economies of scale and scope lead to changes in the traditional industry structure. Many companies are now consolidating to realize economies of scale and scope to recoup their investment in business infrastructure (Hagel & Singer, 1999).
The Observation of Knowledge Separation In many industries, design knowledge is being separated from manufacturing knowledge. This knowledge was once difficult for articulation but now can be made explicitly with substantial organizational implications. Manufacturing organizations can outsource even more to a network of suppliers (Magretta, 1998a). Central to this
54 Chung, Tam & Chan
development is the development of “connected assets” (Blitz, 1999). Today contract manufacturing thrives on this trend with the development of sub-systems template and the setting up of a common product platform that can be readily integrated upon customers’ desire (Meyer & Lehnerd, 1997). The ability to develop and quantify these assets is the key for future business development.
The Self-Adaptive Supply Chain Style of Management The ability for a team of organizations working as an extended enterprise through dynamics partnerships is the key for future success. The existence of a partnership ties the stakeholders of organizations together in developing commitments and sharing a risktaking culture. Corporate competence will have to be integrated to address changing market opportunities. The cost of maintaining a set of vertically integrated competencies is prohibitive for many businesses (Agility Forum, 1997). The ability to interface with others within a value net is the key for future success. The table below illustrates some of the contract manufacturing activities recently found in various manufacturing organizations and industries. The reported contract manufacturing scenarios revealed that manufacturers organize themselves to leverage IT to share information and compete more effectively. The reported cases led manufacturing management to a new era of competition. The new competition is not based on economies of scale, but much depends on how quickly enterprises are aligned together to come up with new business processes tailored to the market requirement. This resembles the business model of mix and match in a real-time exchange market. Thus, contract manufacTable 1: A summary of recent contract manufacturing activities Case 2001 Ericsson and Flextronics (Gartner, 2001) Outsourcing Outsource the manufacturing of mobile phone to Flextronics by transferring plants and employees from Ericsson 2000 Motorola and Flextronics (Thurm, 2000) Outsourcing Motorola hires Flextronics to manufacture consumer electronics Alliance Cross investment in stake-holdings 2000 Arima and Compaq–Notebook Contract Manufacturer in Taiwan (Baum, 1999) Partnering Notebook research center assists the manufacturing of notebook computers 1999 Nortel Network (Royal, 1999) Outsourcing Outsourcing the manufacturing of telecommunication equipment 1999 Sara Lee (Royal, 1999) Outsourcing Selling factories and outsourcing manufacturing of food products. Concentrate on servicing brand name
Integrated QFD and Knowledge Management System 55
turing here is defined as a partnership among manufacturing organizations, customers and suppliers working together as an extended enterprise using IT links. Such an alignment model requires the support of extended enterprise architecture.
Modeling Extended Enterprise An extended enterprise can be described as a partnership between enterprises where the goal is to achieve competitive advantages by forming formal linkages (contracts) and maintaining cooperation distributed throughout the network. In the extended enterprise the collaborating firms are encouraged to focus on their core competencies (Szegheo & Andersen, B, 1999). With the partnering elements becoming more important, extended enterprise modeling seeks a holistic view to avoid the optimization of one partner at the expense of the others. Activities are used as the basic elements for modeling an extended enterprise. The challenge is that the activities are dispersed across the network, crossing organizational boundary, making information collection and modeling very difficult. IT linkages will have to be deployed more systematically to collect the necessary information to enable such a model to become a reality.
CONCEPTUAL FRAMEWORK A knowledge-based view is proposed as the basis for the formulation the extended enterprise architecture, hence the term “knowledge-based extended enterprise architecture.” The idea is to encourage partnering in the extended enterprise to form the basis of developing a knowledge-sharing practice. Four core components of the extended enterprise architecture have been identified. They are market knowledge, design knowledge, process knowledge and production knowledge.
Market Knowledge Market knowledge such as customer lists, market research, data on service or demand patterns are considered to be an important element for the existence of an extended enterprise. Market knowledge serves as the primary input to the extended enterprise and is translated into specific customer needs, perception and preferences. It is a conduit to enable the extended enterprise to interact with the market in order to reconfigure itself for the market. Sometimes, discovering hidden value propositions of the market can assist extended enterprise to move away from manufacturing a “me-too” product.
Design Knowledge Design knowledge is associated with research and development efforts. It embraces the technological content of the product, essential to a business, and is supplied by knowledge workers within the participating firms of the extended enterprise. The design knowledge is very important in supporting the product requirement derived from market knowledge. In many circumstances, combining
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market and design knowledge uncovers hidden value propositions for new customers. Early involvement with customers to design a new product can be beneficial to reduce design lead-time. Thus, participating extended enterprise organizations that contribute or invest in activities to make instrumental goods early in new product development can improve overall extended enterprise performance.
Process Knowledge Process knowledge is a derivative of manufacturing knowledge. It is concerned with workflow management, capacity management, logistic arrangement and scheduling arrangement. Knowledge workers contributing these kinds of knowledge can be sourcing managers, supply chain managers and schedulers. They ensure the order fulfillment process is in place for delivery purposes. The knowledge in this area is generated and acquired through extensive work and know-how in infrastructure management.
Production Knowledge Production knowledge is also a derivative of manufacturing knowledge. It is associated with production of physical goods. Investment in manufacturing technologies and management helps develop such knowledge in a manufacturing organization. Smart use of manufacturing technologies and management of knowledge work give insights into the development of instrumental goods that can be deployed for improving realization activities. The performance is usually associated with volumes, quality and cost. When material costs and overhead costs represent a large proportion of overall product costs, product knowledge is essential in trying to keep the cost structure under control. Production knowledge can combine with other types of knowledge in the extended enterprise architecture to improve performance. For example, early supplier involvement enables the sharing of knowledge by both manufacturers and suppliers, and they can benefit by shortening the time to market.
Common Product Platform – Embedding Knowledge in Product A common product platform is proposed as a vehicle to establish a knowledgesharing routine within the extended enterprise. This provides participants a reference model of a product that can be modified by the different partners of the extended enterprise architecture. Figure 1 shows the concept of a common product platform supporting the use of different knowledge to customize a product. As a knowledge-sharing routine, the common product platform allows knowledge from different areas to interact with each other to create new ones. For example, design knowledge can interact with manufacturing knowledge in a way that the product can be designed more effectively for production.
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Figure 1: The concept of a common product platform (CPP) Customer requirement, market trend
Market Knowledge
Product design, product performance
Delivery, logistics, order fulfillment, scheduling
Design Knowledge
Process Knowledge
Materials cost, capacity planning, etc.
Production Knowledge
Common Product Platform
Develop a System to Leverage the “Voice of Customers” with Knowledge Management Based on the common product platform, a system for integrating quality function deployment (QFD) with a knowledge management system (KMS) is to be developed. It incorporates the QFD and the KMS systems. The QFD is a tool for enterprise modeling that allows internal as well as external staff to communicate and contribute knowledge in a systematic manner. It is a useful modeling approach to discover the interrelationship among customer demands, engineering requirements and manufacturing processes. Central to the QFD is the house of quality that defines the WHAT and HOW as part of the relationship. The WHAT and HOW is captured by a knowledge management system (KMS). Conceptually thinking, the captured interrelationship can be shared and reused. The knowledge management component is to facilitate sharing and reuse by providing functions such as capture, filter, storage, distribution and applications. Figure 2 illustrates the concept of Integrated QFD and KMS (IQKS). To operationalize the common product platform, the BOM can be built into different phases of the quality function deployment to support the phased operation of the QFD process. The four phases of QFD are linked together for information and through sharing of embedded knowledge. The KMS provides the knowledge repository to share information regarding the captured QFD interrelationship. The migration from HOW to WHAT, and the translation of information from one phase to another in QFD, indicates transform knowledge. The information collected and processed serves a common language for exchange in product and process information. As a result, guiding the collection of useful information (transfer information) gradually builds up the knowledge management system. The knowledge layer is an open-type pool, which allows user interface involvement to collect, retrieval knowledge for specific application. The layer captures the relationship within the QFD matrix. The accumulation of this captured knowledge is stored in the knowledge layer embedded within the KMS.
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Figure 2: The common product platform architecture (IQKS) supported by QFD and KMS Data, Procedures, Process
Transform Knowledge I
Transform Knowledge IV
Transform Knowledge III
Transform Knowledge II
QFD+KMS Integration Engine
Knowledge
How Much
PHASE II Design Deployment
Transfer Knowledge II
PHASE III Process Planning
Relationships
PHASE IV Process/Quality Control Transfer Planning Knowledge II
Information Layer Knowledge Layer
KNOWLEDGE MANAGEMENT SYSTEM user 1
user 2
user 3
user 4
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Product Requirements
KMS Interface
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Relationships
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User X
User Interface
Relationships
Process Parameters
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HOW WHAT
How Much
How Much
PHASE I Product Planning
Part Characteristics
Process parameters
Relationships
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Part Characteristics
HOW
Technical Characteristics
Company Measure
Customer Wants
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Customer surveys
HOW
Conflicts
CASE STUDY Background The motor fabrication company (MFC) is one of the world’s largest component manufacturers. It manufactures a range of electric motor products to cater for industries like automotive, home appliance, personal goods, consumer electronics, etc. Their existing customers are associated with well-known brands with their products for worldwide distribution. MFC is engaged with their customers extensively in contract manufacturing initiatives such as outsourcing, partnering and alliance. MFC provides customer support to serve the needs of their customers in new product development. The customers are getting enhanced services from MFC rather than the product itself, and these services include co-design, R&D, engineering, logistics, etc. MFC customers do not need to be concerned with supply chain management, procurement and shipment logistics of the motor products, because all these functions are taken care of by MFC. In many instances, there are a range of products that are similar. For example, MFC manufactures the moving components for the consumer printers’ market. MFC makes assessment on each of the product within the market based on each of their customer’s product mapping. MFC gains competitive advantage through providing better services to customers. By establishing the position of the customer in the product map (Figure 3), the
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Figure 3: Customer-product mapping in consumer printer market Services: For example: Print quality, speed
High
Consumer Printer
Customer B
Customer A
Medium
Customer C Low
Low
Medium
High
Volume
company interacts with customers and gives tailored services of attractive value. Interacting with customers at MFC is a very complex task. It involves contributions from numerous parties from sales, research and development, costing, engineering and procurement to the external suppliers. Figure 4 shows the complexity involved. Interacting with the customers and providing support by answering their queries involves the participation of executives in the organization engaged in knowledgeintensive work. The interaction pattern typically starts with receiving the “customer voice” to the actual commitment of production of electric motors. It is not limited to communication with customers but to a range of parties, including suppliers and logistics services providers. Interactions with the customers at this stage are complicated, and information flow has significant impact on most manufacturing organizations. Success in the venture very much depends on leveraging the use of knowledge for a speedy response. It is critical to embed information into the product. By offering services of value in the value chain, each customer concern may be addressed differently. Products that do not appear as high-value to one customer may appear to be highly valued to another. Furthermore, a previous customer may provide misleading data to MFC in handling other customers. Since the number of parts and associated fixtures proliferate, it requires huge efforts to coordinate. The erosion of value-adding activities creates difficulties for MFC in integrating their performance with the value chain. They can be seen as the limiting factors for
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Figure 4: Organising to provide a service of value to the customers Customer, Sales Product Configuration, Product Requirement
Costing Group Product Cost Estimation
Project Engineering Product Development
Testing and R&D Group
Product Testing and Technical R&D
Cross-Functional Team
Manufacturing Development
Tooling Engineering Tools & Components Development, Fixture Development
Scheduling Schedule Arrangement
Process Engineering
Suppliers/Warehouse
Process Design, Product and Process Specification
Inventory, Material Supply
Production
Production Planning, Direct Production
higher performance from the perspective of customers. There is an urgent need to streamline the information flow for managing the interactions with customers.
A Knowledge Management Process Model An integrated quality knowledge System (IQKS) is conceived at MFC to link all the knowledge silos with the bills-of-materials (BOM) structure of electric motors. The knowledge silos are represented by the pools of knowledge accumulated as a result of specialization in sales and marketing, product design, process planning and production. Each knowledge silo corresponds to a phase of the quality function deployment (QFD). When arranged in this way, the knowledge silos enable knowledge input into the WHAT and HOW of the QFD tool. The BOM information model combines the data in a structure for sharing. The BOM information is embedded into a knowledge management system to support the four core activities of value creation: 1) information and knowledge acquisitions; 2) knowledge filter; 3) knowledge storage; and 4) knowledge distribution (see Figure 5). The BOM information models are run with the support of databases, intranet, extranet and internet. The purpose is to physically enable the communication of more accurate customer requirements, including target price, development cost and product attributes.
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Figure 5: A learning model to link up knowledge silos Information & Knowledge Acquisitions
Knowledge Filtering
Knowledge Storage
New Knowledge Built
Sales QFD Phase I
Knowledge Distribution
Knowledge Application
Design BOM
Process BOM
Productio Production BOM
QFD Phase II
QFD Phase III
QFD Phase IV
In Figure 2, the architecture of the IQKS system consists of information the layer and the knowledge layer. The information layer has BOM databases for internal and external users. The internal BOM databases collect the product design phase information, which is captured from each QFD phase migration under a FORMAL practice. The external BOM databases differ from the internal ones in that they are used to collect any INFORMAL information, which at the collecting moment may not be very useful to anyone but its owner; however, it might also make a difference in the long term. The users can get access to the information using simple searching and browsing criteria. The knowledge layer is a combination of information, data and graphical display. The accumulation of the captured knowledge is stored under the knowledge layer. This knowledge layer is an open-type pool, which allows user interface involvement to collect and retrieve knowledge for specific application. A pre-defined search criterion is created to capture queries. The system is linked with internal departments, customers and suppliers. Users are granted with different access rights depending on their status. The information layer allows the users with a knowledge perspective to add into the BOM databases (under the QFD structure) for knowledge capture and retrieval. The QFD structure enables transition of information on the nature and underlying links between WHAT and HOW. At MFC, the implementation of an integrated QFD knowledge system (IQKS) represents an achievement of leveraging knowledge-based manufacturing technologies to bring in the needed information at the early stage of the internal business operations. The implementation has the steps: a. To link up each QFD phase. b. To link up every individual or department and show the detail job progress, under a common information technology platform. c. To capture the employees’ routine deliverables and output to fulfill the knowledge acquisition
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d. e.
To share the useful information and knowledge for corporate growth. To provide a freedom searching under web-based infrastructure. The IQKS defines the tasks that a knowledge worker needs to perform at each phase of the QFD. These tasks are, as mentioned previously, product planning (house of quality), design deployment (parts deployment), and manufacturing planning (process planning) and production planning (production operations planning) in relation to customer’s want (requirement). The WHAT and HOW govern the formalization of knowledge by introducing a common platform to synchronize the use of information. These represent the knowledge that can be captured and disseminated as the means of future production. The means of production also serves as the manufacturing input to internal business operations in order to make the operation more responsive to the external environment. The knowledge management infrastructure is to be overlaid on the existing corporate environment. It utilizes the IT network to link up the different personnel together including sales, design engineers, process engineers and production engineers (see Figure 6).
Figure 6: Knowledge management infrastructure model
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A Knowledge Management Prototype This section describes the user’s interface for using the system of Web-based knowledge center. The internal portion (intranet) is rolled out first to structure the group use of the knowledge framework, thus allowing the QFD phase IV of information (Figure 2) to be shared and distributed on a daily basis. Next is to establish the web-based knowledge center via the Internet portion. The illustration is shown in Figure 7. The core of the prototype was produced under Lotus Domino Notes environment as shown in Figure 7. While the Lotus Domino Notes enables data to be captured under an Internet framework, the user interface of the IQKS system includes certain database classifications with respect to the usage nature under QFD phases. Figure 7 shows the basic database icons, which formulate the basic IQKS system’s components and are defined below. Different manufacturers (due to product nature’s variance) could have more or less or different icons but the database must be under the common-BOM-platform with monitor and control by a centralized system (IQKS: CAT-D). Figure 7: The IQKS system interface on Lotus Notes
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CAT-D CAT-D is the component architecture technology database. It serves as the global common-BOM platform to link communications of each QFD phase. It is a tool for global transparency of information. Throughout the input from various persons and departments, information is able to be shared and distributed as knowledge through this layer. CAT-D is divided into internal and external portion with support from intranet (organization users) and Internet (customers) respectively.
E-mail Policy E-Mail Policy states and clarifies the company policy as well as security warning to all users, internal and external to MFC.
Bulletin Board This is essential for organization cross-communication (geographical aspects and business unit nature) purposes, as well as capturing and sharing the external information. The board is pre-defined by expert team and it stipulates what kinds of information are allowed to be placed in the system. Since employees or customers already “think” (filter) what should be put on the board, filtered and valued information can be stored.
Discussion Database Discussion Database acts as an Information Record Buffer to temporarily collect and store communication information such as meeting minutes. The users are allowed to write down discussion about anything, but IQKS classifies these as vast unstructured information and are stored for general discussion only. The inside contents are cleaned every two months to free up server space. Any information considered valuable for permanent storage is transferred into Bulletin Board for corporate distribution.
Standard Product Standard Product (motor) holds the records of company standard products. It is linked with the Internet for customer selection or for placing an order. Considered to be the primary form of electronic business over the web, it facilitates the transactions of “commodity” motor products of MFC. It also acts as a product catalog. The “hit-rate” shall be recorded for sales and marketing in support of customer relationship management to analyze the number of interests and demands from real-time market.
Technical Support Technical Support directs question and answer services from internal and external customers. This database has links with standard product databases. This is because a customer, having once selected their favorite products and who might like further customizations, would like to receive direct confirmation of technical possibilities. It is part of introducing engineering services to the front line of customer services.
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Engineering Database Library Under the CAT-D framework, Engineering Database Library captures and stores the engineering information such as product testing record and characteristics. It facilitates the empowerment of internal staff by sharing critical engineering knowledge.
Raw Material Specification This is a library of raw material specifications and is shared among staff at MFC. The main users of such library include supply chain department, purchasing department, engineering department, R&D, and design & product engineers. Each of them uses the information in a very different perspective.
DC Sample Specification This stores product sample records, BOM, specifications and quality test results. Engineers routinely input their design specifications and the BOM information. Product-testing workers and testing engineers input the quality test records. The information is combined in such a way that any new knowledge arising from the know-how in routine capturing can be articulated through some pre-defined information transformation criteria.
Fixture Library Fixture Library facilitates process planning through hyper-linkage to engineering database that contains the process details such as fixture design and setting and so on. The engineering database is dynamically linked with other databases to derive information stored under the fixture library.
Production Specification Production Specification facilitates production and workflow design by recording the production line layout, workflow design, existing line utilization rate and capacity.
Customer Situation Analysis Customer Situation Analysis facilitates quality control. It captures planning records of product complaint history, its subsequent quality improvement analysis along with the failure report. A group sharing of failure know-how from quality audit is preferred to traditional handling by a single person, from a learning perspective.
The IQKS Phases The sales, agent or customer service can use the IQKS system and input the customer requirement into the CAT-D system (through Internet portion). The “hitrate” for specific items and products reflects the market trends and demands and fulfills the QFD phase I where information is retained for future searching and references. The senior management (sales & marketing, business manager) derives appropriate actions.
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Figure 8 shows internal staff accessing the customer historical inquiries through an internal Lotus Notes system. The information regarding customer requirement is stored as list of requirements (LOR), which defines customer specifications of the motor product. The LOR is a combination of information from database and various documentation including drawings and associated graphical presentation on performance. The Lotus Notes system supports these documents with its document management system. In product design phase (QFD phase II), the LOR (or Sales BOM) from phase I is linked to the product design BOM using the same project number. Basically, LOR is translated into product design BOM by a group of experts and stored under a database accessible in the Lotus Notes environment (see Figure 9). This BOM is able to match and merge with the product information; Figure 10 exemplifies the result. Searching and selecting is allowed by referring to product models, component part names or part number. Referring to these keys allows associated information to be extracted. This directly shows whether the product design is capable of fulfilling the customer requirements. With the Lotus Notes documents sharing environment, file attachment and graphical presentation is technically feasible. The following scenario demonstrates what key deliverables can be provided by showing what was known and what was unknown on a daily basis. Figure 8: IQKS phase I
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Figure 9: The translation of LOR to design BOM, QFD phase II
1.
A standardized terminology of products, product attributes (process/production, planning quality) can be mapped out, particularly to products of similar nature (see Figure 10). This serves as a common basis of knowledge sharing as part of the common product platform. Design lead-time can be reduced because inexperienced staff can pick up the product design by using the appropriate information. In short, the common basis of knowledge enables selflearning to occur among inexperienced staff. 2. What is known can be captured explicitly under a structured format. Design processes on product characteristics, causes and solutions can also be captured. The process BOM equivalent to QFD phase III can be accessed under the Lotus Notes environment (see Figure 11). The information is stored in the database dynamically linked with design BOM information. The project numbers, product
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Figure 10: Integrated LOR with design BOM to facilitate learning
Figure 11: Process BOM
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models and components all serve as the common searching criteria. Documents regarding fixtures, machines settings, maintenance record and application records are shared within the document management system of Lotus Notes. The last phase of QFD phase IV gives the production BOM that is linked to the IQKS architecture. It deliberately combines quality control with product specification information together as production and quality control information. The commonly used searching criteria are also supported by the Lotus Notes. Figure 12 illustrates the production BOM interface under the Lotus Notes environment. Traditional practice may only share pieces of information, and it is up to the staff to put them together in a meaningful way. Performance information can be presented in a graphical format important to the user.
DISCUSSION The prototype of the common product platform is currently being expanded to cover suppliers and customers in order to enable a true collaborative mode in the customization of business process. However, since separate organizations are different in many ways (for example, the difference in part specification), the difference in manufacturing environment can very much compromise the progress of the common product platform. If the participating organizations are not quickly realizing the potential benefits of the platform, the implementation team can lose control of the project with a consequence of budget overruns and resulting cooling down of interests of sponsors from senior management. Figure 12: Production BOM and QFD phase IV
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The central accomplishment of this common product platform is the idea to facilitate extended enterprise responsiveness, which in turn represents a large portion of competitive advantage for the participating organizations. Extended enterprise responsiveness focuses on change and dynamic business reconfiguration at the process level. Each participating organization must manage their performance for the benefits of the entire value network through the exchange of information. Since each participating organization must manage their own performance, this means they will perform activities that are most likely to be in their best interest. For example, suppliers that aim to utilize low-cost labor for manufacturing will most likely incline towards economies of scale, thereby producing product in large quantities. Low cost is their order-winning criteria, and they can squeeze reasonable profit margin from the low-cost commodity parts or suppliers. When complex products require the support of these commodity parts, other participating organizations within the extended enterprise can join in the collaboration to deliver complex products while retaining product performance such as low cost or time-to-market. The only challenge is that they must be previously aligned together to allow such business configuration to be engaged. Since manufacturing organizations must manage their own manufacturing environment for performance, more understanding is needed in how collaboration can be materialized in different conditions. More importantly, these conditions govern the coordination of economic activities dispersed across enterprises. Therefore the attempts to make individual enterprise systems compatible to each other are the key concern for extended enterprise operations. New understanding should focus on how the market knowledge, design knowledge, process knowledge and production knowledge impact on different manufacturing environments: make to stock, make to order and engineer to order. A lack of protocol or standardization is the major attributing factor that inhibits individual enterprise systems from being more compatible with each other.
SUMMARY AND CONCLUSION The challenge of managing information for product development must be addressed for many manufacturing organizations entering into the new economy. The creative nature of work or “not invented here syndrome” inherited in generating new products or new services discourages knowledge workers to reuse information and lures them into solutions that involve redesigning the entire part which ultimately affect the efficiency of partnering with suppliers during early supplier involvement. The excessive amount of time spent and cost involved in searching for right information and assuring their relevancy are unnecessary in the first place because parts can be reused with slight modification. This chapter operationalizes the concept of common product platform for contract manufacturers to manage the product development. It makes use of databases to facilitate the reduction of a wide variety of parts in order to simplify
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communication and to provide speedy customer responses. The platform merges the concepts of QFD and knowledge management to form an integrated IQKS system. The core is a knowledge-based system aimed at facilitating communication with customers and empowering knowledge workers. The bill-of-material becomes the backbone of the system in managing knowledge for use and reuse.
ACKNOWLEDGMENT The authors acknowledge that this is the outcome of research supported by the Central Research Grant of The Hong Kong Polytechnic University under project number G-V581.
REFERENCES Agility Forum. (1997). Next Generation Manufacturing. Blitz, A. (1999). Connected Manufacturing Enterprise. Ernst & Young, www.businessinnovation.ey.com. Baum. J. (1999). Small is beautiful. Far Eastern Economic Review, November 18. Deighton, N. (2001). Ericsson to Stop Terminal Manufacturing. www.gartner.com. Gartner Group. Hagel III, J. and Singer, M. (1999). Unbundling the corporation. Harvard Business Review, March-April. Hill, T. J. (1990). Manufacturing Strategy: Developments in Approach and Analysis. PhD Thesis. Yorkshire, UK: The British Library British Thesis Service. Magretta. J. (1998a). The power of virtual integration: An interview with Dell Computer’s Michael Dell. Harvard Business Review, March-April. Magretta. J. (1998b). Fast, global and entrepreneurial: A supply chain management, Hong Kong style. Harvard Business Review, September-October. Meyer, M. H. and Lehnerd, A. P. (1997). The Power of Product PlatformBuilding Value and Cost Leadership. New York: The Free Press. Peppers, D. and Rogers, M. (1997). Enterprise One to One. New York: Doubleday. Royal , W. (1999). Contract manufacturing: Perils and profits. Industry Week, November. Szegheo, O. and Andersen, B. (1999). Modeling the extended enterprise. International Enterprise Modeling Conference, Verdal, Norway. Thurm, S. (2000). Motorola to hire flextronics to make consume electronics. Asian Wall Street Journal, June 1. UNCATDA. (2000). World Investment Report Investment, Cross-Border Mergers and Acquisitions and Development. Zuckerman, A. (2000). Compaq switches to pre-position inventory model. World Trade, April.
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Chapter V
An Expanded Model of the Effects of Organizational Culture upon the Acceptance of Knowledge Management Nancy C. Shaw George Mason University, USA Francis D. Tuggle American University, USA
ABSTRACT Knowledge management (KM) entails several benefits for those organizations that practice it successfully. However, the success of KM is predicated on the organization possessing a suitable corporate culture. We present a model of a generic worker’s daily pattern of behaviors and examine whether or not this behavioral pattern is or is not conducive to the ready implementation of a KM system. Next, we expand upon that model to exhibit the role of corporate culture, especially as manifested in 13 different variables. The expanded model is corroborated by using it to explain why two organizations successfully adopted KM (The World Bank, and RWD Technologies, Inc.) and why two organizations were unsuccessful in their attempts to adopt KM (The Peace Corps, and a Private University). We conclude by offering suggestions for fruitful lines of future research. Copyright © 2003, Idea Group, Inc.
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INTRODUCTION The economies of the industrialized nations have entered into a new age– variously called the “Information Age,” the “Third Wave,” or the “Electronic Economy.” These labels refer to a transition that has taken place in the past few decades. Although there are still a few economies that primarily rely on traditional manufacturing, most economies rely on automated manufacturing and informationdependent service industries. These new economies are known as knowledgebased economies and are heavily reliant on information technology (OECD, 1996). While the means of production in the Industrial Age was industrial capital (plants, equipment, machinery), the means of production in today’s economy is knowledge capital. To succeed in the new world order, companies must adopt a new conceptual framework where knowledge is treated as a core corporate asset, and not as an expense (Housel and Bell, 2001). Knowledge management (KM) is a compelling new information technology that can help organizations leverage their knowledge capital for increased competitive advantage (Davenport and Prusak, 1998). Some benefits of KM include ongoing access to the intellectual assets of the firm (even after key employees leave or retire), the ability to leverage the expertise resident in all facets of the organization, use of tacit knowledge accumulated over decades of experience, etc. However, for a firm to enjoy the advantages of KM, a unique mindset and culture must be adopted by the organization in question. Indeed, having the right organizational culture in place is seen in the literature of KM as being the sine qua non of the field (Davenport, 1997; Mizumori, 1998; Rifkin, 1996). Unless the organization’s culture is sufficiently supportive of the activity and interaction patterns called for by KM, it will suffer the same fate as has befallen many organizations that attempted to implement Total Quality Management or Business Process Reengineering—initial acclaim followed quickly by abandonment. Unless KM systems are embedded within the larger context of organizational work practices, they are unlikely to be used (Nissen et al., 2000). A key issue for KM practitioners is to ensure that their organization creates and maintains an environment in which collective knowing and knowledge-based processes emerge and evolve (Brown and Duguid, 1991). A critical question for organizations that are thinking of attempting to extract the value implicit from KM is to what degree are they ready to have KM successfully adopted by the people in the organization? In this chapter, we help organizations answer that question. We do so by first reviewing the literature on KM and organizational culture—this results in the derivation of 13 different factors that are posited to affect the adoption of technological change, KM in particular. We then integrate these factors into a twolayer model of the effects of organization culture upon the knowledge workers in the organization. We then examine a set of four case studies of firms that wrestled with introducing KM—two did so successfully while two have yet to experience success – in order to test the efficacy of the model. Finally, we show how organizations can use this two-stage model to determine whether or not they are ready for the introduction of KM
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(or any other high technology computer system) and if not, exactly what changes to their culture and system of organizational practices need to be made beforehand.
LITERATURE REVIEW AND GENERATION OF THE MODEL The definitive work on corporate culture is by Schein (1992). Schein’s approach to identifying culture is anthropological—his interests are in developing a system to describe culture fully and accurately, and at those objectives, he has succeeded. Methodologically, Schein’s work implies that a mere typology will not suffice to capture the full richness of organizations. Organizational culture manifests its effects upon the organization through its mediated effects upon each individual in the organization. Therefore, before one can appreciate which cultural factors are salient to the question of the adoption of KM, one must first understand the behavior of a typical individual in an organization, and then one must understand how culture conveys its effects upon that individual. Generally defined as the shared beliefs, values, and assumptions that guide sensemaking and action in organizations (Ott, 1989), organizational culture encompasses both individual and group-level phenomena (Louis, 1985). This leads us to a two-layer model of individual behavior—the first layer is the worker, at one instant in time, in isolation (economists refer to this as a case of static equilibrium). The second layer takes that first layer model as a baseline and adds in cultural effects. Given that organizational culture is ultimately manifested in, and maintained by, the efforts and actions of individuals, in order to fully understand an organization’s culture we must examine the individual-layer manifestations (Harris, 1994). We wish to account for why some individuals readily adopt new computer technologies, such as KM, and why others actively resist such technologies.
First Layer: A Model of the Individual Relating to the Acceptance of KM Following Homans (1950), a worker in a steady-state routine of producing work will maintain that state if and only if three factors are balanced. Specifically, an individual’s daily work life consists of Activities, Interactions, and Sentiments. We shall analyze these three factors relative to the introduction of KM. First, the individual engages in a set of daily work Activities. The individual must be capable of executing these Activities. The Activities could be performed at a desk, in a classroom, on a factory floor, at a client site, or anywhere the company deems fit. Second, the individual engages in a set of Interactions with coworkers. These Interactions may be to exchange task-related information, to perform part of a project task, or simply to engage in social interchange with coworkers. Interactions may occur face-to-face, via telephone or voice mail, via email, via videoconferencing, or in any other modality in which two or more people exchange information.
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Third, the individual holds a set of Sentiments. The Sentiments (attitudes) will cover virtually every dimension of the individual’s work life—in particular, the Activities that are performed, and the Interactions that are conducted (as well as the people with whom this individual interacts). An important element of this first-layer model is that these three facets of an individual’s work life must be in a mutually supportive relationship in order for work homeostasis to be present and to be maintained. That is, for the person to be at equilibrium, the person’s Sentiments must value the Activities the person performs and must value the quality of the Interactions the person carries out (including the people that are interacted with). Similarly, the Interactions must assist the person in carrying out the Activities, and the set of Interactions must convey positive Sentiments for the person. Finally, the person must “enjoy” carrying out the set of Activities, must be competent at carrying them out, and must engage in some requisite set of Interactions in order to fully discharge all the duties and responsibilities of the position (see Figure 1). With that first layer in place, consider what happens when an organization attempts to introduce a new KM system (KMS) into this individual’s work life. The new KMS will likely cause changes to the person’s Activities (the KMS will have to be used), Interactions (knowledge will have to be shared; knowledge will have to be requested from others), and Sentiments (the individual must feel that the effort of learning the new KMS is worthwhile; the individual must feel that using the new KMS is beneficial). Whether or not the new KMS is accepted or resisted is a function of the particular set of Activities, Interactions, and Sentiments held by the target person. Without knowing what the particular pattern of that triad of variables is, it is impossible to predict in advance whether or not the new KMS will be accepted or rejected by the target person.
Figure 1: One-layer model of an individual at work in equilibrium
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Second Layer: A Model of the Effect of Culture on the Individual The second layer of our model has to do with the effects of the organization’s culture upon the way in which the focal worker’s individual behavior is altered. That is, once the focal worker is in a state of equilibrium, the worker’s daily behavior falls into a set routine of activities, patterns of interaction with immediate coworkers, and attitude clusters about activities and interaction patterns. An individual’s motivation to adopt a new system such as KM can be mediated by the culture of their organization. An extensive review of organizational culture literature generated an extended list of factors that may be germane to the adoption of KM. For illustrative purposes, we selected 13 that (looking ahead to the four case studies) are particularly salient. In a very real sense, these 13 factors may be considered to be 13 separate propositions. These will be examined for validity in our four case studies of KM adoption or lack thereof (see Table 1). Now consider the impacts of these 13 cultural factors on the individual (see Figure 2). This model “drills down” on the model presented by De Long and Fahey (2000, p. 116) wherein they merely stipulate that the dimensions of culture affect human behavior, which in turn affects the propensity to adopt or reject a KMS. Our model identifies the salient part of work-related human behavior, and postulates how separate parts of an organization’s culture affect distinct dimensions of an individual’s work-related behaviors. Table 1: Organizational factors that may be germane to the adoption of KM
Is information sharing encouraged? Is there widespread trust in the organization? Do the reward and recognition systems promote initiative and innovation? Are the workers optimistic and curious? Is the culture a strong one?
Davenport and Prusak, 1988 Shaw, 1997 Peters and Waterman, 1982; Nissen et al., 2000
Pfeffer, 1994 Deal and Kennedy, 1982; Brand, 1998; Sieloff, 1999; Pan, 1998 Is the culture a positive one? Goffee and Jones, 1998 Is the culture an adaptive one? Kotter and Heskett, 1992; Caldwell and O’Reilly, 1995; Nemeth and Staw, 1989 Does the firm tolerate well-intentioned errors? Tuggle and Shaw, 2000 Is the reuse of material encouraged? Tuggle and Shaw, 2000 Is teamwork encouraged? Tuggle and Shaw, 2000 Is the firm without the Not Invented Here Tuggle and Shaw, 2000 syndrome? Do the firm’s organizational practices, Tuggle and Shaw, 2000 processes and control systems support KM? Is the firm technologically advanced? Tuggle and Shaw, 2000
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Figure 2: Two-layer model of an individual at work (showing influence of cultural variables with dotted lines)
CASE STUDIES Over the past several years, the authors have closely examined several organizations that were in the process of adopting KM techniques and technologies. While several of these organizations were successful in their endeavors, there were always a few that never quite made it. In an attempt to try to understand why some organizations were successful and some were not, the authors began to examine a variety of cultural aspects of these organizations in order to determine if there were any specific cultural factors that helped or hindered the acceptance of KM—thus, four case studies are examined.
The World Bank The World Bank’s mission is to fight poverty, improve living standards, and improve the quality of life through sustainable growth and investment in people (www.worldbank.org). Owned by 182 member countries, the bank loaned more than $15 billion in 2000. According to Stephen Denning, program director of KM for the
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bank during 1996-2000, the bank underwent a huge cultural transformation from “being a bank” to “being a knowledge-sharing organization” (Barth, 2001). KM at the World Bank is aimed at sharing know-how and experience in order to increase individual effectiveness, transfer information and knowledge to the organizational level, ultimately making it easily accessible so that all individuals are informed to take effective actions. While the preliminary focus of the system was on improving the effectiveness of World Bank staff through providing just-in-time, just-enough knowledge to task teams on call, their goal was to meet the needs of both internal and external users by the end of the year 2000. The KMS is aimed at changing the way the Bank operates internally and transforming the organization’s relationships with external clients, partners, and stakeholders, and will be one of their key strategic thrusts for the 21st century. The creation of the KMS, organized around a matrix structure that combines geographical regions and thematic groups, enabled the bank to identify “communities of practice” which facilitated the building of the knowledge base. Ongoing support and resource commitments from upper management have resulted in a KMS that has been used as a model for many other organizations that are interested in developing a KMS of their own. The main sources of content of the knowledge management system are (a) World Bank operations, (b) answers to questions arising from help desk inquiries, (c) content from external knowledge partners, and (d) feedback and contributions from external users.
Acceptance of Technological Innovation at the World Bank The daily activities of the average employee at the World Bank include numerous opportunities for knowledge sharing (see below). Furthermore, most work, when not performed in meetings or over the telephone, is done electronically. There has been a pronounced shift away from paper towards electronic documents, graphics, spreadsheets, and presentations. Given the skill level of World Bank employees, transitioning to such changes is facile. Almost nothing gets done at the World Bank by an individual acting alone. Teams work on projects, teams that are multicultural, multiethnic, multigender, and diverse along almost every conceivable dimension. Meetings are commonplace; they occur in Washington, they occur in the field, and they (increasingly) occur in cyberspace. There is high esprit d’corps at the World Bank. Virtually all employees believe, deep in their hearts, that their work, individually and collectively, is saving lives, improving the quality of life for the world’s disadvantaged, and making the world a better place for humanity. The nobility of their mission sometimes makes them behave in ways that outsiders might describe as coldly rational, but that modus operandi would be heatedly defended and justified by employees. Partly as a consequence of the strong identification with the mission of the institution, employees are constantly seeking out new ways to improve delivery of services to clients and
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to optimize internal operations. When the then-new CEO, James Wolfenson, proposed knowledge management as a way to improve the World Bank’s operation, the idea was met with little resistance and a great deal of empathy. The culture at the World Bank facilitated the successful adoption of KM. The attributes of the prototypical worker (well-educated, technologically adept, and highly motivated to realize the bank’s objectives) combined with the cultural conditions found at the bank (technological sophistication, continued training and emphasis on skill development, teamwork, empowerment, and egalitarianism) created an environment that made it easy for the workers to accept the new technology. The culture allowed for the employees to easily adapt to changes to their activities, meetings, and attitudes that were a result of the implementation of the KMS.
RWD Technologies, Inc. RWD was founded in January 1988 on the observation that the nature of our society was becoming increasingly technological, resulting in a market opportunity to help organizations operate their high-technology systems and equipment. RWD is headquartered in Columbia, Maryland. There are 11 domestic regional offices and two international regional offices. RWD has clients in 28 countries, clustered in 30 different industries, all in the private sector. RWD is organized as a large matrix structure. On the axis of approaches, it has a set of TCOEs—Technical Centers Of Excellence which represent the high technologies that RWD has expertise in, such as web-based technologies, enterprise information systems, and lean manufacturing. On the axis of customers, there are a set of ICOEs—Industry Centers Of Excellence which represent the customer clusters that RWD has depth of expertise in assisting. In conducting its projects for its clients, RWD utilizes a small number of proprietary, corporate methodologies in its business. These corporate methodologies exist on the company’s intranet—employees across the globe can refer to these corporate methodologies to refresh their memories of the exact details of which step is performed when and how. All projects are performed by teams; a corporate directory allows team leaders to identify who has the right set of skills for a project, who has requisite industry experience, and who has sufficient time availability for the project. One of the important practices at RWD is the notion of continuous improvement. This manifests itself in some cases as a lessons learned database (residing on the corporate computer) and also by questioning how technologies could be improved to deliver better service to the clients. Before any new project is undertaken, it is required that the team leader access the lessons learned database to research knowledge on the client firm and industry, projects of this type, and the technology to be used on the project. At the conclusion of a project, it is required that the team leader enter new lessons learned into the database. The corporate intranet is used for both formal and informal communication. Project information, group communication, and even time sheets are housed on the intranet.
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Acceptance of Technological Innovation at RWD Individuals are recruited for and selected by RWD on the basis of two main considerations: their ability to work in team settings and their technological sophistication. Training is often performed online, and routine organizational matters (the policy manual, the organizational chart, the time billing system, etc.) also require considerable computer proficiency. Software upgrades occur frequently, and they are also carried out online. Software innovation is a valued activity. The annual performance review system explicitly makes note of several of these dimensions. The performance appraisal form is a 20-question evaluation system; three of the questions address the individual’s ability to work effectively in a team setting. Two of the questions explicitly address the individual’s ability to keep abreast of technological chances germane to the person’s client projects. One question on the form explicitly addresses whether or not the person is contributing to the KM lessons learned database as well as using that database on a regular basis. The conclusion is that RWD is well positioned to try new technological innovations as well as developing technological innovations themselves to offer to their clients. RWD has a KM system in place that it expects its employees to use and to extend. Their culture is summed up in their tagline: “We bring people and technology together.”
The Peace Corps Since its establishment in 1961, the Peace Corps and its volunteers have been dedicated to providing opportunities for individuals who want to build a better life for disadvantaged nations. Part of their recent strategic plan included the design and implementation of a KMS that permits secure collection, processing, and redistribution of information throughout the organization (Peace Corps, 1998). The Peace Corps’ mission of aiding people in foreign countries and promoting understanding between Americans and citizens of these countries could be greatly enhanced through increased sharing of knowledge. As a support division within the Peace Corps, the Center for Field Assistance and Applied Research (referred to hereafter as the Center) aids overseas field staff in completing projects in developing countries. A KMS could potentially provide information to all levels of the Peace Corps, thus aiding in upper-level decision making and strategic planning. Part of the Peace Corps’ strategic vision for the year 2000 was to “seek new levels of effectiveness and efficiency.” The Peace Corps required a more effective process for capturing, analyzing, and sharing knowledge resources. In the past, the organization relied heavily on the verbal conveyance of information–an approach that provides limited opportunities to record lessons learned and define solutions to frequently encountered problems. This is particularly problematic due to the Peace Corps’ policy of a maximum of five years’ of employment. In accordance with its strategic goals, the Peace Corps began exploring the potential utilization of a knowledge management system in 1998. The primary goal
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of the Center is to support the Peace Corps’ activities through information management and collection. Information flows through the Center in an informal manner. Generally, employees contact each other directly with requests for information. Some employees act as knowledge brokers, directing individuals who have inquires to the correct person or location. The Project Assistants and other employees who have been working at the Peace Corps for two or three years are perceived as having a wealth of experience and knowledge about the location of needed information and they significantly contribute to the dissemination of knowledge. The country desk units of the Peace Corps act as clearinghouses by obtaining all relevant information from representatives in the field and then deciding where they should go. In addition to a bottom-up flow of information, ad-hoc requests are made that originate from the Center. Peace Corps employees have not been provided with a full appreciation of the process and benefits of KM. This factor is largely due to a lack of sufficient human resources dedicated to this critical area. There are a few knowledge brokers, employees that have relatively extended service with the Corps and have acquired broad-based expertise, who facilitate knowledge sharing. Other experts also exist throughout the organization, but the coordination of their efforts has not been formalized to a sufficient degree.
Acceptance of Technological Innovation at the Peace Corps Two very different types of individuals are recruited for work at the Peace Corps. One type is the individual recruited to work at an office, a staff individual, if you will. The second type is the individual recruited to work overseas in direct contact with a “client.” The skills, motivations, and career aspirations of these two different types of individuals sets up two distinct subcultures within the Peace Corps that practically preordain problems. The Peace Corps (as of this writing) has not been successful in implementing KM, in spite of that being one important objective for the organization. The KM effort at the Peace Corps was hindered by a variety of different factors. In particular, they failed to adequately capture and retain the institutional knowledge of departing employees. There was no substantial commitment (either financial or administrative) from upper management. While there was a great desire to develop an electronic KM system, the informal knowledge-sharing networks that had developed over the years at the Peace Corps continued to be the primary method of knowledge transfer. In addition, the lack of an enterprisewide network that would enable the implementation of compatible systems made access to needed data difficult at best. The employees already had a strong, informal network in place that they were very comfortable with. That, coupled with the lack of technology that would facilitate electronic knowledge sharing, created an environment where KM could not succeed. While they did experi-
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ence some successful knowledge-sharing initiatives, the KM effort has been largely unsuccessful.
Private University The university is a private mid-size university on the East Coast. Established over 100 years ago, it has more than 10,000 students, equally divided between graduate students and undergraduate students, and over 600 full-time faculty members. The university is organized as a college of liberal arts and science and several professional schools within the university. This case study will center upon one of the professional schools, the school of management. The primary goal of the university for the next century is to build a distinctive, global university, marked by the highest levels of academic excellence and creativity. The management school is expected to add to the global distinctive competence of the university. One part of the management school’s efforts in this area is to use computer technology to achieve this end. Describing itself as at the “forefront of business and technology,” the management school has a mission to include technology and innovation in all facets of student and faculty life. The school recently completed major renovations on a multi-story office-classroom building. Included in the renovations were “high-tech” classrooms, intended to be the showcase of the school and used for both executive education as well as normal classroom usage. The classrooms contain Internet ports for each student, as well as a standard AV presentation system for lectures. Numerous break-out rooms for either faculty or student use were worked into the design. Other than the previously mentioned minor technological innovations, there was no additional hardware or software initiative included in the design. The school uses the same platform as the rest of the university (Lotus Notes E-mail) for communication and knowledge sharing. While there has been some interest in using specific technologies for teaching (e.g., Lotus Notes Classroom, online educational courseware, standard Web pages), there is no other knowledge-sharing technology in use by the school. Events and items of interest are sent by standard e-mail group-list technology to the faculty, staff, and students. While the university has an extensive investment in Lotus Notes technology, other than for individual course use, groups within the school (department or research) do not use the Lotus Notes group-ware capability. Knowledge is shared in the traditional manner: during department meetings, over lunch, or while drinking coffee.
Acceptance of Technological Innovation at the Private University Throughout the university, there is the prevalent opinion that computer technology is an enabler of the processes of producing high quality instruction and distinctive research. In particular, computer technology is not a dimension on which this
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university chooses to compete in the academic marketplace—computer technology is a support service, not an end in itself. This school and university present a curious blend of technological sophistication and technological innocence. It is clear that the university and the management school both exhibit fragmented organizational cultures. Specifically, there is a hightech subculture among certain subgroups, and there is a low-tech subculture among other subgroups. These subgroups coexist peacefully on campus because of a tacit agreement not to argue over the proper role of technology in education. There are pockets in the management school that effectively utilize a KM system, and there are pockets in the school that resist the use of any form of computer technology. Consider: one group of technologically literate faculty exchange documents and threaded comments upon works in progress by use of the university’s Lotus Notes groupware system. Consider: several committees in the school schedule meetings by leaving voice mail messages for one another. Consider: at least one department chair refuses to use the desktop computer supplied by the university— documents are hand-typed; this person does not send or receive email or otherwise avail the potential of a networked personal computer. Thus, even in a professional school whose mission includes the promotion of global business through computer technology, there are segments of the school (sometimes important segments) that do not utilize the power of modern computation. That the university tolerates such unevenness is a testimony to the vagaries of tenure coupled with a posture of “satisficing” within its budgetary constraints.
APPLICATION OF THE MODEL These four case studies are broadly consistent with our two-layer model. Let us examine the four case studies in light of the model sketched in Figure 2. First, note that for the two successes in the adoption of KMS (the World Bank and RWD Technologies), all of the 13 posited variables are in the direction supportive of KM adoption success. This corroboration is encouraging. Next, focus attention on the two case studies in which KM failed to be successfully adopted. The difficulties the Peace Corps has had in implementing KM are traceable to many of the cultural factors we have identified in the model. Consider first the factors behind Activities: their personnel are not attuned to high technology, they are tolerant of well-intentioned errors, there is no reward system for sharing and reusing knowledge, and they do possess an adaptive culture. Thus, two of these four factors mitigate against successful KM adoption. Next, consider the factors behind Interactions: trust is present, teamwork is not stressed, information is readily shared, and there is a weak culture present. Again, of the four factors, exactly two of these factors support KM adoption and two counteract it. Finally, consider the factors behind Sentiments: there is not an NIH syndrome, reuse of material is deemed important, they are a governmental bureaucracy with hierarchy, centralized
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decision making and control, and centralized budgetary authority; people are optimistic and intellectually curious, and the culture is a positive one. One out of these five factors works to frustrate the adoption of KM. In summary, five of the 13 factors are opposed to the adoption of KM. That seems to be sufficient in this case to have blocked the adoption of KM. However, it is close—recent inquiries at the Peace Corps reveal that they are still endeavoring to adopt and disseminate a KM system; there has been some limited progress. As noted later, the findings from the case of the Peace Corps raise questions which we are unprepared to answer about the relative strength of the effects of the 13 factors—it is almost certainly the case that not all carry the same weight. Finally, consider the case of the private university. First, for its Activities, it lacks a consistently strong techno-skill base, it is intolerant of errors, it has no reward and recognition system that promotes the use of KM, and it has a rigid culture. All four of these factors work against the adoption of KM. Next, for its Interactions, it is not a trusting organization, it does not operate through teams, there is no history of information sharing, and it possesses a weak culture because of its many subcultures. Again, all four of these factors work against KM. Finally, for its Sentiments, there is not an NIH syndrome, it does not discourage the reuse of materials, it does not follow a set of organizational practices or control systems that are consistent with the use of KM, people in the organization are optimistic and intellectually curious, and the organization has a negative culture—people see it as a means to their own ambitions. Three of these five factors are consistent with the use of KM. Thus, overall, the private university reports that 10 of the 13 factors are not supportive of KM being in place. It should therefore come as no surprise that KM is not even on the planning horizon of this institution.
LESSONS LEARNED Three lessons can be inferred from this work: (1) our two-layer model appears to be descriptively accurate; (2) understanding an organization’s culture appears to give valuable insights regarding the disposition of its people to accept or reject technological innovations; and (3) the first two lessons lead to a prescriptive use of the model. Specifically, organizations can use the two-layer model to assess their readiness to introduce or adopt a KM system. Specific cultural factors that require adjustment can be identified during the assessment process prior to the introduction of any new technology. (There are, of course, other cultural factors that may be pertinent. For example, Christensen and Shu, 1999, identify polite/rude, formal/ informal, and comforting/impersonal among others.) Firms should carry out the following steps in order to understand whether or not their culture is apt for the successful adoption of KM. First, they need to understand the common working environment of individuals in the firm—that is, they need to understand the typical individual’s Activity, Interaction, and Sentiment patterns. What actions does that typical person carry out over the course of a normal day?
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What tools does that person use to get work done? Whom does that individual interact with? Who initiates those interactions? Are those interactions work-related or social interactions, or do they contain elements of each? How much interaction occurs over the course of a day? How many different individuals and groups does that person interact with? What sentiments does this person hold about the job, the work environment, his/her career, the boss, coworkers, managers in general, the organization, and the industry it is in? Second, they need to assess whether or not that pattern is conducive to the successful adoption of KM. Does that typical individual engage in Activities and Interactions and hold a set of Sentiments such that migrating to a KM system is a small step or a big leap? After identifying a “gap” between exhibited and desired patterns, a change program can be designed and implemented. Third, the firm then needs to identify where it stands on each of the 13 organizational cultural factors outlined above that are germane to the firm. Once identified, these cultural factors should be assessed in a similar fashion as the individual factors, and, where one or more gaps exist, to design and undertake additional programs of change.
DIRECTION OF FUTURE RESEARCH Several avenues of future research suggest themselves. For one, our two-layer model is in need of more rigorous and systematic testing. For example, it may be found that not all of our 13 cultural factors are needed to determine the success of KM adoption. Are some of these 13 factors more important than others in determining whether or not KM gets adopted? On the face of it, it would seem unlikely that all 13 factors would carry the same weight in the decision. Are these 13 factors independent of one another or is their presence or absence correlated? One could suspect, for example, that teamwork and information sharing would be correlated with one another. Are there interaction effects between some of the factors? That is, if one factor is there in the presence of a second factor, is the effect of that first factor magnified? Additionally, a program of action research seems called for. This would entail organizational diagnosis, assessment, and change (as warranted) in order to move an organization from a position of being not likely to succeed at KM adoption to the point at which KM was successfully adopted after a change regimen was put in place. Finally, a good part of our two-layer model, with some slight generalization, carries over to an organization’s adoption of any new high-technology system, whether it is the migration to an enterprise resource planning system, a new database system, a new customer relationship management system, or any other substantially new and different IT system. As such, organizations need methods of determining what the barriers to successful implementation are and of designing programs to remove or minimize such barriers. Our two-layer model offers one such approach.
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CONCLUSION This chapter presents a two-layer model that has been used to explain the effects of organizational culture on the adoption or rejection of KMS. As such, this model represents a significant advance over previous models of the effects of culture upon such decisions, e.g., De Long and Fahey (2000) and Shaw and Tuggle (2001). The model was developed through field studies and an extensive literature review on organizational culture and KM. In particular, the model as now formulated stipulates 13 propositions about cultural factors posited to affect an organization’s decision to embrace or reject technological change, KM in particular. An examination of four case studies of organizations undergoing KM implementation projects was conducted to test our model and these 13 cultural factors. We consider the positive results of the case studies to corroborate our model and indicate that it is worthy of more careful examination and testing. For example, we are now more confident that our 13 factors separately operate through the model given in Figure 2 to impact the adoption/rejection decision. However, we do not yet know (1) if some of these factors are relatively more important than others (e.g., if two factors appear simultaneously but in different directions, does one’s effects swamp the impact of the other’s effects?) or (2) if there are any interaction effects among the 13 factors (e.g., if two factors appear simultaneously, do they work together to strengthen the collective impact that they have, more so than just “adding” their independent effects?). We have focused upon the decision to adopt or reject KM in this chapter. Is this model equally valid for other software systems such as ERP, or are different factors or a different model structure called for? We found that an organization’s culture can impede or enhance the acceptance of KM technology and processes. To the extent that the adoption of KM practices and technology creates changes to an individual’s daily Activities, Interactions with coworkers, Sentiments, and to the extent that the organization’s culture allows for or encourages these changes, the acceptance or rejection of the KMS may be predictable. Given the success of our two-layer model at explaining the decision behaviors of four quite disparate organizations, there is a basis for believing that the effects of organizational culture upon the decision to adopt or reject computer technology, KM in particular, may be predictable.
REFERENCES Barth, S. (2001). The knowledge bank. Knowledge Management, 4(6), 24-26. Brand, A. (1998). Knowledge management and innovation at 3M. Journal of Knowledge Management, 2(1), 17-24. Brown, J. and Duguid, P. (1991). Organizational learning and communities of practice: Toward a unified view of working, learning and innovation. Organization Science, 2(1), 40-57.
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Caldwell, D. and O’Reilly, C. (1995). Promoting team-based innovation in organizations: The role of normative influence. Fifty-Fourth Annual Meeting of the Academy of Management. Christensen, C. and Shu, K. (1999). What is an organization’s culture? Harvard Business School Case #9-399-104, Boston, MA, May 20. Davenport, T. H. (1997). Known evils, common pitfalls of knowledge management. CIO Magazine, June 15. Davenport, T. H. and Prusak L. (1998). Working Knowledge: How Organizations Manage What They Know. Boston, MA: Harvard Business School Press. Deal, T. E. and Kennedy, A. A. (1982). Corporate Cultures: The Rites and Rituals of Corporate Life. Reading, MA: Addison-Wesley. De Long, D. W. and Fahey, L. (2000). Diagnosing cultural barriers to knowledge management. Academy of Management Executive, 14(4), 113-127. Goffee, R. and Jones, G. (1998). The Character of a Corporation: How Your Company’s Culture Can Make or Break Your Business. New York: Harper Business. Harris, S. (1994). Organization culture and individual sensemaking: A schema-based perspective. Organization Science, 5(3), 309-321. Homans, G. (1950). The Human Group. New York: Harcourt, Brace and Company. Housel, T. and Bell A. (2001). Measuring and Managing Knowledge. Boston, MA: McGraw-Hill Irwin. Kotter, J. P. and Heskett, J. L. (1992). Corporate Culture and Performance. New York: The Free Press. Louis, M. R. (1985). Introduction: Perspectives on organizational culture. In Frost, P. J, Moore, L. F., Louis, M. R., Lundberg, C. C. and Martin, J. (Eds.), Organizational Culture, 27-30. Beverly Hills, CA: Sage. Mizumori, R. K. (1998). Knowledge management—5 Ws & 1 H. KM Magazine, November. Nemeth, C. and Staw, B. (1989). The tradeoff of social control and innovation in groups and organizations. Advances in Experimental Social Psychology, (22), 175-210. Nissen, M., Kamel, M. and Sengupta, K. (2000). Integrated analysis and design of knowledge systems and processes. Information Resources Management Journal, 13(1), 24-43. OECD Report on Knowledge Based Economies. (1996). Ott, J. S. (1989). The Organizational Cultural Perspective. Chicago, IL: The Dorsey Press. Pan, S. (1998). A socio-technical view of knowledge sharing at Buckman Laboraties. Journal of Knowledge Management, 2(1), 55. Peace Corps. (1998). The Peace Corps 2000: A Strategic Vision for a New Century. Center for Field Assistance and Applied Research. Peters, T. and Waterman, R. H. (1982). In Search of Excellence: Lessons from America’s Best-Run Companies. New York: Harper and Row.
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Pfeffer, J. (1994). The Human Equation: Building Profits by Putting People First. Boston, MA: Harvard Business School Press. Rifkin, G. (1996). Buckman Labs: Nothing but net. Fast Company, June/July. Sieloff, C. (1999). If only HP knew what HP knows: The roots of knowledge management at Hewlett-Packard. Journal of Knowledge Management, 3(1), 47-53. Schein, E. (1992). Organizational Culture and Leadership (second edition). San Francisco, CA: Jossey-Bass. Shaw, R. B. (1997). Trust in the Balance: Building Successful Organizations on Results, Integrity, and Concern. San Francisco, CA: Jossey-Bass. Shaw, N. C. and Tuggle, F. D. (2001). A model of the effects of organizational culture upon the acceptance of a knowledge management system. Working Paper. Tuggle, F. D. and Shaw, N. C. (2000). The effect of organizational culture on the implementation of knowledge management. Proceedings of Florida Artificial Intelligence Research Symposium (FLAIRS 2000), 166-169. Orlando, FL. World Bank. (2001). http://www.worldbank.org.
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Chapter VI
Information-Based Integration for Complex Systems E. Szczerbicki The University of Newcastle, NSW, Australia
ABSTRACT Engineering, operations research, and management science use scientific and engineering processes to design, plan, and schedule increasingly more complex industrial systems in order to enhance performance. One can argue that the systems have grown in complexity over the years mainly due to increased strive for resource optimization combined with a greater degree of uncertainty in the system’s environment. Information is seen as one of the main resources that managers try to use in an optimal way. In this chapter we show how this resource can be used in integration issues. We introduce the problem of information-based integration, propose a solution, and illustrate the proposed solution with an example.
INTRODUCTION Managing complex systems requires a greater understanding and knowledge about the role of information in systems operation. Today, a growing complexity of information flow is a characteristic of enterprises which concerns products to be manufactured, services to be offered, processes and company structures. Complex systems also operate in changing environments surrounded by numerous uncertainties and disturbances. Difficulties arise from unexpected tasks and events and from a multitude of possible failures and other interactions during the attempt to control various activities in dynamic environments. Therefore, management of Copyright © 2003, Idea Group, Inc.
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information is one of the most important aspects to be considered in intelligent systems, which are expected to solve unforeseen problems, even on the basis of incomplete and imprecise information. This chapter starts with some discussion of the importance of information in operation management as well as new challenges in information modelling, visualisation, and communication in information society. Then we turn our attention to one of the challenges, i.e., information-based integration, and discuss it in the context of a system’s design process. We illustrate our approach with an explanatory example, and then conclude the chapter.
INFORMATION MANAGEMENT IN THE NEW MILLENNIUM: SOME CHALLENGES Information becomes an increasingly more important resource in all kinds of business, industrial, and service operations. Changes, uncertainty, imprecision, and complexity became the most important factors affecting the behavior of modern markets. Functioning in such markets requires an increasing amount of information to be processed in substantially shorter periods of time. Therefore the time span left for decision making is dramatically decreased. Some major problems associated with the above facts can be traced down to the level of operational management of a company. The efficiency of management at that level depends mainly on the amount of time needed to react to changes in both internal and external environments in which a given company is functioning. Operational management level is increasingly more often a decisive factor in company’s survival and expansion. We have to concentrate on this level of management as much as on tactical and strategic levels that are usually the main focus of companies today. Operational level at which tasks are processed is no longer a stable one. A number of parameters associated with these tasks can change and will be changing more and more often as the dynamics of the external environment increase.
Demand for Information Functioning in uncertain and imprecise conditions requires predictions of future states of environment in which systems operate. It requires increasingly more efficient and intelligent decision support tools that are able to cope with unexpected changes. Application of such tools usually means significantly bigger demand for information, and therefore a larger amount of information will have to be processed at all management levels. Information that is needed often originates at different, geographically distributed sources and is available in different forms and different coding. Thus, new tools will also be needed to cope with this emerging problem of information diversity. The challenge of the new millennium will be to retrieve and transform huge amounts of different forms of information into knowledge needed to support our decision-making processes.
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Information Theory Managing companies in the new century, the century of information society, will necessitate the use of new means of communication with external environment. It will also require much greater adaptability of companies, it will require the companies of the new millennium to be transformed into intelligent, learning organizations able to cope with globalization of information resources. This globalization means that the main problem will not be the access to information but the ability to mine it and then to transform it into a useful operational and strategic resource. The increasing frequency of changes in the state of the environment in which a company is operating creates a new important challenge associated with time. Time becomes a decisive factor in information retrieval and decision-making processes. Managing complex industrial systems (manufacturing, processing, distribution, servicing, mining, etc.) that function in uncertain information-rich environments requires greater understanding and knowledge about the role of information in systems operation. To gain this understanding, a theory will be needed that could be used to model and evaluate information flow in different situations. In fact, our needs for the new century go well beyond the above in requirement for a theory considering important practical issues of information, i.e., delays, incompleteness, imprecision, and loss in value. The current practice of dealing with such issues are mostly when problems are detected and reactively. This situation may not be desirable and definitely may be a major drawback for complex systems that more and more rely on the timeliness and quality of information for their operation. A theory, in this respect, would greatly enhance the understanding of the various factors that influence the quality of information to the benefit of better decisions in adequate time. Development of such a theory is one of the major challenges in information society of the new millennium. The value of information that flows within a given subsystem is different for different information structures and different environments (Szczerbicki, 1993, 2000). It can be considerably affected by two major attributes of information: incompleteness and delay. The highest value will be possessed by a full information structure (including all relevant information possible). On the other hand, gathering information in a dynamic environment causes its delay. Both delay and incompleteness can be represented by losses in the value of the information structure. Currently, there is no theoretical foundation for such a representation but managers of the new millennium will certainly need it. The delay of information combined with the dynamics in the environment can cause substantial losses in the value of information as a useful resource in decision support. We have to turn huge amounts of information into knowledge needed for our knowledge-based systems very fast. Quick perception of information becomes an important issue. Another challenge emerges here–visualization of information.
Visualization of Information We no longer have time to study pages of reports and columns of data. We have to visualize information quickly and effectively. New tools are needed to support the
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ways we communicate information to the decision maker. Visualization and presentation of information becomes one of the most important areas of research in Cybernetics and Artificial Intelligence. Visualization can help make sense of the flood of data. When applied with some insight into visual perception, and with attention to the nature of the data and how the data are to be used, visualization can become a very powerful tool in future intelligent information systems. Current and future research trends in this area include such important topics as (Rogowitz and Treinish, 1990; Lefkowitz and Herman, 1992; Tufte, 1998, 1994): • color and information; • complexity and clarity of human perception; • use of multi-media, Internet, and WWW screens; • animation for scientific visualizations; • design of efficient computer interfaces. Use of color in presenting data becomes an increasingly more important research topic. The key issue here is the colormap which may be defined as a mapping from a data to a color. In most color maps red is mapped to the highest data value, blue to the lowest, and the other data values are interpolated along the full extent of the rainbow spectrum. An example would be a temperature profile mapped over a land mass on a weather map. But there are some unsolved problems related to color data representation. Color is a perceptual phenomenon. What is commonly called color is only one of three parameters. Another is the brightness of the signal–intensity. The third is the admixture of white–saturation. To add to the complexity of the problem, the above parameters’ relationship to what is perceived is nonlinear. Color perception issue in data mapping is one of the challenges in information management for the new century.
Information-Based Integration From the informational resource perspective, systems are often viewed as sets of components (agents, subsystems) characterized by informational closure and supporting separate functions. Many organizations operate in this highly compartmentalized manner. It appears that the general direction of systems operation in the future, however, is toward linking together function-specific agents into a fully integrated system. An integrated system is a system that consists of agents that efficiently contribute to the task, functional behavior, and performance of a given system as a whole. It is believed that such an integration can be achieved through the flow of information. The proposed solution for this challenge is presented next.
INTEGRATION THROUGH THE FLOW OF INFORMATION Systems become increasingly complex. Their decomposition into smaller units is the usual way to overcome the problem of complexity. This has historically led to
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the development of atomized structures consisting of a limited number of autonomous subsystems that decide about their own information input and output requirements, i.e., can be characterized by what is called an information closure. Autonomous subsystems can still be interrelated and embedded in larger systems, as autonomy and independence are not equivalent concepts. These ideas are recently gaining very strong interest in both academia and industry, and the atomized approach to information flow modeling and evaluation is an idea whose time has certainly come (Gunasekaran and Sarhadi, 1997; O’Grady, 1999). Integration process is of great importance to the theory and practice of a number of practical fields, for example Computer Integrated Manufacturing (CIM), however, it is an area which is not well understood (Tharumarajah, 1998). Integration is concerned with a systematic generation of alternatives in order to create new systems or improve the existing ones. It usually includes the following issues: • selection of appropriate agents (subsystems), • interconnection of the agents through the flow of information. The approach proposed in this section employs a system-theoretic hierarchical integration which provides a practical tool for reasoning with agents and recognizes the importance of interaction in knowledge-based systems and intelligent systems (Pacholski and Wejman, 1995). It is based on a working hypothesis that it is possible to develop hierarchically structured integrated systems by reasoning with autonomous agents (subsystems). As a result, the architecture and information flow at the integration stage takes into consideration the following assumptions: • hierarchical representation of agents with various levels of detail is suited for modeling of integrated systems, • any hierarchical model of an integrated system can be characterized by its agents and connections that were applied during the matching process. Traditionally, system analysts have been solving the integration problem in an ad hoc manner. In this section, a formal integration approach is presented which is suitable for computer implementation.
Related Work Integration of agents (subsystems) represented by models has gained considerable attention. Model development and synthesis (that resembles agent development and integration) is frequently based on the general systems theory, and it uses hierarchical structures and a number of model base concepts (Wyzalek, 1999; Rolstadas and Andersen, 2000; Quinn, 1992; Hassan et al., 1994; Askin and Standridge, 1993). In Askin and Standridge (1993) hierarchical manufacturing systems developed from functional components and integrated by information that is passed between components are discussed. In Raczkowsky and Reithofer (1998) the future development of a hierarchical communication model for coordination of a set of agents performing several functions is addressed. A conceptual modeling approach to represent the complexities in CIM systems, including such issues as information acquisition, storage, dissemination, and the time and costs associated with such informational activities, is proposed in Tharumarajah (1998). The problem
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of coordination of multiagent manufacturing systems developed to fulfill their functional requirements advocating a decentralized approach in which each agent has relative autonomy over its own actions is discussed in Pacholski (1998) . In O’Grady (1999) the application of modular paradigm to integration of systems in which planning, grouping, and scheduling are the central functional areas is described. The role of the flow of information in the process of integration is discussed in Prakken (2000). In Kamrani and Sferro (1999) the integration of manufacturing agents (information islands and automation islands) using knowledge-based technology is proposed for the factory of the future.
The Principles of Agent Integration The following are the basic concepts associated with the integration used for creating hierarchically structured systems at different levels of abstraction: • agents, • connections, • ports, • subsystems. An autonomous agent is a group of people, machines, robots, and/or guided vehicles that decides about its own informational requirements (autonomy) and that represents function(s) that is/are part of the system’s functional requirements (Szczerbicki, 1993). Connections and ports link, through the flow of information, the agents and subsystems. A subsystem is a set of connected agents. A description of the agent includes the following: (i) functions, (ii) informational input, (iii) informational output, (iv) information structure. In the function section of an agent description, the characteristic functions are listed. The characteristic functions are those that have been generated during the functional decomposition stage and then chosen to be represented by the agent during the representation stage. Informational input and output refers to the variables describing the state of the external environment of an agent. For an input, only those variables are included, the information about which is necessary to perform functions represented by an agent. For an output, only those variables are included that are affected by the functions specified for an agent. Internal information structure models an information flow inside an agent. It is defined during the agent representation process. Agents are matched using informational input and output. For example, in the domain of manufacturing systems, information may represent material availability, tool availability, machine availability, number of parts produced, number of products assembled, and the like. After the matching has been accomplished, the informational input variable of a given agent represents the value of the informational output variable of the agent to which it has been connected. In Figure 1, the input variable X of agent A2 is matched with the output variable Y of agent A1. The syntax of an agent connection is given as: Agent.Output_variable ——> Agent.Input_variable or using the symbols in Figure 1: A1.Y ——> A2.X
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Any structure developed during the integration process may be enclosed into an autonomous subsystem using ports. The informational input and output ports provide an interface to the subsystem environment. This interface allows one to develop hierarchical structures. There has been little use of multiple hierarchies of abstraction in the process of systems integration in the literature published, although the idea appears to have considerable merits. To illustrate the notion of ports, in Figure 2 agents A1 and A2 have been matched using the A1.Y ——> A2.X connection. Figure 1: Agent connection X
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The informational input port of the subsystem A1.A2 represents the value of an input variable Z of agent A2: A1.A2.Z = A2.Z The informational output port of the subsystem A1.A2 represents the value of an output variable P of agent A1: A1.A2.P = A1.P The frame of the subsystem A1.A2 is as follows: (Subsystem (A1.A2) (Agents (A1.A2)) (Output_port A1.A2.P = A1.P)) (Input_port A1.A2.Z = A2.Z)) (Agent_connection (A1.Y ——> A2.X))) Subsystem A1.A2 can be later matched with another subsystem or an agent. Different levels of abstraction at the integration stage correspond to various levels of abstraction at the related functional decomposition. For a complex process, the system analyst may begin at the highest level of abstraction with a general view of the process and go into more details by “opening” subsystems (such as A1.A2 in Figure 2) that are elements of the hierarchical structure of the overall system.
Generation of Agent Connections The syntax for connection of agents has been introduced above. It is easy to notice that without any additional mechanism for generation of connections, the bulk of the resulting matchings would not posses either a physical or logical meaning, and could lead to systems that might not be valid, for example, systems without a boundary. In addition, in the absence of constraints guiding the generation of connections, the number of all possible matchings for a complex system (with large initial number of agents) would become difficult to manage. Generation of connections between agents and/or subsystems is guided by production rules. They may represent qualitative knowledge and posses the following important characteristics (Bender, 1996): • Modularity: Each rule defines a small, relatively independent piece of knowledge. • Incrementability: A new piece of knowledge can be added without modification of the existing production rules. • Modifiability: The existing production rules can be changed relatively independently of other rules. • Transparency: Production rules facilitate explanation of the system performance. The rules refer to all possible types of connections, i.e.: • agent ——> agent, • subsystem ——> subsystem, • agent ——> subsystem, • subsystem ——> agent.
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Production rules are structured to ensure that: an overall system is integrated in such a way that all agents are included. The set of agents at the initial state of integration represents all functions specified at the functional decomposition stage. • all boundary informational inputs and outputs are included in the integrated system and they represent the only way that the system communicates with its external environment. • only physically and logically feasible matchings of agents can be explored (i.e., only feasible variants of the integrated system). The agents and subsystems in the production rules supporting the integration are referred to as elements. An informational input boundary element is the one that accepts information from an environment of the system, and an informational output boundary element is the one that provides informational output to the environment. Two production rules that indicate whether the integration solution is feasible are presented next. Rule 34 IF there is only one element left THEN do not generate connections •
Rule 35 IF a single element that is left includes boundary inputs and outputs only THEN it is an overall system Production rule 36 sets a starting point for generation of connections and thus the beginning of the process of overall system building. It requires that the integration process begins with an input boundary element. Rule 36 IF there are more than one element THEN select a connection for an input boundary element Production rule 37 is used for inclusion of all elements in the integrated solution. Rule 37 IF there are elements other than the boundary elements THEN do not specify any connections that involve boundary elements only Production rules 38 and 39 make sure that agents that have been defined as boundary elements at the representation stage also create the boundary of the overall system. Rule 38 IF an element is an input boundary element THEN it cannot accept an input from any other element
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Rule 39 IF an element is an output boundary element THEN it cannot provide an input to any other element Production rules 40 and 41 generate the actual connections. Rule 40 sets the matching priority for agents with exactly the same informational input and output. Since it is not always the case, production rule 41 specifies the priority for generating the connections between elements with only partially identical input and output variables. Rule 40 IF two elements have identical output and input variables AND there are no production rules that prevent from connecting them THEN specify the connections for these elements Rule 41 IF there are no elements with identical input and output variables AND there are elements with partially identical input and output variables AND there are no production rules that prevent from connecting them THEN specify the connection for these elements beginning with the closest match The notion of the closest match can be explained with the following example. Assume that X1, X2, and X3 are the informational output variables of agent A1, and that there are no other elements with identical input variables. There are, however, elements A2 and A3 with input variables X1, X2 and variables X1, X4, and X5, respectively. The match between A1 and A2 (two identical variables) is closer than between A1 and A3 (only one variable is identical). Figure 3 presents the way in which elements with only partially identical output and input variables are connected. Production rule 42 develops a multilevel hierarchy of the inner structure of the overall integrated system. Rule 42 IF a connection for an input boundary element has been specified THEN continue with selecting connections for elements that have not been listed in the specifications Figure 3: Connecting agents with similar input and output variables
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Production rule 43 is applied when all inner (not boundary) elements have already been included in existing subsystems but the integration process has not been completed. Rule 43 IF there are boundary elements only THEN specify connections between them All the above production rules have been structured independently of the application domain and cannot be modified by a system analyst. The analyst may, however, add domain-based production rules. They may follow, for example, the safety requirements, marketing requirements, or other constraints imposed by the analyst.
The Integration Algorithm Integration of agents and subsystems into an overall system begins at the lowest level (level 1) of abstraction represented by the initial number of agents generated at the agent representation stage. It is performed according to the algorithm presented next. Step 1. Open a set AGENT_BASE consisting of all agents defined at the representation stage. Set level = 1. Step 2. Apply production rules to generate connections between elements in AGENT_BASE. Step 3. If no connections are generated, stop; otherwise, match elements in AGENT_BASE into pairs using the existing connections. Step 4. Define informational input and output variables for subsystems generated by the matching process. Step 5. Remove from AGENT_BASE all elements that have taken part in the matching process. Step 6. Add to AGENT_BASE all subsystems generated by the matching process. Step 7. Set level = level + 1 and go to Step 2. According to the above algorithm, in order to enter the next level of integration it is enough to generate only one connection in Step 2. Elements of the agent base that are not matched at level i are considered for matching at level i+1. The algorithm terminates at the level at which it is not possible to match agents and/or subsystems into pairs (no connections are generated). Two cases are possible. First, matching is no longer possible because all agents and subsystems have been exhausted and an overall system has been built. Second, although there are subsystems and/or agents available, it is not possible to match them. In this case, the integration path explored has not led to the overall system and none of the subsystems at the highest level includes all functions specified in the functional space for the designed system.
Illustrative Example As an hypothetical example, let us assume the initial agent base as presented in Figure 4. In Figure 4 agent A1 is an input boundary element. Agents A6 and A8
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Figure 4: The initial state of an agent base X1
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are output boundary elements (note the dotted backgrounds for input and output boundary elements). The integration algorithm is used to build an overall system. Step 1. AGENT_BASE = {A1, A2, A3, A4, A5, A6, A7, A8} (as shown in Figure 4), level=1. Step 2. A1 ——> A4 (rules 36, 37, 38, and 40). Since the agent base includes more than one element, production rule 36 is fired, which means that the search for a connection begins with agent A1 (A1 is an input boundary element). Rule 37 excludes connections with agents A6 and A8. Rule 38 directs the search towards agents that may accept, as their input, the output of agent A1 (it excludes connection such as, for example, A5——>A1). Finally, rule 40 selects agent A4 as the best match for agent A1, i.e., connection A1— —>A4 is specified. The connection A1——>A4 is generated with production rules 42, 39, and 40. Since a connection for the only input boundary element at this level has been specified, production rule 40 requires that the search for connections continues with elements that have not been provided in the specification list. Agent A2 is such an element. Rule 39 excludes connections A6—>A2 and A8—>A8 (A6 and A8 are output boundary elements). Rule 44 excludes connection A2——>A8. Rule 40 specifies agent A6 as the match with the input variables that are identical to the output variables of agent A2, so that the connection A2—>A6 is generated. A5——>A3 (rules 42, and 40).
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The next element that has not been specified in the connections is agent A3 (production rule 42). Rule 40 points towards agent A5 as the best match for A3 (the output of A5 is identical with the input of agent A3). A7——>A8 (rules 42, and 40). Only two elements have been left without connections specified. Rules 42 and 40 lead to generation of the connection A7——>A8. Step 3. Connections specified in step 2 allow to develop the following subsystems: S1 = A1.A4, S2 = A2.A6, S3 = A5.A3, S4 = A7.A8. Step 4. Input and output variables for subsystems generated in step 3 are defined. Step 5. AGENT_BASE = {S1, S2, S3, S4}. Step 6. Level = 2. Step 2. S1——>S3 (rules 36 and 40). At level 2 production rules 44 through 43 are applied to a new state of the agent base at which connections are generated for the four subsystems S1, S2, S3, and S4. Step 3. S5 = S1.S3. Step 4. Input and output variables are defined for S5. Step 5. AGENT_BASE = {S2, S4, S5}. Step 6. Level = 3. Step 2. S5 ——> S4 (rules 36, 41, and 43). Step 3. S6 = S5.S4. Step 4. Input and output variables are defined for S6. Step 5. AGENT_BASE = {S2, S6}. Step 6. Level = 4. Step 2. S6 ——> S2 (rules 36 and 43). Step 3. S7 = S6.S2. Step 4. Input and output variables are defined for S7. Step 5. AGENT_BASE = {S7}. Step 6. Level = 5. Step 2. No connections are generated (rule 34). Step 3. Stop. Note that the overall system S7 is a valid integrated solution as all its input and output variables represent the connections with the system environment (rule 35). The five-level hierarchical tree of system S7 is presented in Figure 5. In the example discussed the integration path explored satisfies the constraints expressed with production rules and, at the same time, leads to a valid overall system.
CONCLUSION Information flow integration is one of the major activities of the design process of an integrated system . The outcome of the integration process is the overall system integrated through the flow of information. In this chapter, a rule-based approach was proposed for the integration problem formulated as follows.
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Figure 5: Five-level hierarchical tree of the overall integrated system S7 SYSTEM 7
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Given the informational inputs and outputs of agents (subsystems), find the overall system being designed that meets the desired functions and is integrated through the flow of information. Elements of an agent base are integrated using an algorithm into an overall system that has a hierarchical structure. General production rules supporting generation of connections for agents and subsystems were developed. The general production rules relate to the underlying systems theory. They are structured independently of the system’s domain and cannot be modified by a system analyst. Production rules ensure that only feasible variants of the designed system are explored. The algorithm and production rules were applied for building of an overall system using a hypothetical example with eight agents at the initial stage of integration.
REFERENCES Askin, R. G. and Standridge, C. R. (1993). Modelling and Analysis of Manufacturing Systems. New York: John Wiley & Sons. Bender, E. A. (1996). Mathematical Methods in Artificial Intelligence. Cocoa Beach, FL: IEEE Press. Gunasekaran, A. and Sarhadi, M. (1997). Planning and management issues in enterprise integration. Concurrent Engineering: Research and Application, 5, 98-100.
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Hassan, A. A., Hershey, J., Schroeder, J., Sohie, G. R. L. and Yarlagadda, R. K. R. (1994). System Design. San Diego, CA: Academic Press. Kamrani, A. L. and Sferro, P. R. (1999). Direct Engineering: Toward Intelligent Manufacturing. New York: Kluwer Academic Publishers. Lefkowitz, H. and Herman, G. T. (1992). Color scales for image data. IEEE Computer Graphics and Applications. O’Grady, P. (1999). The Age of Modularity. Adams and Steele Publishers. Pacholski, L. (1998). Fuzzy logic application in ergonomic renewal of multiagent manufacturing systems. Cybernetics and Systems: An International Journal, 29, 715-728. Pacholski, L. and Wejman, M. (1995). Soft Modelling of the Ergonomicity of the Multiagent Manufacturing Systems. New York: Taylor and Francis. Prakken, B. (2000). Information, Organization and Information Systems Design. New York: Kluwer Academic Publishers. Quinn, J. B. (1992). Intelligent Enterprise. New York: The Free Press. Raczkowsky, J. and Reithofer, W. (1998). Design of consistent enterprise models. Cybernetics and Systems: An International Journal, 29, 525-552. Rogowitz, B. E. and Treinish, L. (1998). Data visualization: The end of the rainbow. IEEE Spectrum. Rolstafas, A. and Andersen, B. (2000). Enterprise Modelling. New York: Kluwer Academic Publishers. Szczerbicki, E., Kubiak, B. F. and Korowicki, A. (2000). Re-engineering and information management issues in concurrent systems analysis for performance enhancement. Systems Analysis, Modelling, Simulation, 38, 141155. Szczerbicki, E. (1993). Acquisition of knowledge for autonomous cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, 23, 13021315. Tharumarajah, A. (1998). A self-organising model for scheduling distributed autonomous manufacturing agents. Cybernetics and Systems: An International Journal, 29, 461-480. Tufte, E. (1994). Envisioning Information. Cheshire, CT: Graphics Press. Tufte, E. (1998). Visual Explanations. Cheshire, CT: Graphics Press. Wyzalek, J. (1999). Systems Integration Success. Auerbach.
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Chapter VII
An Experimental Analysis of the Effectiveness and Efficiency of Teams with Partial Problem Domain Knowledge Dinesh A. Mirchandani and Jaideep Motwani Grand Valley State University, USA
ABSTRACT Knowledge Management Systems are increasingly becoming important to both practitioners and researchers. One area of application of such systems is the formation of organizational teams with appropriate knowledge content to solve complex and novel problems. A common predicament, however, is that teams are often formed with only partial problem domain knowledge. This study examines if teams that have partial problem domain knowledge are more effective and efficient than teams that do not have specific problem domain knowledge. It finds that partial problem domain knowledge may in fact be worse than no problem domain knowledge. Several implications for researchers and practitioners are derived from this result.
INTRODUCTION Information systems that focus on creating, gathering, organizing and disseminating an organization’s knowledge are known as Knowledge Management Systems Copyright © 2003, Idea Group, Inc.
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(KMS) (Markus, 2001; Alavi and Leidner, 1999). The importance of KMS is underscored by the fact that several organizations are now taking steps to manage their knowledge explicitly by appointing chief knowledge officers (CKOs) to do so (Holsapple and Joshi, 2000). CKOs perform a variety of roles, including serving as the chief designer of the knowledge architecture, the head technologist for knowledge technologies, and the primary procurement officer for external knowledge content (Grover and Davenport, 2001). Clearly, there is a growing recognition that knowledge has become an important basis for competitive advantage between firms (Guay, 2001; Nidumolu et al., 2001). Knowledge has even been suggested as the most strategically significant resource for an organization (Pfeffer and Sutton, 2000; Grant, 1996). Typically however, knowledge in organizations is gained through experience and interactions with both processes and individuals (Schulz, 2001; Mulholland and Zdrahal, 2001). The recognition of this nature of knowledge is evident in the emphasis placed in many organizations on “learning by doing, where newcomers to the organization are expected to gain much of their knowledge and skills through a handson approach, even though it may be relearning something that someone else in the organization already knows (Nidumolu et al., 2001). Knowledge management on the other hand aims at knowledge reuse within organizations and the development of organizational memory systems to aid this reuse (Markus, 2001).
REVIEW OF LITERATURE KMS can take the form of knowledge repositories or of data maps. Knowledge repositories are databases of documents written by knowledgeable individuals whereas knowledge maps are searchable indexes of expertise held by individual employees of an organization (Davenport and Prusak, 1998). Using the former, an organization can gain immediate access to knowledge relevant to its needs, and with the latter it can best utilize the individual strengths of its employees. According to Gray (2000), by making knowledge searches more effective, KMS can improve the variety of knowledge present on problem-solving teams. Teams are collectives that have the characteristic of shared interdependent work (Lovelace, Shapiro and Weingart, 2001; Dyer, 1977). They are a useful mechanism for pooling and using the diverse knowledge and skills of employees (Drucker, 1994). Improved team knowledge diversity can lead to more accurate and complete analysis of complex problems, thereby improving the effectiveness of the solutions teams generate. This ability of KMS is important because organizations are increasingly using teams to solve complex problems. In fact, according to Gordon (1992), 82 percent of companies with 100 or more employees use team structures. Also, 68 percent of Fortune 1,000 companies use self-managing teams (Lawler, Mohrman and Ledford, 1995). KMS that can help create teams with appropriate knowledge content to solve problems can be invaluable to an organization. In fact, in companies such as Boeing,
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team members are networked together according to roles, tasks and project deliverables. They also have access to an electronic library of best practices that can be shared and updated on a project-by-project basis thereby improving the company’s performance and reducing costs (Guay, 2001). This chapter thus focuses on examining the performance of knowledge-based problem-solving teams. The formation of such teams is an important application of KMS that can improve organizational performance and create competitive advantage for it (Gray, 2000). Gray’s proposition is also supported by information processing theory (Galbraith, 1973) because to be effective, teams require sufficiently diverse knowledge to properly assess and understand the problems they face. Inadequate problem assessment can lead to poor decisions (Tushman and Nadler, 1978). However a challenge that often arises for managers is that the full scope of a problem is rarely evident when the problem is first encountered. A manager facing such a problem must assemble a team without fully understanding the problem domain. Thus the manager may not identify all the required knowledge bases when putting together the team, and the team may not have the knowledge variety to understand and solve the problem. For most organizations in such situations, a related issue is that knowledge searches require time and effort to identify potential knowledge holders and contact them to assess the relevance of their knowledge. Managers may not be willing to conduct a thorough enough search for the right expertise (Davenport and Prusak, 1998). This increases the probability that decisions will be made using incomplete or deficient knowledge. Inadequate team knowledge variety at inception can however be corrected by searching for and incorporating the missing knowledge into the structure by adding new team members (Gray, 2000). Indeed, most organizations use emergent teams whose structure evolves in response to a problem. But given the fact that team knowledge is often partial or incomplete, it is particularly relevant to examine the effectiveness and efficiency of teams in solving problems about which the team has only partial expertise. Contrasting this performance to the performance of teams that have no expertise in the problem domain can help indicate the usefulness of partial problem knowledge to teams. We undertake this examination to gain a better understanding of team composition and team knowledge content.
METHODOLOGY A large state organization (> 2,000 employees) was solicited to participate in the study. Details of this organization are withheld to respect its desire for privacy. Individual participants were drawn from the organization’s functional areas such as accounting, finance, operations, information systems, and marketing. The functional area from which the participant was drawn was used as that participant’s area of expertise or knowledge domain, with their consent. This simplification replicates the behavior of a satisficing manager (Davenport and Prusak, 1998), and eliminates the conduct of a thorough knowledge search. Another reason for using functional areas
An Experimental Analysis of the Effectiveness and Efficiency of Teams 107
is that many organizations primarily rely on functional area affiliation in determining team composition, e.g., joint application development (JAD) efforts for information systems (Shelly, Cashman and Rosenblatt, 2000). Organizations consider such cross-functional teams as effective and productive ways to work that can provide multiple perspectives and experiences to decision makers (Lovelace, Shapiro and Weingart, 2001; Nidumolu, 2001). To ensure reliability of results, only those participants who had at least some level of college education in their functional area were included in the sample. Sixty teams each comprising two members, were thus initially formed by the researchers. These 60 teams were then divided into two groups each comprising 30 teams. In both groups, each team was presented with a problem to solve related to a particular functional area. The first group (of 30 teams) was presented with a problem related to the functional area of marketing. Of the 30 teams in this group, 15 teams had at least one team member who considered himself knowledgeable about marketing. The remaining 15 teams had both members drawn from functional areas other than marketing. The second group (of thirty teams) was presented with a problem related to the functional area of finance. Of the thirty teams in this group, 14 teams had at least one team member who considered himself knowledgeable about finance. The remaining 16 teams had both members drawn from functional areas other than finance. The purpose of using two groups, each presented with a different problem domain, was to ensure validity of the results and to eliminate the possibility of biases resulting from inappropriate problem selection. To measure the performance of the teams on solving the problems, the researchers used two parameters: (1) the correctness of the solution generated by the team which was used as a measure of problem-solving effectiveness (recorded as a binary variable), and (2) the time taken by the group to solve the problem which was used as a measure of problem-solving efficiency. Two hypotheses were examined: H1. Teams that have at least some knowledge of the problem domain will be more effective than teams with general business knowledge but not specific knowledge of the problem domain. H2. Teams that have at least some knowledge of the problem domain will be more efficient than teams with general business knowledge but not specific knowledge of the problem domain. The alternative hypotheses examined were: H1a. There is no difference in problem-solving effectiveness between teams that have at least some knowledge of the problem domain and teams that have general business knowledge but not specific knowledge of the problem domain. H2a. There is no difference in problem-solving efficiency between teams that have at least some knowledge of the problem domain and teams that have general business knowledge but not specific knowledge of the problem domain.
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The rationale for hypotheses H1 and H2 is derived from the works of Simon (1945) and Mintzberg et al., (1976). Teams that have some problem domain knowledge are likely to understand the problem better and come up with superior solutions.
DATA ANALYSIS Two-way contingency table analyses were conducted to evaluate whether teams with partial problem domain knowledge were more effective than teams with no problem domain knowledge (Hypothesis H1). Significant values of Pearson c2 and Cramer’s V would indicate such a difference in problem-solving effectiveness. As shown in the tables below, partial problem domain knowledge was found to be unrelated to problem-solving effectiveness. Thus we reject H1 and support its alternative hypothesis H1a. For the first group, Pearson χ2 (1, N = 30) = 1.677, p = .195, Cramer’s V = .236 (see Tables 1, 2, and 3 below). This indicates that there is no difference in the effectiveness of teams that have partial knowledge of marketing and no knowledge of marketing in solving a problem derived from the functional area of marketing. For the second group, Pearson χ2 (1, N = 30) = .010, p = .919, Cramer’s V = .018 (see Tables 4, 5, and 6 below). This indicates that there is no difference in the Table 1: Marketing problem analysis–Crosstabulation
Marketing Knowledge
Team has no knowledge of marketing Team has some knowledge of marketing
Total
Count Expected Count Count Expected Count Count Expected Count
Analysis of marketing problem Incorrect Correct Analysis Analysis 2 13 3.5 11.5 5 10
Total 15 15.0 15
3.5
11.5
15.0
7 7.0
23 23.0
30 30.0
Table 2: Chi-square tests
Pearson Chi-Square Continuity Correction Likelihood Ratio Fisher's Exact Test Linear-by-Linear Association N of Valid Cases
Value 1.677 .745 1.721 1.621 30
df 1 1 1 1
Asymp. Sig. (2-sided) .195 .388 .190 .203
Exact Sig. (2-sided)
Exact Sig. (1-sided)
.390
.195
An Experimental Analysis of the Effectiveness and Efficiency of Teams 109
Table 3: Symmetric measures
Nominal by Nominal
Value -.236 .236 30
Phi Cramer's V
N of Valid Cases
Approx. Sig. .195 .195
Table 4: Finance problem analysis–Crosstabulation
Finance Knowledge
Team has no knowledge of finance Team has some knowledge of finance
Total
Count Expected Count Count Expected Count Count Expected Count
Analysis of finance problem Incorrect Correct Analysis Analysis 6 10 5.9 10.1 5 9
Total 16 16.0 14
5.1
8.9
14.0
11 11.0
19 19.0
30 30.0
Table 5: Chi-square tests
Pearson Chi-Square Continuity Correction Likelihood Ratio Fisher's Exact Test Linear-by-Linear Association N of Valid Cases
Value .010 .000 .010
df
.010
1 1 1 1
Asymp. Sig. (2-sided) .919 1.000 .919
Exact Sig. (2-sided)
Exact Sig. (1-sided)
1.000
.610
.921
30
Table 6: Symmetric measures
Nominal by Nominal N of Valid Cases
Phi Cramer's V
Value .018 .018 30
Approx. Sig. .919 .919
effectiveness of teams that have partial knowledge of finance and no knowledge of finance in solving a problem derived from the functional area of finance. We used Mann-Whitney U tests to evaluate the hypothesis (H2) that teams with partial problem domain knowledge are more efficient than teams with no problem domain knowledge. Significant values of z would indicate that there is indeed a difference
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Table 7: Mann-Whitney test Time taken to analyze marketing problem
Groups with some Team has no knowledge of marketing Team has some knowledge of marketing Total
N
Mean Rank
Sum of Ranks
15
17.07
256.00
15
13.93
209.00
30
Table 8: Test statisticsb
Mann-Whitney U Wilcoxon W Z Asymp. Sig. (2-tailed) Exact Sig. [2*(1-tailed Sig.)]
Time taken to analyze marketing problem 89.000 209.000 -.987 .323 a
.345
a. Not corrected for ties. b. Grouping Variable: Knowledge of marketing
Table 9: Mann-Whitney test Time taken to analyze finance problem
Groups with some Team has no knowledge of finance Team has some knowledge of finance Total
N
Mean Rank
Sum of Ranks
16
18.41
294.50
14
12.18
170.50
30
Table 10: Test statisticsb
Mann-Whitney U Wilcoxon W Z Asymp. Sig. (2-tailed) Exact Sig. [2*(1-tailed Sig.)]
Time taken to analyze finance problem 65.500 170.500 -1.951 .051
a. Not corrected for ties. b. Grouping Variable: Knowledge of finance
a
.052
An Experimental Analysis of the Effectiveness and Efficiency of Teams 111
in problem-solving efficiency. However, the results of the tests were insignificant for Group 1 (z = -.987, p = .323) (Tables 7 and 8), and for Group 2 (z = -1.951, p = .051) (Tables 9 and 10). Thus we reject H2 and support its alternative hypothesis H2a.
DISCUSSION OF RESULTS The results of the analysis are somewhat surprising because it is reasonable to expect that teams with partial problem domain knowledge will perform better than teams with general business knowledge but not specific problem domain knowledge (Simon, 1945). The insignificant differences in problem-solving effectiveness and problem-solving efficiency indicate that partial problem domain knowledge is no better than general business knowledge in tackling business-related problems. Further, in 10 out of 29 cases, teams with partial problem domain knowledge incorrectly analyzed the given problem, an error rate of 34% (in contrast to the 26% error rate of teams with no specific problem domain knowledge), which clearly is a cause of concern. This finding places a greater burden on managers responsible for forming teams to ensure that the knowledge content on a team matches the problem domain closely. Putting together teams with only partial problem domain knowledge because it saves on time and effort rather than do a thorough knowledge search may in fact turn out to be more costly to organizations in the long run as the above analysis indicates. This clearly justifies the investment in a KMS for an organization. The results however should also be interpreted with some caution, as in experimental settings it is difficult to replicate organizational settings and informal working relationships. For instance, in this experiment, the team members were brought together solely for the purpose of a single problem-solving episode. Perhaps a continued relationship (as is common in organizational settings) would have improved the team synergy. Also the Hawthorne effect may have negatively affected team performance.
IMPLICATIONS FOR RESEARCH AND PRACTICE KMS are increasingly important to organizations, and researchers are only now beginning to develop theory to support the field as its applications grow (Markus, 2001). Teams are fundamental to any organizational structure and the application of KMS to develop knowledge-based teams is beginning to come to maturity. This chapter highlights the importance of effective knowledge searches during team composition and thus the value of KMS. It brings to fore the potential costs associated with partial problem domain knowledge, thereby cautioning against satisficing human behavior in the formation of problem-solving teams. It thus adds to the growing theory in the field. This research used teams with only two members, one of whom determined whether the team had problem domain knowledge based on his/her functional area affiliation. Typically however organizational team sizes average about nine members
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(Offermann and Spiros, 2001). This simplification made it possible to conduct this experiment. However replicating the study with larger teams would be useful because issues of intra-team communication and group dynamics would become more apparent and perhaps affect the results. Again, for the purpose of simplification, this study used academic cases drawn from the functional areas of marketing and finance to represent the problems that the teams were assembled to solve. Organizational problems however are more complex and multidimensional. They are often novel, unstructured and lacking in clear precedent (Simon, 1977). Thus teams must first understand the problem, analyze their knowledge needs in relation to the problem, and adapt themselves by adding or deleting to the team’s knowledge content in order to solve the problem (Gray, 2000). To experimentally model such organizational problems is difficult because by definition such problems lack precedent, thus an alternative to experimental analysis may be to shadow teams in actual problem-solving episodes. Thus future research could longitudinally shadow the problem-solving episodes of teams. Perhaps this would also reveal the development of informal working relationships and comfort levels among team members, which may be an important component of eventual success. Finally, it was surprising to find that teams with partial problem domain knowledge had a higher error rate in problem analysis than teams without specific problem domain knowledge. In retrospect, one possible reason for this outcome may be poor design implementation. According to Gray (2000), superior analysis may well be wasted if the design is poorly implemented. On the other hand, perhaps this was an influence of the Hawthorne effect where team members affiliated to the functional area from which the problem originated made or contributed to unforced errors because they felt their performance on the problem was being scrutinized more closely than the other team members’. An alternative explanation is that perhaps they overanalyzed the problem, feeling they had to compensate for the other team members’ lack of knowledge of the problem. In either case, the result is unexpected and perhaps indicates that there should be some kind of balance on teams, i.e., teams should be composed of pairs or dyads of members with knowledge about a particular problem area rather than a single member. This issue merits further investigation. The study has clear implications for practitioners. Teams with partial problem domain knowledge are neither effective nor efficient. In forming a team, it is worthwhile to undergo a costly or even time-consuming search for adequate knowledge content, and to adapt the team’s knowledge as appropriate when tackling a problem. This study clearly
