ISSN 0885-8624
Journal of
BUSINESS & INDUSTRIAL MARKETING
Volume 17 Number 4 2002
Organizational learning and industrial marketing Guest Editor: G. Tomas M. Hult
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Access to Journal of Business & Industrial Marketing online . . . . . . . . . . . . 234 Editorial advisory board . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 Abstracts and keywords
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Guest editorial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238 Managing the market learning process George S. Day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240 Market-based success, organizational routines, and unlearning James M. Sinkula . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 Putting people back into organizational learning Robert F. Hurley . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 270 Managing the paradox of inter-firm learning: the role of governance mechanisms Jakki J. Mohr and Sanjit Sengupta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282 A longitudinal study of the learning climate and cycle time in supply chains G. Tomas M. Hult, David J. Ketchen, Jr and Stanley F. Slater . . . . . . . . . . . . . 302 Executive summary and implications for managers and executives . . . . 324 Internet currency Edited by Dennis A. Pitta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331
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JOURNAL OF BUSINESS & INDUSTRIAL MARKETING, VOL. 17 NO. 4 2002
Editorial Advisory Board Riad Ajami Rochester Institute of Technology, USA Syed Tariq Anwar West Texas A&M University, USA Thomas Apaiwongse Clark Atlanta University, Atlanta, USA Siva Balasubramanian Southern Illinois University at Carbondale, USA Donald Barclay University of Western Ontario, Canada Joseph A. Bellizzi Arizona State University, USA James Boles Georgia State University, USA Dennis J. Cahill Cleveland, USA S. Tamer Cavusgil Monash University, Australia Marjorie J. Cooper Baylor University, USA James M. Daley John Carroll University, USA Phil Dawes Wolverhampton Business School, UK Paul Dion Sigmund Weis School of Business, USA Awad B. El-Haddad Suez Canal University, Egypt Eugene Fram Rochester Institute of Technology, USA David Good Grand Valley State University, USA Andrew C. Gross Cleveland State University, USA Steve Henson University of New Orleans, USA Craig A. Hollingshead Texas A & M University, USA George C. Hozier Jr University of New Mexico, USA Tomas M. Hult Michigan State University, USA Michael D. Hutt Arizona State University, USA Daniel E. Innis Ohio University, USA Peter F. Kaminski Northern Illinois University, USA Jeff Kaumeyer Hammond Farrell, USA
Roger A. Kerin Southern Methodist University, USA Key Suk Kim Oregon State University, USA Raymond W. LaForge University of Louisville, USA Richard C. Leventhal Regis University, USA Jeffrey E. Lewin Western Carolina University, USA J. David Lichtenthal City University of New York, USA Donald Lindgren Lindgren Research Associates, USA Ritu Lohtia Georgia State University, USA Michael H. McBride Southwest Texas State University, USA Paul Matthyssens Limburg University Centre, Belgium Laura Milner University of Alaska Fairbanks, USA Michael S. Minor University of Texas – Pan American, USA Thomas G. Noordewier University of Vermont, USA Ben A. Oumlil University of Dayton, USA Michael K. Rich Southwest State University, Marshall, Minnesota, USA Bruce K. Pilling Georgia State University, USA John Ronchetto University of San Diego, USA Theresa M. Rosania Kean College of New Jersey Union, USA Bob Rothberg Rutgers Graduate School of Management, USA Elaine Sherman Hofstra University, USA Richard Spiller California State University, USA Shirley Stretch California State University, USA Dave Wilemon Syracuse University, USA Elizabeth J. Wilson Louisiana State University, USA Thomas R. Wotruba San Diego State University, USA
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Abstracts and keywords
Managing the market learning process George S. Day Keywords Market intelligence, Competitive advantage, Competitor intelligence Organizations continuously learn about their markets through the linked processes of market sensing and sense making. Firms that have mastered these two processes gain an advantage by anticipating market opportunities ahead of their rivals and more accurately forecasting how the market will respond to their moves. Our purposes are first, to describe the primary mechanisms for acquiring market information and turning it into market knowledge; second, to assess the pitfalls and power of the mental models used by organizations to filter, sort and simplify market information into coherent patterns; and then to prescribe some ways to improve a market learning capability. Market-based success, organizational routines, and unlearning James M. Sinkula Keywords Organizational learning, Organizational processes, Organizational change, Information Information, whether it is acquired from an external source or generated internally, is subjected to perceptual filters made up of the organization’s norms, procedures, and beliefs that influence what information the organization attends to and ultimately accepts. This paper examines the role which these organizational filters play in unlearning; viewed here as a specialized form of organizational learning. Unlearning is defined as the ‘‘process by which firms eliminate old logics and make room for new ones’’ by Prahalad and Bettis. The author argues that firms which engage in unlearning activities are better able to cast aside established routines in order to replace them with ones that ultimately result in superior value to their customers. Putting people back into organizational learning Robert F. Hurley Keywords Market orientation, Organizational learning, Customer orientation, Learning styles, Corporate culture, Marketing strategy. There is an overemphasis on an outside-in, macro-organizational view of learning and an under-emphasis on the inside-out view which recognizes that people are the main agents of learning and change. Attempts at building a learning organization should start with an understanding of how adults learn and develop rather than elaborate ideas about competitive strategy, market research and information dissemination. Adult learning theory tells us that people learn primarily by being encouraged to tackle challenges, experiment, fail and correct failures and reflect on their experiences. The challenge in building learning organizations is fighting the bureaucratization that often replaces experimentation with control and routine. This paper examines the literature on market orientation, organizational learning and adult learning theory to identify how individual level learning can be maximized as a mechanism for enhancing organizational learning. Recommendations are made to integrate these streams of research and offer suggestions for further research. Managing the paradox of inter-firm learning: the role of governance mechanisms Jakki J. Mohr and Sanjit Sengupta Keywords Partnering, Alliances, Governance, Skills Organizational learning in inter-firm exchange relationships poses a double-edged sword. On one hand, inter-firm learning is a desirable extension of organizational learning, developing a firm’s knowledge base, and providing fresh insights into strategies, markets, and relationships. On the other hand, inter-firm learning can lead to unintended and undesirable skills transfer, resulting in the potential dilution of competitive advantage. This risk can be exacerbated by disparities in inter-firm learning, resulting in uneven distribution of benefits and risks in the collaborative relationship. This paper articulates 236
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these two different views on inter-firm learning, and second, develops a framework for the role of governance in regulating knowledge transfer. In particular, appropriate governance mechanisms must be crafted which match the learning intentions of the partners, the type of knowledge sought, and the designed duration for the collaboration, so as to maximize the benefits of learning while minimizing the risks. Implications for strategy and future research are offered. A longitudinal study of the learning climate and cycle time in supply chains G. Tomas M. Hult, David J. Ketchen, Jr and Stanley F. Slater Keywords Supply chain, Cycle time, Organizational learning, Corporate culture Drawing on the resource-based view, we posit that the learning climate is an intangible, strategic resource that influences important outcomes. Data from 141 supply chain units within a multinational corporation reveal that four constructs (team-, systems-, learning-, and memory orientations) function as first-order indicators of the higher-order phenomenon of the learning climate. In turn, learning is inversely related to supply chain cycle time. The results are robust across the 1994 and 1999 data, suggesting that learning offers a persistent tool for managing outcomes.
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Guest editorial
About the Guest Editor G. Tomas M. Hult is Director of the International Business Center (e-mail:
[email protected]; www.globaledge.org) and Associate Professor of Marketing and Supply Chain Management in the Eli Broad Graduate School of Management at Michigan State University. His research focuses on knowledge management, marketing strategy, international business, and supply chain management. In addition to JBIM, his research has been published in the Academy of Management Journal, Decision Sciences, Journal of Management, Journal of Marketing, Journal of Retailing, Journal of the Academy of Marketing Science, and Strategic Management Journal, among others. Organizational learning and industrial marketing: an introduction to the special issue This special issue of the Journal of Business and Industrial Marketing focuses on the increasingly important but understudied area of organizational learning as it pertains to industrial marketing. The all-invited issue brings together a set of outstanding authors known for their work on the topic. Their articles are positioned to help managers implement learning-based programs in industrial marketing processes. Additionally, the articles provide cutting edge insights on a variety of learning issues that should advance the scholarly field of market-focused learning. Since Wes Johnston (Editor, Journal of Business and Industrial Marketing) encouraged me to include one of my own articles in the special issue, I decided to keep this introduction short. A lot of my ‘‘scholarly learning’’ over the last decade can be found in the article I co-authored with Dave Ketchen and Stan Slater (see last article in this issue). Other articles I am particularly fond of are Hult (1998), Hult et al. (2000), Hult and Ketchen (2001), Hult, Ketchen and Nichols (2001), and Hurley and Hult (1998); all of those references can be found in the last article in this issue. In this special issue, the series of five articles address the area of organizational learning, but does so from rather different angles. As such, I think the special issue will appeal to a broad variety of marketing scholars interested in organizational learning, knowledge management, industrial marketing, and marketing strategy. In the first article in the special issue, Day expands and extends his notions on ‘‘market sensing’’ which he has presented earlier in a stream of seminal works. In the second article, Sinkula presents a conceptualization centered on ‘‘unlearning’’. In the third article, Hurley focuses on ‘‘people as the main agents of organizational learning’’. In the fourth article, Mohr and Sengupta examine ‘‘inter-firm learning’’. In the last article, together with Ketchen and Slater, I examine longitudinal performance effects of the ‘‘learning climate’’. Finally, I want to recognize the reviewers who provided valuable input to the authors in the process of preparing their articles for publication. Since this issue of JBIM is a collection of all-invited articles, I decided to make the 238
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review process ‘‘open’’ instead of following the standard double blind process. That way, both the authors and reviewers had the opportunity to communicate directly with each other after the initial reviews had been provided through me as the intermediary. In all, I believe this form of peer-reviewed approach was beneficial for this particular special issue. Also important, all reviewers have extensive knowledge of the organizational learning literature. As for the reviewing, I served as the sole reviewer for Day’s article. Stanley F. Slater and Bryan A. Lukas reviewed Sinkula’s article. James M. Sinkula and Simon Bell reviewed Hurley’s article. David J. Ketchen, Jr and Daniel J. Flint reviewed Mohr and Sengupta’s article. Our article (Hult, Ketchen, and Slater) was reviewed in-house by S. Tamer Cavusgil and Destan Kandemir (a PhD student working with me in the area of marketing-focused knowledge management). I hope you enjoy the issue! G. Tomas M. Hult
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An executive summary for managers and executive readers can be found at the end of this issue
Managing the market learning process George S. Day The Wharton School, The University of Pennsylvania, Philadelphia, Pennsylvania, USA
Keywords Market intelligence, Competitive advantage, Competitor intelligence Abstract Organizations continuously learn about their markets through the linked processes of market sensing and sense making. Firms that have mastered these two processes gain an advantage by anticipating market opportunities ahead of their rivals and more accurately forecasting how the market will respond to their moves. Our purposes are first, to describe the primary mechanisms for acquiring market information and turning it into market knowledge; second, to assess the pitfalls and power of the mental models used by organizations to filter, sort and simplify market information into coherent patterns; and then to prescribe some ways to improve a market learning capability.
Slow and ill-advised
Why do firms lose touch with their markets? Why are they surprised by shifts in customer requirements, slow to react to emerging competitors, and unprepared to use innovative channel arrangements? Without an effective capacity to anticipate, they continually miss opportunities and never seem able to do more than catch up. Even their reactions are liable to be slow and ill-advised, or counter-productive because of flawed assumptions, misinformation, or internal disagreements. Market-driven firms, in contrast, stand out in their ability to continuously sense and act on events and trends in their markets. They are better equipped to anticipate how their markets will respond to actions designed to retain or attract customers, improve channel relations or thwart competitors. In these well educated firms, everyone from first-line sales and service people to the CEO is sensitized to identify and seize market opportunities as they arise. Market sensing as a learning process As shown in Figure 1, the linked activities of market sensing and sense making allow organizations to continuously learn about their markets[1]. Market sensing depends on open-minded inquiry rather than looking for information to confirm pre-existing beliefs about the market. The next stage in the market learning process is disseminating the information generated by these inquiries and absorbing the insights into the collective mental models of how the market behaves. These mental models help make sense of information, ensuring that everyone pays attention to the essence and potential of the information. A hallmark of a market-driven firm is broadly shared assumptions about their markets that assures the coherence and timeliness of strategies that anticipate rather than react to market events. This article is adapted from chapter 5 of The Market-driven Organization, The Free Press, New York, NY, 1999. The research register for this journal is available at http://www.emeraldinsight.com/researchregisters The current issue and full text archive of this journal is available at http://www.emeraldinsight.com/0885-8624.htm
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Figure 1. Market-driven processes for learning about markets
Expectations and requirements
This process of learning about markets may be triggered by an impending decision, an emerging problem, or a belief that effective innovation requires deep insights into latent customer needs. This spark begins the active collection and distribution of information about the needs, expectations and requirements of customers, how the market is segmented, how relationships are sustained, and the intentions and capabilities of competitors. Before the information can be used it has to be interpreted so patterns can be revealed and understood. Further learning comes from the feedback about what actually happened[2]. Did the market respond as we expected, and if not, why not? Were our judgments confirmed or disconfirmed? What should we have known that we did not? The cumulative insights are then lodged somewhere in the sprawling memory of the organization, ready to be retrieved (hopefully) when needed. This begins a new cycle of sensing and sense making. Mastery of the complete market sensing process is rare. Most firms suffer disabilities at one or more stages of the process. Their inquiries may be constipated, their mental models myopic, the circulation of information constricted, or the collective memory afflicted by amnesia. The cost of these disabilities is high and mounting rapidly in markets experiencing accelerating rates of change. Yet organizations can learn to better sense their markets, by understanding each step in their process, critically assessing their market learning capability and then correcting the learning disabilities.
Sensing and sense making
As the half-life of usable knowledge shrinks in the face of compressed life cycles, fragmenting markets and proliferating media and distribution channels, it is becoming much harder to stay well educated. This process of sensing and sense making has to be continuous, with knowledge flowing throughout the organizations like the circulatory system in the human body. Sensing the market Market-driven organizations use many devices to open their collective ‘‘mind’’ to new information that can help anticipate emerging opportunities and competitive threats, and more accurately forecast how the market will respond to changes in strategy: .
Creating a spirit of open-minded inquiry.
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Carefully analyzing rivals’ actions.
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Listening to staff on the front lines.
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Seeking out latent needs.
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Active scanning of the periphery of the market.
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Encouraging continuous experimentation.
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Creating a spirit of open-minded inquiry Throughout a market-driven organization there is an openness to trends and events that present market opportunities. Service people do not get upset because their schedule is thrown off by a customer request for a modification to meet their changing needs. Front-line contact people hear complaints or requests for services the firm does not provide as opportunities for new businesses, rather than as nuisance calls to be avoided. Salespeople are motivated to report on customer developments and competitive moves within their territory, for they know their intelligence-gathering efforts will be rewarded and the information they supply will not disappear into a corporate ‘‘black hole’’. Masked by arrogance
The inquiry activities are often threatened by an insidious closedmindedness, that blinds management to emerging possibilities and latent threats by narrowing the scope of the inquiry. This learning disability is often hard to detect, being masked by arrogance (‘‘We know what the market wants because we are out there selling to them’’), complacency (‘‘The information was good enough for my predecessor, so it is good enough for me’’), and an inward orientation that focuses attention on readily available internal measures, activities and standards of performance. Close-mindedness was almost fatal to a maker of high-performance radio transmission equipment. The firm was once a strong second in its market, with a product that was costly and built-to-order, with the flexibility to accommodate thousands of features and options, but took 20 weeks to deliver. Management became concerned about a new competitor with a ‘‘cherry-picking’’ strategy based on three models and immediate delivery through existing dealers. They responded with a cost improvement program that took out most of the extra functions. The decision was based entirely on internal data about the costs of complexity and order reports. Only after the new line was launched – to the dismay of the sales force – was it realized that the distributors were modifying the product in the field and needed lots of flexibility in the base equipment. The absence of direct information about customer preferences and responsiveness to product changes was blamed for a 30 point dip in market share. Carefully analyzing rivals’ actions Most firms routinely do tear-down analyses of their competitors’ products, and monitor news sources and call reports for sightings of unusual competitive activity. Often the outcome of these efforts is an undigested mass of news clippings, and some comparisons of the price and performance of their products with competitors whose products most closely resemble their own. Meanwhile, important information may never come to the attention of those who can act on it: how do manufacturing executives know what to do with evidence that a competitor is ordering large numbers of high performance processing machines? Thus the first step in improving competitor analysis is to develop an organization-wide appreciation of the need for competitor intelligence, and provide a visible and easily accessible focal point in the organization for receiving and interpreting the information and making sure it is acted on quickly.
Firm’s value chain
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Benchmarking takes competitor analysis somewhat further, by comparing costs and performance at every step in the firm’s value chain against the best rivals. This reveals developments in capabilities and processes that could be used to increase their competitive lead. The outcomes of these benchmarking JOURNAL OF BUSINESS & INDUSTRIAL MARKETING, VOL. 17 NO. 4 2002
studies can be used to shake up complacent manufacturing and service groups with the news they are slipping behind. Improve methods and functions
Market-driven firms go further to study the attitudes, values and management processes of nonpareils in different industries that share the same challenges. They recognize they can always learn to improve their methods and the way individual functions work together. These firms also study their direct competitors so they can emulate successful moves before the competition gets too far ahead. This requires thoughtful efforts to understand why the competitor succeeded, as well as further research probes for problems and shortcomings to identify improvements that would be welcomed by customers. The masters of informed imitation are the Japanese electronics firms that quickly rush their versions to market just in case the pioneer’s product is a success. With a target to aim at, they know the innovation is at least technologically feasible and their ongoing marketing research tells them where improvements would be welcomed by customers. Listening to the front lines In most organizations, front-line contact people – who handle the complaints, hear requests for new services, cope with lead users, or lose sales due to competitor initiatives – are seldom motivated to inform management on a systematic basis. They may fear having their job loads increased, suspect the information will not be used, or not know where it should be sent. Unblocking this valuable upward flow requires organizational changes that begin with a recognition of the value of this source of information. Channels for the upward flow of information need to be established, and incentives need to be offered for useful insights. Information technology can play a strong supporting role. For example, Ford is able to electronically forward complaints that have come to the customer service representatives directly to the dealers who are supposed to settle the problem. The same information goes to marketing research and then to engineering, where the need for changes can be readily appreciated. Service people who are motivated to listen carefully are an especially valuable resource. HewlettPackard intercepted an emerging problem when several technicians heard unanticipated negative comments about an innovative service program. This plan offered four levels of service: (1) priority plus meant a service response in two hours or less at a premium price, with successively lower prices for; (2) same day; (3) next day; or (4) regularly scheduled service.
Align service promises
Meanwhile the service organization pushed hard to be able to respond to most calls in two hours. Unhappily, this confused customers while raising their expectations, and made the customers buying the two-hour service contract quite angry. They have now learned to manage customers’ expectations much better by aligning their service promises and service delivery. Seeking out latent needs A whimsical definition of latent needs as ‘‘evident but not yet obvious’’ has a serious message. It addresses the shortcoming of structured research methods that impose fixed attribute descriptions and scaled response categories to
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obtain standardized and comparable responses from large samples. Although these methods are helpful for understanding expressed needs that are close to the surface, they obscure latent needs by forcing responses into fixed categories. Focus groups are not much better for this purpose. As one critic noted, ‘‘they spend two hours competing with ten strangers for five minutes of our time’’. A good focus group can be rich in insights if the participants stimulate each other, but the odds are that a dominant personality will sway everyone else’s opinions. Techniques to help firms anticipate market requirements
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A number of techniques have been devised to help firms surface latent needs and sharpen their ability to anticipate market requirements: .
Problem identification. These are straightforward efforts to get prospective or current customers to describe their problems and frustrations with a product or service or the barriers to adoption. H&R Block has invested heavily to appreciate the difficulties people face when preparing tax forms to identify ways to serve them better. A variant of problem identification is to elicit the ideal purchase and usage situation – what they wish they had rather than what they got. These become good targets for focusing technology development efforts.
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Story-telling. Another kind of dialogue asks customers how they behave and how they truly feel (Berstall and Nitterhouse, 1997; Lieger, 1997). Kimberly-Clark listened over and over to stories from parents before they realized that parents viewed diapers as clothing that signals particular stages of development, not as waste-disposal fodder. Armed with this insight they developed training pants that looked and fitted like underwear, yet still keep accidents on the inside. Such finely detailed stories and case experiences help surface unanticipated purchase criteria. There are formal techniques such as ‘‘laddering’’ that have the same objective, by probing ever deeper for underlying beliefs and motives, such as why many people view soft drinks as rewards.
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Observation. The advantages of observation over direct inquiry are first, that it occurs in a natural setting and does not interrupt the usual flow of activity; second, that people give non-verbal cues of their feelings as well as spontaneous, unsolicited comments that are stimulated by an actual product or prototype; third, trained observers with knowledge of technical possibilities can see solutions to unarticulated needs or problems which users could not conceive (Leonard and Rayport, 1997). This is why firms like Sony and Sharp have set up ‘‘antennae shops’’ so they can watch prospective customers pick up and try to use their new products. The sales people are trained to delve into the reasons for the observed reactions. Similarly, most auto firms have design studios in southern California to see how the leading edge car owners are modifying their cars to meet special needs or make personal statements, that can yield clues for design. Reebok, Nike, and others employ ‘‘cool hunters’’ to watch what teens and street youths are wearing and buying which means it is the coolest fashion to follow.
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Customer economics. Most customers do not know the full costs of acquiring a product or service, including: the costs to use, store and dispose of it; the time consumed in the buying process or ongoing maintenance, insurance, energy and training costs. A firm buying a PC may spend eight times as much on the support costs of maintenance, upgrades and training. These costs are hard to identify, and are often hidden in functional silos that do not share information. Spending ‘‘a day JOURNAL OF BUSINESS & INDUSTRIAL MARKETING, VOL. 17 NO. 4 2002
in the life of your customer’’ is a way to surface these costs and find latent opportunities to deliver superior value. Attentive listening is important
All this deep digging will be fruitless if the listeners impose their judgments and biases to interpret what they are seeing and hearing. Remember the definition of latent needs – it still takes attentive listening to decode the messages in the stories and observed behavior. Active scanning of the periphery All managers scan! They are continually exposed to a wealth of data ranging from the fuzzy imprecision of trade rumors to harder evidence from trade association statistics, product movement data, syndicated market studies, and sales reports. The difference in market-driven organizations is that managers actively scan the periphery to look for new opportunities.
Define market boundaries
Scanning gives managers an illusion of being fully informed that may obscure important shifts in the market. Because most of the data that are scanned come from familiar sources they tend to reinforce existing frameworks that define the boundaries of the market, how it is segmented, who the competitors are, and the benefits customers are seeking. Companies commonly fail to ask for or receive the data they need to understand the full market. They fall into a common trap of relying on outsiders to define the market by virtue of the categories they use to sort the data. Suppliers of syndicated sales data may have no way to access some of the new segments – and may never have been asked. This narrowing of vision means the managers pay attention only to what the data suppliers provide, and the suppliers provide only what they are asked for. Encouraging continuous experimentation and improvement True learning organizations are serious about continuous experimentation. This process of active, ongoing experimentation is where original insights into the market are developed. For example, American Airlines used a set of natural experiments to improve customer perceptions of on-line arrival performance. It found that these perceptions improved markedly if the plane doors were opened less than 16 seconds after gate arrival. The key to this insight was their ability to measure how quickly they opened the doors and then follow up with telephone surveys of the passengers on the flight. In effect they took advantage of a series of natural experiments. Another airline used a tool of statistical process control called ‘‘fishbone’’ analysis to isolate all the reasons for delays in push-backs from the gate. The biggest single cause of delay was accommodating late passengers. These were not passengers who had connection problems, they were simply casual about being at the gate on time. Individual gate agents were making their own decisions to delay the plane, so the airline would not lose the fare, but also out of sympathy. The second cause of delays was because motorized pusher tugs were not available at the time of ‘‘pushback’’. This was solved with better scheduling and the addition of more tugs. As with American Airlines, the raw data were already available – what was added was a desire to improve, a methodology to identify successful and unsuccessful activities and the willingness to monitor the results and act on them.
Experimental mindset
How is experimentation encouraged? First, the organization needs to encourage an experimental mindset. General Electric’s Quick Market Intelligence creates a culture and process for encouraging continuous curiosity and experimentation. In the words of Chairman Jack Welch:
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Quick Market Intelligence is GE’s term for the magnificent boundary-busting technique pioneered by Wal-Mart that allows the entire company to understand, to touch the changing desires of the customer and to act on them in almost real-time. The rhythm of the Wal-Mart intelligence-action cycle encourages experimentation, because whatever doesn’t work is never in place for more than a week. The secret of Wal-Mart is that it keeps its small company speed and behavior as it grows bigger. QMI is a chance for us to get bigger – by acting smaller. The QMI rhythm – that weekly pulsing of customer needs – will become the rhythm of GE in the years to come and one of the key drivers of our top-line growth.
Syndicated prescription data
More formal approaches are being used by pharmaceutical firms who are experimenting with creative approaches to communicating with notoriously hard-to-reach physicians. The different approaches are evaluated with syndicated prescription data that track prescriptions written by specific physicians. Second, there needs to be strong and sustained top management support for experimental learning. This is what Dwight Riskey had when he persuaded the top management of Frito-Lay to conduct a four-year long, in-market test of TV advertising for its key brands (Lodish and Riskey, 1997). This required a significant commitment from the firm but produced extremely useful results. The test gave one group of 15,000 households a normal media plan, while another matched group got no advertising at all. Among the findings were that only 27 percent of advertisements for the largest brands led to significant volume increases, but smaller brands showed much more favorable effects. These experimental findings were distilled into lessons and principles for managing advertising that were a sharp departure from the usual rules of thumb (such as ‘‘to achieve an X percent share you must spend more than X percent of all spending in the category’’). For example, they have learned to always advertise against ‘‘news’’, and that advertisement spending on big brands in the absence of ‘‘news’’ is unlikely to drive sales volume increases. But these lessons are not chiseled in stone; the mind-set in Frito-Lay is constantly challenging entrenched beliefs and assumptions in the search for deeper insights into market behavior.
Few incentives to study danger
Finally, organizations need to tolerate what 3M managers call ‘‘wellintentioned’’ failures. Trial-and-error learning that relies on experimentation is quickly subverted if there is a fear-of-failure syndrome. Organizations that reward people for playing it safe and hold the risk-takers solely accountable for their failures – even when they take calculated risks – soon discourage learning. Although failures should be avoided when possible, they do have a therapeutic role because they contain many instructive lessons. Yet, in most firms there are few incentives to study failures carefully. Audits of strategic initiatives that might sort out causes from their effects are seen as ways of assigning blame rather than a way to learn about new market opportunities. It takes concerted leadership to create a more open climate where learning from failures is possible. Following customers or leading customers Skeptics have argued that sometimes it is better to ignore the customer, on the grounds that they are unable to envision breakthrough products and services (Hamel and Prahalad, 1994). A variant of this fallacy holds that market-driven firms are only reactive, and simply respond adroitly to events and trends in their markets. These criticisms are misdirected because they overlook the difference between asking customers to identify problems or observing their behavior to
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uncover latent needs, versus expecting them to develop solutions. Management conviction that a market exists for a new product must be based on evidence that it is superior to the customers’ present alternatives. This evidence needs to be validated and enhanced through an ongoing process of ‘‘probing and learning’’ about market responses through mindful experiments, and scanning of potential threats at the periphery that come from competing technologies and/or competitors from adjacent markets. Deep down understanding
Effective market sensing means seeing past the superficial responses of customers to impersonal surveys, to gain a deep down understanding of their requirements, frustrations, trade-offs and expectations. It takes an effective sense-making capability to make judgments through effective scanning and experimentation. This is the point at which information becomes knowledge. Sensemaking Before organizations can use the information they have collected, they must classify, sort, and simplify it into coherent patterns. The key to effective sensemaking is the development of mutually informed mental models throughout the organization. Mental models are simplifying frameworks used to order the information received by the organization. These mental models are crucial to making sense of the world and keeping the organization moving in a common direction, but when they are not fully understood they can also blind it to market information and prevent it from developing accurate insights into market realities. Because of this role as filters, mental models have a significant impact on the process of making sense of the information that comes into the organization. They affect both the information managers seek and select during the inquiry stage, as well as the lessons they extract about appropriate actions[3]. The pitfalls and power of mental models Unexamined mental models can become traps. For example, the Multiplex Corporation, a large global manufacturer of industrial materials, was a pioneer in many of its markets. As it shaped its markets, it also developed mental models about how they operated. But these models needed to be re-examined as the markets matured. Early in the life cycle of these markets, when growth came by displacing other materials, the customers were relatively insensitive to premium prices. As competition intensified, large segments of the market were unwilling to pay a premium for the extra value Multiplex offered. This was a well-known and seemingly well understood phenomenon, whose implications had painted Multiplex into a difficult corner.
High-end segments
The company continued to cling to its old mental model, to justify a strategy of focusing on high-end segments in which buyers were still willing to pay a premium for superior value and conceding the low-price markets to rivals. These ‘‘low-end’’ entrants were able to parlay increased volume into ever lower costs. Meanwhile, as the high end of the market continued to shrink, Multiplex faced sagging capacity utilization and rising unit costs, which placed the company at a further disadvantage in the growing commodity segments. Because this strategic track was so unappealing, Multiplex management decided to challenge the premises they were following. Management identified three premises derived from their mental models of the operation of their markets. These premises dealt with the attractiveness of the low end
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of their markets, the process of market saturation, and the ways that customers exercised bargaining power. Potentially misleading
Zero-sum game
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Each of these mental models was well accepted in practice, widely communicated in planning documents and planning meetings – and potentially misleading. In combination they led Multiplex into several pitfalls, including making myopic decisions, triggering a self-fulfilling prophecy that accentuated the problem, and foreclosing the consideration of other strategic options: .
Myopic decisions. Because they believed the low-end market does not count because the profits are in the high end, Multiplex managers did not defend the low-end against new competitors. It lost contact with these customers and rivals moved in. In industry after industry, from mainframes to motorcycles, the long-run threats are more likely to come from below than above, but its mental model kept Multiplex from seeing this threat until the new entrants were too entrenched to dislodge. Collective myopia is especially prevalent within organizations that carefully segment their activities and keep functions separate and distinct. Even when these employees come together on teams, they tend to retain the functional blinders of their independent ‘‘thought worlds’’, and so have difficulty forging mutual mental models.
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Self-fulfilling prophecies. These limited mental models often become ‘‘self-fulfilling prophecies’’. Multiplex apparently had unwittingly contributed to two self-fulfilling prophecies because of flaws in its collective mental models. First, because it assumed market saturation was irreversible, it cut back on marketing and development rather than seeking to attract new customers, further slowing growth and leading to further retrenchment. This pattern was reminiscent of the behavior of US manufacturers of radios in the late 1970s who concluded their market was saturated. By cutting back on new product development and marketing, they not only ensured the prophecy came true but created an opportunity for Japanese competitors to move in. Unencumbered with the models of domestic manufacturers, these new entrants proceeded to flood the market with a variety of new features, colors, and styles. This lifted market growth over expectations by 3 percent per annum in the early 1980s – but this was too late for the US manufacturers who had already begun their exit.
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A second self-fulfilling prophecy was the view of Multiplex managers that interactions with customers were a zero-sum game. Managers assumed the use of their product by their customers created a fixed amount of economic value and powerful customers could use their leverage to take more of this value from the company. The downside of this mindset was the encouragement of adversarial relationships with several of the firm’s largest accounts, which, in turn, led to a zero-sum game. When managers recognized this self-defeating model, they began searching for more cooperative relationships such as joint technical programs and information sharing activities to overcome the climate of distrust.
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Foreclosed options. As with the example of US radio manufacturers, mental models often foreclose options for the firm. An especially influential mental model within the Multiplex management team was the image of a ‘‘stuck in the middle’’ competitor – neither differentiated through superior customer value nor able to achieve the lowest delivered cost. Believing they had to choose between the two options, managers JOURNAL OF BUSINESS & INDUSTRIAL MARKETING, VOL. 17 NO. 4 2002
migrated out of low-cost segments to concentrate on the high end. By running all their decisions through this filter, they failed to see that it might be possible for them to use a ‘‘play the spread’’ strategy that has been employed successfully in other industries. This strategy combines superior customer value and the lowest manufacturing and distribution costs, using a modest price premium and low operating costs to produce gross margins sufficient to cover the extra overhead and marketing costs. But this option was foreclosed by the company’s mental model. Avoid pitfalls
Companies can avoid these pitfalls and improve their mental models only if they can actually see what models they are using. The most important step in understanding and changing mental models is to bring them out into the open. Bring the mental models into the open By identifying its implicit mental models, Multiplex had taken an important step. The danger with mental models is not whether they are right or wrong, for all models are simplifications. The pitfalls arise then the models are tacit (i.e. functioning below the level of awareness) and therefore cannot be scrutinized and challenged. Many managers work on the basis of a tacit model that says product design is a low-level, cosmetic function to be done at the last minute. Because these managers remain unaware of this particular mental model, the model remains unexamined and unchanged. Meanwhile, firms such as Braun excel by recognizing that good design not only appeals to the eye but is reliable and economical to manufacture and service. For firms without this enlightened interpretation, the gap between the tacit mental model and competitive reality leads to increasingly counterproductive actions. Assessing advantage One important dimension that shapes mental models is whether the company focuses on customers or competitors in assessing where and how they have gained a competitive advantage[4].The competitor-centered General Electric Aircraft Engine Business Group strives to ‘‘beat Pratt & Whitney’’, meaning managers are concerned most about points of comparison of the two firms that reveal which has the lead in performance, service coverage, and so on. Conversely, a customer-oriented publisher of specialty magazines argued that it was not necessary to pay close attention to its myriad competitors, because what counted was the firm’s ability to position its magazines to satisfy distinct lifestyle segments.
Narrow market scan
Both the customer and competitor focus, in isolation, will eventually become a misleading mental model, narrowing the scan of the market. A competitorcentered focus not only blocks the view of customer shifts but also of new competitors. When Echard Pfeiffer became CEO of Compaq Computers he recognized the almost exclusive focus on IBM as a major flaw. ‘‘You know ‘IBM did this, and how can we outdo them, how do we make it better and faster?’ It was our total focus. The addition of second tier pricing wasn’t welcome because it deviated from that focus. We didn’t recognize who the new competition would be.’’ But customer-oriented firms are equally vulnerable to myopia. By focusing so heavily on customer sources of information they may overlook shifting competitive forces until it is too late. The magazine publisher whose sole focus was on its market positioning was vulnerable to a consolidation of competitors in adjacent segments of the market that was rapidly changing the economics of printing and distribution.
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Improving market sensing Usually, it takes an outside trigger to create widespread recognition of the need to improve the depth, quality, and timeliness of the base of market knowledge and its availability when decisions have to be made. The usual triggers are large and unexplained performance gaps, unexpected moves by previously ignored competitors, belated recognition of market opportunities missed, or uneasiness with the viability of the strategy. But companies can develop greater sensing capabilities without waiting for a crisis. Better educated
Surprisingly spasmodic
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How should businesses aspiring to become better educated about their markets proceed? Working to improve the sensing activities discussed above is an important step. Other ways to improve the sensing capability include: .
Assess your sensing abilities. Early in the improvement program, a self-assessment or, even better, a benchmark study of best practices is needed to identify learning disabilities and areas where changes are needed. The most compelling motivation for such a program is to find a major deficiency compared to a competitor who represents ‘‘best practices’’. One consumer goods company with a long-standing commitment to understanding its customers, backed up with significant budgets, found to its dismay that it was two to four years behind its major Japanese competitors in its ability to identify emerging or latent needs. Needless to say, current practices were soon overhauled to close the gap.
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Make business and marketing plans true learning tools. Another device for self-improvement is to shift the emphasis of the business and marketing plans from static descriptions of the current situation, with some extrapolations into the future, into true learning tools. In these plans there is often a central section summarizing what was learned about the market during the past year. Each of these lessons is then discussed in terms of its implications for the strategy of the business. This process will help create heightened sensitivity to surprises, emerging trends, and new market requirements and bring them to the attention of others who might be able to use the information.
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Map the market-learning process. Deeper insights into the ability of business to learn about its markets comes from a mapping of the prevailing processes for sensing and sense making. These maps describe sequences of activities with clear beginning and ending points, and reveal where each of the activities is located. Although this process is rarely neat or easy, it is a tremendous learning experience. The mapping itself should be guided by the following questions: Where does the customer and competitor information enter the organization? How is the information provided? How is it distributed? Where is it stored? How is the stored information retrieved? What are the barriers to retrieval? When are specific studies commissioned? Who is responsible at each stage of the process? Is there consistent oversight or sharp disconnects? In particular does the marketing research function play a continuing role or are there hand-offs to outside suppliers?
Another kind of mapping asks where and when in core business processes is market sensing done. One telecommunications equipment firm found it was surprisingly spasmodic when it reviewed the product development process for a major project that had failed. After carefully mapping a sequence of activities and decisions that stretched over four years, there JOURNAL OF BUSINESS & INDUSTRIAL MARKETING, VOL. 17 NO. 4 2002
was collective surprise on realizing there had been only one formal market study. Worse, this study was done at an early stage in the development. Thereafter, the team had become enmeshed in the technical development, prototype testing, and regulatory and budget approval activities. As time passed and the product concept evolved, the conclusions of the study became outdated, but the development team made no visits to customers and did not test their assumptions about customer needs and trade-offs. Competitive activity was monitored, but only to check on their technical performance. Having lost touch with their market early in the project, it was no surprise the team did not anticipate emerging networking requirements of customers their project could not satisfy. Completing the process A superior capability for market sensing is part of a learning process. The organization collects information about the market to become more aware of opportunities to create customer value and position itself to take advantage of those opportunities. But great ‘‘senses’’ – even with effective mental models that do not limit the view of the world – are not enough. The information that is pulled in through this sensing process must be processed into knowledge than can be accessed when needed. Collective memory
Market knowledge
The approaches described above contribute to the superior ability of market-driven firms in sensing their markets. But where does all this information about markets generated by the sensing process and all the knowledge created through the sense-making process go? Unless there is a collective memory – a shared knowledge base – for the organization, all these insights will be lost. To complete a continuous process of learning, the organization has to have a way to capture and retain the information and knowledge it has collected, and needs to be able to access this knowledge quickly and efficiently. Advances in information technology have made the process of designing and building these shared knowledge bases on a large scale much easier. The resulting knowledge base may be one of the firm’s most valuable assets. What ultimately distinguishes a market-driven organization is the depth and timeliness of market knowledge that enables it to anticipate market opportunities and respond faster than its rivals. When this knowledge is widely shared, it is a common reference point and assumption set that ensures the strategy is coherent and precisely targeted. Notes 1. Although researchers have had difficulty defining and studying organizational learning, there is reasonable acceptance of the information processing view of learning that is adopted here (see Levitt and March, 1988; Imai et al., 1985; Huber, 1991). 2. This final stage in the process is the key idea behind double-loop learning, in which efforts are directed beyond solving immediate problems to addressing the underlying reasons for the problem (see Argyris, 1993). 3. A growing body of research suggests persuasively that it is the structure and content of these simplified cognitive portrayals of environments (mental models) that actually drives strategic decisions (see Weick and Daft, 1983; Porac and Thomas, 1990; Walsh, 1995; Warren, 1995). 4. These mental models are sensible adaptations to present realities, as reflected in the pressure points on the market on the emphasis of their strategy (see Day and Nedugandi, 1994). References
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Argyris, C. (1993), On Organizational Learning, Basil Blackwell, Cambridge, MA. Berstall, G. and Nitterhouse, D. (1997), ‘‘Looking ‘outside the box’: customer cases help researchers predict the unpredictable’’, Marketing Research, Summer, pp. 5-13. Day, G.S. and Nedungadi, P. (1994), ‘‘Managerial representations of competitive advantage’’, Journal of Marketing, Vol. 58, April, pp. 31-44. Hamel, G. and Prahalad, C.K. (1994), Competing for the Future, Harvard Business School Press, Boston, MA. Huber, G. (1991), ‘‘Organizational learning: the contributing process and literatures", Organization Science, Vol. 2, pp. 88-115. Imai, K., Nonaka, I. and Takeuchi, H. (1985), ‘‘Managing the new development process: how Japanese firms learn and unlearn’’, in Clark, K., Hayes, R. and Lorenz, C. (Eds), The Uneasy Alliance, Harvard Business School Press, Boston, MA, pp. 337-76. Leonard, D. and Rayport, J. (1997), ‘‘Spark innovation through emphatic design’’, Harvard Business Review, November-December, pp. 102-15. Levitt, B. and March, J.G. (1988), ‘‘Organizational learning’’, in Scott, R. and Blake, J. (Eds), Annual Review of Sociology, Vol. 14, pp. 319-40. Lieger, R.D. (1997), ‘‘Storytelling: a new way to get close to your customers’’, Fortune, 3 February, pp. 102-10. Lodish, L.M. and Riskey, D.W. (1997), ‘‘Expanding the role of the chief learning officer: balancing the costs and value of generating and using new marketing knowledge’’, unpublished working paper, The Wharton School, Wharton, TX. Porac, J. and Thomas, H. (1990), ‘‘Taxonomic mental models in competitor definition’’, Academy of Management Review, Vol. 15, pp. 224-40. Walsh, J.P. (1995), ‘‘Managerial and organizational cognition: notes from a trip down memory lane’’, Organizational Science, Vol. 6, May-June, pp. 281-321. Warren, K. (1995), ‘‘Exploring competitive futures using cognitive mapping’’, Long Range Planning, Vol. 28, pp. 10-21. Weick, K. and Daft, R.L. (1983), ‘‘The effectiveness of interpretation systems’’, in Cameron, K.S. and Whetten, D.A. (Eds), Organizational Effectiveness: A Comparison of Multiple Models, Academic Press, New York, NY, pp. 71-93.
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An executive summary for managers and executive readers can be found at the end of this issue
Market-based success, organizational routines, and unlearning James M. Sinkula Professor of Business, The University of Vermont, Burlington, Vermont, USA
Keywords Organizational learning, Organizational processes, Organizational change, Information Abstract Information, whether it is acquired from an external source or generated internally, is subjected to perceptual filters made up of the organization’s norms, procedures, and beliefs that influence what information the organization attends to and ultimately accepts. This paper examines the role which these organizational filters play in unlearning; viewed here as a specialized form of organizational learning. Unlearning is defined as the ‘‘process by which firms eliminate old logics and make room for new ones’’ by Prahalad and Bettis. The author argues that firms which engage in unlearning activities are better able to cast aside established routines in order to replace them with ones that ultimately result in superior value to their customers.
Managers must learn to ask, on a regular basis and with regard to every product, process, and procedure, whether they would begin producing or practicing it now if they were not doing so already (Peter Drucker, 1992).
Learning organization
Organizations can be characterized as bundles of routines (Kilduff, 1992). Routines emanate from an organization’s capabilities (Day, 1994) and are the natural outcome of lessons learned through encounters with the marketplace (Sinkula, 1994). Routines are deeply ingrained in the organization and are difficult to imitate (Day, 1994) or change (Cohen, 1991). Organizations learn routines as they produce. Learning important capabilities and embedding them in routines is as much an organizational task as is the production of goods and services (Nevis et al., 1995). Organizations even learn routines that guide the way subsequent routines are learned (Argyris and Scho¨n, 1978). Hence, every organization (as long as it remains in existence) learns. Therefore, at some level, every organization is a learning organization. There has been considerable recent effort to integrate the concepts of organizational learning, learning orientation, and learning organization into the marketing literature (Baker and Sinkula, 1999; Day, 1992, 1994; Hurley, and Hult, 1998; Moorman, 1995; Moorman and Miner, 1997; Sinkula, 1994; Sinkula et al., 1997; Slater and Narver, 1995). Good progress has been made in: .
profiling how organizational culture impacts information use, learning and action (Moorman, 1995);
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identifying why organizational learning should be viewed as a means of achieving competitive advantage (Day, 1992, 1994);
The research register for this journal is available at http://www.emeraldinsight.com/researchregisters The current issue and full text archive of this journal is available at http://www.emeraldinsight.com/0885-8624.htm
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circumscribing the learning orientation construct (Sinkula et al., 1997);
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describing the relationship between organizational learning, market orientation and innovation (Hurley and Hult, 1998); and
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identifying characteristics of the organization which are likely to enhance its ability to learn (Slater and Narver, 1995).
Additionally, the value of organizational learning theory has surfaced in work which is not primarily grounded in learning but where the authors have found utility in selected aspects of the theory for conceptualizing ideas and undergirding research hypotheses (Heide and Weiss, 1995). Learn and apply skilled routines
Despite these advances, much of the research that is primarily focused on identifying ‘‘the learning organization’’ could be viewed as misdirected. Organizations exist to learn. To be sustainable the organization must process information in order to learn and apply skilled routines. Indeed some writers define the term ‘‘organization’’ as ‘‘complex arrangements of people in which learning takes place’’ (Nevis et al., 1995). Hence, in the strictest sense, the term ‘‘learning organization’’ is redundant. In this paper, I argue that research that centers on the question: ‘‘What is a learning organization?’’ should be replaced with studies that ask ‘‘How can we speed the rate, improve the accuracy, and convey the relevance of lessons learned in the organization?’’ My primary thesis is that this can be best accomplished through organizational initiatives that encourage ‘‘unlearning’’ (Nystrom and Starbuck, 1984). That is, organizations that will likely reap the most value from learning are those organizations that, at the optimal times and in optimal time, are able to unlearn established routines so as to replace them with ones that ultimately result in superior value for their customers.
Identify critical modes
The paper is organized as follows. First, I will outline current theories that examine unlearning and its relationship to organizational learning. Then, I will propose an ecology of unlearning which is based on Hedburg (1980) and utilizes the individual, the organization, and the external environment as elements. This ecology extends Day’s (1994) work on organizational capabilities by postulating that two organizational-level knowledge factors (Sinkula, 1994) serve as filters of what Day refers to as inside-out/outside-in (IOOI) capabilities. Finally, I will identify the critical nodes of unlearning in the organization and advance some recommendations for managers and academicians on how to integrate the concept of unlearning into their organizations or their research agendas. Success, routine, and organizational learning ‘‘In Stinchcombe’s perspective, improving the speed of routines and changing their detailed contents, along with the accurate switching among existing routines, are major sources of competitive advantage or other forms of organizational success’’ (Cohen, 1991, p. 136). But organizations cannot succeed forever. In the capitalist system, it is expected that there will be constant turnover among corporations and the products they innovate (Lawler and Galbraith, 1994). In the early 1900s, the leading US companies included National Sugar, US Leather, National Lead, and General Electric (Lawler and Galbraith, 1994). What caused GE to prosper while the others faded out of existence? One might argue that, while in their heyday all were learning, not all these organizations were learning to apply the appropriate routines and capabilities at an appropriate rate. Many may have allowed their successes to inhibit learning (March, 1991), acquiring and interpreting new information in
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only a passive fashion as their adopted routines were reinforced by continuing success (Daft and Weick, 1984). Such organizations would, of course, remain successful only until the environment changed. Clearly, GE is a very different company from the one that existed in the early 1900s. It has survived by continual learning and adaptation. Weick (1979) contends that adaptation can be viewed as the unlearning of a specific response (routine) in order to learn a new one. If this is so, unlearning could be considered the genesis of competitive advantage. Examine the following formula[1] for organizational learning: Lt
¼ f½ðUðt1Þ þ Sðt1Þ Þ þ Aðt1Þ
½1
where: ¼ learning in period t Lt Uðt1Þ ¼ unlearning in period t 1 Sðt1Þ ¼ substitution in period t 1 Aðt1Þ ¼ accretion in period t 1: While this formula for organizational learning is under-specified, it does allow for a discussion of learning, unlearning, substitution, accretion, and time.
Behavior change
Changing cognitive structures
Learning For purposes of this discussion, organizations are seen as learning by encoding inferences from history into routines that guide behavior (Levitt and March, 1988). Consider organizational learning to manifest itself in shared knowledge, which is codified in a social system (Dixon, 1992). Codified knowledge can take the form of mission statements, white papers, information systems, operating procedures, organizational stories and routines (Slater and Narver, 1994). While some argue that the prime requirement for organizational learning is a change in organizational behavior (Garvin, 1993), a central tenet of this research is that firms also engage in learning for sense making (Sackmann, 1991) and that for learning to occur it is merely the potential for behavior change that is required (Huber, 1991). Hence, if an organization unlearns a philosophy or a set of shared values (sans behavior change) and replaces it with a new one, it has learned on one level. If it has processed information, changed certain key routines or behaviors, and improved its performance, it has learned on a different (perhaps more imperative) level. Unlearning Consider unlearning to be the ‘‘process by which firms eliminate old logics and make room for new ones’’ (Prahalad and Bettis, 1986). For unlearning to occur on an organizational level, forgetting or ‘‘extinction’’ (Klein, 1989) must first take place on an individual level. Consider individuals in new, young organizations. They operate with a ‘‘clean slate’’. Hence, what they (and the organization) learn is not jaded by the experience lessons of history. But in more experienced organizations it is unlearning that makes way for new responses and mental maps (Hedberg, 1980). The unlearning process has, at its heart, an attempt to reorient organizational values, norms and/or behaviors by changing cognitive structures (Nystrom and Starbuck, 1984), mental models (Day and Nedungadi, 1994), dominant logics (Bettis and Prahalad, 1995), and core assumptions which guide behavior (Shaw and
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Perkins, 1991). Not many organizations encourage their members to test the validity of their beliefs about the cause and effect relationships that guide their behavior. This is, in part, due to the liability of success. ‘‘The presumed correctness of past actions and interpretations is reinforced by repeated success, and the ensuing complacency breeds rejection of information that conflicts with conventional wisdom’’ (Day, 1994, p. 24). New ideas
While nothing in the above formula dictates that to learn in period t the organization must first unlearn in period t–1, it is the case that past learning inhibits new learning. Nystrom and Starbuck (1984, p. 53) argue that ‘‘before organizations will try new ideas, they must unlearn old ones by discovering their inadequacies and discarding them’’. In many cases learned routines are so organizationally inculcated that only in times of crises will managers begin to question them. Klein (1989) notes that, while unlearning can be attained through ‘‘extinction’’ (the removal of undesirable knowledge from individuals), it can also come about through ‘‘exorcism’’ (the removal of individuals from the organization). Hence: ULt ¼ f½Tðt1Þ þ Oðt1Þ
ð2Þ
where: UL t ¼ unlearning in period t Tðt1Þ ¼ extinction in period t 1 Oðt1Þ ¼ exorcism in period t 1 Unlearning through extinction may be next to impossible, particularly in the successful organization. Consider that: Success vests individuals with special interests in the status quo. Certain positions and departments become powerful, wealthy, and prestigious. Because these e´lite have a lot to lose, they become the organizations conservative party. They turn a deaf ear to both problems requiring change and innovations that would improve performance. They become risk averse – rather than playing the game to win, they play the game ‘‘not to lose’’. The degree to which they dominate in their organization is directly proportional to the organization’s vulnerability (Lawler and Galbraith, 1994, p. 7, emphasis added).
Dramatic impact
Organizational memory is said to transcend the comings and goings of personnel (Sinkula, 1994). Yet there is, perhaps, no clearer way to signal the desire to end a dominant logic (Bettis and Prahalad, 1995) or theory of action (Argyris and Scho¨n, 1978) than to remove a top manager who espouses the logic. Nystrom and Starbuck (1984, p. 53) argue that top managers’ ideas dominate organizational learning, but during crises ‘‘organizations . . . often remove their top managers as a way to erase the dominating ideas, to disconfirm past programs, to become receptive to new ideas, and to symbolize change’’ (emphasis added). While such exorcisms will not completely erase organizational memory, they do have a dramatic impact on redirecting routines because the first step in changing a routine is communicating to organizational members that the old one is no longer satisfactory. Substitution The highest level of organizational learning involves discarding (unlearning) the present way of doing something and substituting it with a new way. To
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substitute an unlearned routine with a new one is to risk failure. Yet the role of failure in organizational learning is so essential as to be definitional to it (Klein, 1989). One of the major aspects of organizational unlearning is an understanding of the mechanisms that can be used to enable the organization to deviate from the culture in which it is embedded (Simon, 1991). Acceptance of failure should be viewed as one of these mechanisms. Organizations (even those in equilibrium) should not strive only to master routines that exploit current opportunities. Rather, a balance must be attained between the exploitation of old certainties and the exploration of new possibilities (March, 1991). As shown in cell 1 of Figure 1, learning through the unlearning/substitution route is commonly referred to as double-loop (Argyris and Scho¨n, 1978) or generative (Senge, 1990a) learning. Decision-making process
Single-loop learning
Learning on such a level is tantamount to paradigm shifting and is, occasionally, a requisite for long-term organizational sustainability. When Harley-Davidson, for example, was bought back from AMF by a group of its employees, it discarded (unlearned) its pre-existing routines for organizational decision making. Production, quality control, and even marketing issues are now evaluated at lower levels in the organizational hierarchy, allowing first-line supervisors and laborers much more input into the decision-making process. Clearly, organizations do not double-loop learn on a routine basis, for learning on such a level is exhausting and unnecessary. More often, learning will take place in some lesser form. Accretion In the case of accretion, less risk is involved because the existing routine is not discarded (unlearned). Rather, incremental behaviors are added to the existing routine which are expected to have the end result of improving it. Hence, no attempt is made to change the paradigm, only to improve on it. This scenario, where the firm augments an existing routine rather than unlearn it, is represented in cell 2 of Figure 1. Argyris and Scho¨n (1978) refer to this type of learning as single-loop learning. The bulk of organizational learning takes place at this level. Improving the speed of routines, reducing their negative by-product, and codifying more accurate principles for selecting among sub-routines are examples of single-loop learning that can serve as sources of competitive advantage. For example, utilizing scannerbased data rather than data derived from traditional paper-and-pencil
Figure 1. The role of unlearning in determining learning JOURNAL OF BUSINESS & INDUSTRIAL MARKETING, VOL. 17 NO. 4 2002
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techniques speeds the market research process, thereby improving the organization’s market sensing routines and capabilities. Pruned, eliminated or divested
Also represented in Figure 1 (cell 3) is the scenario where the organization unlearns but does not substitute a routine. While one could argue that to substitute a routine with nothing is still substitution, let us, for the purposes of this discussion, place such occurrences in a separate category. Routines surrounding products, people, and units of the organization that have been pruned, eliminated, or divested could be placed in this category, for such routines are quickly unlearned via the process of elimination. Because the products, people, or units have been exorcised, there is not a need to replenish the discarded routines. While whether such learning should be considered single or double-loop is primarily a semantic issue, here it is regarded as double-loop learning because it clearly goes beyond the ‘‘routine modifying’’ nature of single-loop learning. Ecology of market-based unlearning Why do marketing managers cling to routines, dominant logics, and mental models that are out of date? What, other than the fact that individuals do not like change, drives organizations to be lulled into complacency, often at the very pinnacle of their success? Understanding unlearning requires understanding how individuals act within the context of their organizations and how organizations, in turn, act within the context of their environments. Figure 2 illustrates an ecology that includes the individual, the organization and the environment. Let us now turn to a discussion of the interactions of
Figure 2. An ecology of unlearning 258
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these three components in the context of market-directed learning and unlearning processes. Axiomatic knowledge, acceptable stimuli, and the market sensing capability We will begin by discussing organization-level (as opposed to individual) factors that impact unlearning processes. Such factors revolve around truths, beliefs, knowledge and values that are organizationally shared. Axiomatic knowledge is a principal factor which affects the unlearning process. Sinkula (1994, p. 39) defines axiomatic knowledge as consisting of ‘‘fundamental beliefs [which] appear as organizational values which are set a priori and cannot be further reduced’’. Sackmann (1991) argues that, in the etiology of decision making, the organization’s axioms are truly exogenous factors that drive (or filter) the way it interacts with its environment. Institutional membership
Consider religious persons and institutions. Such persons and institutions might see crises, catastrophe, or success through a different ‘‘lens’’ than would an atheist. And this lens, which is largely constructed through the shared values of the institutional membership, will filter the way the individual sees the world. When, in an organization, one asks ‘‘why’’ and gets the answer ‘‘because that is just the way we do things here’’, a number of underlying axioms are usually at the heart of the true answer. For example, the axiomatic knowledge at Analog Devices is that the rate at which the organization learns is the key to its competitive advantage (Stata, 1992). This is a fundamental and deeply held belief. Usually such beliefs emanate from the CEO. When accepted across the firm, such axioms serve to create a lens, with filtering capabilities no less powerful than those of religion, through which a business sees its environment.
Accepted set of axioms
Axiomatic knowledge is construed by Sinkula (1994) to exist in the mid- to upper-range of the knowledge hierarchy. That is, new organizations might not have fully developed axiomatic memories, endeavoring instead to more fully understand ‘‘what is’’ (dictionary knowledge) or ‘‘what has been’’ (episodic knowledge) before they settle into an accepted set of axioms. Hence, axioms develop slowly and are slow to change. In Figure 2, axiomatic knowledge is specified as an organization-level factor which filters environmental stimuli and which, in turn, mandates a subset of stimuli that are processed (perceived) at the individual level. Axioms dictate what the organization hears when it hears the voice of its customers. Therefore, given its preponderant nature, it is reasonable to posit that axiomatic knowledge filters what Day (1994) refers to as outside-in processes. These processes include channel bonding, customer linking, and market sensing, all of which serve to ‘‘connect the processes that define the other organizational capabilities to the external environment and enable the business to compete by anticipating market requirements ahead of competitors and creating durable relationships with customers, channel members, and suppliers’’ (Day, 1994, p. 41).
Market sensing capability
The axiomatic knowledge filter has particular impact on market sensing. Day (1994, p. 43) argues that ‘‘the process of market sensing follows the usual sequence of information processing activities that organizations use to learn’’. This sequence of organizational information processing typically follows a pattern of information acquisition, distribution, interpretation, and utilization (Huber, 1991; Sinkula, 1994). Day notes that organizational memory affects market sensing capability. Since axiomatic knowledge exists in organizational memory, it is not unreasonable to suggest that it, as a
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specific form of organizational knowledge residing in memory, affects market sensing. Procedural knowledge, sanctioned routines, and theories in use Sinkula (1994, p. 38) describes procedural knowledge as ‘‘a task system, governed by tacit rules’’ which typically differs from the espoused way of doing things. Thus, procedural knowledge is what mandates the true nature of organizational routines[2] rather than just the norms and policies that initially codify them. Argyris and Scho¨n (1978) refer to this tacit knowledge system as the organization’s ‘‘theory in use’’ and contrast it against the organization’s more explicitly codified ‘‘theory of action’’.
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Organizationally sanctioned routines
Procedural knowledge differs from axiomatic knowledge in the following ways. First, procedural knowledge means knowing what to do, not necessarily why. Second, procedural knowledge is shared knowledge about routines that are acceptable, not shared knowledge about organization-wide truths. As such, an individual having procedural knowledge is able to select the appropriate, accepted, organizationally sanctioned response (routine) when assigned a task. Third, procedural knowledge is of a lower level than axiomatic knowledge. Procedural knowledge is a requisite for axiomatic knowledge because one must know what the organizationally sanctioned routines are before one asks why the routines are sanctioned. Thus, a marketing manager’s knowledge structure might be that ‘‘it is advisable to disseminate customer research throughout all functional units of the organization’’ (procedural knowledge) ‘‘because we are a market driven company’’ (axiomatic knowledge).
Elicits a response
In Figure 2 procedural knowledge is designated as an organizational-level factor, which specifies (filters) acceptable responses for individuals who receive and process incoming, organizationally filtered stimuli. That is, if on the individual level a stimulus elicits a response that is not organizationally sanctioned, it is less likely to be a chosen response (routine). Procedural knowledge has much to do with an individual’s success given the context of organizational constraints. Cyert and March (1963) note that ‘‘the likelihood that a routine will be used increases when it is associated with success in meeting a target, decreases when it is associated with failure’’. Hence, procedural knowledge filters inside-out processes. These processes ‘‘are those that are deployed from the inside-out and activated by market requirements, competitive challenges, and external opportunities’’ (Day, 1994, p. 41). Manufacturing, logistics, and recruiting are examples of such processes. Providing slotting allowances to gain distribution in some product categories while not in others might be sanctioned (procedural knowledge) because it is accepted that retailers have more channel control in certain categories (axiomatic knowledge).
Acts as a buffer
Hence, there are two types of knowledge which reside in organizational memory that serve to filter IOOI processes and, thereby, shape the organization’s capabilities. These filters serve to reduce the amount of incoming environmental stimuli and the potential set of responses available to organizational members, allowing organizations to make better sense of their worlds and, in theory, operate more efficiently. Thus, in the ecology represented in Figure 2, the organization (with its two knowledge filters) acts as a buffer between the environment and the individual. As such, individuals are engaging in stimulus-response learning, but only in the context circumscribed by the organization. Interestingly, the degree to which an individual’s learning context is circumscribed by the organization is circular JOURNAL OF BUSINESS & INDUSTRIAL MARKETING, VOL. 17 NO. 4 2002
in that the two knowledge filters are a function of the individual’s own learning patterns.
Cause and effect relationships
Shared beliefs about cause and effect People are hypothesis stores. That is, in order to simplify and make sense of their worlds, individuals develop beliefs (hypotheses) about cause and effect. In Figure 2, the individual’s learning pattern is brought into an organizational learning context through shared beliefs about cause and effect (Argyris and Scho¨n, 1978). Without social consensus about cause and effect relationships, learning on an organizational level would not exist. Therefore, individual learning is a necessary but insufficient condition for organizational learning. A set of beliefs about a number of cause and effect relationships might develop into a mental model (Day and Nedungadi, 1994); a more global, perhaps managerial, representation of reality. These models are managers’ ways of simplifying, sorting, and classifying everyday realities. A manager might view a drop in demand, for example, as primarily a function of competitors, customers, or both (Day and Wensley, 1988). Clearly this representation of reality, the chosen mental model, will affect the strategy that the manager takes in rectifying the situation. If the strategy is successful, the mental model will be reinforced and applied again. Once the shared beliefs about cause and effect are fixed in social cognition, they feed back into the organization-level knowledge factors which, when mediated by individuals, initially shaped them. Hence, I argue and show in Figure 2 that neither axiomatic nor procedural knowledge are exogenous. Both are, therefore, subject to change, albeit slow, as beliefs about cause and effect relationships change.
Current procedural knowledge
Nodes for unlearning Drawing from Hedberg (1980), the ecology presented here specifies three nodes that are critical in organizational unlearning processes. Node 1 exists where axiomatic knowledge funnels acceptable stimuli from the environment to organizational members (Ka?S). This is an ‘‘across-level’’ linkage as it crosses from organization-level factor to individual-level factor. Node 2 (also an across-level linkage) exists where the current procedural knowledge mandates an organizationally sanctioned repertoire of responses to a given stimulus (Kp?R). Node 3 occurs between stimulus and response on the individual level (S?R). It is a ‘‘within-level’’ linkage. This final node is, perhaps, the most important since much of the change in axiomatic or procedural knowledge has its genesis in gradual, long-term unlearning and re-learning taking place on an individual S?R level. These three nodes for unlearning are not entirely discrete. Completely disentangling node 1 from 3 or 2 from 3 may be impossible, given that node 3 is critical in facilitating internally generated shifts in axiomatic or procedural knowledge. Let me now turn to a discussion of market-directed unlearning at each node. Unlearning at the Ka?S node Consider a production oriented firm. Presumably, such an organization would place less value on the market sensing capability (Day, 1994) in that the degree to which market-based information is acquired, disseminated, and acted upon would be less than that in a market oriented firm (Jaworski and Kohli, 1993; Kohli and Jaworski, 1990). Hence, environmental stimuli from customers and other marketplace constituencies would be more likely to be averted by the organization before it reaches the individual. Let us also
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suppose that, because it has pioneered a break-through technology, this firm is presently successful. Hence, although the history of the industry shows that competitors cannot rely on a technology orientation alone, there is no immediate incentive for the organization to change. Forced unlearning
As it faces its future, this organization might be forced to unlearn selected axioms. In so doing, the organization will be free to substitute new ones which will, in turn, funnel a new set of stimuli to organizational members. Forced unlearning is often caused by environmental uncertainty and external phenomena. Two of the most common examples are new leaders and environmental shocks, and unlearning efforts often take place because of the interaction of both. Along with new owners, leaders, and stakeholders often comes a new dominant logic and, hence, a shift in organizational axioms. Before the new logic can be substituted, old logics must be unlearned. A new leader (one who places value in the tenets of market orientation) might take the helm of our production oriented firm. Not only must he/she convince organizational members of the value of a market orientation, but also of the problems that might result from a rigid adherence to a production orientation. For a shift to be successfully generated by new management, Schein (1993) argues that they must be masters at creating sufficient anxiety, of which he delineates two types: Anxiety1 is the feeling associated with an inability or unwillingness to learn something new because it appears too difficult or disruptive . . . Anxiety2 is the fear, shame, or guilt associated with not learning anything new (Schein, 1993, pp. 86, 88).
Anxiety of change
Schein notes that while the first type of anxiety is dysfunctional, the second type of anxiety must be created by managers in order for unlearning to occur. Indeed managing the anxiety of change according to Schein means making sure that anxiety2 outweighs anxiety1. While new leadership might bring new paradigms to organizations, most unlearning is problem driven (Hedberg, 1980; Nystrom and Starbuck, 1984). Often, radical unlearning is set in motion by some external shock to the organization.
Deregulated marketplace
Shock-driven unlearning is taking place in the electric utilities industry because of deregulation. The utilities that thrive in the deregulated marketplace will owe their success largely to the unlearning efforts of corporate leaders who take them though a landscape much like the one the airline industry went through almost 20 years ago when it was deregulated. This new environment has mandated new thinking from executives like Richard C. Green, CEO of UtiliCorp of Kansas City, whose mentor corporations are McDonalds, Southwest Airlines, and Wal-Mart (Nulty, 1995). Consider the following scenario: The fight among power titans to win over consumers will likely play out house by house. Otter Tail Power Co . . . in Fergus Falls, Minnesota could well call you at your home in Atlanta, say, offering a year’s supply of free light bulbs if you disconnect from Southern Co. and buy some Minnesota juice instead (Nulty, 1995, p. 200).
One can argue that, in many organizations, the adoption of business philosophies such as market orientation are reactionary; often occurring too late in the organization’s development to assure survival. Whether such an environmentally mandated axiom shift maintains any temporal stability 262
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depends upon the ‘‘stimulus-response-reinforcement-shared belief’’ loop illustrated in the unlearning ecology. Unlearning at the Kp?R node Our hypothetical production oriented firm would have developed its philosophy of the marketplace through its outside-in processes and, therefore, would have built a repertoire of sanctioned routines devoid of the notion of market orientation. Consider this set of sanctioned routines to be a fixed set, a result of organizational members’ reactions to the subset of environmental stimuli, which were originally filtered by the organization’s axioms. Organization-wide discord
Unlearning at the Kp?R node is tantamount to ‘‘un-sanctioning’’ one or more routines. Typically, this occurs by mandate. Mandated extinction of sanctioned routines can take place for a variety of reasons. Government regulation, for example, can force change in policies, procedures, rules and routines which require, first, unlearning the former ones. A change in corporate ownership or governance might have a similar effect. Such mandates may happen without a concomitant change in axiomatic knowledge. While the resulting organization-wide discord between axioms and mandated routines typically diminishes with time, there is no hard-andfast rule that this will occur. Therefore, in many cases unlearning which originates through forced sanctions is sub-optimal. As discussed earlier, whether routines sanctioned this way remain sanctioned depends upon the ‘‘stimulus-response-reinforcement-shared belief’’ loop illustrated in the unlearning ecology. Unlearning at the S?R node In the prior two nodes, unlearning the axiomatic or procedural knowledge occurred largely through mandate. Unlearning axioms or procedures does not only occur in this rather discrete, reactionary manner. Internally generated axiom shifts, for example, might result from unlearning in a more fluid fashion, stemming from activities at the individual S?R node. Stimulus-response learning occurs in individuals and is a necessary but insufficient condition for organizational learning (Argyris and Scho¨n, 1978). Stimulus-response learning here is different from traditional discussions of stimulus-response learning in only one respect. This difference is that, while the individual is pivotal in the ecology of unlearning, the stimulus pool to which he/she may be exposed (as well as the response pool from which he/she may draw) is buffered by the organization. Hence, the individual is subject to an organizationally circumscribed stimulus-response set.
Reward and measurement systems
Unlearning at the S?R node takes place through extinction (Klein, 1989). Extinction is facilitated by breaking the correlation between response and reward – i.e. by removing the reward or by introducing rewards which are not correlated with the response (Rothschild and Gaidis, 1981). Hence, the nature of the reward and the definition of success is key. General Electric, for example, has been experimenting with reward and measurement systems involving what it refers to as ‘‘stretch goals’’, which are essentially extremely ambitious goals (Sherman, 1995a). A senior manager might, for example, ask for product development time or for costs to be cut in half while full well knowing such a goal to be unattainable. Steve Kerr, chief learning officer at GE, notes that: ‘‘Stretch targets are an artificial stimulant for finding ways to work more efficiently. They force you to think ‘out of the box’’’ (Sherman, 1995a, p. 231). Stretch goals force managers to unlearn traditional ways of thinking about problems.
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Once a given S?R pairing is unlearned, the individual, when presented with a recurring stimulus, can substitute something other than the sanctioned routine. Here, the individual may substitute through experimentation by:
Able to motivate
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selecting a routine sanctioned for some other stimulus;
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combining a number of sanctioned routines into a new one; or
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devising a completely new routine.
Certain individuals are more willing to substitute new routines than are others. McKinsey and Company director, Jon R. Katzenbach, who calls such individuals ‘‘change leaders’’, suggests that whether new routines are sanctioned organizationally is dependent on a select group of middle (not top) managers (Sherman, 1995b). They are focused, determined, willing to break routine, and, perhaps most importantly, able to motivate other organizational members. You almost never find them using existing management systems, whether designed by the corporation or by consultants. They will take whatever structure is available and modify it to suit their needs. They redesign on-line. And they’ll take a system they designed themselves and change that when they think it’s necessary (Sherman, 1995b, p. 198, emphasis added).
Unlearning and substitution at the S?R node will fail to become unlearning and substitution at the organizational level if it does not translate into shared beliefs about cause and effect. Thus, discarding routine X in favor of Y must result from a majority assessment (formal or informal) that Y is, somehow, better. Consider the organizational side-effects in our stretch goal example: One issue is self-punishment. If you’re truly setting stretch goals, by definition you can’t have a high degree of success. For individuals who are high achievers, it’s not their style to miss goals. So you end up making people who are winners feel as if they’re losers (Sherman, 1995a, p. 231).
Value structures and behaviors
When individuals in our production oriented firm are no longer reinforced (for whatever reason) for behavior grounded in the tenets of production orientation, their personal value structures and behaviors will change. If this happens to a critical threshold of organizational members, the shared belief about cause and effect will change and, in certain cases, could shift organizational axioms and procedures. Salespeople in our production oriented firm, for example, may find little continuing reinforcement in closing small accounts. Hence, they may begin to focus their efforts on larger accounts that also, let us say, have a common end use for the product. This might result in an axiom shift that favors the larger end-users, a change in the way the organization segments its markets (say, from geographic to end-user segmentation), and ultimately a change in the organization’s structure to one that is oriented around end-user markets. In this example, our production oriented firm has made significant incremental progress toward becoming market oriented. When should organizations unlearn? Clearly, the primary incentive for market-driven organizational learning is the ability for companies to harvest rewards from their learning efforts. Market-driven organizations do not develop successful programs, which result in superior value for customers, only to face a climb up the learning curve again in the immediate future. One measure of a successful marketing strategy is the length of time that it, and the routines associated with it, can be exploited (Bettis and Prahalad, 1995; March, 1991). Yet, a fundamental
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question lies in understanding how long one should exploit marketing endeavors that have become successful. So it would seem critical that market-driven organizations hone an ability to sense when unlearning efforts should be initiated. This ability is, perhaps, more critical than knowing exactly how to engage in unlearning. Accordingly, this section of the paper proposes a set of cues that, though admittedly non-comprehensive, will serve to alert marketing managers that unlearning efforts may be indicated in their organizations. These cues are categorized as environmentally based and organizationally based drivers. Environmentally-based drivers Marketers who face dynamic, hostile environments must unlearn more frequently and faster than those who face static, benevolent environments. For the purposes of this discussion, I will examine three major environmentally based drivers for unlearning. These are categorized as customer, competitor, and partner cues. Acting upon customer information
Process information better
Customer cues. Market-driven firms should watch for cues from customers as they evaluate established products and programs. Hence, the degree to which the firm is connected to the customer is proportional to the timing and amount of unlearning that ensues (Lawler and Galbraith, 1994). By definition, market-driven firms will be more adept at systematically acquiring, distributing, and acting upon customer information (Jaworski and Kohli, 1993). Market-driven firms will tend to rely on (Day and Nedungadi, 1994, p. 34): .
customer comparisons of the attributes of the firm versus competitors using choice models, market maps, or conjoint analysis;
.
customer satisfaction and loyalty surveys; and
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relative shares of end-user segments.
Such information is critical to timing the unlearning effort. But in marketdriven firms, particular attention should be paid to serendipitous, unsolicited customer information, particularly that which revolves around complaints. Marketing managers must do two things to better listen to customers and, thereby, gauge when the unlearning effort should take place. First, they must process the information better. That is, while organizations seem to be quite capable of acquiring customer information, they must improve upon their ability to distribute, interpret, act upon, and store the information in organizational memory (March and Shapira, 1982). Second, marketing managers must become more open to criticism (Senge, 1990b). They must overcome their tendency to shun information which disconfirms their expectations (Deshpande and Zaltman, 1982) and therefore might serve to cast doubt on their competency. To this end, top executives should create a climate where open-minded inquiry (Day, 1991) and experimentation are rewarded. Competitor cues. Market-driven firms view competitors as equally important to customers in their representations of strategic reality (Day and Nedungadi, 1994). Therefore, a change in competitive intensity should be viewed as a cue for unlearning efforts. Such cues are usually grounded in comparisons which marketing managers make between the organization and its competitors. These comparisons might revolve around (Day and Nedugadi, 1994, p. 34):
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Switch business orientation
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management judgments of strengths and weaknesses;
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comparisons of resource commitments and capabilities;
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value chain comparisons of relative costs; and
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market share and relative profitability.
Marketing managers should watch their product categories vigilantly for competitive shifts. Market entry of new competitors, as well as exit of former ones, might serve to trigger unlearning processes. These processes would focus on the replacement of prior routines with ones that are expected to provide superior value to customers. When Silicon Graphics, for instance, faced the onslaught of competition in its 3D computer graphics business from Hewlett Packard, Intel, Sun and Microsoft, it came to the realization that it must switch its business orientation from that of a technology driven organization to a customer driven one (Malone, 1995). During this critical period of transition, SGI executive Ken Coleman said: ‘‘Great companies, like H-P or Intel, have passed that test and proven they have it. We haven’t yet’’ (Malone, 1995, p. 126). Partner cues. Much like the case of competitors or customers, cues for unlearning can come from strategic partners of the firm. Unlike cues from customers or competitors, cues from partners are more likely to arise from passive scanning (Daft and Weick, 1984) of the environment rather than from systematic, proactive information acquisition efforts on the part of the firm. The firm’s suppliers, for example, might make suggestions about procedures for streamlining order scheduling, delivery, and payment; suggestions to which the firm may react. By and large, partner cues are less frequent, occur on an intermittent schedule, and are more serendipitous than other environmentally based drivers. Rather than accepting the passive nature of such information, organizations interested in unlearning must become more active in acquiring cues from partners. Organizationally-based drivers Changes in organizational structure. Companies face stages in their development that beg for the unlearning of routines established in earlier stages. A successful change from simple structure (Mintzberg, 1979) to a departmentalized structure, for example, will probably require that the founder unlearn the routines that once allowed for tight control and substitute them with routines that allow for delegation. Hence, any proposed change in an organization’s structure should serve as a cue for re-examining everything, including successful products, processes and procedures.
Unlearning at Dell
Size. Attaining threshold size levels can trigger the need to unlearn past rules and routines. Dell Computer’s growth provides an example. ‘‘Like the kid in junior high who comes back from summer vacation a foot taller than the rest of the class, Dell Computer’s rapid rise had left it a gangly, dysfunctional mess’’ (Jacob, 1995, p. 118). Dell’s key to unlearning was to bring in experienced executives from Motorola, Hewlett-Packard, and Apple Computer. Klein (1989) refers to this as unlearning through salvation (i.e. unlearning through the recruitment of a mythical manager-messiah). Success. Hedberg (1980) writes that there are times when organizations should treat their memories as enemies. Routines that have been successful in the past are quick to inhabit organizational memory and narrow an organization’s vision. Numerous writers have written about the poisonous side effects of success (Daft and Weick, 1984; Lawler and Galbraith, 1994;
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Nystrom and Starbuck, 1984; Sinkula, 1994). Successful products, processes and procedures and the routines surrounding them should be ongoing foci for unlearning in organizations. Marketers may have notions about the optimal amount of time in which a successful product or process can be exploited. Yet each success must be questioned on its own merits.
Organizational competencies
Conclusion It has been my premise that all organizations learn at some level, whether or not they act on what they learn. The key to competitive advantage, however, is action. This action should have its genesis in an organization-wide understanding that the routines that emanate from organizational competencies must be continually examined, questioned and, if necessary, unlearned. Market driven organizations have expanded their axiomatic knowledge to allow for greater openness to outside in processes. At the same time, however, they possess a keen ability to sort noise from truth. Market driven organizations, in this regard, retrieve cues from customers, competitors, and other environmental entities that allow them set a trajectory for unlearning. Unlearning in market driven organizations results in routines that exploit optimally, but also are replaced at optimal points in strategic time. Because of the ability to unlearn, the accuracy of procedural and axiomatic knowledge in market driven organizations allows organizational members an ability to see the right stimuli and choose the appropriate routine. All of this allows the organization to provide superior value for its customers. Notes 1. Adapted from Bettis and Prahalad (1995) and Klein (1989). 2. Throughout this discussion, we refer to the term ‘‘routine’’ as synonymous with the term ‘‘response’’. References Argyris, C. and Scho¨n, D.A. (1978), Organizational Learning: A Theory of Action Perspective, Addison-Wesley, Reading, MA. Baker, W.E. and Sinkula, J.M. (1999), ‘‘The synergistic effect of market orientation and learning orientation on organizational performance’’, Journal of The Academy of Marketing Science, Vol. 27, Fall, pp. 411-27. Bettis, R.A. and Prahalad, C.K. (1995), ‘‘The dominant logic: retrospective and extension’’, Strategic Management Journal, Vol. 16, pp. 5-14. Cohen, M.D. (1991), ‘‘Individual learning and organizational routine: emerging connections’’, Organization Science, No. 2, February, pp. 135-9. Cyert, R.M. and March, J.G. (1963), A Behavioral Theory of the Firm, Prentice-Hall, Englewood Cliffs, NJ. Daft, R.L. and Weick, K.E. (1984), ‘‘Toward a model of organizations as interpretation systems’’, Academy of Management Review, Vol. 9, April, pp. 284-95. Day, G.S. (1991), ‘‘Learning about markets’’, Marketing Science Institute Report Number 91-117, Marketing Science Institute, Cambridge, MA. Day, G.S. (1992), ‘‘Continuous learning about markets’’, Planning Review, Vol. 20, September-October, pp. 47-9. Day, G.S. (1994), ‘‘The capabilities of market-driven organizations’’, Journal of Marketing, Vol. 58, October, pp. 37-52. Day, G.S. and Nedungandi (1994), ‘‘Managerial representations of competitive advantage’’, Journal of Marketing, Vol. 58, April, pp. 31-44. Day, G.S. and Wensley (1988), ‘‘Assessing advantage: a framework for diagnosing competitive superiority’’, Journal of Marketing, Vol. 52, April, pp. 1-20.
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Sackmann, S.A. (1991), Cultural Knowledge in Organizations, Sage Publications, Newbury Park, CA. Schein, E.H. (1993), ‘‘How can organizations learn faster? The challenge of entering the green room’’, Sloan Management Review, Vol. 34, Winter, pp. 85-92. Senge, P.M. (1990a), The Fifth Discipline: The Art and Practice of the Learning Organization, Doubleday, New York, NY. Senge, P.M. (1990b), ‘‘Mental models’’, Planning Review, Vol. 20, March-April, pp. 4-10, 44. Shaw, R.B. and Perkins, D.N.T. (1991), ‘‘Teaching organizations to learn’’, Organization Development Journal, Vol. 9, Winter, pp. 1-12. Sherman, S. (1995a), ‘‘Stretch goals: the dark side of asking for miracles’’, Fortune, Vol. 132 No. 10, 13 November, pp. 231-2. Sherman, S. (1995b), ‘‘Wanted: company change agents’’, Fortune, Vol. 132 No. 12, 11 December, pp. 197-8. Simon, H.A. (1991), ‘‘Bounded rationality and organizational learning’’, Organization Science, Vol. 2, February, pp. 125-34. Sinkula, J.M. (1994), ‘‘Market information processing and organizational learning’’, Journal of Marketing, Vol. 58, January, pp. 35-45. Sinkula, J.M., Baker, W.E. and Noordewier, T. (1997), ‘‘A framework for market-based organizational learning: linking values, knowledge behavior’’, Journal of The Academy of Marketing Science, Vol. 25, Fall, pp. 305-18. Slater, S.F. and Narver, J.C. (1994), ‘‘Market oriented isn’t enough: build a learning organization’’, Report No. 94-103, Marketing Science Institute, Cambridge, MA. Slater, S.F. and Narver, J.C. (1995), ‘‘Market orientation and the learning organization’’, Journal of Marketing, Vol. 59, July, pp. 63-74. Stata, R. (1992), ‘‘Management innovation’’, Executive Excellence, Vol. 9, June, pp. 8-9. Weick, K.E. (1979), The Social Psychology of Organizing, Addison-Wesley, Reading, MA.
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An executive summary for managers and executive readers can be found at the end of this issue
Putting people back into organizational learning Robert F. Hurley Associate Professor, Fordham University, New York, USA
Keywords Market orientation, Organizational learning, Customer orientation, Learning styles, Corporate culture, Marketing strategy Abstract There is an overemphasis on an outside-in, macro-organizational view of learning and an under-emphasis on the inside-out view which recognizes that people are the main agents of learning and change. Attempts at building a learning organization should start with an understanding of how adults learn and develop rather than elaborate ideas about competitive strategy, market research and information dissemination. Adult learning theory tells us that people learn primarily by being encouraged to tackle challenges, experiment, fail and correct failures and reflect on their experiences. The challenge in building learning organizations is fighting the bureaucratization that often replaces experimentation with control and routine. This paper examines the literature on market orientation, organizational learning and adult learning theory to identify how individual level learning can be maximized as a mechanism for enhancing organizational learning. Recommendations are made to integrate these streams of research and offer suggestions for further research.
Important insights
Introduction General Electric (GE) is often cited as one of the most impressive examples of the learning organization in practice (Garvin, 1993; Marquardt, 1996; Ghoshal and Bartlett, 2000). Also of interest to readers of this journal, GE is one of the largest global business and industrial marketing companies in the world. In a recent seminar at Fairfield University, Jack Welch, the outgoing CEO of GE, provided some important insights into how his company operates. Mr Welch’s words merit careful study by scholars of market orientation and organizational learning. Mr Welch described his company using some of the following words (Welch, 2001): We are a people development center. Our core competence is that we are in the people development business. I don’t know that much about television networks and plastics. The only way to run such a diversified company is to hire great people and develop them. Seventy to seventy five percent of my time is devoted to developing people. The rest of my time is spent doing mundane paperwork that is meaningless. In our E-commerce efforts we made every mistake there was to make. You learn from your mistakes; from your experiences. You have to stretch your people. Corporations are people.
Organizational learning
This paper explores a view of organizational learning that is consistent with Jack Welch’s description and, perhaps, at odds with some of the current research in this area. To summarize the argument at the outset, there is an overemphasis on an outside-in, macro organizational view of learning and an under-emphasis on the inside-out view which recognizes that people are the The research register for this journal is available at http://www.emeraldinsight.com/researchregisters The current issue and full text archive of this journal is available at http://www.emeraldinsight.com/0885-8624.htm
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main agents of learning and change. Organizations learn only when people learn and in turn affect the theories in use operating inside the firm. As such, a learning organization requires a critical mass of people in the organization who are learning. Further, attempts at building a learning organization should start with an understanding of how adults learn and develop rather than elaborate ideas about competitive strategy, market research and information dissemination. Adult learning theory tells us that people learn primarily by being encouraged to tackle challenges, experiment, fail and correct failures, and reflect on their experiences. The real challenge in building learning organizations is actively fighting the bureaucratization that inevitably comes with size and that sometimes replaces challenge and experimentation with control and routine. When this occurs, it inhibits learning and innovation in such a way that increased focus on collection and dissemination of market information will not correct. Research concerning market orientation and organization learning should be redirected towards understanding the effect that risk taking, experimentation, proactivity and learning from failure have on organizational adaptation and performance. Individual level learning
This paper will proceed first by examining the individual and organizational unit of analysis question. Second, the literature concerning market orientation and organizational learning will be reviewed to isolate what we know about the role of individual level learning in organizational learning. Third, the literature on adult learning theory is examined to identify how individual level learning can be maximized in organizations as a mechanism for enhancing organizational learning. Finally, an attempt is made to integrate these streams of research and offer suggestions for further research. Organizational learning or individual learning Research concerning organizational responsiveness to environments began with a focus on market orientation and then led to an interest in organizational learning. It is interesting that researchers label this area of research ‘‘organizational’’ learning. In the effort to institutionalize learning and enhance firm performance researchers have anthropomorized the firm and lost sight of a key element in the process of organizational learning. This emphasis on macro organizational aspects of organizational learning begs the question: How much of the process of organizational adaptation is dependent on individual level learning? Clearly, not everyone in an organization has to be learning and innovating for the organization to do so; however, does the engine of organizational learning and innovation operate on the fuel of individual learning and innovation? I believe that the answer is yes and by ignoring the human dimension of learning we have misdirected our research.
Market orientation
Kohli and Jaworski’s (1990) efforts to understand market orientation started with a fairly micro view utilizing in-depth interviews with managers about their efforts to compete in markets. From these rich interviews with managers, who emphasized things such as risk taking and senior managers reinforcing the need for innovation, a model was developed that abstracted the antecedents, moderators and consequences of a market orientation. Narver and Slater (1990) started with the literature on market orientation and also develop a model linking market orientation to performance. The central focus of this early work by these two teams of researchers was market orientation at primarily the organizational level with little attention to individual level factors. In both cases, models of market orientation were developed that reflected the human dimension of market orientation but this dimension was a fairly minor part of these models.
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Individual learning necessary
Sinkula (1994) introduced the concept of organizational learning in marketing and defined it as a process by which organizations as collectives learn through interaction with their environment. Sinkula (1994, p. 36) indicated that individual learning is a necessary but insufficient condition for organizational learning. His perspective was that individuals come and go and that organizational learning is about memory and the preservation of learning so that it can be ‘‘used by individuals other than its progenitor. Slater and Narver (1995) pick up on this macro perspective on organizational learning. They focus on firm-wide mechanisms for information acquisition, dissemination and shared interpretation. Unlike Sinkula (1994), Slater and Narver (1995) slip into a focus on organization level learning without directly addressing the unit of analysis questions and the role of individual level learning. Sinkula et al. (1997) seemed to understand that there is a link between individual learning and organizational learning. In defining organizational learning they cited Argyris and Schon (1978, p. 23) who suggested that ‘‘organizational learning occurs when members of the organization act as a learning agent for the organization, responding to changes in the internal and external environment of the organization by detecting and correcting errors in organizational theory in use, and embedding the results of their inquiry in private images and shared maps of organization’’. While Sinkula et al. (1997) emphasize that organizational values of commitment to learning, open-mindedness and shared vision promote a learning orientation, they go further down the organization level path by suggesting that organizations are cognitive systems. Like most other researchers in the field, they then focus on organizational level properties that facilitate organization learning. Once again, the people are left out or assumed away at the group level.
Disciplinary boundary traps
Most of the scholars that emphasize the macro-organizational aspects of learning do so because they fall into disciplinary boundary traps or because they agree with Hedberg’s (1981) reasoning on the subject. Hedberg (1981) suggested that ‘‘although organizational learning occurs through individuals, it would be a mistake to conclude that organizational learning is nothing but the cumulative result of their members’ learning. Organizations do not have brains, but they have cognitive systems and memories. As individuals develop their personalities, personal habits, and beliefs over time, organizations develop worldviews and ideologies. Members come and go, and leadership changes, but organizations’ memories preserve certain behaviors, mental maps, norms, and values over time.’’ This focus on organization level learning is at odds with some scholars’ view of how learning occurs. For example Argyris and Schon (1978, p. 20) argued that ‘‘there is no organizational learning without individual learning’’. Jelinelk (1979) indicated that only people are capable of learning through mental activity. Probst and Buchel (1997) suggested that individual and organizational learning are distinctly different and that individual learning requires a bridge to lead to organizational learning. Klimecki et al. (1994) suggested that this bridge consists of a communication, storage and integration process. Senge (1994, p. 236) also supported this view, suggesting that ‘‘organizations learn only through individuals who learn. Individual learning does not guarantee organizational learning, but without it no organizational learning occurs.’’ Redding (1994, p. 3) makes the connection between individual learning and organizational change, indicating that ‘‘individual learning is essential to the continuing transformation of the organization, to expand the firm’s core competencies,
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and to prepare everyone for an unknown future’’. Even Sinkula et al.’s (1997, p. 306) own view of organizational learning supports more of an individual level focus. Citing Lee et al. (1992), they suggest that: ‘‘The organizational learning process is cyclical. Individuals’ actions lead to organizational interactions with the environment, and outcomes are interpreted by individuals who learn by updating their beliefs about causeeffect relationships.’’ The same individual action leading to organizational action which changes individual beliefs and ultimately effects individual action as a cycle of learning was also proposed by March and Olsen (1975). Kim (1993) supports this view by suggesting that organizational learning occurs through the transfer of individual level learning via shared mental models. He argues that eventually the organization’s worldview evolves slowly to encompass individuals’ thinking. Shared understandings
Kim (1993) offers a model of organizational learning that takes into account individual and organizational learning. Kim’s (1993) OADI-SMM model suggests that individuals observe (O) stimuli in the environment, assess (A) them, design (D) actions and implement (I) these actions which affect individual mental models and eventually affect shared mental models (SMM) at the organization level. Crossan et al. (1999) offer a similar clarification and resolution to the macro-micro dilemma, but shed further light on the transfer process as a dialogue among communities of organization members. They suggests that organizational learning is multilevel and operates at the individual, group and organization levels of analysis. Insight and innovative ideas occur to individuals, not organizations (Nonaka and Takeuchi, 1995), but learning is manifest in the organization only when ideas are shared, actions taken, and common meaning developed at the group and organization level (Argyris and Schon, 1978; Huber, 1991). Thus, learning occurs through ad hoc and formal groups or communities of individuals. Some shared understandings developed by groups of individuals become institutionalized in the organization (Hedberg, 1981). The learning cycle operates in reverse order to the culture cycle. An organization’s culture is held at the group level but operates on the individual level (cf. Hurley and Hult, 1998). Norms, which are held and enforced at the group level, operate to shape the behavior of individuals. Learning occurs in a reverse cycle. While individuals’ thinking is to some extent influenced by culture, people are also capable of independent thought, which can change the cultural milieu (Bandura, 1977). Thus employees can learn and act within an organization and thereby affect the beliefs and cognitive maps and, ultimately, the behavior of other organization members. What started as individual insight and learning can eventually become an organizational habit or assumption about how things work which can become embedded in the culture of the organization as a form of tacit knowledge. This transfer process happens via modeling (Bandura, 1977) and dialogue (Senge, 1990).
Social imprinting process
Interestingly, when tacit knowledge is imparted to new organization members they may simply execute the behaviors without really understanding why. This is why strong cultures often become rigid. The social imprinting process leads to a form of ‘‘group think’’ and can become a learning disability where people do not challenge conventional thinking, unlearn and change (Janis, 1982). It is only when the individual learning cycle operates again to challenge assumptions and habits that new learning occurs; that is, theories in use at the firm level are questioned and organizational change results. Thus, learning starts at the micro or individual
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level, affects the group level and may also be codified in memory at the organizational level. Paid little attention
There is a reasonable degree of consensus that a theory of organizational learning needs to consider the individual, group, and organizational levels (Crossan et al., 1999). As we will explore in the next section, researchers in marketing have paid little attention to how individuals learn, experiment, practice, change and develop as the mechanism for organizational adaptation and performance. This is in direct contrast to the comments by Jack Welch reviewed earlier describing GE as a learning organization. If researchers are to make a credible contribution in this field, it seems some critical questions are: .
does the major impetus for organizational learning come from individual learning; and
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how leveragable are learning mechanisms that affect organizational processes relative to those that affect individuals?
Market orientation and organization learning In addition to understanding the appropriate unit of analysis it is important to glean from the market orientation and organizational learning literature what we can about how learning occurs. Interestingly, with the exception of Sinkula’s (1994) work, most of the research concerning market orientation and learning orientation has not been connected to prior research on how people learn. Kohli and Jaworski (1990) defined market orientation as the organization-wide generation of market intelligence pertaining to current and future customer needs, dissemination of intelligence across departments, and organization-wide responsiveness to it. They make the case that the antecedents to market orientation fall into three broad factors: (1) senior management; (2) interdepartmental dynamics; and (3) organization-wide systems. Response to customer needs
Jaworski and Kohli (1993) focus on facilitating individual learning in that they suggest that risk aversion on the part of top management inhibits the introduction of new products. In their earlier work (Kohli and Jaworski, 1990), they are more explicit in suggesting that if top management exhibits a willingness to take risks and to accept failure as natural, employees are more likely to propose and introduce new offerings in response to customer needs. Largely, Kohli and Jaworski’s work ignores individual learning and its impact on market orientation and organizational learning. Deshpande et al. (1993, p. 27) take a macro-organizational focus and concentrate on customer orientation and conceptualize it ‘‘as being part of an overall, but more fundamental, corporate culture’’. While they did not focus on the more micro role of individuals, their findings suggest that hierarchical or bureaucratic organizations perform poorly whereas more innovative organizations achieved better performance. Similar findings come from research on the use of market information which suggests that formalization and centralization have a negative affect on the use of knowledge (Deshpande and Zaltman, 1982; Jaworski and Kolhi, 1993). Like Deshpande et al. (1993), Day (1994, p. 39) took an organization culture perspective. He indicated that ‘‘management systems’’ was one of the core capabilities of a market oriented firm and that norms and values which are
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part of a management system ‘‘define the content and interpretation of knowledge, transcend individual capabilities and, unify these capabilities into a cohesive whole’’. Day further suggests that market driven behaviors are more likely when there is committed and involved top management and where the locus of decision making is as close to customers as possible. Knowledge and commitment
Slater and Narver (1995) emphasized that entrepreneurship, market orientation, organic structure, facilitative leadership, decentralized strategic planning and a challenging external environment combine to promote organizational learning. In their focus on entrepreneurship, Slater and Narver (1995) argue that learning occurs from exploration and experimentation. Entrepreneurial cultures, they said, value risk taking, proactiveness, innovation and resistance to bureaucratization. Slater and Narver (1995) cite Jack Welch as an example of facilitative leadership and suggest that such leadership fosters inquiry, challenging the status quo, motivating people to do more than is expected and encouraging breakthrough learning. According to Slater and Narver (1995), facilitative leaders have a keen focus on developing the people around them. The emphasis on decentralized strategic planning is due to the need in a dynamic marketplace for a bottom-up participative approach to gain knowledge and commitment from key stakeholders. The role of top management in such a process is to encourage experimentation and nurture the development of the highest potential ideas.
Innovative culture
Hurley and Hult’s (1998) work further points to the criticality of individual learning. They demonstrated that innovativeness of an organization’s culture had a direct effect on the capacity to implement new ideas. More importantly, they demonstrated that an emphasis on learning and development of individual employees was by far the most important contributor to creating an innovative culture. Decentralized decision making was another factor that contributed to creating an innovative culture. To summarize, the literature on market orientation and organizational learning has shed some light on how learning occurs. Learning is facilitated by:
Individual level
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an emphasis on individual learning and development (Hurley and Hult, 1998);
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decentralization of decision making and low formalization (Deshpande et al., 1993; Hult et al., 2000; Hult, 1998; Hurley and Hult, 1998; Slater and Narver, 1995);
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top management support of risk taking and treating failure as an opportunity to learn (Slater and Narver, 1995);
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management systems that provide for an integrated interpretation of information (Day, 1994);
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facilitative leadership that encourages stretch (Slater and Narver, 1995; Jaworski and Kohli, 1993; Narver and Slater, 1990); and
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top management nurturing good ideas (Slater and Narver, 1995).
Most of these findings in the literature are under-emphasized by researchers who focus on organizational properties that aid in the generation, dissemination and interpretation of market information. The argument in this paper is that these factors, which affect individual level learning, are central to the whole mechanism of organizational learning, change and adaptation. The literature on adult learning theory will be examined next to shed light on
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how organizations can facilitate individual learning in order to increase organizational learning and adaptation.
Individual learning
How adults learn If individual learning is critical to responsiveness, change, and organizational performance, then an understanding of how adults learn becomes crucial to building the learning organization. Table I summarizes some of the findings on adult learning from the literature. Based on the literature, promoting individual learning requires that the organization create Arousal for learning Awareness of inadequacy of present behaviors Clarity about required new behavior
Brundage and Mackeracher, 1980; Schein, 1993 Miller, 1964; Schein, 1993
Miller, 1964; Brundage and Mackeracher, 1980 Collaborative and participative modes of Brundage and Mackeracher, 1980; teaching Darkenwald and Merriam, 1982; Conti, 1985 Experiential learning approaches employed Gibb, 1960; Smith, 1982; Conti, 1985 Experimentation allowed Smith, 1982; Schein, 1993 Failure with help to locate correct Shank, 1997; Schein, 1993 approaches Failure with the ability to talk about errors Edmonson, 1999 Feedback concerning progress Gibb, 1960; Brundage and Mackeracher, 1980; Shank, 1997; Merriam and Brockett, 1997 Freedom to examine experiences after Gibb, 1960 they have occurred Learning is meaningful to learner Gibb, 1960; Knox, 1977; Brundage and Mackeracher, 1980; Smith, 1982; Darkenwald and Merriam, 1982; Brookfield, 1986 Motivation to change exists on the part of Miller, 1964; Smith, 1982; Schein, 1993 the learner New behaviors are related to past Knox, 1977; Smith, 1982 experience Non-threatening and low stress Brundage and Mackeracher, 1980; Smith, environment 1982; Schein, 1993; Edmonson, 1999 Positive self-concept exists Brundage and Mackeracher, 1980 Practice opportunities are available Miller, 1964; Darkenwald and Merriam, 1982; Shank, 1997 Problem-solving approaches are employed; Gibb, 1960; Knox, 1977; Brundage and there is an active adaptation and Mackeracher, 1980; Smith, 1982; searching for solutions Brookfield, 1986; Conti, 1985; Knowles, 1975 Reinforcement of learned behaviors Miller, 1964; Brundage and Mackeracher, 1980; Darkenwald and Merriam, 1982; Schein, 1993 Self-directed, goals are set and pursued by Gibb, 1960; Brundage and Mackeracher, the learner, learner has responsibility for 1980; Smith, 1982; Brookfield, 1986; Conti, 1985; Merriam and Caffarella, 1991 what and how of learning Sequence of appropriate learning material Miller, 1964 Support for change Brundage and Mackeracher, 1980; Smith, 1982 Value status of learner Brundage and Mackeracher, 1980 Voluntary participation Brundage and Mackeracher, 1980 Motivation is intrinsic rather than extrinsic Darkenwald and Merriam, 1982
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an arousal and motivation for learning and change, avoid levels of stress and threat that will inhibit learning, increase experimentation, treat failure and mistakes as learning opportunities, emphasize accumulating learning experiences, make the role of learner a valued one, use problems as opportunities for learning, locate the responsibility and control over learning with the learners, provide feedback on learning, reinforce learning activities, and reinforce new behaviors that are learned. Adult learning
This review of research on adult learning theory shows an interesting parallel with the comments of Jack Welch from GE. The literature on adult learning and Mr Welch both seem to be saying that in order for learning to occur people must be challenged, supported, encouraged to experiment, to take action, and to learn from their mistakes. Conclusion Respected leaders from global industrial organizations such as ABB, GE and Allied Signal emphasize the importance of individual initiative in organizational learning and performance (Welch, 2001; Bossidy, 2001; Ghoshal and Bartlett, 2000). This is at odds with much of the research on market orientation and organizational learning coming out of the marketing field. This stream of research has not integrated individuals as agents of learning with the necessary macro-organizational properties required to nurture, promote and institutionalize peoples’ ideas. This is perhaps due to the fact that learning is a complex, multi-level phenomenon whereas the theories and measurement approaches tend to be more narrow and constrained by traditional disciplinary boundaries. Marketing researchers focus more on external factors and management scholars focus more on internal factors, but do so usually at either a macro or micro level. The reality of organizational learning falls between the cracks of theories of organization cognition and individual cognition. We are left with an incomplete understanding of how organizations adapt to changes in markets.
Emergent change
The importance of individual level factors concerning organizational learning and change is supported by Ghoshal and Bartlett (2000, p. 196) who found that companies that were successful at change ‘‘recognized that transformation is as much a function of the behaviors of individuals within the organization as it is of strategies, structures and systems that top management introduces’’. Weick (2000, p. 237) also argued that real change occurs not in planned programmatic initiatives but in an emergent process of change ‘‘when people re-accomplish routines and when they deal with contingencies, breakdowns and opportunities in everyday work’’. Organizational adaptation is emergent change created by choices made on the front line. The manager’s job is to aid sense making by interpreting and labeling these choices and legitimizing and accelerating adaptation (Weick, 2000). Recognizing that individual learning is perhaps the main engine for organizational learning has implications for both managers and researchers. These implications are reviewed below. Implications for leaders Creating mechanisms for the collection and dissemination of market information is the easy part of leading a learning oriented company. Unfortunately, creating a bureaucracy of information flow will not create a true learning organization. The inherent conservatism and bureaucratization of large organizations was discussed by Rockefeller (1973, p. 72), who
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suggested that an ‘‘organization is a system, with a logic of its own, and all the weight of tradition and inertia. The deck is stacked in favor of the tried and proven way of doing things and against the taking of risks and striking out in new directions.’’ Create learning environment
The major challenge in creating a learning organization is to fight the process of bureaucratization and shape a culture where people take the initiative to learn and innovate as if it was their company. Research on adult learning theory clearly points out the role of experimentation, experience, and using challenging problems, where failure is possible, as opportunities for learning. Creating an environment that promotes learning requires that leaders strike the appropriate balance between control and creativity. At some level employees must care enough and be free enough to notice challenges, take them on, struggle, learn and innovate in the process. This requires a delicate balance of top-down and bottom-up influence. Such a balance must assure that new ideas come up through the organization but that there is enough integration and coordination to avoid the chaos that would result if decisions were not made and resources were not ultimately allocated to the best ideas.
Learning requires skills
Creating an organization culture that promotes individual learning requires leadership development on the ‘‘soft’’ skills or what some refer to as emotional intelligence. A quick glance at Table I which summarizes the literature on adult learning theory reveals that managers cannot compel learning. Facilitating learning requires skills in establishing a challenging and inspiring vision, building trust, supporting, taking risks, controlling at more of a distance by shaping the agenda, empowering, and allowing leadership vacuums to be created for others to fill and expecting people to fill them. A critical mass of leaders engaging in these and other practices will facilitate individual learning. This, when combined with some attention to infrastructure and integration, will undoubtedly lead to a learning organization. I believe this is what Jack Welch was describing at GE. Implications for researchers There is a great deal of good research being done in marketing concerning market orientation and organizational learning. The fact that individual level learning has been somewhat neglected is due to a number of factors that should be examined. First, research has been guided more by disciplinary boundaries than by the natural boundaries of the problem to be solved. Individual level learning is the discipline of psychology or organizational behavior and not the traditional territory of marketing, strategy or even organizational theory scholars. This same disciplinary myopia explains why researchers in marketing largely ignored the vast literature on innovation as they developed theories about market and learning orientation. Perhaps increasing cross-disciplinary collaboration would yield more robust and powerful ways of understanding about market and learning oriented companies.
More complete view
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Second, it appears that research in marketing moved too quickly to measurement and model building without enough qualitative research on how learning occurs in organizations. Fine-grained qualitative research is needed to achieve a more complete view of the complex causal chain of events from changes in markets to changes in organizations. The argument made in this paper is that if we had a rich description of the process we JOURNAL OF BUSINESS & INDUSTRIAL MARKETING, VOL. 17 NO. 4 2002
would see more clearly the importance of the role of people as the agents of organization learning and adaptation. Some specific areas of the research that need more attention are noted below in the form of research questions:
Research agenda
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How important is action orientation and experience to individual and organizational learning?
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How does individual learning lead to group and organizational learning?
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Are there factors that will inhibit organizational learning even if individual level learning is occurring in the organization?
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Is self-directed individual learning critical to organizational learning?
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What is the relative impact of training, action learning and other individual or group learning initiatives on overall organizational learning?
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What is the effect of having well-developed intelligence systems when the organization’s culture is not innovation or learning oriented?
There are many more interesting research questions that could be added to this list. Making progress on this research agenda would certainly go a long way to putting the people back into research on organizational learning. References Argyris, C. and Schon, D.A. (1978), Organizational Learning: A Theory of Action Perspective, Addison-Wesley, Reading, MA. Bandura, A. (1977), Social Learning Theory, Prentice-Hall, Englewood Cliffs, NJ. Bossidy, L. (2001), ‘‘The job no CEO should delegate’’, Harvard Business Review, March, pp. 46-9. Brookfield, S.D. (1986), Understanding and Facilitating Adult Learning, Jossey-Bass, San Francisco, CA. Brundage, D.H. and Mackeracher, D. (1980), Adult Learning Principles and Their Application to Program Planning, Ministry of Education, Ontario. Conti, G.J. (1985), ‘‘Assessing teaching style in adult education: how and why’’, Lifelong Learning: The Adult Years, Vol. 8 No. 8, pp. 7-11, 28. Crossan, M., Lane, H. and White, R.E. (1999), ‘‘An organizational learning framework: from intuition to institution’’, Academy of Management Review, July, pp. 1-23. Darkenwald, G.G. and Meriam, S.B. (1982), Adult Education, Harper & Row, New York, NY. Day, G. (1994), ‘‘The capabilities of market-driven organizations’’, Journal of Marketing, Vol. 58, October, pp. 37-52. Deshpande, R. and Zaltman, G. (1982), ‘‘Factors affecting the use of market research information: a path analysis’’, Journal of Marketing Research, Vol. 19, February, pp. 14-31. Deshpande, R., Farley, J. and Webster, F.E. Jr (1993), ‘‘Corporate culture, customer orientation, and innovativeness in Japanese firms: a quadrad analysis’’, Journal of Marketing, No. 57, January, pp. 23-37. Edmonson, A. (1999), ‘‘Psychological safety and learning behavior in work teams’’, Administrative Science Quarterly, Vol. 44 No. 2, pp. 350-84. Garvin, D.A. (1993), ‘‘Building a learning organization’’, Harvard Business Review, Vol. 71, July-August, pp. 78-91. Gibb, J.R. (1960), ‘‘Learning theory in adult education’’, in Knowles, M.S. (Ed.), Handbook of Adult Education in the United States, Adult Education Association of the USA, Washington, DC. Ghoshal, S. and Bartlett, C.A. (2000), ‘‘Rebuilding behavioral context: a blueprint for corporate renewal’’, in Beer, M. and Nohria, N. (Eds), Breaking the Code of Change, Harvard Business School Press, Boston, MA, pp. 195-222.
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Hedberg, B. (1981), ‘‘How organizations learn and unlearn’’, in Nystrom, P. and Starbuck, W. (Eds), Handbook of Organizational Design, Oxford University Press, New York, NY. Huber, G.P. (1991), ‘‘Organizational learning: the contributing processes and the literatures’’, Organization Science, Vol. 2, February, pp. 88-115. Hult, G.T. (1998), ‘‘Managing the international strategic sourcing function as a market-driven organizational learning system’’, Decision Sciences, Vol. 29, Winter, pp. 193-216. Hult, G.T., Hurley, R.F., Giunipero, L.C. and Nichols, E.L. Jr (2000), ‘‘Organizational learning in global purchasing: a model and test of internal users and corporate buyers’’, Decision Sciences, Vol. 31 No. 2, pp. 293-325. Hurley, R.F. and Hult, G.T. (1998), ‘‘Innovation, market orientation, and organizational learning: an integration and empirical examination’’, Journal of Marketing, Vol. 62, July, pp. 42-54. Janis, I.L. (1982), Groupthink, 2nd ed., Houghton-Mifflin, Boston, MA. Jaworski, B.J. and Kohli, A. (1993), ‘‘Market orientation: antecedents and consequences’’, Journal of Marketing, Vol. 52, July, pp. 53-70. Jelinek, M. (1979), Institutionalizing Innovation: A Study of Organizational Learning Systems, Praeger, New York, NY. Kim, D.H. (1993), ‘‘The link between individual and organizational learning’’, Sloan Management Review, Fall, pp. 37-49. Klimecki, R., Probst, G. and Eberl, P. (1994), Entwicklungsorientiertes Management, Poeschel, Stuttgart. Knowles, M.S. (1975), Self-directed Learning: A Guide for Learners and Teachers, Cambridge Books, New York, NY. Knox, A.B. (1977), Adult Development and Learning: A Handbook on Individual Growth and Competence in the Adult Years, Jossey-Bass, San Francisco, CA. Knox, A.B. (1993), ‘‘Market orientation: antecedents and consequences’’, Journal of Marketing, Vol. 52, July, pp. 53-70. Kohli, A. and Jaworski, B.J. (1990), ‘‘Market orientation: the construct, research propositions, and managerial implications’’, Journal of Marketing, Vol. 54, April, pp. 1-18. Lee, S.J., Courtney, J.F. Jr and O’Keefe, R.M. (1992), ‘‘A system for organizational learning using cognitive maps’’, OMEGA International Journal of Management Science, Vol. 20, Spring, pp. 23-36. March, J.G. and Olsen, J.P. (1975), ‘‘The uncertainty of the past: organizational learning under ambiguity’’, European Journal of Political Research, Vol. 3, pp. 146-71. Marquardt, M.J. (1996), Building the Learning Organization, American Society for Training and Development, McGraw-Hill, New York, NY. Miller, H.L. (1964), Teaching and Learning in Adult Education, Macmillan, New York, NY. Narver, J. and Slater, S. (1990), ‘‘The effect of a market orientation on business profitability’’, Journal of Marketing, Vol. 54, October, pp. 20-35. Nonaka, I. and Takeuchi, H. (1995), The Knowledge Creating Company, Oxford University Press, New York, NY. Probst, J.B. and Buchel, B. (1997), Organizational Learning: The Competitive Advantage of the Future, Simon & Schuster, London. Redding, J. (1994), Strategic Readiness: The Making of the Learning Organization, JosseyBass, San Francisco, CA. Rockefeller, J.D. (1973), The Second American Revolution, Harper-Row, New York, NY. Schein, E.H. (1993), ‘‘How can organizations learn faster? The challenge of entering the green room’’, Sloan Management Review, Winter, pp. 85-92. Senge, P.M. (1990), The Fifth Discipline: The Art and Practice of the Learning Organization, Doubleday, New York, NY. Shank R. (1997), Virtual Learning, McGraw-Hill, New York, NY. Sinkula, J.M. (1994), ‘‘Market information processing and organizational learning’’, Journal of Marketing, Vol. 58, January, pp. 35-45. Sinkula, J.M., Baker, W.E. and Noordewier, T. (1997), ‘‘A framework for market-based organizational learning: linking values, knowledge and behavior’’, Journal of the Academy of Marketing Science, Vol. 25 No. 4, Fall, pp. 305-18. 280
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Slater, S. and Narver, J.C. (1995), ‘‘Market orientation and the learning organization’’, Journal of Marketing, July, Vol. 59, pp. 63-74. Smith, R.M. (1982), Learning How to Learn: Applied Learning Theory for Adults, Cambridge Books, New York, NY. Weick, K.E. (2000), ‘‘Emergent change as a universal in organizations’’, in Beer, M. and Nohria, N. (Eds), Breaking the Code of Change, Harvard Business School Press, Boston, MA, pp. 223-41. Welch, J. (2001), ‘‘A conversation with Jack Welch’’, part 1 of 1 of a CSPAN program featuring Jack Welch at Fairfield University, the tape can be ordered by calling 1-877ONCSPAN. videotape 163647.
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An executive summary for managers and executive readers can be found at the end of this issue
Managing the paradox of inter-firm learning: the role of governance mechanisms Jakki J. Mohr Ron and Judy Paige Faculty Fellow, Associate Professor of Marketing, University of Montana, Missoula, Montana, USA
Sanjit Sengupta Associate Professor of Marketing, San Francisco State University, San Francisco, California, USA
Keywords Partnering, Alliances, Governance, Skills Abstract Organizational learning in inter-firm exchange relationships poses a doubleedged sword. On one hand, inter-firm learning is a desirable extension of organizational learning, developing a firm’s knowledge base, and providing fresh insights into strategies, markets, and relationships. On the other hand, inter-firm learning can lead to unintended and undesirable skills transfer, resulting in the potential dilution of competitive advantage. This risk can be exacerbated by disparities in inter-firm learning, resulting in uneven distribution of benefits and risks in the collaborative relationship. This paper articulates these two different views on interfirm learning, and second, develops a framework for the role of governance in regulating knowledge transfer. In particular, appropriate governance mechanisms must be crafted which match the learning intentions of the partners, the type of knowledge sought, and the designed duration for the collaboration, so as to maximize the benefits of learning while minimizing the risks. Implications for strategy and future research are offered.
Another important dimension of the learning organization’s architecture is its openness to external ‘‘learning partners’’ [customers, distributors, suppliers, alliance partners and others] . . . The development of long-term, stable relationships with ‘‘learning partners’’ leads to information sharing that benefits both partners. These partnerships provide access to a greater number of information sources, force the development of mechanisms that facilitate the sharing of information, and offer alternative perspectives of the meaning of critical information that could lead to generative learning (Slater and Narver, 1995, p. 70). . . . when collaborating with a potential competitor, failure to ‘‘out-learn’’ one’s partner could render a firm first dependent and then redundant within the partnership, and competitively vulnerable outside it . . . The fact that a firm chose to collaborate with a present or potential competitor could not be taken as evidence that that firm no longer harbored a competitive intent vis-a`-vis its partner. Indeed, when it came to the competitive consequences of inter-partner learning, the attitudes of some managers shifted from naivete to paranoia . . . The competitive consequences of skills transfers, as well as the actual migration of skills, were often unintended, unanticipated, and unwanted by at least one of the partners (Hamel, 1991, pp. 84, 92). The authors gratefully acknowledge the helpful comments of Daniel Flint and David Ketchen in revising this article. The research register for this journal is available at http://www.emeraldinsight.com/researchregisters The current issue and full text archive of this journal is available at http://www.emeraldinsight.com/0885-8624.htm
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Key mechanism
Recent research in marketing on the topic of strategic alliances is increasingly taking a learning-based perspective. For example, Rindfleisch and Moorman (2001) and Sividas and Dwyer (2000) highlight the role of communication and information sharing in achieving alliance outcomes. Learning from others is one of the key mechanisms to generate new knowledge, and is often an express purpose of collaborative relationships. Indeed, ‘‘know-how transfer is widely cited as the major objective behind alliance participation’’ (Rindfleisch and Moorman, 2001, p. 2). Organizational learning has been conceptualized and defined in a variety of ways; most common conceptualizations agree that organizational learning is a process of acquiring, disseminating, interpreting, using, and storing information within or across organizations. The process leads to new knowledge or insights that affect organizational strategies (Sinkula, 1994; Slater and Narver, 1995). The types of useful information that can be shared between firms might include, for example, learning new technological skills, learning about new markets and customers and how to access them effectively, and learning about new ways to approach the product development process. In addition, inter-firm learning can include learning about partners, which allows firms to respond more efficiently to each other’s – as well as market – demands, creating a form of switching cost. Furthermore, learning about how to partner – the skills and procedures necessary to enhance collaborative advantage – can, in and of itself, be an important source of competitive know-how (Johnson and Sohi, 1997; Simonin, 1997).
Inter-firm learning
However, as the quotes at the outset of this article indicate, acquiring and using information from alliance partners offers benefits to a firm, but the downside risks of inter-firm learning must also be accounted for. For example, inter-firm learning can make alliance participants more aware of the difficulties in the relationship, which can lead to its termination (Doz, 1996). With respect to inter-firm learning, possibly the greatest risk comes in the potential loss of tacit knowledge to a partner (Dutta and Weiss, 1997), in which a firm’s source of competitive advantage is diluted when its partner acquires or internalizes its knowledge and skills. As stated by a manager in Hamel’s (1991, p. 87) study: If they were really our partners, they wouldn’t try to suck us dry of technology ideas they could use in their own product. Whatever they learn from us they are going to use against us worldwide.
The downside risks of inter-firm learning are emphasized in work by Bucklin and Sengupta (1993), Ring and Van de Ven (1994), Littler et al. (1995), and Lei (1997), among others. Indeed, Littler et al.’s (1995) study of collaborative efforts in inter-firm product development found that the leakage of information was the most frequently mentioned risk cited by product managers. Account for the factors
Hence, learning in inter-firm relationships poses a paradox for managers and scholars: while one wants to learn as much as possible from one’s partners in order to maximize the effectiveness and efficiency of the partnership, one also must limit transparency and leakage of information in the partnership so as not to dilute the firms’ sources of competitive advantage. If we can account for the factors that lead to both the upside potential as well as the downside risks of inter-firm learning, then we can structure inter-firm relationships in such a way to enhance the upside benefits, but mitigate the downside risks.
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Conceptual model
The purposes of this paper, therefore, are first to explicitly articulate the factors that are related to the two sides of inter-firm learning. Although prior literature on inter-firm relationships and organizational learning acknowledges both the upside benefits and the downside risks (e.g. Hamel et al. 1989; Kale et al., 2000), there is no integrated treatment of the underlying issues which are related to the two views of inter-firm learning. With a more complete understanding of the two perspectives of inter-firm learning, a second purpose of this paper is to offer a conceptual model to help managers and scholars deal with the paradox of inter-firm learning. Theories of inter-firm governance (Williamson, 1985, 1996; Heide, 1994; Gulati and Singh, 1998), which focus on the mechanisms used to structure and manage inter-firm relationships, offer one possible way to address the paradox posed by inter-firm learning. In particular, appropriate governance mechanisms must be crafted which match the learning intentions of the two partners involved, so as to maximize the possible benefits of learning while minimizing the risks. This paper builds on the existing literature in organizational learning and inter-organizational relations by exploring the role of governance mechanisms as an interface in effective knowledge transfer. Organizational learning and inter-firm relationships Overview Collaboration between firms can take place through a variety of different arrangements, including relationships with suppliers, intermediaries, and customers. In addition, alliances with current or potential competitors, as well as firms which offer complementary offerings (Bucklin and Sengupta, 1993) may be formed to facilitate a firm’s ability to achieve a strategic objective or enhance its competitive position in the marketplace.
Collaboration enhances learning
Because useful knowledge does not reside exclusively within a firm, the degree to which firms learn useful knowledge can be a function of their collaborative partnerships with suppliers, intermediaries, customers, and other firms and organizations (Powell, 1990; Simonin, 1997). Such collaboration enhances organizational learning (Kogut, 1988; Kanter, 1994; Powell et al., 1996), ‘‘short-circuiting’’ the learning process (Lei, 1997). By enlarging one’s knowledge base and accessing information which can augment a firm’s sources of expertise, collaborative learning can help a firm become a stronger competitor in the marketplace (Story and Mohr, 1997). Huber (1991) refers to the process by which firms internalize knowledge not previously available within the organization as ‘‘grafting’’. However, the benefits associated with cooperative relationships and alliances do not accrue automatically to the firms involved, but require conscious management action to ensure that useful knowledge is actually integrated into the firm’s activities (Nicholls-Nixon, 1993). For firms to maximize the value of inter-firm learning, they must first recognize that competitive advantage can be gained from collaboration (Kanter, 1994), and put the appropriate structures and mechanisms in place to ensure such learning (Crossan and Inkpen, 1995).
Internalize knowledge and skills
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Inter-firm learning is a function of the form and strength of the organization’s interdependence with its learning partners (Slater and Narver, 1995), as well as its openness – its willingness to share knowledge and to interact closely with a partner (Aadne et al., 1996). Relationships that involve close inter-partner collaboration help firms learn, absorb, and internalize the tacit knowledge and skills possessed by their partners JOURNAL OF BUSINESS & INDUSTRIAL MARKETING, VOL. 17 NO. 4 2002
(Lei, 1997). Operating-level employees must be receptive to the gathering and sharing of useful knowledge (Hamel, 1991; Kanter, 1994). Channels of interaction
Based on the implicit assumption that knowledge transfer is desirable, empirical research on inter-firm learning has examined factors related to inter-firm learning and knowledge transfer. In their test of a model of the effectiveness of knowledge transfer, Wathne et al. (1996) found that openness, prior experience on related projects, channels of interaction, and trust were positively related to the effectiveness of knowledge transfer. Furthermore, Lane and Lubatkin (1998) tested a model of the effects of absorptive capacity (cf. Cohen and Levinthal, 1990) on inter-firm learning; their findings showed support for their hypothesized positive relationships between three dimensions of absorptive capacity (ability to recognize and value new knowledge, ability to assimilate new knowledge, and ability to commercialize new knowledge) on inter-firm learning. Both of these studies measured inter-firm learning (or the effectiveness of knowledge transfer), not as the amount of information shared, but as the degree to which the knowledge acquired offered new insights, new ways of performing tasks, or new skills (Lane and Lubatkin, 1998; Wathne et al., 1996)[1]. A closer examination The synergies of partnering and enhanced learning can, in some relationships, lead to both parties becoming more competitive through a winwin situation. Yet in others, a cooperative relationship can strengthen both companies against outsiders even as it weakens one partner vis-a`-vis the other (Hamel et al., 1989). This more risky view of inter-firm learning, based on the resource-based view of the firm, sees the firm as a unique bundle of assets, skills, and capabilities (Wernerfelt, 1984; Prahalad and Hamel, 1990; Barney, 1991). Collaboration with business partners is viewed as a tool to augment skills deficiencies or to acquire new skills. Inter-firm learning is desirable – but only when one firm out-learns the other. The inherent risk comes from either intended or unintended leakage of information.
Resource-based view
One of the assumptions of the resource-based view of the firm is that strategic assets and core competencies are so deeply embedded in the fabric of the organization that they are difficult to transfer across organizational boundaries. Even when personnel leave an organization, the high degree of tacitness of some knowledge, with ambiguous connections between specific behaviors and outcomes, can make it hard to use elsewhere. Despite the ambiguity inherent in some types of knowledge, we argue that given a sufficient length of time and degree of closeness in an inter-firm relationship, the partner may risk losing some of its valuable information[2]. For example, in the NUMMI joint venture between Toyota and GM, GM learned valuable information that it was able to apply in its Saturn plant. Indeed, strategy researchers have documented that, for each focal firm which is learning, there is the partner firm which faces the risk of being ‘‘de-skilled’’ or seeing its own skills, knowledge, and expertise used against it in the future (Hamel, 1991). Thus, knowledge that diffuses across porous organizational boundaries can jeopardize a firm’s sources of competitive advantage. As stated by Ring and Van de Ven (1994, p. 108): Increasing transfers of proprietary resources among parties over time implies that their identities or unique domains may gradually shift from being complementary to being undistinguished, which increases the likelihood of disputes, conflict, and competition.
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More favorable view
One implication of these risks – which runs directly counter to the implications of the more favorable view of inter-firm learning – is that firms must not partner too closely (Hamel et al., 1989; Dutta and Weiss, 1997), and that they must guard against interpersonal ties which are too collegial. Hence, the literature on inter-firm relationships and organizational learning offers two distinct views of inter-firm learning. These views, shown in Table I, highlight its paradoxical nature. In painting the ‘‘rosy’’ picture of inter-firm learning, the focus is on the efficient and effective transfer of knowledge from the partner to the focal learning organization. The risks involved in the partner attempting to internalize knowledge are minimized or overlooked in favor of the benefits likely to accrue from effective partnering. On the other hand, the ‘‘risky’’ view of inter-firm learning explicitly alerts managers to the potential seepage of information when the partner has an intent to internalize knowledge. Here, loose organization with unrestricted interaction is potentially problematic – a signal that one partner may be naı¨ve to the other’s intent. Rather, tighter controls may be necessary to mitigate the risks of undesirable knowledge transfer.
Partnership success
One’s view of inter-firm learning is likely to color the desirability of cooperative, harmonious relational ties. Healthy, productive, functional, cooperative inter-firm relationships typically are characterized by a high degree of trust, commitment, information sharing, and high levels of balanced interdependence (Gulati, 1995; Kumar et al., 1995; Doney and Cannon, 1997; Gulati and Singh, 1998). Such relational characteristics are facilitated by the development of close collegial, interpersonal ties between people in the two organizations (Kanter, 1994). Indicators of partnership success include satisfaction, integrative conflict resolution, harmony, or longevity of the relationship (Mohr and Spekman, 1994). However, when explicitly considering the downside risks of learning in inter-firm relationships, the desirability of these relational characteristics becomes suspect. If, in fact, one’s partner is trying to internalize the knowledge that forms the basis of a firm’s competitive advantage (regardless of how likely it is to do so), harmony and a lack of contentiousness may Rosy
Risky
Partnering is associated with: Enhanced competitive position Skills augmentation
Potential leakage of valuable information Deskilling and vulnerability
Effective knowledge transfer is associated with: Outlearning one’s partner (race to learn) Interdependence More restricted interaction to mitigate Openness undesirable knowledge transfer Close collaboration Trust Many channels of interaction Characteristics of functional partnering: High levels of trust, commitment, and information sharing High, symmetrical interdependence Close, collegial interpersonal ties Indicators of partnership success: Integrative conflict resolution harmony Relationship longevity
Cautious levels of trust, commitment, and information sharing Asymmetrical interdependence More distant interpersonal ties Some contentiousness Relationship ends when partner’s learning objectives achieved
Table I. Two views of inter-firm learning 286
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signal a lack of attention to protecting proprietary information (Reich and Mankin, 1986; Hamel, 1991; Lei and Slocum, 1992). Moreover, while interpersonal ties are important, interpersonal ties which are too close and collegial can, in fact, lead to unrestrained information sharing (Hamel et al., 1989). In addition, if learning occurs asymmetrically in a relationship, the firm which is out-learning its partner may become more powerful as its partner adds less value to the relationship (value being based on useful knowledge) (Inkpen and Beamish, 1997). Explicit learning objective
These two views of inter-firm learning are not mutually exclusive, but rather co-exist and create tension in managing collaborative ventures. Hence, managers and scholars need assistance in understanding ways to manage the paradox to maximize the upside potential of inter-firm learning while limiting its downside risks. Underlying this paradox is the reality that two different parties come to the relationship with potentially conflicting goals and objectives. One party may be content with mere access to a partner’s knowledge and skills, while the other is desirous of internalization. Learning risks arise when a partner has an explicit learning objective, particularly in terms of gaining access to more tacit or embedded knowledge over a long-term partnership. Such a situation makes a firm vulnerable to the risk of potential opportunism by its partner. The role of governance in inter-firm learning Organizational (Williamson, 1985, 1996) and marketing theory (Bergen et al., 1992; Heide, 1994; Rindfleish and Heide, 1997) addresses the problems inherent in conflicting goals and objectives by implementing governance mechanisms to protect one party from the opportunistic tendencies of another. The two views of inter-firm learning explicated in the prior section highlight the need for effective governance mechanisms. For example, many scholars note the limitations of formal governance mechanisms (typically, the formal contractual agreement) in stanching knowledge flows between partners (e.g. Harrigan, 1986; Hamel, 1991; Kanter, 1994). For example, Lei (1997, p. 216) observes: . . . disparities in organizational receptivity to learning, knowledge embeddedness, and strategic intent will work to favour one partner’s ‘‘outlearning’’ the other in absorbing and internalizing skills over time, regardless of the amount of formal, legal ownership that is demarcated in the alliance structure.
Formal controls and mechanisms
One might surmise that unilateral governance (based on one firm’s controlling authority) inhibits inter-firm learning, while bilateral governance (based on mutual relational norms) would facilitate such learning. With formal controls and mechanisms put in place to manage the unilateral exchange relationship, personnel involved in the day-to-day management of the relationship would have rules for information sharing and tools to monitor and limit knowledge flows. On the other hand, bilateral mechanisms, with their emphasis on trust, relational norms, and mutuality in the relationship, would facilitate inter-firm learning. As stated by Helper and Levine (1992), interdependent relations governed by trust encourage the transfer of proprietary know-how. Dutta and Weiss (1997) examined the relationship between a firm’s level of technological innovativeness and its pattern of partnership agreements. They found that the higher the firm’s technological innovativeness, the less likely it was to use more transparent types of partnering arrangements such as joint ventures and research-and-development agreements, compared to marketing and licensing agreements. Because technology licensing agreements (where
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one firm sells the rights to use its technology to another firm) and marketing agreements (for marketing and/or distribution of another firm’s products) do not involve the same degree of face-to-face communication of personnel at joint ventures and closer forms of collaboration, they do not necessarily entail the same degree or intensity of sharing of information (Dutta and Weiss, 1997). Tighter structures
On the other hand, joint ventures and other types of close collaboration typically span many functions and levels across the two organizations, and entail close working relationships between operating personnel; hence, they tend to result in more permeability of the ‘‘collaborative membrane’’ (Hamel, 1991). Gulati and Singh (1998) and Pisano (1989) empirically found that alliances involving a technology component were more likely to use these tighter, hierarchical structures than those that did not. The equivocal empirical evidence – with some suggesting more arm’slength, and others suggesting more hierarchical relationships under conditions of greater risk – highlights the need for improved understanding of the link between governance mechanisms and the underlying conditions of inter-organizational learning. Indeed, these findings highlight the possible role of governance mechanisms in moderating the degree of inter-organizational learning. Those who subscribe to the more rosy view of inter-firm partnering may focus on the potential benefits, overlooking the risks that tightly-knit and highly relational partnerships can pose. On the other hand, those operating under the more risky view see the inherent risks in inter-firm learning and may adopt either more rigorous or more arm’s-length governance structures to limit unintended leakage of valued information.
Pose a dilemma
While either form of governance may have its risks, more loosely-knit or arm’s-length partnerships may pose a dilemma to the firm which is desirous of learning from its partner: while it needs a tightly-linked relationship to access information, it may be stymied in an attempt to form a tightly-linked relationship due to the partner’s concerns. Moreover, the formation of relationships that are only loosely linked could have implications for the success of the partnership. Some partnerships may require greater depth and breadth of interaction between personnel (Doz, 1996) in order to maximize their potential. But, concerns over inter-firm learning may hamper such development. Hence, simple one-way predictions about the linkage between governance and learning do not account for the fact that: many relationships include a combination of unilateral and bilateral governance mechanisms (Bradach and Eccles, 1989); and second, governance must be matched to the nature of the risks inherent in the relationship (Rindfleisch and Heide, 1997). In order to address these complexities, we develop a conceptual framework for effective knowledge transfer to a focal firm, contingent on matching the degree of ex ante learning risks in the relationship with an appropriate governance mechanism. As mentioned previously, learning risks arise depending on the partner’s intent, type of knowledge shared, and the anticipated duration of the alliance. In the spirit of Cannon and Perreault (1999), we identify governance mechanisms that are likely to have a high degree of bearing on the degree/type of learning that occurs in an inter-firm relationship. Firms can be connected in myriad ways:
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Some may be connected with formal contracts and others simply by trusting agreements; some may be connected with open communications and others may treat every piece of information as a secret; some may be connected by a shared sense of cooperation and others may act as if they were totally independent (Cannon and Perreault, 1999, p. 440).
Provide governance mechanism
Propensity to view
Cannon and Perreault’s (1999) findings show that firms can use a menu of devices to manage and govern exchange relationships, and that these devices do not necessarily co-vary in a systematic fashion. They identify information exchange as expectations of open sharing of information that may be useful to both parties. Information sharing and communication have been identified as critical governance mechanisms in both the relational norms and the channels literature (Macneil, 1980, 1981; Mohr et al., 1996). Operational linkages are the degree to which systems, procedures, and routines of the two firms have been linked to facilitate operations. Such linkages range from fairly independent, arm’s length to closely inter-coupled systems. With close operational linkages, shared activities and processes facilitate the flow of information. Cooperative norms refer to expectations about working together to jointly achieve mutual and individual goals, and capture the idea of relational norms. In relationships operating under norms of cooperation, problems are joint responsibilities, parties would not take advantage of the other’s bargaining positions, and each is concerned about the other. Legal bonds are contractual agreements that specify the obligations and roles of both parties in the relationship. They ‘‘provide a governance mechanism that may be used to simulate hierarchy in exchange when vertical integration is impractical’’ (Cannon and Perreault, 1999, p. 443). Credible commitments, or relationship-specific investments, create a self-interest stake in maintaining the relationship and avoiding opportunistic behavior (cf. Anderson and Weitz, 1992; Gundlach et al., 1995). Conceptual framework As shown in Figure 1, we posit that effective knowledge transfer requires an appropriate fit between governance mechanisms and the underlying ex ante learning risks in the alliance including: the type of knowledge the focal firm seeks, the learning intent of the partner firm, and the duration that the partners have in mind for the alliance. The type of knowledge is based on a common typology categorizing knowledge as either explicit or tacit (Badaracco, 1991; Kogut and Zander, 1993; Nonaka, 1994; Inkpen and Dinur, 1998), both of which are defined subsequently. Learning intent refers to the partner firm’s initial propensity to view collaboration as an opportunity to learn the focal firm’s knowledge and skills (Hamel, 1991; Story and Mohr, 1997; Inkpen and Beamish, 1997). The duration of the partnership refers to the ex ante anticipated timeframe of the alliance. As
Figure 1. Inter-firm learning and governance JOURNAL OF BUSINESS & INDUSTRIAL MARKETING, VOL. 17 NO. 4 2002
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shown in Figure 1, if there is a fit between the underlying conditions and the governance mechanism, effective knowledge transfer takes place. Sensitive information access
Our conceptualization of ‘‘effective knowledge transfer’’, in order to be compatible with this more complex view of inter-firm learning, incorporates two dimensions. First, most conceptualizations of inter-firm learning address the degree to which the focal firm has gained access to new knowledge. Such knowledge can include new insights about the market, products, or customers, ways of performing current tasks, and knowledge spillovers (Lane and Lubatkin, 1998; Parkhe, 1993; Rindfleisch and Moorman, 2001; Saxton, 1997; Wathne et al., 1996). However, from a focal firm’s perspective, effective knowledge transfer, because of its dyadic nature in inter-firm relationships, must also include protection against a partner’s accessing the focal firm’s own proprietary information (Kale et al., 2000). So, the second dimension of effective knowledge transfer is the degree to which a partner’s access to sensitive information is carefully controlled and monitored. Next, we explicitly consider the impact of partner’s intent, type of knowledge, and duration on the learning risks inherent in the relationship and try to minimize these risks through appropriate governance mechanisms. Ex ante conditions Type of knowledge Knowledge can be categorized in terms of its nature or properties. A common typology categorizes knowledge as either explicit or tacit (e.g. Kogut, 1988; Teece, 1988; Badaracco, 1991). Explicit, or migratory, knowledge is that which can be written down, encoded, and explained. Examples include blueprints, technical specifications, product designs, steps in the manufacturing process, and so forth. Importantly, explicit knowledge is not shrouded in organizational routines, practices, and culture. Because such knowledge is transparent – anyone with a comparable knowledge or skills base can understand and decipher it – some firms turn to patent protection and other forms of intellectual property rights for ownership.
Product-market knowledge
On the other hand, tacit knowledge is unwritten know-how and ‘‘know-why’’ (Lane and Lubatkin, 1998) that is part of the organization which possesses it, embedded in skills and routines (Nelson and Winter, 1982). It can include a way of approaching and solving problems, imagination, continuous improvement techniques, or artisan-like skills (Lei, 1997). For example, product-market knowledge tends to be explicit and more easily transferred than skills in technology development, manufacturing, or continuous quality improvement techniques (Hamel, 1991; Inkpen and Beamish, 1997)[3]. Tacit knowledge is less transparent than explicit knowledge, and has a ‘‘sticky’’ quality to it, making it difficult to learn and absorb. Tacit knowledge has causal ambiguity (Reed and DeFillippi, 1990) in which the relationship between a firm’s knowledge/actions and outcomes is highly uncertain, which makes it more difficult to transfer (Simonin, 1999). Because of this, to the extent that the most valuable knowledge is tacit – which is also the most difficult to transfer – a partnering firm desirous of learning from the other has an incentive to structure the closest type of partnership agreement possible[4]. Indeed, the only way to learn skills and competencies that are highly embedded in organizational routines is to partner closely (Badaracco, 1991). The learning risks in an alliance increase as the type of knowledge transferred goes from explicit to tacit. Tacit knowledge forms the basis of
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core skills and competencies, which on the one hand is harder to share and imitate than explicit knowledge due to its deeply embedded nature, but on the other hand potentially presents the greatest value to partners. Even though such tacit knowledge may be difficult to imitate, industry programs for learning through ‘‘best practices’’ indicate that managers hope to learn enough to transfer to their own organizations. Firms seeking to transfer tacit knowledge often desire collaborative agreements such as joint ventures and R&D consortia (Lei, 1997), where organizational routines are examined and understood, learning what is done, why it is done, and how it is done. Those who tend to emphasize the upside potential in inter-firm learning may overlook the potential ‘‘bleed-through’’ of tacit knowledge that is a very possible side-effect of partnering relationships. Even when such bleedthrough is explicitly recognized, some view it as more helpful to collegiality than harmful to competitive advantage (Harrigan, 1985). Partner’s learning intent Partner learning intent refers to the partner firm’s initial propensity to view collaboration as an opportunity to learn the focal firm’s knowledge and skills (Hamel, 1991; Story and Mohr, 1997; Inkpen and Beamish, 1997). Sometimes referred to as a learning axiom (Sinkula et al., 1997) or a predisposition/ motivation to learn (Story and Mohr, 1997), intent is an important condition which fosters learning. Partners’ intents in forming collaborative arrangements can range from mere access to information, to actual internalization of the focal firm’s skills and knowledge (Hamel et al., 1989; Inkpen and Beamish, 1997). The risk to the focal firm increases as the partner’s intent goes from access to internalize. Remedy skills deficiency
Often, a partner firm desires access to a focal firm’s skills and knowledge as a substitute for its own lack of competitiveness in a particular arena (Hamel, 1991). To actually remedy the underlying skills deficiency may be viewed as costly or time-prohibitive, and hence the firm chooses to partner as a solution to its problem. When a partner firm desires access to a focal firm’s skills, it may view the inter-firm agreement as truly collaborative, rather than a venue for becoming a more powerful competitor in the future (Inkpen and Beamish, 1997). In essence, the collaborative alliance is viewed as a viable, less painful alternative to internal efforts. Many vertical relationships with suppliers or intermediaries may be designed to access a partner’s skills. Consistent with the literature on make-versus-buy decisions (Venkatesan, 1992), it makes sense for a firm that is focusing on its core skills and capabilities to outsource non-critical supplies, parts, and components, as well as non-critical functions (such as human resources, billing, etc.). In addition, a firm which is deficient in technological skills or new product development may have ‘‘access’’ intents in partnering with another firm, each providing complementary skills.
Stated learning intent
Alternatively, a partner firm’s intent may be to actually internalize the skills and knowledge of the focal firm. Partners with such a stated learning intent are ‘‘more likely to view collaboration as a race to get to the future first, rather than a truly cooperative effort to invent the future together’’ (Hamel, 1991, p. 89). In this sense, the partnership is a transitional device, to be used only until the firm has amassed the knowledge it was seeking. From the perspective of the learning firm, termination of the agreement is viewed as evidence of successful learning, rather than of a failed collaborative venture. As discussed in Inkpen and Beamish (1997), it is the combinations of the two partners’ intents that lead to interesting insights. In situations where both
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partners have access intents, the risks to either party are likely to be low. Where both partners have internalization intents, both parties are likely to be very vigilant in structuring the exchange relationship. The most interesting situation, from a governance perspective, arises when the parties have asymmetrical intents: when one desires access but the other seeks internalization. Learning motives
To model the dyadic nature of the learning risks in an alliance, we capture the two different parties’ learning motives with these first two ex ante conditions. We model the partner’s learning intent based on the focal firm’s perceptions of the partner’s motives: does the focal firm perceive the partner’s learning intents to be based on access or internalization? We model type of knowledge sought from the focal firm’s perspective, and make two assumptions (see Table II). (1) If the focal firm is seeking explicit knowledge in the alliance, then its learning intent is likely to be access-oriented. (2) If the focal firm is seeking tacit knowledge in the alliance, then its learning intent is more likely to be internalization-oriented. In this way, we capture the four dyadic possibilities in the alliance: .
Both firms have access intents: perceived partner’s intent is access; focal firm seeks explicit knowledge (access).
.
Both firms have internalization intents: perceived partner’s intent is internalization; focal firm seeks tacit knowledge (internalization).
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One firm has access intent while the other has internalization intent: – perceived partner’s intent is access; focal firm seeks tacit knowledge (internalization); – perceived partner’s intent is internalization; focal firm seeks explicit knowledge (access).
In this manner, we address the possibility of asymmetrical risks. Duration While the literature on strategic alliances has sometimes used the terms ‘‘duration’’ and ‘‘age’’ interchangeably (e.g. Simonin, 1999), we focus on designed duration, ex ante. Duration is the ex ante anticipated timeframe for an alliance. Some alliances are designed for a shorter duration, e.g. co-marketing alliances for jointly promoting complementary products during the holiday shopping season (Bucklin and Sengupta, 1993), while others are expected to continue in perpetuity, e.g. joint ventures. Value creation logic
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Doz and Hamel (1998) argue that value creation logic dictates the duration of the alliance. The duration is designed keeping in mind the nature of work and available resources to achieve value objectives. The greater the benefits of the alliance to the partners, the longer the alliance should continue. Moreover, the longer the duration of the alliance, the more knowledge transfer can take place between partners. Indeed, if the partner firm decides to act opportunistically, a longer duration creates a bigger problem for the focal firm than a shorter duration. So, from an organizational learning perspective, duration might be one factor significantly related to anticipated future opportunism of a partner. Hence, the risk to the focal firm increases as the duration of the alliance goes from short term to long term. JOURNAL OF BUSINESS & INDUSTRIAL MARKETING, VOL. 17 NO. 4 2002
Type of knowledge
Predictions regarding the moderating role of governance on interfirm learning As the partner is perceived as having internalization (versus access) intents, as the type of knowledge sought by the focal firm goes from explicit to tacit, and as the duration of the alliance goes from short term to long term, risk can be minimized by crafting appropriate governance mechanisms. Specific predictions are presented in Table II and discussed in this section, along with illustrative examples and supporting logic. The underlying assumption is that with the appropriate governance structures in place, effective knowledge transfer will occur.
Partner’s intent
Type of knowledgea
Access
Focal firm seeks explicit (access)
Duration Short
Long
Focal firm seeks tacit (internalization)
Short
Long
Internalize
Focal firm seeks explicit (access)
Short
Long
Focal firm seeks tacit (internalization)
Short
Long
Governance mechanisms (from focal firm’s perspective) Cell 1. Traditional arm’s-length relationship with low levels of information exchange, operational linkages, cooperative norms, specific investments; greater reliance on contractual terms; ‘‘give and take’’ Cell 2. Close ongoing relationship with a moderate level of regular information exchange, operational linkages, specific investments and contractual terms; greater reliance on cooperative norms; ‘‘cooperative sharing’’ Cell 3. Rich information exchange; less reliance on operational linkages, cooperative norms, specific investments; may have contractual terms; ‘‘asymmetrical learning intents’’ Cell 4. Rich information exchange, close operational linkages, development of interpersonal relationships, specific investments; contractual protections for partner; ‘‘risks to partner from naı¨vete´’’ Cell 5. Controlled information exchange, loosely coupled operations, low cooperative norms, specific investments; contractual protections for focal firm; ‘‘keep away’’ Cell 6. Controlled information exchange, low operational linkages, specific investments; high cooperative norms, contractual protections for focal firm; ‘‘cooperate with caution’’ Cell 7. Guarded information exchange, low cooperative norms; high operational linkages, specific investments, contractual protections; ‘‘share as needed’’ Cell 8. Guarded information exchange; high operational linkages, high cooperative norms, joint credible commitments, contractual protections; ‘‘guard crown jewels’’
Note: a Please see additional explanation in prior section under ex ante conditions
Table II. Matching governance mechanisms to ex ante conditions JOURNAL OF BUSINESS & INDUSTRIAL MARKETING, VOL. 17 NO. 4 2002
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Confluence of conditions
In cell 1, the partner is perceived to desire mere access to the focal firm’s knowledge, the type of knowledge sought by the focal firm is explicit, and the task-dictated duration of the project is short. Given the combination of conditions, both the potential for opportunism and the coordination costs are anticipated to be low. This confluence of conditions represents low learning risks, and therefore the corresponding governance mechanisms can be crafted without undue worry about loss of proprietary knowledge. An arm’s-length relationship characterized by limited communication, few operational linkages, low levels of cooperative norms, and low levels of specific investments by partners is an efficient governance mechanism for these conditions. Because the nature of the exchange is fairly concrete, with few unforeseen contingencies, it would be fairly easy to rely on a contractual agreement. An example might be a company (the partner) buying a mailing list from a magazine (the focal firm) so that it can send a direct mail offer to the magazine’s readers. This cell might best be characterized as a ‘‘give and take’’ situation in which the nature and value of what each party is to give and receive is clear.
Cooperative norms
In cell 2, the partner is perceived to desire mere access to the focal firm’s knowledge, the type of knowledge sought by the focal firm is explicit, and the task-dictated duration of the project is long. The longer duration of the partnership adds an element of risk compared to cell 1. However, this longer duration of the partnership allows for the emergence of cooperative norms, which can help mitigate the risk arising from the longer duration. Because the relationship is ongoing (long term), each party must coordinate its efforts with the other, but no real safeguards must be put into place, because there is generally little risk involved. A moderate level of information sharing via regular communication (possibly via electronic sharing) with cooperative norms should suffice as a governance mechanism. Because of the explicit nature of the exchange, contractual terms may be clearly specified. There is likely to be less need for specific investments (other than those related to communications). Operational linkages will depend on the nature of the exchange. Manufacturers and distributors of low-priced, frequently purchased, consumer packaged goods illustrate this type of relationship. For the more committed of these efforts, both parties typically call the joint effort a partnership. For example, Procter & Gamble and Wal-Mart have this type of strategic alliance that has enabled them to achieve enormous operational efficiencies and boost profit margins (Dow et al., 1999). This would be a cooperative sharing situation.
Short time horizon
In cell 3, the partner is perceived to desire mere access to the focal firm’s knowledge, the type of knowledge sought by the focal firm is tacit, and the task-dictated duration of the project is short. This may be the case when a focal firm studies another firm for benchmarking purposes, as in the Malcolm Baldrige Quality Competition. Because the focal firm seeks to transfer tacit knowledge to itself, it requires a closer relationship with the partner than in cells 1 or 2. The focal firm has an incentive to have rich communication and a contractual agreement in order to enhance its ability to learn. However, the short time horizon of the relationship indicates that cooperative norms may not have time to develop; furthermore, returns from close operational linkages or investments in specific assets may be too low to justify the investment. Note that in this cell, there is an asymmetry in the partner’s learning risks. The focal firm desires internalization of the partner’s tacit knowledge, but the partner has only access intents. Indeed, the partner may or may not be
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aware of the learning risks involved. If the partner is aware of the learning risks, they may be fairly guarded in the extent of their information sharing, and rely on formal terms of the agreement to protect themselves. Mere access intents
In cell 4, the partner is perceived to desire mere access to the focal firm’s knowledge, the type of knowledge sought by the focal firm is tacit, and the task-dictated duration of the project is long. This may be the case when US firms partner with Japanese firms to gain access to their manufacturing expertise, and the Japanese firms end up internalizing the useful knowledge of their US partners with respect to marketing, distribution, and customer knowledge of the US market, e.g. Ford and Mazda. Again, this cell exhibits asymmetric risk, with one firm having mere access intents, while the other seeks to internalize the more valuable tacit knowledge from the other. So, from the focal firm’s perspective, a governance strategy that maximizes the breadth and depth of interaction between the parties, with close operational linkages, is desired. Moreover, in this case (compared to cell 3), the longer term time horizon may work in favor of the focal firm, allowing cooperative norms and interpersonal ties to develop. Indeed, Kale et al.’s (2000) study of learning in alliances found that ‘‘relational capital,’’ or close, intense interaction between individual members in the alliance enhances learning, but is unrelated to the ability to protect proprietary knowledge. While their study did not examine the parties’ learning intents, their findings could signal that a partner is naı¨ve to the other’s learning goals, and faces learning risks because of it.
Manage the risk
In cell 5, the partner is perceived to desire internalization of the focal firm’s knowledge, the type of knowledge sought by the focal firm is explicit, and the task-dictated duration of the project is short. For example, a focal firm might contract with an outside trainer to deliver a sales training program for its field personnel. Such a trainer may be looking to learn more about the focal firm’s sales operations so that it can use this information in future training programs with other clients. Although there are again asymmetrical learning risks, in this particular case the focal firm is aware of the partner’s learning intent. The goal is to manage this risk by having controls on information sharing by maintaining a safe distance in the relationship with a contract that only loosely couples the firms and limits communication. From the focal firm’s perspective, a governance strategy of ‘‘keep away’’ makes sense.
Adequate safeguards
In cell 6, the partner is perceived to desire internalization of the focal firm’s knowledge, the type of knowledge sought by the focal firm is explicit, and the task-dictated duration of the project is long. Because of the longer time horizon over which the partner is exposed to the focal firm’s activities and capabilities, this longer duration of the partnership increases the riskiness to the focal firm. Although the long-term time horizon allows the development of cooperative norms, the riskiness posed by the partner firm’s learning intent over a longer term time horizon warrants caution in information sharing. Such a situation calls for adequate safeguards for the focal firm that match the focal firm’s risks and costs in the relationship. For example, the explicit nature of the focal firm’s knowledge goals imply that close operational linkages or specific investments may not be warranted, but contractual safeguards like exclusivity or exit penalties can minimize the risks to the focal firm. In other words, a ‘‘cooperate with caution’’ governance strategy is appropriate. As an example, Apple partnered with Microsoft in the mid-1980s to have the latter develop application programs for the Macintosh. There were not
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adequate safeguards in this agreement (from Apple’s perspective, as the focal firm) and Microsoft, its partner, developed Windows as a direct competitor to the Apple Macintosh graphical interface. When the two companies partnered again in 1997 to have Microsoft develop Office 98 for PowerPC, Apple made sure there was a $150 million minority equity investment by Microsoft in the company. Protect potential loss
In cells 7 and 8, the tacit nature of the knowledge sought by the focal firm from its partner and the partner’s intent to internalize the focal firm’s knowledge pose something of a paradox for the focal firm: it wants to absorb its partner’s tacit knowledge (presumably via a closely collaborative partnership), but it does not want the partner to inadvertently learn from it. These cells exhibit the highest degree of learning risks for both parties from unrestrained information sharing. Indeed, because the focal firm may not know a priori the degree to which its partner may be able to absorb a part of the tacit knowledge that comprises its unique, strategic assets, it must act in such a way to protect the potential loss of such valuable knowledge. However, because both parties are aware of the risks, appropriate governance strategies can be crafted to mitigate these risks.
Guarded communication
In cell 7, the task-dictated duration of the project is short which precludes cooperative norms from being established. So, it would be important for the focal firm to have rather guarded communication, and to set up some sort of contractual protections should the relationship go awry. For example, Fogdog (the focal firm), a pure-play Internet e-tailer of sporting goods including Nike products, wanted to learn about sports marketing from Nike, its partner. Although Fogdog wanted to learn from Nike, it was wary about sharing its own e-commerce knowledge because Nike had its own Web site marketing its products in competition with Fogdog. To the extent that Nike had internalization intents about Fogdog’s e-commerce knowledge, the risk to Fogdog in this partnership was high. As a result, it was careful about sharing information, despite the fairly high operational linkages and specific investments. The two firms signed a six-month alliance whereby Nike took a 10 percent equity stake in Fogdog, and Fogdog was Nike’s exclusive Internet e-tailer (Wall Street Journal, 1999). The minority equity investment from Nike and its grant of short-term exclusivity were safeguards that protected Fogdog from potential opportunism. Hence, a ‘‘share as needed’’ policy should guide interactions in this situation.
High degree of control
In cell 8, the partner is perceived to desire internalization of the focal firm’s knowledge, the type of knowledge sought by the focal firm is tacit, and the task-dictated duration of the project is long. This cell represents the greatest risk because the partner firm is seeking to internalize the focal firm’s knowledge while the focal firm is seeking tacit knowledge from its partner in the context of a long-term relationship. This is a ‘‘guard the crown jewels’’ situation; joint ventures are often seen under such circumstances. The relationship should work under structured communication, careful monitoring, and guarded interpersonal ties, offering a high degree of control to protect the focal firm. Joint commitments can also provide a situation where each firm has a disincentive to act opportunistically in the relationship. Although most such ventures have fairly detailed contracts at the outset, such contracts do not typically determine the nature of the interactions over time. The NUMMI joint venture in Fremont, California between General Motors and Toyota that has lasted more than 15 years provides a good illustration of this (Inkpen and Beamish, 1997; Doz and Hamel, 1998). Toyota’s objectives
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in the joint venture were centered on minimizing financial risk; GM was able to learn valuable lessons that were subsequently applied in its Saturn plant. From GM’s perspective, it was able to protect itself while gaining access to valuable knowledge. Achieve learning objectives
Taken as a whole, these predictions, summarized in Table II, reflect the focal firm’s need to achieve its learning objectives while protecting itself from possible risks posed by its partner’s intent, the type of knowledge to be shared, and the duration of the relationship. The examples used are illustrative in nature, and suggestive of the dimensions in the model. Ultimately, the goal is to structure governance mechanisms and processes in the partnership to enhance the functioning of the relationship while minimizing undue risks. The final section explores implications of this model and suggestions for future research. Implications and suggestions for future research This model highlights the need for managers to pay careful attention to both the underlying learning risks in collaborative relationships, and the appropriate governance mechanisms that are crafted in response. Inter-firm learning can present real opportunities in terms of accessing market knowledge, technical know-how, knowledge about partners and the partnering process, thereby enhancing the competitive position of exchange parties (Garratt, 1987; Kogut, 1988). At the same time, however, inter-firm learning can also lead to a de-skilling and dilution of the knowledge that forms the basis of competitive advantage. Because of its dual role, inter-firm learning cannot be treated as just another form of opportunistic behavior. Much valuable information is tacit in nature; close inter-firm relationships with dense ties and thick boundary spanning structures are associated with greater flows of tacit information. Many firms encourage inter-firm learning for its ability to offer win-win outcomes.
Key contribution
Prior research has not explicitly addressed the paradox inherent in inter-firm learning. Hence, one key contribution of this work is its explicit articulation of the presence of this paradox. Viewing the phenomenon of inter-firm learning as a paradox promotes divergent thinking which can generate enhanced understanding. A paradox embraces mutually contradictory elements that are equally necessary to convey a more imposing, illuminating . . . insight into truth than either [element] can in its own right (Lado et al., 1997, p. 112).
Understanding the duality inherent in inter-firm learning is particularly important for managers in today’s era of knowledge-based competition (Grant, 1996; Spender, 1996). Success in today’s business world often requires that firms pursue both competitive and cooperative strategies simultaneously (cf. Brandenburger and Nalebuff, 1996; Lado et al., 1997). Learning from others is a key way to access useful knowledge, and success is increasingly linked to the firm’s ability to leverage its knowledge through collaboration. Upside and downside views
A second key contribution of this work, with corresponding managerial implications, is the focus on governance. Articulating the upside and downside views of inter-firm learning highlights the role of inter-firm governance in moderating the relationship between the dynamic interplay of the firms’ learning intents and the degree of knowledge transfer that actually occurs. Governance can either aid in enhancing the unimpeded transfer of information or stymie such transfers. Appropriate governance structures
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must be crafted which match the learning intents of the parties involved, the type of knowledge sought, and the designed duration of the project. Indeed, rather than being explained solely by the type of partner (i.e. Japanese or US origins), the paradox of inter-firm learning is more likely to be explained by the differences in partner’s intent, the type of knowledge sought by the focal firm, and the duration of the collaboration. Our recommendations to managers on matching appropriate governance mechanisms to the underlying conditions are summarized in Table II. Different view of governance
The nature of the predictions for the type of governance mechanisms warranted under different learning conditions presents a somewhat different view of governance than traditional views on relationship marketing. For example, take the notion that despite the presence of cooperative norms, information sharing should be guarded where learning risks are high. This represents the reality that knowledge spillover can occur in alliances, and the partner may use that information in the future to their advantage. The ideas of ‘‘cooperate with caution’’, ‘‘share as needed’’ and ‘‘guard crown jewels’’ reflect these learning risks even in closely collaborative contexts. Hence, an important research implication arising from the development of this model is to assess learning risks in collaborative alliances. The ideas presented here warrant empirical testing from either a descriptive perspective (what governance structures are actually used under different combinations of the underlying conditions) or a prescriptive perspective (does matching governance structures to the underlying conditions effectively regulate knowledge transfer). Moreover, additional factors that affect the presence of the paradox would add richness to the model. There may be subtle but important differences in learning from a partner compared to teaching a partner. Similarly, there may be value in exploring issues – about how to learn from a partner as well as teaching a partner how to learn. We hope this paper will stimulate new research to facilitate better understanding of the paradox of inter-firm learning. Notes 1. In addition, some research on outcomes of strategic alliances has included ‘‘knowledge spillover’’, or knowledge from one partner that unintentionally ‘‘spills over’’ to the other during the course of the alliance, as part of the construct of inter-firm learning (Parkhe, 1993; Saxton, 1997). 2. The degree to which information shared between partners constitutes a unique, strategic asset is one that warrants close attention. Moreover, it may be that managers must act to protect deeply embedded assets because of the mere threat of dilution, even when the actual threat of a firm truly duplicating its skills and abilities is unlikely. 3. Some (e.g. Hamel, 1991) suspect that differences in the type of information shared in Japanese-U.S. partnerships gave rise to the disparity in outcomes, although this is disputed by others (Jones and Shill, 1993). 4. An alternative would be to convert tacit knowledge into explicit form (Nonaka, 1994). But, as Grant (1996, p. 116) notes, such conversion typically involves substantial knowledge loss, without shared understanding and common cognitive schema to interpret and use the information. References Aadne, J., von Krogh, G. and Roos, J. (1996), ‘‘Representationism: the traditional approach to cooperative strategies’’, in von Krogh, G. and Roos, J. (Eds), Managing Knowledge: Perspectives on Cooperation and Competition, Sage Publications, London, pp. 9-31. Anderson, E. and Weitz, B. (1992), ‘‘The use of pledges to build and sustain commitment in distribution channels’’, Journal of Marketing Research, Vol. 29, February, pp. 18-34. Badaracco, J.L. Jr (1991), The Knowledge Link, Harvard Business School Press, Boston, MA.
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An executive summary for managers and executive readers can be found at the end of this issue
A longitudinal study of the learning climate and cycle time in supply chains G. Tomas M. Hult Director, International Business Center and Associate Professor of Marketing & Supply Chain Management, Eli Broad Graduate School of Management, Michigan State University, East Lansing, Michigan, USA
David J. Ketchen, Jr Associate Professor of Management, Florida State University, College of Business, Tallahasse, Florida, USA
Stanley F. Slater Professor of Strategic Management and Marketing, Department of Business Administration, University of Washington, Bothell, Washington, USA
Keywords Supply chain, Cycle time, Organizational learning, Corporate culture Abstract Drawing on the resource-based view, we posit that the learning climate is an intangible, strategic resource that influences important outcomes. Data from 141 supply chain units within a multinational corporation reveal that four constructs (team-, systems-, learning-, and memory orientations) function as first-order indicators of the higher-order phenomenon of the learning climate. In turn, learning is inversely related to supply chain cycle time. The results are robust across the 1994 and 1999 data, suggesting that learning offers a persistent tool for managing outcomes.
Development of new knowledge
Organizations are forced to confront a variety of challenges, including unpredictable advances in technology and the globalization of many industries. Organizational learning has long been viewed by management scholars as crucial to overcoming such challenges (e.g. Cangelosi and Dill, 1965; Cyert and March, 1963). Organizational learning can be broadly defined as the development of new knowledge that has the potential to influence behavior in an organization (Huber, 1991; Hurley and Hult, 1998). Most research on learning is either conceptual (e.g. Argyris and Scho¨n, 1978; Simon, 1991) or adopts a cross-sectional empirical approach (e.g. Baker and Sinkula, 1999; Hult et al., 2000; Parkhe, 1991; Simonin, 1997). Few studies have investigated learning longitudinally (cf. Barkema and Vermeulen, 1998), yet such efforts are needed in order to gain a thorough understanding of the nature and extent of learning’s role in surmounting key organizational challenges. This paper is devoted to helping to fill this need. The context of our investigation is the supply chains involving a Fortune 500 multinational corporation (MNC) (cf. Hult et al., 2001; Monczka et al., 1998). A supply chain is defined as ‘‘the network of facilities and activities that performs the functions of product development, procurement of material from vendors, the movement of materials between facilities, the This longitudinal research has been supported since 1993 by the FedEx Center for Cycle Time Research and the Federal Express Corporation. The research register for this journal is available at http://www.emeraldinsight.com/researchregisters The current issue and full text archive of this journal is available at http://www.emeraldinsight.com/0885-8624.htm
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manufacturing of products, the distribution of finished goods to customers, and after-market support for sustainment’’ (Mabert and Venkataraman, 1998, p. 538). As such, a supply chain represents an organization of ‘‘linked suppliers and customers; every customer is in turn a supplier to the next downstream organization until a finished product reaches the ultimate end user’’ (Handfield and Nichols, 1999, p. 2). Given supply chains’ complex and dynamic nature, these organizations represent an area in which learning is a part of creating a positional advantage (Day and Wensley, 1988; Hult and Ketchen, 2001) in the form of what has been labeled as ‘‘cultural competitiveness’’ (Hult et al., 2001), ultimately leading to a competitive advantage in the marketplace (Day, 1994; Hult, 1998; Hult et al., 2000). Conversely, a corporation’s ability to provide value to external customers can be severely impeded by a dysfunctional supply chain system (Venkatesh et al., 1995), particularly involving MNCs where effective supply chain management can be constrained by cultural barriers (Kale, 1986). Thus, understanding what allows some supply chains to succeed while others fail may be valuable (Spekman et al., 1994; Venkatesh et al., 1995). Within each supply chain, we devote particular attention to the pivotal ‘‘sourcing unit’’ composed of a corporate buyer and an internal customer (Handfield and Nichols, 1999; McCabe, 1987). Supply chain functioning
Success and failure are characterized in this study in terms of cycle time. Cycle time is the duration of the period between the internal customer’s recognition of a need to the provision of a good or service to that customer (Narasimhan and Jayaram, 1998; Wetherbe, 1995). Our focus on cycle time lies in contrast to the usual focus on financial (e.g. return on assets) and market-based (e.g. stock price) performance found within organizational studies (e.g. Combs and Ketchen, 1999). Financial and market measures are driven by a variety of organizational traits and decisions (Capon et al., 1990), many of which lie far beyond the domain of the sourcing unit and the overall supply chain. In contrast, cycle time directly reflects the functioning of the supply chain and thus is a preferred measure (Handfield and Nichols, 1999; Hult, 1998). The resource-based view (RBV) offers a promising perspective for investigating the relationship between the learning climate and cycle time. The RBV posits that organizations should strive to improve their performance through amassing and utilizing ‘‘strategic’’ assets and capabilities (Chi, 1994; Hauser et al., 1996; Wernerfelt, 1984). Strategic resources are those that are valuable, rare, and difficult to imitate (Barney, 1991). Some are tangible (e.g. patents) while others are intangible (e.g. culture – Barney, 1986). A firm possessing strategic resources can develop competitive advantages over rivals lacking such resources and can leverage these advantages to gain sustained superior performance (Barney, 1991).
Second-order construct
Building on the RBV, we argue that the learning climate can serve as a strategic resource. Specifically, learning is viewed as an intangible resource that is deeply embedded in the fabric of an organization. Consistent with this intangibility, learning is conceptualized as a second-order construct that arises from four firstorder ‘‘orientations’’ introduced by Hult and colleagues: team, systems, learning, and memory orientations (e.g. Hult, 1998; Hult et al., 1995, 2000). We also contend that, as a strategic resource, learning influences the success of the supply chain (cf. Barney, 1986; Dyer and Singh, 1998). Specifically, to the extent that a supply chain is disposed toward developing new, behaviorenhancing knowledge, cycle time will be reduced. Accordingly, we develop hypotheses pertaining to the composition of the higher-order phenomenon of the learning climate and its subsequent effect on
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supply chain cycle time. The hypotheses are tested using data drawn from surveys obtained in 1994 and 1999 from internal customers within 141 supply chains of a Fortune 500 MNC. Theory and hypotheses Figure 1 identifies the key constructs and main relationships examined in the study. As shown, the learning climate is depicted as a higher, second-order construct arising from the first-order constructs of team, systems, learning, and memory orientations. Specifically, the proclivity of the sourcing units (i.e. corporate buyers and internal customers) in the supply chain toward being team, systems, learning, and memory-focused is proposed to give rise to an intangible resource (learning climate) which embodies the proclivity to develop new, behavior-enhancing knowledge (Handfield and Nichols, 1999; Hauser et al., 1996). This higher-order construct of the learning climate is expected to influence cycle time, one of the most important performance variables in the supply process (Bhaskaran, 1998; Handfield and Nichols, 1999; Wetherbe, 1995).
Resource-based view
The composition of the learning climate The theory underlying our model is the resource-based view of the firm (Wernerfelt, 1984). Although the roots of the RBV date back at least to the 1950s (e.g. Penrose, 1959), formal presentation of the perspective was offered in the mid-1980s. In a landmark piece, Wernerfelt (1984, p. 173) argued that resources (defined as ‘‘those assets that are tied semipermanently to the firm’’) are central to competition among firms. Further, strategic resources are much more important than others. Non-strategic resources such as slack capital are possessed by many firms and are easily bought and sold. Thus, owning non-strategic resources does not distinguish a firm’s ability to be competitive. In contrast, competitors struggle to duplicate or identify substitutes for strategic assets like patents and strong, positive reputations. Thus, possession of strategic resources serves as a powerful form of differentiation (Barney, 1991). We contend that ‘‘learning’’ is a strategic resource. To be strategic, a resource must meet three criteria (Barney, 1991). First, the resource must be valuable, meaning it helps create outputs that are important to customers. Learning appears to surmount this hurdle, particularly in the supply chain context
Figure 1. A conceptual model of the learning climate in supply chains: the perspective of the internal customer 304
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examined in this study. Meyer (1993) argues that implementing a fast cycle time climate requires adopting a paradigm focused on organizational learning, where flexibility, responsiveness, creativity, and timeliness are stressed. From this perspective, learning is a valuable resource to supply chains in that it subtly but persistently steers behavior toward effectively satisfying the needs of internal customers (cf. Hauser et al., 1996). Structural element of learning
A strategic resource must also be rare, meaning that the resource is found infrequently and that close substitutes are not available. Organizational learning is a complex concept, encompassing both a process and a structure. The process of learning refers to the development of new knowledge that has the potential to change behavior (Huber, 1991; Slater and Narver, 1995). The structural element of learning refers to the organization’s ability to implement behaviors suggested by the new wisdom it develops (Garvin, 1993). Thus, organizations stressing learning must learn and then behave accordingly to be effective. Relatively few organizations are able to meet these dual challenges (Slater and Narver, 1995). Further, other assets cannot easily substitute for learning, especially in supply chains (Hult, 1998; Hult et al., 2000). Indeed, Hult et al. (1995) suggest that learning is at the heart of sustained competitive advantage in the supply chain context. Finally, a strategic resource must be inimitable, meaning that purchasing or duplicating the resource is difficult. Learning appears to meet this third criterion for strategic resource status in that learning is ‘‘history-dependent’’ (Levitt and March, 1988). Organizations adapt their behavior based on interpretations of past organizational events. The behaviors that result from this process can be made evident to other organizations, but the idiosyncratic history that underlies learning cannot be duplicated (Levitt and March, 1988). Thus, learning appears to be inimitable. More specifically, learning is an intangible phenomenon; one that cannot be easily transferred or purchased because it is deeply embedded in the fabric of the organization (Barney, 1991; Meyer, 1993).
Higher-order construct
The intangibility of learning is at the core of our initial hypotheses. Because the learning climate is posited to be intangible, it is depicted in Figure 1 as a higher-order construct. Although a variety of first-order indicators are possible, our choice of indicators is driven by our study’s focus on the supply chain. Consistent with previous supply chain research, we adopt the four climate learning indicators suggested by Hult and colleagues (e.g. Hult, 1998; Hult et al., 2000; Hult et al., 1995): 1) Team orientation is defined as the degree to which the members of the focal supply chain organization stress collaboration and cooperation in performing supply chain activities and in making supply chain decisions. (2) Systems orientation is defined as the degree to which the members of the focal supply chain stress the interconnectedness and mutual dependence of the activities in the supply chain process. (3) Learning orientation is defined as the degree to which the members of the focal supply chain stress the value of learning for the long-term benefit of the supply chain organization and the specific strategic sourcing unit. (4) Memory orientation is defined as the degree to which the members of the focal supply chain stress the distribution and preservation of supply chain knowledge.
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Collectively contribute
We do not contend that team, systems, learning, and memory orientations are strategic resources independently, but rather that they can collectively contribute to the creation of a strategic resource. Specifically, these elements are each necessary, but not individually sufficient, for creating a strategic resource that we have labeled ‘‘learning climate’’. It is also important to note that the predictions linking team, systems, learning, and memory orientations with learning are not causal. Instead, they address the issue of construct validity. In other words, the first-order constructs are not expected to ‘‘cause’’ learning, but rather they are all necessary elements to develop the strategic resource that we have labeled the learning climate (cf. Bagozzi and Phillips, 1982; Jo¨reskog et al., 1999).
Closely aligned
In past studies of supply chains, the four ‘‘orientations’’ have been shown to exhibit good reliability and validity (e.g. Hult, 1998; Hult et al., 2000). In addition, a number of other cross-sectional studies on learning using similar frameworks and measurement properties support the use of constructs closely aligned with the four selected for our research (e.g. Baker and Sinkula, 1999; Menon et al., 1999; Sinkula et al., 1997). Based on the robustness of the four orientations in cross-sectional designs, we test the contention that these indicators also function as first-order indicators of the higher, second-order construct of the learning climate when using a longitudinal design. Stated in testable form: H1. Across time, team orientation is an indicator of the higher-order phenomenon of the learning climate within supply chains. H2. Across time, systems orientation is an indicator of the higher-order phenomenon of the learning climate within supply chains. H3. Across time, learning orientation is an indicator of the higher-order phenomenon of the learning climate within supply chains. H4. Across time, memory orientation is an indicator of the higher-order phenomenon of the learning climate within supply chains. Learning climate and cycle time Cycle time is defined as the duration of the supply chain process from initiation (the recognition of a need by the internal customer) to completion (the delivery of a product or service to satisfy the internal customer’s need) (Wetherbe, 1995). Cycle time is considered by many organizations to be a key indicator of supply chain performance and, as such, is increasingly viewed as important to the success of the overall corporation (Handfield and Nichols, 1999; Hult et al., 1995). Indeed, increased global competitive pressures and the shortening of product life cycles have led many firms to focus on reducing cycle times in order to maintain their edge in the marketplace (Griffin, 1997; Wetherbe, 1995).
Provide a finite set
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The resource-based view of the firm provides the theoretical basis for our model’s expectation that the learning climate reduces cycle time. Wernerfelt (1984) argued that the accumulation of resources explains important outcomes. Specifically, the outputs (i.e. products or services) that can be derived from a firm’s resource endowments are idiosyncratic. Each firm must attempt to provide value to customers through its outputs. As economic actors, however, potential customers are more strongly attracted to some firms’ outputs than to those of other firms (cf. Meyer, 1993). If possible, each firm would tailor outputs to uniquely fit each customer’s desires, but each firm is limited by its resource array to providing a finite set of potential outputs. As a result, performance differences emerge between competitors. JOURNAL OF BUSINESS & INDUSTRIAL MARKETING, VOL. 17 NO. 4 2002
The longevity of performance differences depends on how difficult it is to access resources similar to those owned by successful firms. Strategic resource
As described above, we argue that learning serves as a strategic resource within supply chain organizations. Accordingly, learning should be tied to important outcomes such as cycle time (Meyer, 1993). Supply chains are critical to a corporation’s ability to satisfy external customers. Supply chain members are aware of this (Mabert and Venkataraman, 1998), and thus they should take seriously the prospects offered by interaction with other members to develop and/or implement new knowledge. The resultant knowledge should serve to reduce cycle time, given cycle time’s status as a key objective in the supply chain process (Handfield and Nichols, 1999; Hult, 1998; Hult et al., 2000; Wetherbe, 1995). Thus, we believe that the second-order construct of learning climate and a key sourcing outcome, cycle time, are intimately intertwined longitudinally. Formally stated: H5. Across time, the learning climate in supply chains is inversely related to cycle time. Method Sample The sample used to test the hypotheses was drawn from the sourcing field managers (i.e. the internal customers) of an MNC’s strategic business units (SBUs). These field managers were sampled in both 1994 and 1999 to obtain longitudinal data on learning and cycle time involving the MNC’s supply chains. The SBUs represented by the field managers are a part of a major air express transportation company operating in more than 200 countries. The transportation system of the MNC includes a comprehensive network of regional ‘‘hubs’’ and sort centers, and over 1,300 service centers and airport facilities around the world.
Pivotal relationship
The study focused on examining learning and cycle time in the MNC’s supply chains based on the perceptions of the corporation’s internal SBU customers. As such, we echo Barkema and Vermeulen’s (1998) view that the study of MNCs can be advanced by examining elements of learning relative to the MNC’s operations (such as our focus on supply chain management). The internal SBU customers were asked to assess learning in the relationship with their assigned corporate buyer. This is perhaps the most pivotal relationship within the supply chain (Handfield and Nichols, 1999; McCabe, 1987). Each internal customer sampled was designated to a different corporate buyer in the MNC’s ‘‘strategic sourcing and supply unit’’ – a unit composed of 400 corporate buyers. In the dyadic relationship between the internal SBU customer and the corporate buyer, the roles of each party are determined by the MNC’s formal sourcing policy. The role of the corporate buyer includes: .
to assume leadership of the activities in the supply chain;
.
to maintain contact/relationship with suppliers; and
.
to be responsible for the implementation of supply chain objectives in line with overall corporate objectives.
The SBU field manager is charged with the responsibilities of: .
making the initial sourcing request to the corporate buyer; and
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.
Direct examination
working with the corporate buyer to achieve the supply chain objectives broadly determined by the initial request while achieving the overall corporate objectives set forth by the MNC.
Studying the supply chains involving the internal supply chain participants (corporate buyer and internal SBU customer) of one MNC allows for the direct examination of learning phenomena without the potentially confounding effects of variation in practices that would appear across multiple corporations (Harrigan, 1985). Following the completion of pretests with eight academics and seven sourcing executives and a pilot study of 36 sourcing executives, the survey was mailed internally in 1994 to the sourcing representatives of the MNC’s 416 worldwide SBUs. English was used for all of the questionnaires since English-based questionnaires are commonly used in internal communication and research conducted in the MNC. The response rate in 1994 was 83.2 percent; 346 of the 416 targeted internal customers responded to the questionnaire. The same respondents were targeted in 1999 in order to obtain longitudinal data. Of the original 346 respondents, 141 both were in the same position as in 1994 and responded to the survey. This response rate of 40.8 percent of the original respondents compares favorably with rates found in similar studies (e.g. Menard, 1991). Only the respondents who completed both the 1994 and 1999 surveys were included in our study to focus on the longitudinal aspects of the learning climate and cycle time. Measurement The Appendix contains the indicators used to measure the learning climate and cycle time. The results of the measurement analysis are presented in Tables I-IV. Table I summarizes the correlations between all study variables in the 1994 sample. Table II provides corresponding correlations for the 1999 sample. Table III reports the means, standard deviations, and item comparisons between the 1994 sample and the 1999 sample. Table IV summarizes the factor loadings and t-values for the two samples (including the two-sample multi-sample analysis as well as the analyses of the 1994 and 1999 samples independently). In addition, the composite reliabilities, average variances extracted, shared variances, fit indices, and a comparison among each item across the two samples are reported in Table IV. Overall, the measurement scales exhibited good reliability and validity across the 1994 and 1999 samples.
Scale of learning
The learning climate was measured via a set of 17 items adopted from Hult and colleagues (e.g. Hult, 1998; Hult et al., 2000) via the four subdimensions of team, systems, learning-, and memory orientations (cf. Baker and Sinkula, 1999; Sinkula et al., 1997). The overall scale of learning assesses the degree to which the corporate buyer and internal SBU customer in their dyadic relationship emphasize: cross-functional teamwork; the broad functions of the supply chain process; the value of organizational learning; and distribution of supply chain knowledge among participants in the system. Four items were used to measure team orientation as a first-order construct of the learning climate. Attributes such as ‘‘team spirit’’, ‘‘cross-functional teamwork’’, ‘‘commonality of purpose’’, ‘‘organizational vision’’ and ‘‘sharing . . . vision’’ were used to assess the degree to which the corporate buyer and the internal SBU customer stress collaboration and cooperation in performing supply chain activities and in making supply chain decisions.
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1.00 0.65 0.44 0.38 0.48 0.31 0.26 0.35 0.28 0.24 0.37 0.32 0.26 0.23 0.24 0.30 0.28 0.15 0.34 0.41 0.33 0.40 0.41
1.00 0.47 0.44 0.50 0.33 0.28 0.38 0.33 0.29 0.37 0.39 0.28 0.20 0.21 0.32 0.24 0.17 0.33 0.34 0.32 0.35 0.34
TO1 TO2
1.00 0.66 0.51 0.48 0.45 0.52 0.49 0.23 0.33 0.33 0.24 0.27 0.28 0.29 0.35 0.22 0.40 0.33 0.41 0.42 0.34
TO3
1.00 0.46 0.41 0.31 0.51 0.41 0.20 0.31 0.28 0.20 0.27 0.24 0.31 0.36 0.16 0.34 0.30 0.37 0.43 0.29 1.00 0.48 0.42 0.53 0.46 0.30 0.37 0.33 0.30 0.37 0.33 0.35 0.37 0.21 0.37 0.41 0.42 0.42 0.44 1.00 0.68 0.56 0.65 0.14 0.26 0.20 0.24 0.42 0.33 0.37 0.41 0.18 0.26 0.22 0.32 0.31 0.24
TO4 TO5 SO1
1.00 0.58 0.65 0.08 0.23 0.22 0.25 0.36 0.30 0.31 0.38 0.17 0.31 0.29 0.31 0.30 0.29
SO2
1.00 0.73 0.18 0.26 0.29 0.21 0.27 0.24 0.32 0.46 0.18 0.38 0.34 0.47 0.47 0.40
SO3
1.00 0.20 0.25 0.21 0.20 0.36 0.28 0.34 0.43 0.20 0.33 0.32 0.39 0.35 0.34
SO4
1.00 0.45 0.39 0.49 0.19 0.16 0.12 0.17 0.13 0.26 0.23 0.27 0.25 0.24
LO1
1.0 0.51 0.53 0.19 0.17 0.25 0.28 0.08 0.25 0.33 0.30 0.24 0.30
LO2
1.0 0.50 0.12 0.12 0.15 0.22 0.08 0.28 0.24 0.24 0.25 0.23
LO3
1.0 0.19 0.22 0.16 0.23 0.17 0.29 0.19 0.30 0.22 0.20
LO4
1.00 0.55 0.34 0.45 0.11 0.15 0.21 0.16 0.17 0.22
MO1
1.00 0.27 0.33 0.12 0.19 0.16 0.18 0.19 0.19
MO2
1.00 0.34 0.15 0.21 0.24 0.23 0.25 0.24
MO3
1.00 0.12 0.21 0.24 0.24 0.27 0.24
MO4
1.00 0.40 0.18 0.31 0.34 0.22
CTOBJ
1.00 0.63 0.72 0.73 0.55
CT1
1.00 0.61 0.64 0.80
CT2
1.00 0.83 0.60
CT3
1.0 0.65
CT4
1.00
CT5
Table I. Correlation matrix for the 1994 data (n = 141)
Notes: All correlations are significant at the p < 0.05 level except the CTOBJ correlations below 0.10. For readability, the negative signs between the objective cycle time item (CTOBJ) and the reverse-coded subjective cycle time items (CT1 to CT5), and the items in the the learning climate scale are not included in the matrix
TO1 TO2 TO3 TO4 TO5 SO1 SO2 SO3 SO4 LO1 LO2 LO3 LO4 MO1 MO2 MO3 MO4 CTOBJ CT1 CT2 CT3 CT4 CT5
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1.00 0.85 0.82 0.69 0.74 0.80 0.71 0.66 0.72 0.49 0.49 0.39 0.47 0.62 0.62 0.55 0.65 0.14 0.45 0.49 0.48 0.46 0.48
1.00 0.82 0.67 0.66 0.77 0.77 0.64 0.70 0.55 0.54 0.36 0.49 0.61 0.61 0.62 0.64 0.14 0.46 0.49 0.47 0.46 0.46
1.00 0.69 0.74 0.75 0.71 0.63 0.70 0.57 0.53 0.34 0.48 0.61 0.59 0.56 0.70 0.04 0.49 0.52 0.51 0.50 0.50
TO3
1.00 0.75 0.84 0.69 0.68 0.72 0.47 0.52 0.25 0.43 0.65 0.56 0.52 0.69 0.03 0.46 0.51 0.49 0.56 0.56 1.00 0.80 0.75 0.67 0.68 0.54 0.53 0.37 0.47 0.65 0.63 0.61 0.76 0.02 0.45 0.49 0.47 0.58 0.56 1.00 0.79 0.79 0.83 0.50 0.56 0.32 0.47 0.72 0.63 0.63 0.72 0.05 0.53 0.59 0.56 0.61 0.62
TO4 TO5 SO1
1.00 0.72 0.74 0.61 0.62 0.49 0.49 0.64 0.63 0.65 0.67 0.06 0.44 0.44 0.43 0.48 0.46
SO2
1.00 0.86 0.50 0.49 0.24 0.37 0.64 0.59 0.58 0.65 0.03 0.53 0.50 0.48 0.51 0.55
SO3
1.00 0.52 0.57 0.34 0.49 0.69 0.67 0.65 0.70 0.01 0.54 0.61 0.59 0.58 0.60
SO4
1.00 0.73 0.62 0.52 0.50 0.52 0.54 0.49 0.05 0.47 0.46 0.45 0.40 0.39
LO1
1.0 0.70 0.59 0.57 0.56 0.62 0.52 0.12 0.49 0.48 0.46 0.45 0.43
LO2
1.0 0.53 0.38 0.35 0.45 0.33 0.08 0.24 0.28 0.23 0.28 0.17
LO3
1.0 0.60 0.47 0.49 0.48 0.11 0.36 0.40 0.37 0.45 0.35
LO4
1.00 0.72 0.66 0.80 0.10 0.54 0.57 0.50 0.58 0.54
MO1
1.00 0.77 0.78 0.20 0.50 0.51 0.49 0.50 0.49
MO2
1.00 0.72 0.29 0.46 0.51 0.43 0.46 0.41
MO3
1.00 0.14 0.58 0.61 0.56 0.65 0.62
MO4
1.00 0.17 0.17 0.24 0.22 0.19
CTOBJ
1.00 0.86 0.83 0.68 0.78
CT1
1.00 0.87 0.76 0.77
CT2
1.00 0.80 0.86
CT3
CT5
1.0 0.77 1.00
CT4
Table II. Correlation matrix for the 1994 data (n = 141)
Notes: All correlations are significant at the p < 0.05 level except the CTOBJ correlations below 0.10. For readability, the negative signs between the objective cycle time item (CTOBJ) and the reverse-coded subjective cycle time items (CT1 to CT5), and the items in the the learning climate scale are not included in the matrix
TO1 TO2 TO3 TO4 TO5 SO1 SO2 SO3 SO4 LO1 LO2 LO3 LO4 MO1 MO2 MO3 MO4 CTOBJ CT1 CT2 CT3 CT4 CT5
TO1 TO2
Item
1994 sample Means SD
1999 sample Means SD
Mean 1994 vs. 1999
TO1 TO2 TO3 TO4 TO5
4.05 3.95 3.78 3.25 3.71
1.56 1.48 1.41 1.38 1.53
3.73 3.73 3.69 3.24 3.73
1.76 1.69 1.76 1.73 1.70
p < 0.05 ns ns ns ns
SO1 SO2 SO3 SO4
3.35 3.91 3.01 3.20
1.61 1.65 1.50 1.47
3.38 3.85 3.23 3.31
1.71 1.74 1.68 1.70
ns ns ns ns
LO1 LO2 LO3 LO4
4.66 4.55 4.97 4.94
1.36 1.43 1.38 1.31
4.57 4.49 5.04 4.59
1.82 1.74 1.69 1.85
ns ns ns p < 0.05
MO1 MO2 MO3 MO4
3.73 3.92 3.66 3.27
1.50 1.50 1.58 1.62
3.54 3.54 3.98 3.47
1.60 1.70 1.72 1.59
ns p < 0.05 p < 0.05 ns
CTOBJ CT1 CT2 CT3 CT4 CT5
48.4 3.69 3.91 3.53 4.15 3.45
53.5 1.28 1.33 1.50 1.36 1.52
36.7 3.29 3.49 3.15 3.33 3.08
44.3 1.54 1.67 1.61 1.83 1.59
p p p p p p
< < < < < <
0.05 0.05 0.05 0.05 0.05 0.05
Table III. Means and standard deviations of scale items
Focal supply chain
Four items composed the systems orientation scale. Attributes such as ‘‘interconnectedness’’, ‘‘value chain’’, ‘‘activities . . . clearly defined’’, and ‘‘understand . . . activities’’ were used to measure systems orientation as the degree to which the corporate buyer and the internal user in the focal supply chain stress the broad picture of the activities in the supply chain process and the justification that certain activities exist. Four items were also used to capture learning orientation. Learning orientation was assessed using attributes such as ‘‘ability to learn is a key to improvement’’, ‘‘values . . . include learning’’, ‘‘once we quit learning . . . we endanger our future’’, and ‘‘learning is an investment’’ to measure the degree to which the corporate buyer and the internal user in the focal supply chain stress the value of learning for the long-term benefit of the supply chain process and the specific strategic sourcing unit.
Specific mechanisms
Memory orientation, the fourth learning climate subdimension, incorporated four attributes also. Items reflecting elements such as ‘‘we have specific mechanisms for sharing lessons’’, ‘‘we audit unsuccessful endeavors’’, ‘‘organizational conversation’’, and ‘‘formal routines’’ to measure the degree to which the corporate buyer and the internal user in the focal supply chain stress communication and distribution of supply chain knowledge. The construct of cycle time was measured both subjectively and objectively to obtain data which comprehensively assessed the intricacies of the construct. The subjective items were adopted from Hult (1998) and Hult et al. (2000). The items were reverse coded to conform to the objective cycle time indicator that was measured in number of days. The subjective items asked respondents about attributes such as ‘‘length of the sourcing process’’, ‘‘improvement in the cycle time . . . recently’’, ‘‘satisfied with the
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Item
Independent sample analysis Multi-sample analysis 1994 1999 1994/1999 1994 1999 sample sample (equal ’s) sample sample
TO1 TO2 TO3 TO4 TO5 Composite reliability Variance extracted Shared variance range
0.66 0.68 0.76 0.70 0.73 0.83 49.8% 6.9-35.6%
0.90 0.88 0.88 0.82 0.84 0.94 74.8% 37.8-77.4%
0.78 0.79 0.82 0.76 0.79 0.89 62.0%
0.79 0.83 0.90 0.82 0.86 0.90 65.0%
0.77 0.76 0.77 0.73 0.75 0.88 60.4%
SO1 SO2 SO3 SO4 Composite reliability Variance extracted Shared variance range
0.78 0.77 0.80 0.85 0.88 64.5% 4.3-35.6%
0.93 0.85 0.86 0.90 0.94 78.5% 45.8-77.4%
0.86 0.81 0.84 0.88 0.91 71.5%
0.94 0.91 0.94 0.99 0.93 76.0
0.81 0.75 0.77 0.81 0.90 68.0%
LO1 LO2 LO3 LO4 Composite reliability Variance extracted Shared variance range
0.62 0.75 0.68 0.72 0.79 48.3% 4.7-24.2%
0.81 0.91 0.75 0.67 0.87 62.3% 20.6-42.5%
0.72 0.83 0.71 0.70 0.83 55.0%
0.69 0.86 0.73 0.75 0.84 56.0%
0.74 0.81 0.69 0.66 0.82 54.0%
MO1 MO2 MO3 MO4 Composite reliability Variance extracted Shared variance range
0.71 0.61 0.53 0.66 0.72 39.5% 2.1-30.7%
0.86 0.86 0.81 0.91 0.92 74.3% 27.4-63.7%
0.78 0.74 0.69 0.79 0.84 56.3%
0.84 0.72 0.63 0.81 0.84 56.3%
0.76 0.75 0.71 0.78 0.84 56.5%
CT objective CT1 CT2 CT3 CT4 CT5 Composite reliability Variance extracted Shared variance range
0.37 0.81 0.75 0.88 0.91 0.74 0.89 58.5% 2.1-6.9%
0.22 0.89 0.92 0.95 0.84 0.89 0.92 67.7% 20.6-46.8%
0.29 0.84 0.85 0.90 0.86 0.83 0.90 62.9%
0.38 0.87 0.85 0.94 0.94 0.83 0.91 64.7%
0.21 0.82 0.85 0.88 0.80 0.83 0.90 61.5%
t-value range (p < 0.01) Delta2 RNI CFI 2 df
4.35-13.62 0.97 0.97 0.97 266 220
2.61-15.01 0.90 0.90 0.90 560 220
4.42-14.45 0.86 0.86 0.86 1,172 496
4.74-19.24 0.86 0.86 0.86 1,156 473
Notes: 2 = 1,172-1,156 = 16; df = 496-473 = 23 not significant; all loadings (TO, SO, LO, MO and CT) were found to be equal using the 2 test. The 2 ranged from 0.00 to 2.12, which is below the 3.84 threshold for significance at the p < 0.05 level with df = 1
Table IV. Scale item loadings
speediness’’, ‘‘involving the corporate buyer in the decision making’’, ‘‘based on our knowledge [as internal SBU customers] of the sourcing process, we think it is short and efficient’’. The objective cycle time item was 312
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based on company records of the actual cycle time of the supply chain process involving a specific strategic sourcing unit. Collectively, the subjective and objective cycle time items were used to measure the time it takes from initiation to completion of the supply chain process. Confirmatory factor analysis
After the data were collected, the measures were subjected to a series of reliability and validity assessments. The psychometric properties of the five constructs were evaluated by using the method of confirmatory factor analysis (CFA) via the use of LISREL (Jo¨reskog and So¨rbom, 1996; Jo¨reskog et al., 1999). To rigorously assess the reliability and validity of the measures, we conducted one multi-sample CFA in LISREL (using the correlation matrices from the 1994 and 1999 samples as independent input into the same ‘‘multi-sample’’ model). In addition, we also performed independent CFA analyses on the two samples. The model fits were evaluated using the DELTA2 index (Bollen, 1989), the relative noncentrality index (RNI) (McDonald and Marsh, 1990), and the comparative fit index (CFI) (Bentler, 1990). These fit indices have been shown as the most stable fit indices in LISREL models by Gerbing and Anderson (1992). The specific items are evaluated based on the item’s error variance, modification index (< 3.84), and residual covariation (< | 2.58 | ) (e.g. Anderson and Gerbing, 1988; Fornell and Larcker, 1981; Jo¨reskog and So¨rbom, 1996; Jo¨reskog et al., 1999).
Parameter estimates
Utilizing these criteria, the two-sample CFA model resulted in a good fit to the data (DELTA2 = 0.86, RNI = 0.86, CFI = 0.86, 2 = 1,171.52, df = 496) when constraining the parameter estimates to be the same across the two samples (i.e. loadings, factor correlations, and error variances). Thus, this analysis reveals two solutions, one for each of the 1994 and 1999 samples, but the parameter estimates are identical. To test the validity of the constrained model, we allowed the factor loadings to be different across the two samples. Allowing the loadings to be estimated independently from each other in the two samples resulted in similar fit statistics (DELTA2 = 0.86, RNI = 0.86, CFI = 0.86, 2 = 1,155.81, df = 473). Using the 2-difference test suggested by Anderson and Gerbing (1988), we find that the constrained and unconstrained measurement models do not differ (2 = 15.71, df = 23). Similarly, no difference is found between the models when the error variances of the items were allowed to be freely estimated across samples in addition to the loadings (2 = 1,155.80, df = 471), or when the loadings are invariant but the error variances are allowed to be different across samples (2 = 1,170.99, df = 493).
Reliability and validity
To provide for an additional layer of rigor in assessing the measurement properties, we also conducted separate CFAs on the 1994 sample (DELTA2 = 0.97, RNI = 0.97, CFI = 0.97, 2 = 265.88, df = 220) and the 1999 sample (DELTA2 = 0.90, RNI = 0.90, CFI = 0.90, 2 = 559.69, df = 220); both models resulted in excellent fit indices. Based on the multi-sample analysis and the independent analyses of the 1994 and 1999 samples, the structure of the four dimensions of the learning climate and the unidimensional cycle time construct can be considered robust across the longitudinal data in this study. Thus, we can now continue by assessing the robustness of each item included in the study, followed by the calculation of the reliability and validity of the individual constructs. After conducting the analysis of general differences among the 1994 and 1999 samples, we examined the potential differences between each of the item loadings across the sample groups. This test involves constraining
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appropriate pairs of estimates, one pair at a time, to be equal and different across the two groups, and then evaluating whether the resulting change in the 2 value is significant with one degree of freedom (Bagozzi and Heatherton, 1994; Jo¨reskog and So¨rbom, 1996). The results indicate that all 23 items were robust across the 1994 and 1999 surveys. The 2 ’s ranged from 0.00 to 2.12 (with 2’s ranging between 1,169.40 to 1,171.52) with a df = 496-495 = 1, which is lower than the 3.84 to be significant at the p < 0.05 level. Assessed average variance
Next, we assessed the reliability of the measures. Within the CFA setting, composite reliability is calculated using the procedures outlined by Fornell and Larcker (1981) based work by Werts et al. (1974). The formula P on the P specifies that: CR = ( i )2/[( "i Þ, where CR = composite reliability for scale ; yi = standardized loading for scale item i , and "i = measurement error for scale item i . We also examined the parameter estimates and their associated t-values, and assessed the average variance extracted for each construct (Anderson and Gerbing, 1988). As is shown in Table III, the reliabilities for team, systems, learning, and memory orientations and cycle time range between 0.66 and 0.94, indicating acceptable levels of reliability for the 1994 and 1999 surveys (Fornell and Larcker, 1981). In fact, the memory orientation scale in the 1994 sample is the only scale below a composite reliability of 0.79, suggesting that the scales’ reliabilities are excellent (Gerbing and Anderson, 1992).
Five individual constructs
Discriminant validity was established by calculating the shared variance between all possible pairs of constructs and verifying that it was lower than the average variances extracted for the five individual constructs (Fornell and Larcker, 1981; Jo¨reskog et al., 1999). The shared variance was calculated as: 2 = 1 – , where 2 = shared variance between constructs, and with the diagonal element of indicating the amount of unexplained variance. Since and " are standardized, 2 is equal to the r2 between the two constructs. Average variance extracted P was calculated using the P P following formula: V ¼ i2 /( i2 þ "i ) where V = average variance extracted for ; yi i = standardized loading for scale item i , and "i = measurement error for scale item i . The shared variances between pairs of all possible scale combinations ranged from a low of 2.1 percent to a high of 77.4 percent between the various scale combinations (Table III). The average variances extracted ranged between 39.5 percent and 78.5 percent, with all but team orientation in the 1999 sample having higher average variances extracted than the shared variances among all possible pairs of scales (Table III). Thus, the measurement scales can be considered reliable, valid, and stable across the 1994 and 1999 surveys used in this study.
Test of potential differences
Hypothesis tests and results The results of the hypothesis tests are provided in Table V, including the parameter estimates for both the 1994 and 1999 data points as well as the test of potential differences across samples. Initially, testing of the hypothesized relationships was accomplished through a multi-sample (two-group), higherorder structural equation model (SEM) via the use of LISREL (Jo¨reskog and So¨rbom, 1996; Jo¨reskog et al., 1999). Next, segmented SEM analyses were conducted for the 1994 and 1999 samples. Overall model testing In the multi-sample analysis, four structural equation models were analyzed – simultaneously involving the two groups of 1994 and 1999 responses to assess the stability of the hypothesized model parameters across time. To clarify, the two data points (1994 and 1999) were not averaged but instead
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Hypothesis H1. Team orientation
/ + Learning climate
H2. System orientation
/ + Learning climate
H3. Learning orientation
/ + Learning climate
H4. Memory orientation
/ + Learning climate
H5. Learning climate
/ – Cycle time
Note: All parameter estimates are
1994 sample (n = 141) Significant loading = 0.94 t-value = 2.69 Significant loading = 0.78 t-value = 5.92 Significant loading = 0.61 t-value = 5.18 Significant loading = 0.71 t-value = 5.09 Significant loading = 0.66 t-value = j6.11j significant at p <
1999 sample (n = 141) Significant loading = 0.95 t-value = 5.71 Significant loading = 0.97 t-value = 3.83 Significant loading = 0.72 t-value = 7.13 Significant loading = 0.89 t-value = 7.49 Significant loading = 0.69 t-value = j7.42j 0.01 level
2 1994/1999 Not significant 2 = 0.10 Not significant 2 = 0.02 Not significant 2 = 0.81 Significant 2 = 6.67 p < 0.01 Not significant 2 = 0.04
Table V. Summary of the hypothesis testing
the correlation matrix of each dataset was used as input into the same multisample run of LISREL. Thus, the 1994 data were directly tested against the 1999 data to analyze the effects of the higher-order construct of the learning climate on cycle time longitudinally. Effect on cycle time
In MODEL1, the model parameters were assumed as equal in the two subgroups (i.e. loadings, factor correlations, and error variances), similar to the reliability and validity testing of the CFA models. The 2 statistic for this two-sample model was 1,182.20 (df = 496). However, to test the developed framework, a need existed to determine if the impact of the four dimensions of the learning climate and its effect on cycle time varied between the 1994 and 1999 surveys. As such, we determined if the loadings were different within the two sample groups, while retaining the invariance of the factor correlation and the error variances (MODEL2). Thus, the only difference between MODEL2 and MODEL1 was that the relationships in MODEL2 were estimated independently from each other in the 1994 and 1999 groups. The 2 value of MODEL2 was 1,123.99 (df = 468). Similar to the CFA analysis, the difference between MODEL1 and MODEL2 was determined by comparing the difference in 2 values for the two multisample models (Anderson and Gerbing, 1988). Anderson and Gerbing (1988) state that the 2 differences can then be tested for statistical significance with the appropriate degrees of freedom being the difference in the number of estimated coefficients for the models, with the assumption that they are nested models. The 2 for MODEL2 was significantly lower than that of MODEL1, suggesting a difference may exist between the 1994 and 1999 survey data in terms of the composition of the learning climate and its effect on cycle time (2 difference = 1,182.20-1,123.99 = 58.21, dfdifference = 496-468 = 28, p < 0.01).
Final data analysis
To further check the validity of the results, the two-group model was also allowed to have both the loadings and error variances vary between the groups (MODEL3). In MODEL3, the p-value was 847.62 (df = 443). The difference between MODEL2 and MODEL3 was also significant (2 difference = 1,123.99-847.62 = 276.37, dfdifference = 468-443 = 25, p < 0.01), indicating the possibility of the error variances being different between sample groups. As a final analysis of the data, we constrained the loadings and factor
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correlations to be the same in the two groups, while the error variances were allowed to be different (MODEL4). The 2 statistic for MODEL4 was 895.96 (df = 471). Based on this analysis, MODEL2 and MODEL4 were significantly different (2 difference = 1,123.99-895.96 = 228.03, dfdifference = 468-471 = |3|, p < 0.01). MODEL3 and MODEL4 were also significantly different (2 difference = 847.62-895.96 = |48.34|, dfdifference = 443-471 = |28|, p < 0.01). These results indicated that potential differences exist between the 1994 and 1999 surveys, but primarily only in error variances between samples. As such, we conducted separate LISREL analyses on the 1994 and 1999 data to assess the intricacies of the model relationships in greater detail.
Positive indicator
Relative ability
Specific hypothesis testing The hypothesized model (Figure 1) resulted in a good fit to the data in both the 1994 data (DELTA2 = 0.97, RNI = 0.97, CFI = 0.97, 2 = 276.20, df = 225) and the 1999 data (DELTA2 = 0.90, RNI = 0.90, CFI = 0.90, 2 = 571.42, df = 225). The results provided support for H1, suggesting that team orientation is a positive indicator of the second-order construct of the learning climate. The factor loadings were high in both the 1994 sample (loading = 0.94, t-value = 2.63, p < 0.01) and the 1999 sample (loading = 0.95, t-value = 5.71, p < 0.01). Systems orientation was furthermore a strong indicator of the learning climate in both the 1994 (loading = 0.78, t-value = 5.92, p < 0.01) and 1999 (loading = 0.97, t-value = 3.83, p < 0.01) samples and, thus, H2 was supported. Similarly, learning orientation was also a positive indicator of the learning climate in both samples (loadings = 0.61, 0.72, t-values = 5.18, 7.13, p < 0.01), and H3 was supported. The final first-order indicator of the learning climate, memory orientation, was also found to be a good indicator of the higher-order construct (loadings = 0.66, 0.69, t-values = 6.11, 7.42, p < .01); thus, H4 was supported. Based on the 1994 survey, the relative ability of the second-order construct of the learning climate to explain variation in the hypothesized first-order indicators of team, systems, learning, and memory orientations, as measured by the R2-value, were 88 percent, 61 percent, 37 percent, and 51 percent respectively. Corresponding results for the 1999 sample were 90 percent, 94 percent, 52 percent, and 79 percent respectively. The results also provided support for H5, suggesting that the higher-order construct of the learning climate has a negative relationship with cycle time. This relationship has a loading of –0.66 (t-value = |6.11|) in the 1994 survey and a loading of –0.69 (t-value = |7.42|) in the 1999 survey. The relative ability of the higher-order construct of the learning climate to explain variation in cycle time performance was 43 percent (1994 sample) and 47 percent (1999 sample). Asymmetries in effects between the 1994 and 1999 survey The asymmetries in effects between the 1994 and 1999 data were tested via a two-step process. First, the effect of each indicator of the learning climate was examined in both subgroups to understand the significant pattern of results. In both the 1994 and 1999 samples, all four first-order constructs (team, system, learning, and memory orientations) were significant. Second, since all indicators were significant in both samples, we compared the loadings across the two groups. The test involved constraining appropriate pairs of estimates, one pair at a time, to be equal and different across the two groups, and then evaluating whether the resulting change in the 2 value was significant with one degree of freedom (Bagozzi and Heatherton, 1994; Jo¨reskog and So¨rbom, 1996). The results indicate that the loadings of team, systems, and learning orientations did not differ significantly across the 1994
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and 1999 samples (Table V). However, the loadings involving memory orientation differed across the two samples (2difference = 6.67, p < 0.01). Memory orientation was a stronger indicator of the learning climate in 1999 than in 1994 (loadings = 0.71 vs. 0.89). As such, the difference in explanatory power found in the multi-sample analysis of the overall model can be traced to the difference between the loadings involving memory orientation in the 1994 and 1999 analyses. Additionally, the effect of the learning climate on cycle time did not differ across samples (Table V). Overall, team, systems, learning, and memory orientations were significant first-order constructs of the higher-order intangible phenomenon of the learning climate, which, in turn, significantly affected cycle time in the supply chain longitudinally. Discussion The usual caveats relevant to survey research apply to our study. Given our heavy reliance on survey data, common method bias may have influenced our results. However, the consistency of the results across the 1994 and 1999 data makes it more likely that actual relationships were assessed than if only one survey were conducted. Another concern is that our data were gathered from the supply chains involving one large multinational corporation. Although focusing on one corporation allowed us to avoid the potential confounding effects of variation in corporate practices (Harrigan, 1985), the possible generalization of the results beyond this corporation’s supply chains should be approached with caution. Overcome challenges
Despite these limitations, the study is a valuable addition to an important stream of research. Organizational learning has long been viewed as an important tool for organizations in their efforts to overcome challenges and enhance success (e.g. Cangelosi and Dill, 1965; Cyert and March, 1963). After four decades of research on learning, new approaches may represent a means to generate new insights (Thomas et al., 1997). Accordingly, our study departs from the traditions developed in learning research in several ways. Our study: .
relies on the theoretical foundation provided by the resource-based view of the firm;
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uses a longitudinal design;
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adopts the supply chain as the unit of analysis; and
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assesses success in terms of cycle time.
The results supported our predictions that four first-order constructs (team, systems, learning, and memory orientations) would give rise to a second-order construct (the learning climate), which in turn would be inversely related to supply chain cycle time longitudinally. Below, we discuss the implications of our findings. We then offer several suggestions for future research.
Resource-based view
Implications of the findings H1-H4 each predicted that a particular first-order construct would help give rise to the second-order construct labeled the learning climate. These hypotheses were grounded in the resource-based view of the firm. Each of the hypotheses was supported, thus our study can be viewed as offering empirical support for the resource-based view. As Miller and Shamsie (1996) note, the theoretical development of the resource-based view has substantially outpaced empirical examination. Studies of strategic resources that are intangible (such as the learning climate) are particularly scarce, perhaps because the predominantly logical positivist orientation of the
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management field discourages researchers from conducting such studies (Godfrey and Hill, 1995). To overcome the unique challenges presented by learning’s intangibility, we built on the foundation laid by Barkema and Vermeulen’s (1998) study of international expansion. In both studies, constructs that are theoretical antecedents to learning were used as indicators. We extended this approach by using structural equation modeling to investigate the presence of the latent construct of learning. Studies such as this and that of Barkema and Vermeulen (1998) provide indirect, but much needed, evidence about the nature and effects of learning within organizations.
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Relative magnitudes
Insights can be gleaned by examining the relative magnitudes of the loadings of the four first-order indicators on learning. Although all four had significant loadings, the loadings associated with team orientation and systems orientation were particularly strong (0.95 in 1994, 0.95 in 1999 for the former; 0.78 in 1994, 0.97 in 1999 for the latter). The context of our investigation may explain the relative strength of these two constructs. Supply chains consist of interwoven functions that depend on each other in order for the chain as a whole to operate efficiently (Mabert and Venkataraman, 1998). Given these conditions, the ability to learn seems likely to rely heavily on participants’ willingness to collaborate (team orientation) and to adopt a system-wide, broad view (systems orientation).
Stronger indication
In other contexts, learning and memory orientations might be stronger indicators of learning. For example, in a high technology setting with vast information processing requirements, such as space exploration (Hurley and Hult, 1998) and aircraft carriers (Weick and Roberts, 1993), learning might be driven largely by organizational members’ abilities to first constantly monitor and interpret the environment and, second, share and preserve the insights developed. Thus, while we anticipate that learning may usually depend on the presence of all four orientations, specific orientations may be more important under particular sets of circumstances (cf. memory orientation’s increased importance in 1999 compared with 1994). From a practical perspective, managers seeking to enhance learning within their organizations may be wise to analyze their organization’s context and then use reward systems to encourage the most critical orientations. Given the importance of team and systems orientation in our study, for example, the managers of supply chains might tie each employee’s raises and bonuses to the entire chain’s productivity and to the individual’s responsiveness to others’ needs.
Straightforward implication
The results related to H5 revealed that learning was inversely related to supply chain cycle time. This finding has both practical and theoretical importance. The implication for managers is straightforward – if a supply chain is disposed toward developing new, behavior-enhancing knowledge, cycle time may be reduced. This is a valuable piece of information for managers, given the increasing strategic importance of the supply chain (Spekman et al., 1994; Venkatesh et al., 1995), and the potential of the supply chain to serve as a source of competitive advantage (Hult, 1998; Hult, et al., 2000). It is important to recognize, however, that there are limits to learning imposed by organizational characteristics such as structure (Barkema and Vermeulen, 1998). At some level, additional learning will either not be possible or will consume more resources than its added benefits provide. Thus, managers should view learning as an important means, but not the only means, to influence cycle time. JOURNAL OF BUSINESS & INDUSTRIAL MARKETING, VOL. 17 NO. 4 2002
For researchers, the significant results related to H5 contribute to a small but growing body of literature linking strategic resources with important outcomes. A central tenet of the resource-based view is that possession and deployment of strategic resources enhance success (Wernerfelt, 1984). A limited number of studies have linked resources with financial and market performance (e.g. Combs and Ketchen, 1999; Miller and Shamsie, 1996; Rumelt, 1991), but attention to key operational measures such as cycle time has been scant. Yet, understanding organizational outcomes requires attention to both financial and operational elements (Venkatraman and Ramanujam, 1986). Thus, our study represents an initial step toward understanding the potential influence of strategic resources on a largely ignored but important category of outcomes.
Design and results
Suggestions for future research Several implications for future research arise from our design and results. From a broad perspective, our study calls attention to the role that operations management issues may play in achieving and maintaining marketing success. These issues have received little attention in the marketing literature. As a starting point to help fill this gap, below we outline key issues that might be examined in subsequent inquiry. Future studies should focus on linking the constructs examined in our study to traditional performance measures such as profit and stock price, and more marketing-specific outcomes involving market share and product development. Given our focus on supply chains within one corporation, we were unable to examine the effect, if any, of learning and cycle time on such measures. According to strategic management researchers, the ultimate test of whether or not a phenomenon is critical to organizations is its effect or lack thereof on profitability and, ultimately, shareholder value (e.g. Summer, et al., 1990). Thus, before learning can be confidently added alongside culture (Barney, 1986), top management experience (Castanias and Helfat, 1991), and brand name reputation (Combs and Ketchen, 1999) on the emerging list of strategic resources, empirical tests grounded in the resourcebased view that link learning to profitability and stock market measures are needed.
Holistic assessment
Future studies might benefit from using a broader empirical approach than was adopted in our study. Whereas we gathered data from one node within the supply chain (i.e. the internal SBU customer), a more holistic assessment of chain processes could be achieved through measurement of nodes throughout the chain (e.g. external supplier, corporate buyer, external customer). Inevitably, however, researchers will face a dilemma in that gathering data longitudinally becomes more challenging as the research design becomes more complex. Perhaps a reasonable hope is that a series of studies will be conducted, with some based on longitudinal data from a limited set of informants (as in our study), and a second group drawing on cross-sectional data from a variety of supply chain participants. Taken together, these studies could collectively provide both breadth and depth to our knowledge about supply chains. One node of particular interest in future studies might be the corporate buyer, who links the internal customer with external suppliers. A key issue surrounding the corporate buyer is whether or not this node adds value in the modern corporation. As supply chain systems continue to grow in sophistication, suppliers and internal customers increasingly have the opportunity to interact directly (Hult et al., 2000). One implication is that as
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major corporations develop electronic marketplaces to service their supply needs, the brokering role played by the corporate buyer may become obsolete. Indeed, the joint creation of an on-line business-to-business supply system by long-time, often vicious, rivals Ford, General Motors, and DaimlerChrysler sends a clear signal about the increased efficiencies that corporations envision through supply chain networks. The implications of such strategic moves for supply chain management warrant future research attention.
Crucial units
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Appendix
Scale
Indicators
Team orientation
A team spirit pervades our ranks in the sourcing process Cross-functional teamwork is the common way of working in the sourcing process There is a commonality of purpose in the sourcing process There is total agreement on our organizational vision in the sourcing process We are committed to sharing our vision with each other in the sourcing process All activities that take place in the sourcing process are clearly defined We understand how our work fits into the value chain of the sourcing process We have a good sense of the interconnectedness of all parts of the sourcing process We understand where all activities fit in the sourcing process The sense around here is that employee learning is an investment not an expense. The basic values of this sourcing process include learning as a key to improvement Once we quit learning in the sourcing process we endanger our future We agree that our ability to learn is the key to improvement in the sourcing process Organizational conversation keeps alive the lessons learned from sourcing history We always audit unsuccessful sourcing endeavors and communicate the lessons learned We have specific mechanisms for sharing lessons learned in the sourcing process Formal routines exist to uncover faulty assumptions about the sourcing process The length of the sourcing process is getting shorter every time We have seen an improvement in the cycle time of the sourcing process recently We are satisfied with the speediness of the sourcing process Involving the corporate buyer in decision making shortens the sourcing process Based on our knowledge of the sourcing process, we think it is short and efficient The average length of our sourcing process from initiation to completion is (as indicated by company records in number of days)?
Systems orientation
Learning orientation
Memory orientation
Subjective cycle time
Objective cycle time
Notes: a all subjective items were measured via seven-point Likert-type scales ranging from ‘‘strongly disagree’’ = 1 to ‘‘strongly agree’’ = 7; objective cycle time was measured in number of days
Table AI. Measurement scalesa
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This summary has been provided to allow managers and executives a rapid appreciation of the content of this issue. Those with a particular interest in the topics covered may then read the issue in toto to take advantage of the more comprehensive description of the research undertaken and its results to get the full benefit of the material present
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Executive summary and implications for managers and executives Papers in this special issue explore the increasingly important but understudied area of organizational learning in the context of industrial marketing, and seek to help managers to implement effective learning-based programmes. Sensing the market Day argues that organizations continuously learn about their markets through market sensing and sense making. Sensing the market depends on: .
Creating a spirit of open-minded inquiry. Throughout a market-driven organization there is an openness to trends and events that present market opportunities, and an aversion to closed-mindedness that blinds management to emerging possibilities and latent threats by narrowing the scope of inquiry.
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Carefully analysing rivals’ actions. Firms should develop an organization-wide appreciation of the need for competitor intelligence, and provide a visible and easily accessible focal point for receiving and interpreting the information and ensuring it is acted upon quickly. Benchmarking studies of direct competitors and of organizations in different industries that share the same challenges can be used to shake up complacent manufacturing and service groups with the news that they are slipping behind.
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Listening to staff on the front lines. Organizations should ensure that proper channels exist for the upward flow of information from front-line contact people who handle complaints, hear requests for new services, cope with lead users or lose sales because of competitor initiatives. Incentives should be offered for useful insights, and full use made of the supporting role of information technology.
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Seeking out latent needs. Firms should try to get prospective or current customers to describe their problems and frustrations with a product or service, or the barriers to adoption. Customers should be asked how they behave and how they truly feel towards the product or service. Observers should be used to see how customers react to and use the product or service in the natural setting. Spending ‘‘a day in the life of your customer’’ can help a firm to highlight ‘‘hidden’’ costs – such as costs to use, store and dispose of the product or service, or the time consumed in buying it or in ongoing maintenance – and find latent opportunities to deliver superior value.
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Active scanning of the periphery of the market. Managers of marketdriven organizations actively scan the periphery to look for new opportunities. They avoid the trap of paying attention only to what the data suppliers provide – since the suppliers tend to provide only what they are asked for.
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Encouraging continuous experimentation. True learning organizations develop original insights into the market through continuous experimentation. They encourage an experimental mindset, and give strong and sustained top management support for experimental learning. Moreover, they tolerate well-intentioned failures. JOURNAL OF BUSINESS & INDUSTRIAL MARKETING, VOL. 17 NO. 4 2002
Sense making Before organizations can use the information they have collected, they must classify, sort and simplify it into coherent patterns. Mental models are simplifying frameworks used to order the information received by the organization. Organizations must fully understand the mental models they use in order to avoid being blinded to market information and prevented from developing accurate insights into market realities. The most important step in understanding and, if necessary, changing mental models is to bring them into the open. One important mental dimension that shapes mental models is whether the company focuses on customers or competitors in assessing where and how they have gained a competitive advantage. Both the customer and competitor focus, in isolation, will eventually become a misleading mental model, narrowing the scan of the market. A competitor-centred focus blocks the view of customer shifts and of new competitors. Customer-orientated firms may overlook shifting competitive forces until it is too late. Improving market sensing Firms aspiring to be better educated about their markets should, early in the improvement programme, carry out a self-assessment or benchmarking study of best practices to identify where change is needed. Companies should use business and marketing plans as true learning tools, and should map the prevailing processes for sensing and sense making. Firms should develop a collective memory – a shared knowledge base – so that the insights can be retrieved when needed. This begins a new cycle of sensing and sense making. The process of sensing and sense making has to be continuous, with knowledge flowing throughout the organization like blood in the human body. Firms that have mastered market sensing and sense making, says Day, gain an advantage by anticipating market opportunities ahead of their rivals and more accurately forecasting how the market will respond to their moves. The key role of organizational unlearning Like Day, Sinkula argues that companies subject information to perceptual filters made up of the organization’s norms, procedures and beliefs, that influence what information the firm attends to and ultimately accepts. Sinkula examines the role which these organizational filters play in unlearning, which is the process by which companies eliminate old logics and make room for new ones. He contends that the organizations which will reap the most value from learning are likely to be those that, at the optimal times and in optimal time, are able to unlearn established routines so as to replace them with ones that ultimately result in superior value for their customers. Past learning inhibits new learning. Before organizations will try new ideas, they must unlearn old ones by discovering their inadequacies and discarding them. In many cases, learned routines are so deeply ingrained in the organization that managers will begin to question them only in times of crisis. Unlearning can take place through extinction (the removal of undesirable knowledge from individuals) or by exorcism (the removal of individuals from the organization). The former can be particularly difficult to achieve in successful organizations. Removing top managers, in contrast, can have a dramatic impact on redirecting routines because the first step in changing a JOURNAL OF BUSINESS & INDUSTRIAL MARKETING, VOL. 17 NO. 4 2002
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routine is communicating to members of the organization that the old one is no longer satisfactory. Substitution and accretion Firms must achieve a balance between exploiting old certainties and exploring new possibilities. Unlearning old routines and substituting new ones is commonly referred to as double-loop or generative learning, and is occasionally needed for long-term organizational sustainability. But organizations do not double-loop learn on a routine basis, for learning on such a level is exhausting and unnecessary. More often, learning will take place in some lesser form. In the case of accretion, or single-loop learning, less risk is involved because the existing routine is not discarded. Rather, incremental behaviours are added to the existing routine, which are expected to improve it. Most organizational learning takes place at this level. The individual, the organization and the environment Two types of knowledge reside in organizational memory and shape the organization’s capabilities. Axiomatic knowledge consists of the fundamental beliefs which appear as organizational values which are set a priori and cannot be further reduced. They make up ‘‘the way we do things here’’ and can be viewed as a kind of ‘‘lens’’ which filters the way in which individuals within the organization see the world. Procedural knowledge is shared knowledge about routines that are acceptable, rather than shared knowledge about organizational truths. An individual who has procedural knowledge is able to select the appropriate, accepted, organizationally sanctioned response when assigned a task. Both axiomatic and procedural knowledge are subject to change, albeit slowly, as beliefs about cause and effect relationships change. Sinkula specifies three areas that are critical in organizational unlearning processes: .
where axiomatic knowledge funnels acceptable stimuli from the environment to organizational members;
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where the current procedural knowledge mandates an organizationally sanctioned repertoire of responses to a given stimulus; and
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between stimulus and response on the individual level – which is perhaps the most important, since much of the change in axiomatic or procedural knowledge has its genesis in gradual, long-term unlearning and relearning taking place on an individual stimulus-to-response level.
When should organizations unlearn? Marketers who face dynamic, hostile environments must unlearn more frequently and faster than those who face static, benevolent environments. Market-driven firms should watch for cues from customers as they evaluate established products and programmes. Particular attention should be paid to serendipitous, unsolicited customer information, especially that which revolves around complaints. Marketing mangers must learn how to process information well, and must become more open to criticism. A change in competitive intensity should also be seen as a cue for unlearning efforts. Marketing managers must watch their product categories vigilantly for competitive shifts. The market entry of new competitors, like the exit of former ones, might serve to trigger unlearning processes. 326
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Cues for unlearning can also come from strategic partners of the firm. These cues are more likely to arise from passive scanning of the environment than from the systematic acquisition of information. Partner cues tend to be less frequent and more serendipitous than those from customers and competitors. As companies develop – for example, from a simple structure to a departmental one – they may need to unlearn former routines. Attaining threshold size levels can also trigger the need to unlearn past rules and routines. Moreover, routines that have been successful in the past are often quick to inhabit organizational memory and narrow an organization’s vision. Successful products, processes and procedures – and the routines surrounding them – should be ongoing foci for unlearning in organizations. Putting people back into organizational learning Hurley believes that too much emphasis has been placed on an outside-in, macro-organizational view of learning and too little on the inside-out view which recognizes that people are the main agents of learning and change. Organizations learn only when people learn and, in turn, affect the theories in use inside the firm. A learning organization therefore requires a critical mass of people in the organization who are learning. (Clearly, not everyone in an organization has to be learning and innovating for the organization to do so.) Attempts at building a learning organization, says Hurley, should start with an understanding of how adults learn and develop, rather than elaborate ideas about competitive strategy, market research and information dissemination. How adults learn Adult-learning theory tells us that people learn primarily by being encouraged to tackle challenges, experiment, fail and correct failures and reflect on their experiences. Promoting individual learning requires that the organization creates an arousal and motivation for learning and change, avoids levels of stress and threat that will inhibit learning, increases experimentation, treats failure and mistakes as learning opportunities, emphasises accumulating learning experiences, makes the role of learner a valued one, uses problems as opportunities for learning, locates the responsibility and control over learning with the learners, provides feedback on learning, reinforces learning activities and reinforces new behaviours that are learned. Creating an environment that promotes learning Creating an environment that promotes learning requires that leaders strike the appropriate balance between control and creativity. The real challenge in building learning organizations is fighting the bureaucratization that inevitably comes with size and that sometimes replaces challenge and experimentation with control and routine. When this occurs, it inhibits learning and innovation in such a way that increased focus on collection and dissemination of market information will not correct it. Employees must care enough and be free enough to notice challenges, take them on, struggle, learn and innovate in the process. This requires a delicate balance of top-down and bottom-up influence. Such a balance must ensure not only that new ideas come up through the organization, but also that there is enough integration and co-ordination to avoid the chaos that would result if decisions were not made and resources were not ultimately allocated to the best ideas. JOURNAL OF BUSINESS & INDUSTRIAL MARKETING, VOL. 17 NO. 4 2002
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Creating an organizational culture that promotes individual learning requires leadership development on the ‘‘soft’’ skills. Managers cannot compel learning. Facilitating learning requires skills in establishing a challenging and inspiring vision, building trust, supporting, taking risks, controlling at more of a distance by shaping the agenda, empowering and allowing leadership vacuums to be created for others to fill and expecting people to fill them. A critical mass of leaders engaging in these and other practices will facilitate individual learning. This, when combined with some attention to infrastructure and integration, will lead to a learning organization. Risks and rewards of external learning partnerships Mohr and Sengupta examine the issues that arise when companies enter learning partnerships with external organizations, such as suppliers, intermediaries, customers or even current or potential competitors. The types of useful information that can be shared between firms might include, for example, new technological or product development skills, or how to gain access to new markets and customers. Indeed, learning how to develop effective partnerships can itself be an important source of competitive advantage. The synergies of partnering and enhanced learning can, in some relationships, lead to both parties becoming more competitive through a winwin situation. In others, a co-operative relationship can strengthen both companies against outsiders even as it weakens one partner vis-a`-vis the other. Perhaps the greatest risk is the potential loss of tacit knowledge to a partner, resulting in the company’s own skills, knowledge and expertise being used against it in the future and so jeopardizing its sources of competitive advantage. Healthy, productive, functional, co-operative inter-firm relationships are characterized by a high degree of trust, commitment, information sharing and high levels of balanced interdependence. They usually involve the development of close collegial, interpersonal ties between people in the two organizations. Indicators of partnership success include satisfaction, integrative conflict resolution, harmony and longevity of the relationship. But if the partner is trying to internalize the knowledge that forms the basis of the firm’s competitive advantage, harmony and lack of contentiousness may signal a lack of attention to protecting proprietary information. Moreover, interpersonal ties that are too close and collegial can lead to unrestrained information sharing. The role of appropriate governance mechanisms The benefits and risks of inter-firm learning are not mutually exclusive, but co-exist and can create tension in managing collaborative ventures. Mohr and Sengupta believe that appropriate governance mechanisms can match the learning intentions of the two partners involved, so as to maximize the possible benefits of learning while minimizing the risks. In particular, attention should be paid to:
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the type of knowledge the focal firm seeks, and in particular whether it is explicit or tacit (the learning risks in an alliance increase as the type of knowledge transferred goes from explicit to tacit);
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and knowledge (the risk to the focal firm increases as the partner’s intent goes from access to internalize); and .
the duration that the partners have in mind for the alliance (the risk to the focal firm increases as the duration of the alliance goes from short term to long term).
Taking account of these factors, Mohr and Sengupta make recommendations to those responsible for managing collaborative ventures. The recommendations range from ‘‘give and take’’ to ‘‘guard the crown jewels’’. How learning can help organizations to surmount their key challenges Hult et al. investigate how learning can help an organization to surmount the key challenges it faces. They concentrate on 141 supply chains involving a Fortune 500 multinational corporation. Because supply chains are so complex and dynamic, they appear to represent an area in which learning can help to produce a competitive advantage for a firm. Within each supply chain, the authors devote particular attention to the pivotal ‘‘sourcing unit’’ made up of a corporate buyer and an internal customer. The authors characterize success and failure in terms of cycle time. This is the length of time between the internal customer’s recognition of a need to the provision of a product or service to that customer. The resource-based view is that organizations should strive to improve their performance through amassing and using ‘‘strategic’’ assets and capabilities. Strategic resources are those that are valuable, rare and difficult to imitate. Some, such as patents, are tangible. Others, such as culture, are not. The authors argue that the learning climate can serve as a strategic resource. They view learning as an intangible resource that is deeply embedded in the fabric of an organization, and argue that four elements are each necessary (although not individually sufficient) for creating a learning climate: .
team orientation – the degree to which the members of the focal supply chain organization stress collaboration and co-operation in performing supply chain activities and in making supply chain decisions;
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systems orientation – the degree to which the members of the focal supply chain stress the interconnectedness and mutual dependence of the activities in the supply chain process;
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learning orientation – the degree to which the members of the focal supply chain stress the value of learning for the long-term benefit of the supply chain organization and the specific strategic resourcing unit; and
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memory orientation – the degree to which the members of the focal supply chain stress the distribution and preservation of supply chain knowledge.
Based on information gathered from surveys administered five years apart, the authors show that the proclivity of corporate buyers and internal customers in the supply chain towards being team, systems, learning and memory focused gives rise to a learning climate that embodies the likelihood of developing new, behaviour-enhancing knowledge, and that learning climate can help to reduce cycle time which is among the most important variables in the supply process. In particular, the authors reveal that team orientation and systems orientation have a particularly strong effect in the context of supply chains, where the ability to learn seems to rely heavily on JOURNAL OF BUSINESS & INDUSTRIAL MARKETING, VOL. 17 NO. 4 2002
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people’s willingness to collaborate and to a system-wide, broad view. However, Hult et al. believe that, in other contexts, learning and memory orientations might be stronger indicators of learning. For example, in a high technology setting with vast information processing requirements, learning might be driven by people’s abilities constantly to monitor and interpret the environment, and share and preserve the insights developed. The authors advise that managers seeking to advance learning in their companies should analyse their organization’s context and then use the reward system to encourage the most critical orientations. The authors admit, however, that at some level additional learning will either not be possible or will consume more resources than its added benefits provide.
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Internet currency Edited by Dennis A. Pitta University of Baltimore
Supply chain management: the next phase of B2B In a series of articles published last year a variety of authors described the two early ‘‘waves’’ of business-to-business (B2B) e-commerce. They concentrated on the Net market makers and B2B consortia. There were predictions of the demise of B2B on the Internet, the demise of public exchanges and a basic retreat from using ecommerce for industrial marketing. Others, like several Ziff-Davis Computer publications, predicted that the emphasis and biggest challenges ahead were in ‘‘developing the new business processes, standards, and organizational capabilities to execute a good B2B strategy, regardless of the technology used’’. Since then, B2B on the Internet has undergone dramatic change. As some firms have faltered, there have been secondary effects. Some of the software companies which served them have suffered declining revenues. As a consequence, firms are no longer concentrating on the demand side. As the pundits predicted, astute companies have now concentrated on supply chain basics. There are a growing number of B2B models showing good early results – particularly in streamlining supply chain processes. The process is incremental rather than revolutionary. However, as supply chain problems are solved, firms are growing more robust. It is more important to document the behind the scenes developments that have offered evidence of a new direction in the evolution of B2B marketing. The basic marketing principles have always been important. While some decision makers, mostly non-marketing managers, concentrated more on technology and how far it came, others focused continuously on consumers and the degree of their satisfaction. What became clear is that behind the scenes improvements were vital in what e-business thinkers call a disintermediated environment. All that simply says, is when you do not have an intermediate, when you don’t have a direct, information rich contact with your customer, the challenge is great. Everything has to work effectively and quickly. The technological challenge has been met by a variety of firms. The successful ones exploit existing e-business technology to develop robust offline processes. Information systems management has a reputation for over budget, over time limit, business solutions that do not work as designed. Starting from scratch is a gamble. Thus, tested solutions are the components of new supply chain management systems. We must stress that technology alone or founding a public marketplace are not automatic steps to success. Instead increased efficiencies and improvements in supply chain processes are the keys. In supply chain management terms, Herman Miller, other furniture companies and automobile manufacturers engage in complex replenishment processes. In general, they build well-established, moderately complex products. Little new technology is involved. The most complex tasks are keeping track of supplies, components, multistep manufacturing processes, and distribution logistics. They comprise the critical functions, which are key success factors. Add to that issues of intrachannel inventory and transportation exchange and they complete the conceptual issues that must be managed. [Hermanmiller.com] Herman Miller is an international manufacturer of office furniture with $2 billion in sales. The company is well known but recently attracted attention when DCI (www.dci.com), the largest US-based producer of information technology conferences, presented the company’s supplier portal, MySIGN, with its Portal Excellence Award in the ‘‘Best eBusiness Portal’’ category. Herman Miller developed MySIGN (Supplier Information Global Network) in 1998 to operate as a self-service portal for its direct material suppliers. Using it, suppliers can access Herman Miller’s production needs through the Internet using a Web-based browser technology. JOURNAL OF BUSINESS & INDUSTRIAL MARKETING, VOL. 17 NO. 4 2001, pp. 331-332, # MCB UP LIMITED 0885-8624
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The company was featured in the book, The Seven Steps to Nirvana: Strategic Insights into e-Business Transformation. The authors quoted top company executives ‘‘on subjects ranging from business incubation and designing business models for customers, to investing in business partners’ e-business processes and infrastructure’’. The authors cite Herman Miller as a ‘‘world-class example of e-business strategy and execution’’. Herman Miller is in excellent company. The authors also praise Microsoft, Procter & Gamble, and Sears, Roebuck & Company. This information is prominently featured on the Web site. The information underlines the company’s experience in serving customers. In a clear manner, the book emphasizes that e-business is still business, and that marketing efforts must focus on the customer. One challenge is that the customer is out there – somewhere. Since customers do not physically come in to the store, different methods of communication and satisfaction become important. Since the interaction is indirect, serving such customers requires the marriage of technology, strategy, processes, infrastructure, user friendliness and reliability to insure satisfying those consumers. What was the company like before? It suffered from operations related problems like costly and extended lead times. One cause was an unwieldy order entry process. Another compounding problem was its order fulfillment process. The result was a growing work-in-progress inventory and a back-up that caused extended order times and lengthy delays for delivery. The mix was a recipe for dissatisfied customers. The company was forced to either take action or suffer problems. Its actions were designed to eliminate inefficiencies on the supply side. Specifically, Herman Miller sought to increase coordination among its supply chain participants. In order to get control of the process, the company chose to use a single point of order entry for all products. The arrangement is remarkably logical and avoids the chaos that might arise from multiple order portals. To make it work requires that the system work with supply chain software that communicates with vendors and company plants daily or hourly. That link is used to generate component or manufacturing requirements to allow orders to be fulfilled. The software can even synchronize orders to meet customer delivery requirements and eliminate inventory costs. Herman Miller has since decreased inventory costs, lowered manufacturing lead-time, and increased inventory turnover. The system allows the company to be fast and flexible and serve even small companies and home offices efficiently. As a result it has increased annual small-market volume. Site characteristics The site features a homepage with several customer-focused links. They include ‘‘Buy it Online’’, ‘‘For Contract Customers’’, ‘‘For the Home’’ and ‘‘For Small Business’’. The online buying link works easily and well. Each of the other links is informational and provides a great amount of detail including extensive documents in Adobe Acrobat (PDF) format. If a visitor wishes to purchase but wishes to visit a store, pop-up lists, keyed to the customer’s area, are available. The most important aspect is that supply chain processes that will complete the customer’s order process support the customer friendliness and ease of purchase on the order side. Summary Herman Miller represents a clear example of how B2B must evolve to address the traditional marketing and business issues. No firm really succeeds by becoming a slave to the customer. Instead, firms succeed by satisfying the customer at a profit. In B2B, that means managing both demand and costs. Herman Miller shows that managing supply chain processes can result in more satisfied consumers – and profit. Reader requests Please forward all requests to review innovative Internet sites to: Dr Dennis Pitta, University of Baltimore, 1420 North Charles Street, Baltimore, MD 21201-5779, USA. Alternatively, please send e-mail to
[email protected] for prompt attention.
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