Organizational Learning: Creating, Retaining, and Transferring Knowledge. – Review – book review

Theresa K. Lant

Linda Argote. Boston: Kluwer Academic, 1999. 212 pp. $89.95.

Research on organizational learning has been plagued by widely varying theoretical and operational definitions and a lack of empirical study. Argote’s book goes a long way toward addressing these complaints. This book is a coherent and comprehensive treatment of the state of knowledge about organizational learning. Argote has purposely limited this book to a clearly articulated definition of organizational learning and a related set of questions. She focuses on contexts in which the outcomes of learning can be identified and evaluated and in which feedback on the efficacy of acquired knowledge is clear, timely, and understandable. Thus, the emphasis is on the learning of knowledge and practices that produce some measurable outcome, such as production quantity and quality. The book does not delve into issues such as the role of interpretation in organizational learning or the dynamics of learning processes across levels of analysis. There is some discussion of the micro underpinnings of organizational learning , in particular, the evidence around group dynamics and group structure in relation to knowledge creation and evaluation. Argote’s stated goal for the book is to describe and integrate the results of research on “factors explaining organizational learning curves and the persistence and transfer of productivity gains acquired through experience” (p. xvi).

If you are looking for a book on the social construction of knowledge, then this is not the book for you. If you would like to read a cogent assessment of research on organizational learning that has been based on solid theorizing and empirical study, then look no further. This book is a well-crafted, readable overview of issues such as learning curves, organizational memory, and knowledge transfer. I outline the key issues that Argote explores and summarize the key findings that she reports. Many of these findings are based on an extensive body of empirical research conducted by Argote and her students and colleagues.

The section on learning curves goes beyond the standard application of learning curves to an understanding of how learning-curve patterns are influenced by factors such as organizational forgetting and knowledge transfer. The benefit of Argote’s approach is twofold. First, she links the formal models and sophisticated empirical testing associated with the learning-curve literature with the broader literature on organizational learning. Second, she describes specific and quantitative ways of testing the degree of learning, forgetting, and transfer of knowledge within and across organizations. This is a significant contribution to a body of literature that has suffered from a lack of precise definition, measurement, and estimation (Miner and Mezias, 1996).

The findings reported suggest that organizational learning might explain the significant performance variations that are evident at the firm level of analysis. Three broad categories of organizational factors appear to influence the rate at which organizations learn and their subsequent productivity: proficiency of individuals performing both production and managerial activities, technology, and an organization’s routines, structures, and means of coordination. To understand the role of learning and subsequent outcomes, Argote chooses to decompose the learning concept into three components of the learning process: knowledge acquisition, knowledge retention, and knowledge transfer. Research on these components is summarized throughout the book.

A critical contribution of Argote’s elaboration of the learning curve concept is the finding that acquired knowledge does not stay at a constant level; it depreciates. Like water in a bathtub, knowledge tends to seep out of organizations. Several mechanisms cause this seepage. Knowledge can be lost when the physical substrate on which knowledge is encoded decays. Argote gives the example of old recordings of film or data stored on magnetic tape. Knowledge can also be lost through poor record keeping. Both of these causes of knowledge depreciation are significant, but solutions can be readily imagined, if not implemented.

A more complex cause of knowledge depreciation is personnel turnover. To the extent that knowledge is held by people rather than in technologies, structures, or routines, knowledge leaves when people leave. A significant body of research supports this conclusion. But turnover is a double-edged sword. Knowledge is not necessarily related to a constant level of productivity increases. Knowledge can become obsolete; changing technologies is a common cause. Under these circumstances, turnover can be beneficial for two reasons. First, individuals with obsolete knowledge may be resistant to new knowledge. Newcomers may be more willing to learn new skills. Second, newcomers may be more likely to have knowledge relevant to new technologies when hired. For instance, young newly hired employees are more likely than older workers to understand how to use the Internet. To determine the relationship between turnover and knowledge depreciation, an organization must have a good understanding of the degree to which knowledge is embedded in technologies, structures, and procedures versus people. The more knowledge resides in people, the higher the rate of depreciation due to turnover.

The concept of organizational memory is highly related to that of knowledge depreciation. Organizational memory can be thought of as repositories or retention bins for knowledge acquired through experience. As noted above, organizational memory that depends on individual memory results in a higher risk of knowledge loss than organizational memory that consists of technologies, structures, and routines. The embedding of knowledge in artifacts and practices can be seen most clearly in the case of manufacturing machinery. For instance, the knowledge of glass blowing possessed by artisans has been embedded in glass-making machinery. Machines recreate the actions of the artisans and their tools. Now, individuals who know nothing of the glass blower’s art can produce large quantities of glassware. In continuousprocess technologies, so much knowledge has been embedded in the technology that few individuals are needed, and the transformation of inputs is virtually invisible.

Memory also takes the form of structured tasks that follow procedures created as the result of prior learning. Technologies and procedures are memories of how to accomplish a goal (Garud, 1997), but organizational memory is much more differentiated than this. More ambiguous and social forms of knowledge are also very important, such as the knowledge of who is good at what tasks, the knowledge of how to coordinate and communicate with others, and the knowledge of whom to trust. These forms of knowledge reside in transactive memory systems (Moreland, 1999).

Technological memory is very stable and reliable; it is also fairly rigid and resistant to change. Structures and procedures can be changed more easily than technologies, but they require people to use them. Individuals have the most flexibility–they have subtle, tacit knowledge that they can apply to related tasks without being “retooled.” The differentiated memory that exists in organizations has implications for how organizations can transfer knowledge within or between firms.

The simpler and more codified knowledge is, the easier it is to transfer. For instance, if technology can be copied precisely, the knowledge embedded in this technology can be transferred with ease. Even for the most routinized tasks, however, there is usually some tacit understanding that makes the transfer error-prone. Argote finds that knowledge seems to transfer best in the presence of a superordinate relationship, such as a franchise or chain. This finding may be attributable to motivation and communication. Transfer can be hindered by between-group competition and in-group/out-group conflict, which motivates groups to withhold information rather than share it. The richness of the information transmitted also enhances the transfer process. Much of the literature on knowledge transfer has emphasized the tacit and sticky nature of knowledge that makes it difficult to transfer (Szulanski, 1996). The findings in this book also suggest that the situated, context-specific nature of knowledge makes it difficult to transfer (Wenger, 1998).

The findings of this book have many important implications given the organizational and economic trends that we face today. Questions of knowledge transfer become especially important and difficult given the globalization of business organizations with value-chain activities dispersed around the world. Issues of organizational memory and learning curves are particularly important in this age of flexible production, mass customization, and an externalized and/or virtual workforce. This book should be required reading for scholars doing research related to organizational learning and for practitioners trying to implement knowledge management and transfer programs in organizations.


Garud, R.

1997 “On the distinction between know-how, know-why, and know-what.” In P. Shrivastava, A. S. Huff, and J. E. Dutton (eds.), Advances in Strategic Management, 14: 81-101. Greenwich, CT: JAI Press.

Miner, A. S., and S. J. Mezias

1996 “Ugly duckling no more: Pasts and futures of organizational learning research,” Organization Science, 7: 88-99.

Moreland, R.

1999 “Transactive memory: Learning who knows what in work groups and organizations.” In L. Thompson, D. M. Messick, and J. M. Levine (eds.), Shared Knowledge in Organizations: 3-31. Mahwah, NJ: Erlbaum.

Szulanski, G.

1996 “Exploring internal stickiness: Impediments to the transfer of best practice within the firm.” Strategic Management Journal. 17: 27-43.

Wenger, E.

1998 Communities of Practice: Learning, Meaning, and Identity. Cambridge: Cambridge University Press.

COPYRIGHT 2000 Cornell University, Johnson Graduate School

COPYRIGHT 2001 Gale Group

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