The Competitiveness of Nations in a Global Knowledge-Based Economy

Robin Cowan (a), Paul A. David (b) & Dominique Foray (c)

The Explicit Economics of Knowledge: Codifcation and Tacitness (cont'd)


Page 1


1. Introduction: What’s All this Fuss over Tacit Knowledge About?

2. How the Tacit Dimension Found a Wonderful New Career in Economics

2.1 The Roots in the Sociology of Scientific Knowledge, and Cognitive Science

2.2 From Evolutionary Economics to Management Strategy and Technology Policy

Page 2

3. Codification and Tacitness Reconsidered

4. A Proposed Topography for Knowledge Activities

Page 3

5. Boundaries in the Re-mapped Knowledge Space and Their Significance

6. On the Value of This Re-mapping

6.1 On the Topography Itself

6.2 On Interactions with External Phenomena

Page 4

7. The Economic Determinants of Codification

7.1 The Endogeneity of the Tacitness - Codification Boundary

7.2 Costs, Benefits and the Knowledge Environment

7.3 Costs and Benefits in a Stable Context

7.4 Costs and Benefits in the Context of Change

8. Conclusions and the Direction of Further Work



HHC:  added



Industrial and Corporate Change, 9 (2), 2000 , 211-253

page 4

7. The Economic Determinants of Codification

The preceding exposition focused first upon the conceptual distinctions separating types of knowledge activities and then upon the locations of knowledge activities in the space thus delineated.  In any topographic discussion there is a temptation to treat boundaries between regions as having been imposed from outside the system that is under examination.  In a sense this is proper, in that structures are often in principle distinct from the activities that make them.  But inasmuch as we are dealing here with knowledge, and the latter is seen today to be so central to the process of economic growth, a treatment of the subject would be more useful were it to deal with the genesis of our structural boundaries and the forces that determine their positions in different areas of knowledge formation.  This becomes all the more relevant inasmuch as the main concern here is not primarily taxonomic; we are less interested in delineating the nature and varieties of human knowledge than in being able to explain and predict the changes taking place in the character of economically significant knowledge activities.

Another way of highlighting this issue is to return briefly to the previous discussion of the critique of the implicit assumptions of new growth theory in regard to the composition of the knowledge stock.  Both sides in this incipient debate over the economic role of the tacit dimension have tended to accept a view of the ‘composition of knowledge by type’ (i.e. the codified-tacit mix) as being determined exogenously, outside the sphere of economics -


and, therefore, as a matter that may be left to the epistemologists, cognitive scientists and students of human psychologists.  But, in focusing upon those extra-economic conditions bearing upon the supply side of knowledge production and distribution activities, that implicit approach ignores the influence of the range of considerations that impinge upon the demand for codified versus uncodified knowledge.  Some of these factors involve institutional arrangements affecting the structure of relative rewards for codification activities, whereas others have to do with the state of available technologies affecting the costs of rendering knowledge in codified form and the storage, retrieval and transmission of information.  In the remainder of this section, therefore, we make a start towards a more systematic economic analysis of the matter.


7.1 The Endogeneity of the Tacitness - Codification Boundary

Any individual or group of agents makes decisions about what kind of knowledge activity to pursue and how it will be carried on.  Should the output be codified or remain uncodified?  Are the inputs to be made manifest or latent in the production process?  For an economist, there is a simple one-line answer: the choices will depend on the perceived costs and benefits.  The implication is that where knowledge activities are located (the extent to which agents codify their knowledge for example) will depend on economic considerations, and that the boundaries may move in response to changes that are external to the knowledge system per se.  The significance of this requires some further discussion, if only because it represents a novel (if obvious) departure from the usual way in which the problem of tacitness has been framed.

In analyzing the economics of this choice, we need - even more so than above - to consider only knowledge which is codifiable.  Several different situations can arise: knowledge can be in a state of true tacitness but codifiable; the codebook can exist or not; and it can be displaced or not.  Each situation generates its own cost-benefit structures, which we will address through the concept of the knowledge activity environment.

The endogeneity of the tacit-codified boundary (or the Merton-Kuhn boundary in Figure la) refers to the fact that the agents pursuing a knowledge activity have a choice regarding whether or not to codify the knowledge they use and produce.  In practice, the extent to which both ‘new’ and ‘old’ knowledge becomes codified in a particular environment is determined by the structure of the prevailing costs and benefits of doing so.  Many factors - such as the high cost of codifying a certain type of knowledge, to take the simplest example - can decrease the incentives to go further, by lowering the private


marginal rate of return on codification investments.  A low rate of return may in turn result in the existence of a large community of people possessing tacit knowledge.  In other words, there will be a market for the workers whose functions include the storage and transfers of the knowledge from firm to firm.  Of course, the presence of a thick labor market as a medium through which knowledge can be accessed further reduces incentives to codify, provided that the heterogeneity, perishability and autonomy of these organic knowledge repositories does not give rise to offsetting costs.  (See the discussion of policy issues in section 2, above.)

A self-reinforcing, positive feedback process of that kind can generate multiple equilibria.  If, for example, there are high returns to codification, more knowledge will be codified.  This will decrease the value of alternative (thick labor market) means of maintaining and distributing (tacit) knowledge.  As the market for labor to perform that function shrinks, the relative value of codification would tend to increase further.  Thus there are two possible equilibria: one with significant resources devoted to codification and a resulting high incentive to codify; and one with few resources so devoted, a thick, active market for skilled labor as the mechanism for storing and disseminating knowledge, and thus low incentives to codify.  This conclusion rests on there being substitutability in the production process between the types of knowledge transferred by these two mechanisms.

This focus on endogenous limitations indicates that costs and benefits and the resulting incentive structures are pivotal in shaping the dynamics of codification.  Emphasizing the role of the incentive structures by no means implies that the codification of new forms of knowledge is an instantaneous process: moving the boundaries between codified and tacit parts of the stock of knowledge is a matter of long-term technological and institutional evolution, involving changes in incentive structures, and in costs and benefits.


7.2 Costs, Benefits and the Knowledge Environment

In order to understand the sources and magnitudes of costs and benefits, it is necessary to put them in the context of the knowledge environment.  A first and straightforward point is that the incentives will depend to a very great extent on the possibility of proceeding to codification on the basis of pre-existing codebooks [languages, models and techniques, in the terminology of Cowan and Foray (1997)).

When the language and the model already exist, the fixed costs, those born to generate the now standard models and languages, have already been sunk: languages and models have been developed by past work, and are known by


codifiers and users.  Such a situation describes both cases in which codebooks are manifest and those in which codebooks are displaced.  The idea here is that some existing body of well-developed, stable, codified knowledge, often one that is displaced, contains the necessary concepts and relations with which to codify the knowledge in question.  The only cost then is the variable one.  On the other hand, if codebooks do not exist, or are incomplete or ambiguous, costs of codification entail more than simply the variable costs.  Further, before a language has been standardized and is stable, linguistic ambiguity implies that codes which appear to represent codified knowledge can change their meanings as the language is developed and refined, and as vocabulary expands and changes.  It is thus useful to differentiate between contexts of stability and contexts of change.


7.3 Costs and Benefits in a Stable Context

In a stable context - when there is a community of agents who have made the necessary initial investments to develop a language and to maintain efficient procedures of language acquisition for new entrants - the transfer of messages can be assimilated to transfer of knowledge, and storing messages means recording knowledge.

On the benefits side, the efficiency gains from codification will be greater in very large systems that must coordinate the complementary activities of many agents.  We identify five classes among such situations: (i) systems involving many agents and many locations; (ii) systems strongly based on recombination and reuse, and which take advantage of the cumulativeness of existing knowledge (rather than on independent innovation); (iii) systems that require recourse to detailed memory; (iv) systems which need particular kinds of description of what (and how) the agents do; and (v) systems characterized by an intensive usage of information technologies.  We take these up for further discussion ad seriatim.

First, codification will provide high benefits in stable systems characterized by specific requirements of knowledge transfer and communication.  Such needs may arise from a tendency towards delocalization and externalization, or from the development of cooperative research, entailing a spatial distribution with activity at many places.  This first effect can be appreciated without any ambiguity, for example, in science.  It operates, however, within a given ‘clique’ or network - that is, a community which shares common codes and codebooks (whether or not the latter are manifestly present to hand) and such tacit knowledge as is used in interpreting messages exchanged among the members.


Second, in (stable) systems of innovation where advances and novelties mainly proceed from recombination, reuse and cumulativeness, benefits of codification are important.  Gibbs (1994, 1997) claims that the very limited progress in the productivity of software engineering is due to an excessive dependence on craft-like skills (in contrast, for example, with chemical engineering).  The schema that Gibbs has in mind is that once an algorithm is written as a piece of code, it can be used in many applications - at least in principle.  The practical difficulty in doing so arises in part because of a lack of standardization both in the way code is written and the way algorithms are employed.  This lack of technological rationalization impedes the full realization of the opportunities provided by the reuse and recombination model.

Third, codification holds out great benefits for systems that require extensive memory and retrieval capacities (e.g. firms and organizations that have extended product or process development cycles or high rates of personnel turnover, and institutions confronted by a major technological bifurcation).  In those settings, under-investment in codification increases the day-to-day costs of locating frequently applied knowledge; and, where there are critical bodies of knowledge that are not kept in more-or-less continuous use, inadequate codification and archiving heightens the risks of ‘accidental uninvention’.  For example, according to Mackenzie and Spinardi (1995), in the nuclear weapons design process specific local and uncodified knowledge was so important that there was a constant appreciable risk that critical elements of the knowledge base would be lost simply through the turnover of scientists and engineers - a risk of technological retrogression, or at best of costly reconstruction of the organization’s previous capabilities (competencies).

The same argument is readily extended to apply in situations where knowledge has been thoroughly codified in the form of algorithms, or operating instructions, but the text of the ‘source code’ for these - or an understanding of the language in which it was recorded - has ceased to be readily decipherable, or has simply been misplaced or destroyed.  The result is a paradoxical one: the technology in which the knowledge has been embedded may continue to work, as is the case when the computer implements the machine-language version of its instructions.  But, as has been found to be the case with some major pieces of ‘legacy software’, the human agents, being no longer able to read or write, the source code, are unable to emend or elaborate those machine-language encoded instructions.  Nor can they locate and correct defects in the original source code, defects whose existence has become painfully evident.  It is possible that even beyond the range of such algorithmic


technologies, cultural inventions and culturally transmitted skills important for activities upon which social welfare depends - such as those involved in dispute resolution - may become lost because ‘the market’ for agents possessing tacit knowledge of that kind is undermined by the competition of more fully codified (legal) procedures.

Fourth, systems that require accurate descriptions of what agents are doing (either to meet quality standards constraints, to patent innovations or to enter into contractual relations with a partner) would greatly benefit from codification.  Here we can also include systems confronted with inefficient market transactions, where the traditional mechanisms of legal warranty, insurance, reputation and test are not efficient means to mitigate the effects of information asymmetry in regard to product and service quality (Gunby, 1996).  If, however, it is feasible to record production practices, some of the asymmetry can be removed, as the buyer given this information is in a better position to judge the prospective quality of the output.  The widely diffused procedural certification standards belonging to the ISO 9000 series were based upon what was, in essence, a linguistic innovation aimed at facilitating codification of quality assurance practices.

Fifth, and last but not least, a sort of cross-situation deals with the lack of productivity gains from the use of information technology (IT), due to incomplete codification.  Fully taking advantage of the potential productivity gains of IT typically demands not only the adoption of the technology but also organizational change (see e.g. David, 1991, 1994, 2000; Cowan, 1995, and references therein).  But a firm undergoing organizational change does not want to lose functionality in the process.  The firm must develop jointly the new technology and organizational structures that will reproduce old functions and create new ones (see David 1991, 1994).  It is obvious that if too much of the old functionality resides in tacit knowledge, or depends heavily on it, this task will be extremely difficult.  When the presence of tacit knowledge operates as a bottleneck, impeding the full realization of productivity potential, the firm can expect great benefits from codification (Baumol et al., 1989).  This, indeed, may be a critical role played by management consultants, to whom earlier reference was made.

In all these cases, where important operations of transfer, recombination, description, memorization and adaptation of existing knowledge (to IT) are required, it would be very costly and inefficient to keep this knowledge tacit.  Thus, there can be under-investment in codification, coexisting with ‘excess of tacitness’.  Given the nature, degree and pace of recent technical change, it is likely that the current equilibrium involves an allocation of resources


devoted to knowledge generation and transmission under conditions of incomplete codification and deliberate under-documentation.

Nonetheless, private resources continue to be poured into the production of differentiated ‘information’ that is idiosyncratically coded, whether deliberately or inadvertently, because such practices support the producers’ intentions to capture private ‘information rents’.  Such practices also occur within business corporations (and other bureaucracies) where the internal reward mechanisms have failed to align the interests of individuals (possessing specialized knowledge) with those of the larger organizational entity.  There, as in the cases where there are social inefficiencies due to the persistence in an uncodified state of knowledge that could be made more widely available in codified form for use by competing business entities, the design of incentive mechanisms is likely to prove more effective than the provision of less costly codification technologies, or the imposition and enforcement of formal disclosure requirements, in eliciting a collectively beneficial change in strategic behaviors.

Many other, rather more subtle issues are involved in considering the means through which firms and other entities can manage a process of codification where a large portion of the critical knowledge base required for functioning of the organization (its so-called ‘core competencies’) has not been articulated.  Quite often one hears of businesses which (in times of stress) apply for help from some external management consultant, who will try to identify what things the troubled firm really ‘knows how’ to do well.  A large body of modern management literature has been spun around that conceptualization of the consultant’s role, so it may be reassuring to notice this implication of our topographic structure: collective procedural knowledge may remain unarticulated even though, at some cost, it is perfectly (or at least workably) ‘codifiable’.

A more interesting issue for the skeptical economist to ponder in that connection is simply why is it that the organization - having somehow acquired and successfully deployed its ‘core capabilities’ without needing to make them explicit - should suddenly require, or find it profitable to employ, the costly services of outside management consultants to break the spell of ‘tacitness’.  In most of the specific cases discussed in the literature of professional business management that question is not posed explicitly.  But, there is a suggestion that the organization, perhaps through the attrition of key personnel, may have ‘forgotten’ what it once understood tacitly and was therefore able to act upon collectively.  Another possibility is that the operating environment of the firm might have been radically altered, without prompting a timely revision of the collective awareness of the mismatch


created between the opportunities now facing the enterprise and its capabil­ities for exploiting them. The presumption, therefore, is that it will take too long, or be too risky, to go through a tacit, trial-and-error learning process. Bringing explicit analysis to bear—and so codifying the organiza­tion’s understanding of itself and its new situation—then is deemed to prove either more expedient or less costly, or both, than continuing to operate in tacit mode (see Cobenhagen, 1998).


7.4 Costs and Benefits in the Context of Change

While many knowledge activities take place in a relatively stable context, some particular domains or sectors are characterized by knowledge environments exhibiting ongoing rapid transformations.

Models and languages are fluid, and the community of agents conversant with the models and languages is itself changing.  The fluidity of the language implies that there is uncertainty about what the messages actually mean, because there is uncertainty, and perhaps change, with regard to the vocabulary in which they are written.  Even when scientific papers express new discoveries, or re-examine old results in some ‘natural’ language, much jargon specific to the subject matter remains; ‘terms of art’ are employed whose meanings are lost on outsiders; and, in formal modeling, definitions of variables specific to the model may remain in flux as the model itself is modified and reconciled with observational data.  In an important sense, the progress of research involves - and requires - the stabilization of meanings, which is part of the social process through which the stabilization of beliefs about the reliability of knowledge comes about.

To the extent that codification is taking place under those conditions, the benefits deriving from it have substantial ‘spillover’ elements, as they contribute largely to the modeling and language development parts of the exercise.  There may be competition among different basic models, and so among the basic tenets and vocabulary of the language.  Until this competition is resolved, the community of potential knowledge generators and users will have difficulty communicating, and the value of knowledge codification that arises from dissemination will be reduced.  Thus the codification process in this environment generates some immediate value, which derives both from worth of the content of the messages that agents can transmit and interpret with less effort and expense, and from the value to the agent of storage and retrieval of his own knowledge.  However, it has greater value as an investment good: a contribution to the resolution of the competition among variant languages and models.


It is in the context of change that we expect to find situations of ‘excess codification’.  That is to say, the accumulation of successive generation of codes can prevent the development of radically new knowledge, simply because explicating and understanding it would require entirely new codes.  As argued by Arrow (1974, p. 56), codification entails organizational rigidity and uniformity while increasing communication and transaction efficiency:

the need for codes mutually understandable within an organization imposes a uniformity requirement on the behavior of participants.  They are specialized in the information capable of being transmitted by the codes, so that they learn more in the direction of their activity and become less efficient in acquiring and transmitting information not easily fitted into the code.

It is clear, therefore, that codification can have unfortunate consequences for creativity and radical changes.  Like a larger category of coordination mechanisms to which technical interoperability standards belong, codified knowledge can be a potent ‘carrier of history’ - encapsulating influences of essentially transient and possibly extraneous natures that were present in the circumstances prevailing when particular codes took shape.  Having that power, it can become a source of ‘lock in’ to obsolete conceptual schemes, and to technological and organizational systems that are built around those. [23]

The second problem we have thus identified deals with ‘excess inertia’.  There are high fixed costs to be borne in the process of codification, especially when the cognitive environment is changing.  Roughly put, costs of learning and developing languages in which new codes are being written will be incurred during the period when the knowledge environment is in flux, whereas benefits will accrue (from some of those investment) during a subsequent period of stabilization and widespread dissemination of the information.  During a period of change, infrastructure is developed, languages and models are built, learned and standardized, and a community of agents with shared tacit knowledge grows.  All of these investments contribute to a reduction in the fluidity of the knowledge environment, and conduce to hastening the enjoyment of the increasing returns from more widespread application that are permitted by the stabilization of organizational and technological knowledge.  As a network of users of the

23. The argument follows that developed by David (1994) regarding the sources of path-dependence in the evolution of organizations and institutions, without reiterating the important respects in which those social entities differ from technological constructs.


knowledge expands, learning costs continue to decline and coordination externalities are likely to grow more significant as a source of social benefits.

If developing new languages and models allocates the fixed cost to one generation while many future generations benefit from the new infrastructure to codify knowledge, there is an intergenerational externality problem which can result in a lack of adequate private (or social) incentives for allocating resources to the development of more powerful codes and systematizing those that already exist.  Solutions that would help mitigate this kind of time inconsistency problem entail the development of relevant markets (which may significantly increase the benefits even for the first generation of developers), or the creation of infinitely lived institutions that do not discount the future so strongly.  Alternatively, society may rely upon the cultivation of altruistic preferences for the welfare of coming generations, to whom a greater stock of useable knowledge can be bequeathed (see Konrad and Thum, 1993).


8. Conclusions and the Direction of Further Work

This paper has looked intensively and critically at one of the several dimensions David and Foray (1995) identified in their schematic description of the space in which ‘knowledge-products’ were distributed. [24]  Our focus has been maintained on the most problematic and, for many economists, the most esoteric of the three axes defining that space: the dimension along which codification appeared at one extremum and tacitness occupied the other.  This has permitted some further unpacking of the economic determinants of codification decisions, and the resources committed thereto, and has revealed that the term tacit is being used so loosely in the current economics of science and technology literature that important distinctions, such as the one separating that which is uncodified in a particular context and that which will not (likely) be codified at all, are blurred or entirely lost.

Also lost from view in too many treatments now appearing in the economics literature dealing with tacit knowledge and experience-based learning (learning ‘by doing’ and ‘by using’) is the important difference between procedural knowledge (know-how) and declarative propositions (know-what and know-why) about things in the world.  Although the subject of tacit procedural knowledge, and its regeneration in the process of working with previously codified routines, has been highlighted by Cowan and Foray (1997) and touched upon at several places here, the nature of the technological

24. The other two dimensions of that space are the continuum between secrecy and full disclosure, and the spectrum of asset ownership status ranging from legally enforced private property rights to pure public goods.  See David and Foray (1995, 1996) for further explication.


constraints and the role of economic factors affecting the scope for codification in ‘cycles of learning and knowledge transformation’ are topics that deserve and are likely to repay more thorough exploration.

In drawing out the important distinction between knowledge that is codifiable (in the sense of articulable) and that which actually is codified, and in focusing analytical attention upon the endogenous boundary between what is and what is not codified at a particular point in time, it has not been possible to adequately discuss some quite important ‘conditioning’ influences.  Most notably, this essay has had to leave for future treatment the ways in which the nature of the intellectual property rights regime and the disclosure conventions of various epistemic communities affects private strategies concerning the degree of completeness with which new knowledge becomes codified.

Those interactions, as much as the effects of changes in information technology, will have to be studied much more thoroughly before economists can justly claim to have created a suitable knowledge base upon which to anchor specific policy guidelines for future public (and private) investments in the codification of scientific and technological knowledge.



This article originated in a report prepared under the EC TSER Programme’s TIPIK Project (Technology and Infrastructure Policy in the Knowledge-Based Economy - The Impact of the Tendency Towards Codification of Knowledge).  That draft was presented for discussion by the 3rd TIPIK Workshop, held at BETA, University of Louis Pasteur, in Strasbourg, April 2-4, 1999, where it elicited many helpful comments and suggestions from our colleagues.  We acknowledge the contributions of the TIPIK teams lead by Patrick Cohendet, Franco Malerba and Frieder Meyer-Kramer to improving both the substance and the exposition of our arguments, even though it has not been possible for us to do justice to all of their good ideas in the present paper.  We are grateful also to Keith Pavitt for his probing critique of an earlier draft, and to W Edward Steinmueller and an anonymous referee for their editorial questions and recommendations.



Anderson, E. 5. (1994), Evolutionary Economics: Post-Schumpererian Contributions. Pinter: London.

Arrow, K. J. (1955), Economic Aspects of Military Research and Development, RAND Corporation Memorandum D-3l42, August 30.


Arrow, K.J. (1962), Economic Welfare and the Allocation of Resources to Inventive Activity, in R. Nelson (ed.), The Rate and Direction of Technical Change. National Bureau of Economic Research: New York.

Arrow, K. J. (1974), The Limits of Organization. Norton: New York.

Arundel, A. (1996), Enterprise Strategies and Barriers to Innovation: Preliminary Descriptive Results for the CIS Subjective Questions, paper prepared for ElMS, DGxIII, Luxembourg, May.

Arundel, A. (1997), Enterprise Strategies and Barriers to Innovation, in A. Arundel and R. Garrelfs (eds), Innovation Measurement and Policies, pp. 10 1-108. European Commission, EIMS publication 50.

Arundel, A. and I. Kabla (1998), What Percentage of Innovations are Patented?  Empirical Estimates for European Firms, Research Policy, 27, 127-141.

Balconi, M. (1998), Technology, Codification of Knowledge and Firm Competences, Revue Internationale de Systémique, 12, 63-82.

Baumol, W, S. Batley Blackman and E. Wolff (1989), Productivity and American Leadership: The Long View. MIT Press: Cambridge.

Bell, M. and K. Pavitt (1993), Technological Accumulation and Industrial Growth: Contrasts between Developed and Developing Countries, Industrial and Corporate Change, 2, 157-201.

Callon, M. (1995), Four Models of the Dynamics of Science, in S. Jasanoff(ed.), Handbook of Science and Technology Studies, pp. 29-63. Sage: Thousand Oaks, CA.

Cobenhagen,J. (1999), Managing Innovation at the Company Level: A Study on Non-sector-specific Success Factors, PhD dissertation (to be defended), Maastricht University.

Collins, H. M. (1974) The TEA Set: Tacit Knowledge in Scientific Networks, Science Studies, 4, 165-186.

Cowan, R. and D. Foray (1997), The Economics of Codification and the Diffusion of Knowledge,’ industrial and Corporate Change, 6, 595-622.

Cowan, R. (1995), The Informatization of Government as an Economic Opportunity, STJ Review, 16. Dasgupta, P and P A. David (1994), ‘Towards a New Economics of Science,’ Research Policy, 23, 487-521.

Davenport, T. H. and L Prusak (1998), Working Knowledge: How Organizations Manage What They Know. Harvard Business School Press: Boston, MA.

David, P A. (1991) General Purpose Engines, Investment and Productivity, in E. Deiaco, E. Hornell and G. Vickery (eds), Technology and Investment, pp. 141-154. London: Pinter Publishing.

David, P A. (1994) Why are Institutions the “Carriers of History”?: Path Dependence and the Evolution of Conventions, Organizations and Institutions,’ Economic Dynamics and Structural Change, 5, 205-220.

David, P A. (1997), From Market Magic to Calypso Science Policy: A Review of T Kealey’s Economic Laws of Scientific Research, Research Policy, 26, 229-255.

David, P A. (2000), Understanding Digital Technology’s Evolution and the Path of Measured Productivity Growth: Present and Future in the Mirror of the Past, in E. Brynolfsson and B. Kahin (eds), Understanding the Digital Economy. MIT Press: Cambridge, MA (forthcoming).

David, P A. and D. Foray (1995): Accessing and Expanding the Science and Technology Knowledge Base, STI, 16, 13-68.

David, P A. and D. Foray (1996), Information Distribution and the Growth of Economically Valuable Knowledge: a Rationale for Technological Infrastructure Policies, in M. Teubal et al. (eds), Technological Infrastructure Policy: An International Perspective, pp. 87-116.  Kluwer Academic Publishers: Dordrecht.

David, P A., D. Foray and W E. Steinmueller (1999), The Research Network and the New Economics of Science: from Metaphors to Organizational Behavior, in A. Gambardella and F. Malerba (eds), The Organization of Inventive Activity in Europe, pp. 303-342. Cambridge: Cambridge University Press.

Dosi, G. (1988), Sources, Procedures and Microeconomic Effects of Innovation , Journal of Economic Literature, 26, 1120-1171.


Dosi, G. and M. Egidi (1991), Substantive and Procedural Uncertainty, Journal of Evolutionary Economics, 1, 145-168.

Dosi, G., C. Freeman, R. Nelson, G. Silverberg and L Soete(eds)(l988), Technical Change and Economic Theory. Pinter: London.

Ferguson, E. S. (1992), Engineering and the Mind’s Eye. MIT Press: Cambridge, MA.

Geuna, A. (1999), Resource Allocation and Knowledge Production: Studies in the Economics of University Research. E. Elgar: Cheltenham.

Gibbons, M., C. Limoges, H. Nowotny, S. Schwartzman, P Scott and M. Trow (1996), The New Production of Knowledge. Sage: London.

Gibbs, W W (1994), The Crisis in Software, Scientific American, 241, 261-267.

Gibbs, W W (1997), Taking Computers to Task, Scientific American, 277, 282-289.

Gunby, P (1996), Explaining Adoption Patterns of Process Standards, PhD Dissertation, Department of Economics, The University of Western Ontario.

Hofstader, D. (1979), Gödel, Escher, Bach: An Eternal Golden Braid. Basic Books: New York.

Kay, J. (1999), Money from Knowledge, Science and Public Affairs, April, 12-13.

Kealey, T (1996), Economic Laws of Scientific Research. Macmillan: London,

Konrad, K. and M. Thum (1993), Fundamental Standards and Time Consistency, Kyklos, 46, 607-632.

Kuhn, T (1962), The Structure of Scientific Revolutions. University of Chicago Press: Chicago, IL.

Latour, B. (1987), Science in Action: How to Follow Scientists and Engineers Through Society. Harvard University Press: Cambridge, MA.

Larour, B. and S. Woolgar (1979), Laboratory Life: The Construction of Scientific Facts. Princeton University Press: Princeton, NJ.

Leonard-Barton, D. (1995), Well-Springs of Knowledge. Harvard Business School Press: Boston, MA.

Machlup, Fritz (1980), Knowledge: Its Creation, Distribution and Economic Significance. Vol. 1. Knowledge and Knowledge Production. Princeton University Press: Princeton, NJ.

Mackenzie, D. and G. Spinardi (1995), Tacit Knowledge, Weapons Design and the Uninvention of Nuclear Weapons, American Journal of Sociology, 101.

Nelson, R. R. (1959), The Simple Economics of Basic Scientific Research, Journal of Political Economy, 67, 323-348.

Nelson, R. R. (1992), What Is “Commercial” and What is “Public” about Technology and What Should Be?, in N. Rosenberg, R. Landau and D. Mowery (eds), Technology and the Wealth of Nations, ch. 3, pp. 57-72. Stanford University Press: Stanford, CA.

Nelson, R. R. and S. Winter (1982), An Evolutionary Theory of Economic Change. Harvard University Press: Cambridge, MA,

Newell, A. and H. A. Simon (1972), Human Problem Solving. Prentice-Hall: Englewood Cliffs, NJ.

Nonaka, I. and H. Keuchi (1995), The Knowledge Creating Company. Oxford University Press: New York.

Patel, P and K. Pavitt (1995), Patterns of Technological Activities: Their Measurement and Interpretations, in P Stoneman (ed.), Handbook of the Economics of innovation and Technical Change, pp. 14-51. Blackwell: Oxford.

Pavitt, K. (1987), The Objectives of Technology Policy, Science and Public Policy, 14, 182-188.

Penrose, R. (ed.) (1997), The Large, the Small and the Human Mind. Cambridge University Press: Cambridge.

Penrose, R. and M. Gardner (1989), The Emperor’s New Mind: Concerning Computers, Minds, and the Laws of Physics. Oxford University Press: Oxford.

Polanyi, M. (1967), The Tacit Dimension. Doubleday: New York.


Polanyi, Mi. (1958), Personal Knowledge: Towards a Post-critical Philosophy. Routledge & Kegan Paul: London.

Schutz, A. and T Luckmann (1973), The Structures of the Life-World (translated by R. M. Zaner and A. T Enge[hardtJr]. Northwestern University Press: Evanston, IL.

Shank, R. (1998), What is AI, Anyway?, in D. Partridge and Y. Wilks (eds), The Foundations of Artificial Intelligence: A Sourcebook. Cambridge University Press: New York, pp. 3-13.

Skinner, R. E. (1994), Making Tacit Knowledge Explicit, Working Paper, Stanford Knowledge Systems Laboratory, Stanford University.

Traweek, 5. (1988), Beam Times and Life Times. The World of High Energy Physicists. Harvard University Press: Cambridge, MA.

Vincenti, W G. (1990), What Engineers Know and When They Knew It: Analytical Studies in Aeronautical History. The Johns Hopkins University Press: Baltimore, MD.

von Hippel, E. (1993), Trading in Trade Secrets, Harvard Business Review, February/March, 59-64.

von Hippel, Eric (1994), Sticky Information and the Locus of Problem Solving: Implications for Innovation, Management Science, 40, 429-439.

Winter, S. G. (1987), Knowledge and Competence as Strategic Assets, in D. J. Teece (ed.), The Competitive Challenge, ch. 8, pp. 159-184. Ballinger: Cambridge, MA.