Abstract
Having agreed to review this book because of my longstanding dissatisfaction with the entire domain of what has come to call itself “knowledge” management (KM), I am pleasantly surprised to find that there is much in this book to commend, both as fodder for thought for the present reader and as a potential lever for shifting the center of gravity in the KM community away from computers and data and toward general management and the thorny problems of human interaction. I confess that in agreeing to review this book, I had hoped to use it as a foundation for a jeremiad against computationally based paradigms of what is erroneously labeled “knowledge.” Happily, these authors have not been willing subjects for such an auto-da-fé, and their work deserves a more appreciative review.
My antipathy to the so-called “knowledge management” literature is longstanding. My doctoral dissertation in 1992 was focused on the then-novel notions of knowledge work, knowledge workers, and the knowledge-intensive organization. After studying these constructs and the discourse of which they were part for a considerable time in my academic career, I became engaged as a strategic consultant to the general manager of a New Zealand agricultural biotech company, which provided us both with extensive experience trying to apply yet-inchoate theory to the rigors of producing economic value, in practice, on a timeline. Our discussions, over a period of several years, centered on the challenge of having to rationally manage resources (e.g. an eight-figure annual budget) in order to (non-rationally) produce game-changing innovations—something one knows how to do rationally only after having done it. How does a manager mobilize the collective abilities of his or her organization to achieve outcomes and how does one change this knowledge base to create significant innovations? In the process of these discussions, our efforts were hampered by the promiscuous and often contradictory ways the notions of data, information, knowledge, and learning were used in theory and in practice.
My hope at the time was that the literature would advance my thinking and that of my client in this area, but triangulating the extensive literature search and experience that he brought to our discussions with my knowledge and research, we still found little that aided our quest. It appeared that the KM field had defined knowledge in a manner that circumvented the general management issue. One indication of this is the assumption that surfaces occasionally in the volume under review that knowledge can be stored and retrieved. That is a computer-based, not a management-based, definition. The managers with whom I worked all agreed that defining knowledge and learning was both essential to their work and impossible, but they generally agreed that knowledge was something that was done, not simply something that was cognized. That is, the test of knowledge is whether something can be done as a consequence of possessing it. Therefore, if there is an inherently group behavioral component to organizational knowledge, what can be stored and retrieved can only support knowledge; it cannot be knowledge.
There is a progressive hierarchy, oft-repeated in the KM world, in which a foundational notion of Symbol leads progressively through Data, Information, and Knowledge to Wisdom. Leaving wisdom aside for a moment as being beyond the scope of this discussion, and assuming we have adequate agreement on the relationship between symbols and data, let us consider the other three terms: Data can demonstrably be stored and retrieved because there are calculative tests for this. If my accounting software agrees with the money I actually have, the data are valid. Whether these data constitute information is dependent upon user interpretation. Most readers of this review will have encountered this phenomenon at meetings where supposedly objective data become the basis for highly varied interpretations of its meaning. Where there is a high level of agreement about the meanings of data, it may have high information content, but shared user perceptions become an inherent determinant of the degree to which data constitute information. There is a social component to the transformation of data into information, a component not amenable to binary storage. If knowledge is something that is done with information, the element which can be stored and retrieved is still farther from being that. If knowledge is distinguished from information as something that is achieved—whether it is greater sale performance, innovation, response to environmental threats, or something else—knowledge is not merely a form of cognition but a form of group ability.
I must add another essential term that is not conventionally part of the hierarchy mentioned above, learning. Learning relates to knowledge analogously to the way acceleration relates to speed. If knowledge is a static ability, learning is a capability of changing that ability. Learning at the organizational level involves a group change in the knowledge base. This is something that cannot be stored at all since its content is known only after it has been produced. Yet, contradictorily, what has come to be called KM is largely legitimated by its presumed ability to support innovation, something which cannot happen without learning.
The reason I make much of these distinctions is to refute an elision that has stuck in my craw since the advent of KM discourse. As somebody who remembers (vaguely) when computers had vacuum tubes, I have seen electronic data processing (EDP) morph into information technology (IT) and then into KM. Since the 1950s, the area of artificial intelligence has richly demonstrated that as a field with access to nominally artificial brains, there is a certain amount of terminological hyperbole accessible to those who control the flow of binary bits through the Von Neumann bottleneck. The less glamorous areas of cyber-culture have benefited from the public-relations halo around “electronic brains” to some degree. But has substantive change been paradigmatic or simply progressive? I see the changes from data processing (DP) to IT to KM as largely quantitative changes in the amount of computing power available, plummeting processing cost and broader accessibility. This has certainly expanded organizational applications in a half century, but if there has been a point of paradigmatic change, I missed it. The goal continues to be using machines that are ultimately merely calculative to provide data to support management decision making. That is undeniably valuable, but does this mean that what is stored and retrieved is “knowledge”? In my experience to date, the KM field has defined knowledge in a manner that largely circumvents the general management issue of supporting effective organizational responses to environmental change, whether they be defensive responses such as competitor threats or changes in organizational context, or more offensive responses in the domain of innovation.
I am critical of much of the KM literature to date for having co-opted “knowledge” as the domain it manages when it is more properly concerned with data/information and with communicating it. That domain is strategically essential, so I am not dismissing it as unimportant, but it is critical to distinguish it from the strategic general management function of mobilizing data, information, and the ability to elicit group and intergroup cooperation to achieve the sort of market success one might deem an application of knowledge. This is where I see the present collection as having great potential value.
In Chapter 3 of this book, after two chapters which do a very good job of reviewing work to date, Myers sets out a normative model for KM that he refers to as the Seven C’s model (Connection, Competencies, Contacts, Communication, Catalysts, Culture, leading to Capability). While one might propose different models reflecting these factors, this type of model—one which makes individual, group, and intergroup behavioral issues central—is essential if the KM discourse is going to move beyond being an IT support discipline and actually be relevant to senior managers who have to create something from nothing—which was a normal day at the office for the biotech manager in my introductory anecdote.
Theoretical models in the management disciplines have a sad tendency to go directly from conjecture to either perdition or dogma with no space in between for critical assessment, elaboration, and possible replacement by more elegant explanatory models (Maslow’s Hierarchy, McGregor’s alleged Theory X and Y, eustress/distress … the list goes on). My hope would be that the Seven C’s model leads the KM discourse away from a data-centered emphasis and toward a behavioral-managerial emphasis. This has been desperately needed if the expertise of the KM area is to be supportive of strategic goals and not limited to the IT area. It is not that this model should be accepted or rejected, but that models which address these factors should be discussed, tested, and criticized. To quote the (lost) words of McGregor (1960),
It is not important that management accept the assumptions of Theory Y. These are one man’s interpretations … and they will be modified—possibly supplanted—by new knowledge within a short time … The purpose of this volume is not to entice management to choose sides over Theory X or Theory Y. It is, rather, to encourage the realization that theory is important. (pp. 245–246)
It is too late to undo the ossification, dogmatization, and misrepresentation of McGregor’s essay which challenges us to look at emergent forms of organizing, but the present volume provides ample fodder for theoretical and practical debate about the door it opens with the Seven C’s model. More than a dozen chapters which follow the elaboration of this model report on its application in a wide variety of industry segments, occupations, and cultural contexts. I would hope that this provides rich fodder, not merely for validation, invalidation, and/or elaboration of the model but for reflecting on what the very phenomena of knowledge and managing knowledge for organizational purposes are.
Although I have spoken disparagingly about the terminological inflation of DP to IT to KM above, this book has provisionally convinced me that there may be valid reasons to try to direct the boundaries of discourse beyond what has been branded as KM to a successor domain, Knowledge Integration (KI). This term, introduced by Antonacopoulou in Chapter 21, suggests the possibility to move past KM toward a broader perspective focused more on overall organization outcomes than on bits and bytes. Whether this is justifiable is an open-ended question that I hope all of those with an interest in this area will approach open-mindedly, passionately, and critically because in this area we need all three.
A widely known comment of Michel Foucault about theory in general is that it is our duty to “make it groan.” It is my sincere hope that this volume will receive the kind of critical attention in the field that will do just that. Were these chapters either forgotten or ossified in citationality as final authority (as we are so wont to do that it is hardly noticed), both fates would negate the efforts of the authors. A central problem of articulating the theoretical and practical difficulties which stood between a 20th-century discourse of managing production and a 21st-century discourse of managing knowledge has been the presumption, from the advent of the KM discourse, that the central theoretical, paradigmatic, and epistemological issues were already understood, but they were not. Most of that potential (and essential) debate was skipped in the rush to create normative pronouncements about the new millennium of knowledge management, a task usually accomplished by recycling bits and pieces of whatever was lying around from those whose reputations anchored the old millennium. While critical, group self-examination of problems, paradigms, and proposals in the KM area would be a couple of decades late, it could—may I hope—lead to a discipline of KI founded on greater reflexivity, dialogue with practitioners, and relevance to general management. I heartily recommend that those for whom the KM area is meaningful come at this volume hammer and tongs, challenge it, and make it groan. It merits that degree of respect.