The codification of knowledge: a conceptual and empirical exploration

The Codification of Knowledge: a Conceptual and Empirical Exploration P A T R I C K C O H E N D E T a and W. E D W A R D S T E I N M U E L L E R b (aBETA,

Université Louis Pasteur, Pole Européen d’Economie et de Gestion, 61 Avenue de la Fôret Noire, F-67 085 Strasbourg, France and bSPRU, Science and Technology Policy Research, University of Sussex, Falmer Brighton BN1 9RF, UK. Emails: [email protected] and [email protected])

Industrial and Corporate Change Volume 9 Number 2 2000

1. Introduction to this Special Issue This special issue is devoted to sharpening the distinctions between information and knowledge, and to investigating how some of the tools for generating and distributing information are influencing the production and use of both information and knowledge. The need to sharpen our distinctions is provoked by the continued acceleration of innovations in information and communication technologies. After a half century of development, it is increasingly clear that the ubiquity and diversity of the more advanced information and communication technologies have contributed to the development of an information ‘flux’ (the growing flow of information of all types that is available to organizations and individuals). The effective use of this information ‘flux’ is essential to the creation of organizational capabilities that provide the basis for organizational success. Efforts to devise routines and procedures for managing information and knowledge are being undertaken even when information and communication technologies are not directly involved. Despite the importance of an ever-expanding information ‘flux’ to organizations, the problems of organizing and selecting useful information continue to be difficult and expensive to address. Why is this? The simplest possible answer is that the problems of organizing and selecting information arise from the very different contexts in which it is employed. This answer is unsatisfactory because the array of possible contexts for the use of information can be rendered part of the information itself. For example, by combining conditional with informational statements, the costs of organizing and © Oxford University Press 2000

195

The Codification of Knowledge selecting information should be reduced. Recognition of the existence of context-dependence information does, however, produce two fundamentally different interpretations of the distinction between information and knowledge. The first is that it is primarily the cost and complexity of creating the appropriate conditional statements that give rise to the distinction. In this view, the reproduction and exchange of knowledge principally are problems of finding an effective means of ‘codifying’ knowledge as information (that is, creating the appropriate conditional statements). This viewpoint may admit the possibility that certain human capabilities and understandings cannot be ‘articulated’ at any cost as conditional statements, while contending at the same time that convincing examples of such ‘inarticulable’ knowledge are difficult to find. A second interpretation of the distinction between information and knowledge arises from questioning what is required for the creation and evaluation of conditional statements. If the creator of the conditional statement does not understand that statement in the same way as another individual, then the meaning of the statement cannot be reproduced. In other words, conditional statements must take into account not only the context of the information, but also the identity and capabilities of the ‘receiver’ of the statement. In our view, these two interpretations underlie many important divisions within the social sciences. The failure to clearly identify these viewpoints as premises of research produces mutually incomprehensible interpretations of social behaviour. Our purpose in this issue has been to commission papers that would provide a better explication of these two alternative interpretations and the nature of the debate between those who espouse them. For some participants in this debate, the growth of the information ‘flux’ provides new, and perhaps more effective, means of creating and reproducing knowledge. For example, strong claims made about the potential for creating ‘artificial intelligence’ are instances of what Cowan et al. (2000) refer to as the ‘algorithmic model’ of knowledge production.1 Cowan et al. (2000) observe that this viewpoint leads to the conclusion that ‘what an (algorithmic) economic agent “knows” is nothing more nor less than “information”’. In practice, the ‘algorithmic economic agent’ is a familiar figure in formal economic analysis.2 Thus, there exists a ‘strong programme’ with regard to 1 One representative of the ‘strong programme’ of artificial intelligence (AI) is Hofstadter, who has argued that ‘every aspect of thinking can be viewed as high-level descrition of a system, which, on a low level, is governed by simple, even formal, rules’ (Hofstadter, 1979, p. 559). While this research programme has gradually fallen out of favour and been replaced by more limited efforts at knowledge representation or human augmentation, it is an idea that remains embedded in the popular imagination. 2 One example may serve to illustrate this point. Laffont (1989) proceeds in the following way to define the state space for economic decision-making, ‘Let (Ω, O, µ) be a probability space that represents the

196

The Codification of Knowledge codification that asserts either that there is real potential for universal codification of knowledge or that it is meaingful to proceed ‘as if’ such a programme could be implemented. A different and arguably far more relevant approach to the issues of codification is represented by the contribution of Cowan et al. (2000), who focus upon a series of boundaries. While admitting the existence of ‘uncodifiable knowledge’, they focus their attention on knowledge that can be codified and seek to create a framework for establishing the conditions under which such knowledge will be codified. In setting the problem in this way, Cowan et al. transform what they argue is a fruitless effort to define the epistemology of ‘tacit’ knowledge into a series of questions that can be addressed both theoretically and empirically. In particular, if we focus on the margin between knowledge that is codified and knowledge that remains uncodified only because of economic incentives or costs, we may examine the potential for altering these incentives and costs and thereby increase the extent of codified knowledge. For example, it becomes an empirical question as to whether information and communication technologies that reduce the costs of codifying knowledge provoke greater efforts to produce codified knowledge. Knowledge that has been codified into informational messages can be reconstituted at a later time, in a different place, or by a different group of individuals with varying degrees of effectiveness depending upon the ‘cognitive framework’ of those attempting to use this information. The Cowan et al. (2000) framework thus also suggests that an important set of issues surround the problems of ‘de-codification’, issues that will influence the economic value of codification. This leads them to consider the processes by which collective efforts may be organized to define and use standards for knowledge codification. Thus, for Cowan et al. (2000) the processes of knowledge codification lead to efforts to understand the social processes, economic incentives and technological possibilities influencing these processes. For other participants in the debate, the growing information ‘flux’ is seen primarily as a complement to the general extension of knowledge-based work and competence within modern economies. These participants, exemplified in this issue by Ancori et al. (2000), are sceptical that knowledge is becoming more codifiable. Instead, they contend that the growing ‘flux’ of information is accompanied and mediated by social activities and networks that serve to reproduce the capabilities of comprehending and utilizing this information. space of the states of nature. Ω is the set of the states of nature; ω ∈ Ω is a complete description of the exogenous variables of the model considered; O is a σ-algebra of events which, in the case where Ω is finite, is simply the set of subsets of Ω. Then an event is a subset E of Ω and µ is a measure of the objective probability over the events of O’ (p. 6). Laffont does note that ‘objective’ probabilities may not exist for particular economic problems (p. 15).

197

The Codification of Knowledge They argue that these capabilities involve high degrees of ‘tacit’ skills, knowledge and cognition, and that the mechanisms for reproducing these capabilities are essentially distinct from the processes of codification. When cast in these terms, the debate clearly has both irreconcilable and reconcilable elements. For those who argue, for the strong programme of artificial intelligence, that knowledge can be formally represented and transmitted as information without the benefit of prior acquisition of tacit skills and understanding, the two positions are irreconcilable through theoretical argument. Inasmuch as the promises of artificial intelligence remain prospective, the two positions cannot be resolved empirically either. Resolving the contrasting issues presented by Cowan et al. (2000) and Ancori et al. (2000) is, however, possible through empirical observation. The former authors maintain that shared contexts for the exchange of codified knowledge exist or can be constructed if the investment is economically worthwhile. The latter argue that the social processes are intrinsically and inevitably bound to or required in the growth of codified knowledge. This leads to a familiar type of specification in economic analysis, the distinction between processes characterized by increasing and constant returns. If, as Cowan et al. (2000) suggest, investments in building shared contexts are of limited duration and are followed by more intensive exploitation, the investments in ‘codebook’ (their terminology for the construction of shared context and language) building should be subject to increasing returns with respect to their inputs. If, on the other hand, such exchanges require continuous investment in social overheads and organizational learning, they will be better represented by constant returns with respect to inputs. Resolving this debate is even more important because of the increasing emphasis that many scholars are placing upon the consequences of a very broadly defined view of ‘tacit’ knowledge, which embraces not only knowledge that cannot be codified but also knowledge that has not been codified for various reasons. In other words, if empirical evidence demonstrates that the social processes involved in reconstituting knowledge from information are becoming easier to reproduce, or more capable of utilizing the expanding information ‘flux’, the two positions should be resolved in favour of greater attention to the processes of codification. By higher attention, we mean that a much higher priority should be given to the policy implications and research programme of improving and extending knowledge codification efforts, and to the social mechanisms by which these efforts are accomplished. On the other hand, the empirical evidence may demonstrate that social mechanisms accompanying, but not directly dedicated to, knowledge codification are becoming more elaborate or that their costs are growing in parallel with the expansion of the 198

The Codification of Knowledge information ‘flux’. In this case, the policy and research priorities should shift toward strengthening, expanding and reducing the costs of these social mechanisms as well as to assessing the significance of their inter-dependency with knowledge codification processes. As Cowan et al. (2000) illustrate, it is possible to reach conclusions about both policy and research agendas that are unhelpful or misleading if the boundary between codified and uncodified knowledge is regarded as an immutable wall. This issue offers three exploratory efforts to mobilize the empirical evidence with a view to further assessing this debate. The paper by Malerba and Orsenigo (2000) extends and elaborates on a range of issues that are empirically relevant to the debate. They argue, primarily from first principles, that much of what we understand as organizational ‘competence’, an important type of knowledge, is not readily ‘codifiable’. Differences in such competencies are a major source of observable variety in the conduct and performance of organizations. From their viewpoint, the ‘codifiability’ of knowledge is only one of several characteristics influencing the economic significance of knowledge. Thus, their paper suggests that the resolution of the debate may not be as straightforward as suggested by the previous two paragraphs. The social mechanisms associated with firms’ knowledge bases may be growing in significance as the result of strategic efforts to extend appropriability or to manage cumulativeness, just as there may be an expansion of the mechanisms related directly to knowledge codification. The two remaining papers in this issue examine the technology of knowledge codification (Steinmueller, 2000) and the implications of changes in the ‘information flux’ for industry structure (Nightingale, 2000). Both papers reveal the difficulties of bringing empirical evidence to bear in a way that creates a resolution of the ‘codification’ debate. Steinmueller reflects on the recent history of the use of information and communication technologies related to codification. He argues that the contribution of these technologies to achieving direct ‘codification’ of knowledge that is related to individual and organizational memory has been relatively modest. He also argues, however, that the relatively recent development of techniques for capturing collective or ‘group’ processes by employing software technology, and for reconstituting a broader context for scientific and technological investigation through the use of computer-assisted simulation and modelling, show more promise for knowledge codification. Nightingale (2000) is antagonistic to the view that knowledge can be reproduced through the transmission of information. His paper nonetheless provides a very rich description of the changes in the information ‘flux’ which plays such a major role in stimulating the ‘codification’ debate. A principal 199

The Codification of Knowledge conclusion of his paper is that the scale of information generation available in pharmaceutical research serves to entrench the existing advantages of large organizations. Regardless of whether this information can or cannot be viewed as ‘codified knowledge’, Nightingale clearly demonstrates that technological change, in the context of pharmaceutical research, is expanding the ‘flux’ of information very dramatically. One of several implications of this paper for the ‘codification’ debate is that some of the new technologies involved in the information creation process may be particularly proficient in embedding the generation of ‘conditional statements’ in the experimental process. Thus, in the process of drug discovery, it is not only the automated generation of new chemical compounds (combinatorial chemistry) with pharmaceutical potential, but also their assessment and evaluation in situ as they are created, that is important. While Nightingale may contest that this process represents ‘knowledge representation’, analysis of this process clearly has the potential to generate a much richer starting point for further work. A second important feature of Nightingale’s work is what it reveals about the potential for new information generating technologies to influence the returns to ‘fixed’ cost structures of companies. From an historical perspective, the ability to automate and accelerate dramatically the trial-and-error process within a particular context chosen by researchers is a powerful tool for knowledge creation. The fact that a single researcher or small group of researchers can establish and implement such a trial-and-error process on a scale that would have required the dedicated efforts of hundreds or even thousands of researchers in the past challenges our understanding of the research process. Were researchers in the past who engaged in trial-and-error processes effectively performing as technicians, only occasionally employing their tacit knowledge and skills to guide them and to correct the course of the laborious process of sifting among possibilities? The empirical investigations of the papers in this special issue raise as many issues as they resolve in the codification debate. This is, however, a measure of the fruitfulness of the debate itself. In attempting to sharpen the distinctions between information and knowledge, the codification debate challenges existing ‘habits of thought’ that are subject to diminishing returns. These habits of thought are clearly apparent in discussions of the knowledgebased economy and knowledge management, subjects that are often devoid of analytical content. Once sensitized to the issues of the ‘codification’ debate, many contemporary questions appear in a different light. For example, what is the value of ‘distance education’ or ‘World Wide Web based learning’ and how should it be organized? If knowledge cannot be codified and transmitted effectively through the use of information outside a given social context, such 200

The Codification of Knowledge initiatives will require considerable attention to social organization if they are to succeed. Can organizations reduce the costs of acquiring improved technology by improving their access to global data communication networks? If knowledge exchange requires very substantial social interaction, the extent of cost reduction through access to information alone should be modest. Should enterprises give more emphasis to providing tools for employees to access information or to raising the skills of employees to utilize information? This is simply a restatement of the codification debate in different terms. For many, the knowledge-based economy simply refers to the growth of firms whose competitive position in the world economy is influenced by their ability to use and expand scientific and technical knowledge effectively. The apparent significance of this kind of knowledge in a growing array of industries that historically would not have been classified as ‘science-based’ is of particular importance. In analysing these trends, the issue of ‘knowledge codification’ is linked to questions about the difficulties of technology transfer or the sources of, and ability to mobilize, the ‘core capabilities’ of enterprises. Not surprisingly, these concerns often lead to a conservative position on ‘knowledge codification’ issues and a relatively greater emphasis on the importance of tacit understanding and skill. An alternative viewpoint, however, is that the growing concern with the knowledge-based economy is an outgrowth of our improving abilities to use new techniques to facilitate information exchange and organizational change. This view places a greater emphasis on the value of making ‘explicit’ a wide range of practices and habits of thought that previously were embedded within the division of labour of the organization. In achieving a greater degree of explicitness, it is possible to achieve more dramatic and effective ‘re-structuring’ or ‘re-engineering’ of the organization, or to build entirely new structures that are better suited to the evolving challenges presented by markets and technologies. Yet, recent experience with such re-engineering efforts has had mixed and uncertain results. Considerably more analysis of the incentive mechanisms and barriers influencing the ability of organizations to make information or knowledge ‘explicit’ seems to be warranted. In 1997, the European Commission, through the then Directorate General XII Targeted Socio-Economic Research Programme, funded a project entitled ‘Technology and Infrastructure Policy in the Knowledge-based Economy— The Impact of the Tendency Toward Codification of Knowledge’, known as TIPIK, which proposed to examine the issue of codification from both the conceptual and empirical viewpoints.3 The members of the resulting TIPIK 3

The views expressed in this introduction and the papers of this special issue represent the research

201

The Codification of Knowledge research consortium agreed on the significance of a better understanding of the growing information ‘flux’ and on the value of examining the processes of codification of knowledge as a core issue influencing the rate and direction of changes in the accumulation of information. Members of the consortium were interested particularly in the implications of the digital revolution for intensifying information generation and distribution opportunities, and the specific potential for extending or changing the processes underlying knowledge codification. The papers in this issue are the first of the planned outputs from this project and the research programme in which the project consortium members are engaged. The remainder of this introduction highlights some of the broad features of this research programme and is intended as an invitation to other researchers to join in the discussion and to help develop linkages with related efforts.4

2. Potential Benefits and Costs of Knowledge Codification To the extent that knowledge can be exchanged as information (codified) it will be a core process for economic activity and development for the following main reasons: • Knowledge codification produces information that has some of the properties of a commodity that either can be bought and sold directly or used as a signal of the desirability of entering into other forms of commercial relationships such as joint ventures, consultant relationships, or strategic technology development partnerships. • To the extent that knowledge can be codified, it is possible to exploit some of the non-standard commodity features of information including the possibility of non-rivalry in use (the stock is not reduced by its sale) and the low marginal cost of reproduction. These features in principle may reduce the costs of technology ‘transfer’. • Codification allows the ‘modularization’ of bodies of knowledge and its output of the authors and do not necessarily reflect the views or policies of the European Commission. TIPIK is based on initial conceptual proposal developments by Paul A. David, D. Foray and W. Edward Steinmueller as elaborated, extended and refined by P. Cohendet and his colleagues at BETA, University of Louis Pasteur, Strasbourg, who serve as the project coordinators. TIPIK’s project officer is Ronan O’Brien. 4 We would be most interested in receiving references, working papers and other communications c/o P. Cohendet, BETA, University of Louis Pasteur, Strasbourg, France, and we will endeavour to recognize these contributions in future publications or future World Wide Web sites. Communications to individual authors identified with TIPIK may be circulated, if the correspondent so wishes, within the consortium.

202

The Codification of Knowledge distribution and specialization in different phases or domains. It also facilitates knowledge externalization and allows a firm to acquire more knowledge than previously for a given (but not necessarily lower) cost. This contributes to modifying the spatial organization and division of labour and can contribute to the outsourcing of activities. • Codification directly affects the speeding-up of knowledge creation, innovation and economic growth. It thus has the potential to alter the rate and direction of knowledge generation and distribution dramatically. The potential benefits from the codification of knowledge that may be obtained in all these ways should carefully be compared with the costs of codification, in order to assess the conditions under which the codification of knowledge actually is beneficial to an organization or society. There are significant costs associated with the codification of knowledge, including the observations that,

The opportunity cost of making knowledge more explicit is often that fewer resources will be available for generating new knowledge. Encoding knowledge may be a costly process, particularly when the knowledge is deeply contextually embedded in experience and related understanding and decoding knowledge in contexts other than those originally anticipated may also be costly. Making knowledge explicit makes it more portable and available for ‘capture’ by opposing interests. By codifying knowledge, companies may more easily lose control of proprietary knowledge or be disadvantaged in legal action. The codification of knowledge may serve the interest of some individuals at the expense of others (‘You want to codify the knowledge I have built over the last 20 years of employment in this company?’). This will create difficult or costly issues of incentive compatibility. Thus, the assessment of the costs and benefits of the codification of knowledge raises important new challenges for economists. While economists are accustomed to analysing the ability of agents (individual or collective) to process information, codification moves the focus to the transformation processes involved in creating as well as using ‘codified’ knowledge. This requires careful examination of the notion of knowledge and its use in economics and, in particular, the need to specify the extent to which the relevant body of knowledge differs in its characteristics from information. 203

The Codification of Knowledge

3. A ‘Neutral’ Distinction between Information and Knowledge and its Implications for Knowledge Codification Information can be considered as a ‘flux’ in that each piece of information brings with it a ‘quantum of novelty’ for one or more ‘receivers’. Defining knowledge is less straightforward because it represents the capacities or capabilities of an individual or a social group. These capacities are multifaceted and span the cognitive or ‘holistic’ processes associated with meaning and understanding, as well as the abilities to organize, interpret and assess information. In relation to information, the capacity of having knowledge has both a generative and adaptive dimension. A person who has knowledge is capable of generating both new information and new knowledge from his or her pre-existing knowledge. It is also a characteristic of having knowledge that a person’s knowledge-generating capabilities adapt both to the receipt of new information and to the feedback provided by the generative process of using that knowledge. Knowledge is thus not simply the accumulation of information in a stockpile and it cannot be comprehended independently of the process through which it is obtained. The cognitive capabilities of agents differ; in particular, in their knowledge about how to use knowledge, how to transmit knowledge and how to manage knowledge. While this approach better distinguishes between information and knowledge, it does not resolve the ‘codification’ issue where the aim is explicitly to devise information messages that will serve to ‘reproduce’ the capacities and capabilities that comprise knowledge. What this approach does do, however, is to call attention to the fact that information will not spontaneously organize itself into knowledge or the reverse. Taking into account the cognitive abilities of agents sheds some light on the nature of the process of knowledge codification. This process can be defined as that of reduction and conversion of knowledge into messages that can then be processed as information (David and Foray, 1995). Knowledge codification may be analysed as a specific process that entails a three-step activity of creation (Cowan and Foray, 1997): (i) creation of models; (ii) creation of languages; and (iii) creation of messages. When a knowledge activity takes place in a new sphere or discipline, models must be developed together with the informational vocabulary with which to express these models (Cowan et al., 2000). This initial codification activity involves high fixed costs. When models and a language have been developed, a ‘codebook’ may be said to exist; knowledge can be codified as information and created, circulated and reconstituted. At this stage, assuming that codification is achieved, agents are able to carry out certain operations 204

The Codification of Knowledge at very low marginal costs. The pre-existence of standards of reference (numerical, symbolic, pictorial, geometrical languages, taxonomies of many kinds) and performance, and a vocabulary of precisely defined and commonly understood terms, contribute greatly to reducing the time and effort required to produce non-ambiguously codified messages. When the codification of the models and of the language becomes large enough to stabilize the language, the ‘flux’ of information can grow rapidly. In turn, the new codified knowledge inevitably will introduce new concepts, notation and terminology, so that stabilization does not imply a complete cessation of dictionary building. The codification of knowledge thus inherently involves further creation of knowledge. In this process it is worth noting that codifying knowledge simultaneously draws upon the pre-existing content of the codebook, and adds content to the codebook. This simplified vision of the process of codifying knowledge as a three-step process does not encompass all the complex cognitive issues associated with the codification process (there may be problems in aligning cognitive understanding with the language by which models and messages are constructed). However, this approach offers a useful reference point for establishing ‘common ground’ in the codification debate and a basis for identifying and understanding some of the key economic phenomena related to the codification of knowledge.

4. Codification and Technologies of Information and Communication Advances in the information and communication technology infrastructure may contribute to lowering the costs of codifying knowledge. Simulation and approximation techniques have improved the ability to model complex phenomena and software designers have produced new types of languages. Improved storage media have resulted in a dramatic reduction in the cost of information storage and in the cost of using information. The declining cost of telecommunication services is facilitating a reduction in the cost of diffusing codified knowledge. Through systems such as the Internet, many more potential users can rapidly access codified knowledge. These changes clearly increase the potential values of codified knowledge and may make it more attractive to allocate resources to the process of codification. It should not be assumed, however, that the codification of knowledge is synonymous with the use of information and communication technologies. Major examples of codification, such as Taylorist efforts to record and model movement or ISO procedures of quality certification, do not explicitly require, 205

The Codification of Knowledge nor were they prompted by, innovations in or the diffusion of advanced information and communication technologies. Moreover, the costs and complexities of using information and communication technologies may outweigh efforts to codify knowledge.

5. Links to Other Research Programmes The central role of social processes in both the knowledge codification process and in the potential necessity for reproducing ‘tacit’ capabilities to make codification or decoding feasible aligns the insights that are highlighted here with several other programmes of research. Three such areas of research have particular value in this discussion. The communities responsible for producing codified knowledge are likely to be closely identified with its effectiveness and value in use. Thus, in many important respects, effective codification cannot be separated from the influences governing the coherence, homogeneity, and sustainability of particular communities. As Cowan et al. (2000) note, the concept of an ‘epistemic’ community, defined as a collective effort to achieve a common purpose governed by a procedural authority, is a useful starting point for examining knowledge codification efforts. The existence of shared objectives and values defining the epistemic character of these communities helps with the alignment of language and incentives to engage in codification processes. The existence of procedural authority aids in the resolution of potential disputes and provides a reference point for achieving ‘closure’ in various stages of the codification process. Similar advantages may be present in particular communities of practice, as noted by Creplet et al. (2000). Such communities, which Wenger and Lave (1990) identify as arising from relatively loose associations of individuals engaged in similar activities, regularly communicate with one another about their activities. The absence in communities of practice of a ‘procedural authority’, as well as the likelihood that they include individuals with a greater diversity of objectives, should make knowledge codification activities more difficult to accomplish. Nevertheless, it is possible that some communities of practice may have greater incentives for knowledge codification or that particular epistemic communities may encounter obstacles or crises to knowledge codification that overcome the advantages of procedural authority. Empirical work on specific instances of knowledge codification, such as standards-making processes, codes of practice and efforts to capture ‘best practice’, would shed light on these issues. The process of knowledge codification can also be related to numerous organizational issues, as Malerba and Orsenigo (2000) illustrate. The 206

The Codification of Knowledge potentially close relationship between knowledge codification and the process of creating new ‘routines’ within the organization is of particular importance. Nelson and Winter (1982) indicate the significance of routines in creating organizational inertia and the potential for this inertia to lead to a failure of the organization to adapt to changing circumstances. We suggest that the process of codification is a fairly dynamic process, requiring continuous feedback and exchange between codification and use. If the dynamic features of this process contribute to a periodic re-examination of the organization’s routines, knowledge codification may serve as a mitigating influence on the organization’s tendency to develop fixed routines that create problems of adaptation to changing circumstances. A similar, but more proactive, suggestion is made by Nonaka and Takeuchi (1995), who argue that the process of knowledge management is directly connected to the management of the interface between what is implicit and what is explicit within the organization, concepts that are closely related to the codification debate. This suggests examination of the relationship between organizational adaptation and the codification process, and assessment of whether codification tends to create more rigid or flexible organizations or whether it has a measurable influence on organizational performance. Finally, knowledge codification raises numerous issues with regard to the social organization of knowledge generation and distribution activities. In recent years, considerable attention has been devoted to examining changes in the process of knowledge generation. Some of the findings include changes in the extent of trans-disciplinarity in research, in the division of labour of research between public and private performers, and in the role of policies aimed at creating new inter-organizational linkages [for contrasting views on some of these issues see Gibbons et al. (1994), David et al. (1998) and associated references]. David and Foray (1995) have called explicitly for a closer examination of the knowledge distribution process, indicating that investments devoted to knowledge generation may, at the margin, generate lower returns than re-deploying these investments to knowledge distribution. Clearly, this argument hinges on the extent to which users of such distributed knowledge must also be participants in the knowledge creation process to achieve benefits from improvements in knowledge distribution. These examples are a partial sampling of the related lines of enquiry that are directly connected with the issues in the knowledge codification debate. They strongly favour the literature with which the readers of this journal are most familiar. It is an important characteristic of the knowledge codification research programme, however, that it provides an avenue for a richer interdisciplinary discussion of the issues as they relate to the cognitive sciences, 207

The Codification of Knowledge business studies of organizational memory, computer science, and many other disciplines and research agendas.

6. Conclusion The knowledge codification debate highlights contemporary changes in the rate and direction of technical change associated with the growing use of information and communication technologies, and the new ‘habits of thought’ that arise in parallel with the use of these technologies. In highlighting these changes, it opens a range of specific issues for economists and those engaged in business studies, such as the costs and benefits of codification activities, the incentive compatibility of various approaches to knowledge codification, and, ultimately, the impact of these activities on organizational performance. It also touches on many deeper issues concerned with the representation of knowledge as information, cognitive processes, and the potential to achieve social cohesion and closure in the process of knowledge exchange. Although it would be premature to draw specific policy conclusions from this line of research, it is not too early to indicate some of the policy areas that are likely to benefit from advances in our understanding of knowledge codification processes. Many public policies are directed at the dissemination of the information resulting from the research process. The examination of the knowledge codification process will provide deeper insights into how dissemination activities can be more effective and achieve a greater degree of cumulative value rather than simply increasing the volume of the information ‘flux’. Understanding the features of knowledge codification that make for the effective use of the results and the relative importance of the ability of agents in a given society to absorb and interpret such knowledge will provide a better basis for assessing the balance between knowledge generation and distribution activities. Critical assessment of the potential costs of knowledge codification in reducing organizational flexibility versus its potential benefits in stimulating re-examination of organizational routines and practices should provide better guides to the desirability of funding projects where the aim is to improve the codification process. In particular, assessment of the value of new methods for codifying and communicating the results of scientific and technical research would be of substantial value in ensuring that public, and often private, investments in research generate benefits. Ultimately, the knowledge codification debate is likely to be influential in assessing what organizational structures should be favoured and what practices should be adopted in both public and private research programmes, in the development 208

The Codification of Knowledge of systems for distant learning, and in the public provision of information regarding health, safety, the environment and government affairs.

References Ancori, B., A. Bureth and P. Cohendet (2000), ‘The Economics of Knowledge: The Debate about Codification and Tacit Knowledge,’ Industrial and Corporate Change, 9, 255–287. Cowan, R. and D. Foray (1997), ‘The Economics of Codification and the Diffusion of Knowledge’, Industrial and Corporate Change, 6, 595–622. Cowan, R., P. A. David and D. Foray (2000), ‘The Explicit Economics of Knowledge Codification and Tacitness’, Industrial and Corporate Change, 9, 211–253. Creplet, F., O. Dupouet, F. Kern and F. Munier (2000), ‘Knowledge and Expertise: Toward a Cognitive and Organisational Duality of the Firm’, Working Paper, TIPIK Project, BETA, University of Louis Pasteur, Strasbourg. David, P. A. and D. Foray (1995), ‘Accessing and Expanding the Science and Technology Knowledge Base’, STI, 16, 13–68. David, P. A., D. Foray and W. E. Steinmueller (1998), ‘The Research Network and the New Economics of Science: from Metaphors to Organizational Behaviours’, in A. Gambardella and F. Malerba (eds), The Organisation of Innovative Activities in Europe, pp. 303–342. Cambridge University Press: Cambridge. Gibbons, M., C. Limoges, H. Nowotny, S. Schwartzman, P. Scott and M. Trow (1994), The New Production of Knowledge: The Dynamics of Science and Research in Contemporary Societies. Sage: London. Hofstadter, D. (1979), Gödel, Escher, Bach: an Eternal Golden Braid. Basic Books: New York. Laffont, J.-J. (1989), The Economics of Uncertainty and Information. MIT Press: Cambridge, MA. Malerba, F. and L. Orsenigo (2000), ‘Knowledge, Innovative Activities and Industrial Evolution’, Industrial and Corporate Change, 9, 289–314. Nelson, R. and S. Winter (1982), An Evolutionary Theory of Economic Change. Belknap Press of the Harvard University Press: Cambridge, MA. Nightingale, P. (2000), ‘Economies of Scale in Experimentation: Knowledge and Technology in Pharmaceutical R&D’, Industrial and Corporate Change, 9, 315–359. Nonaka, I. and H. Takeuchi (1995), The Knowledge-creating Company: How the Japanese Companies Create the Dynamic of Innovation. Oxford University Press: Oxford. Steinmueller, W. E. (2000), ‘Will New Information and Communication Technologies Improve the “Codification” of Knowledge?’, Industrial and Corporate Change, 9, 361–376. Wenger, E. and J. Lave (1990), Situated Learning: Legitimate Peripheral Participation. Cambridge University Press: Cambridge.

209