Knowledge, Innovative Activities and Industrial Evolution F R A N C O M A L E R B A and L U I G I O R S E N I G O (CESPRI—Bocconi University, Via Sarfatti 25, 20136 Milan, Italy. Email:
[email protected])
We argue that the distinction between tacit and codified knowledge is indeed very important, but it constitutes only a part of the categorization of the dimensions of knowledge relevant for understanding innovative activities and firms’ and industrial evolution. In particular, we emphasize the relevance of the notion of competencies and of some further properties of knowledge, like technological regimes (opportunities, accessibility and cumulativeness), domains of knowledge (in terms of technology, demand and applications) and knowledge complementarities (and the related issues of coordination and integration of these complementarities).
Industrial and Corporate Change Volume 9 Number 2 2000
1. Introduction The idea that knowledge has a crucial importance for the performance and growth of firms, regions and countries has become in recent years so widely diffused that, as such, it now dangerously borders on triviality. What is more interesting is to understand how knowledge can be characterized and how it produces its impact on the economy. In very generic terms, all this amounts to the statement that we need better conceptualizations of what knowledge is, what its relevant dimensions are and, as a consequence, what the mechanisms are through which knowledge improves welfare. Indeed, some important progress has been achieved in this direction. The discussion about tacit and codified knowledge in this special issue of Industrial and Corporate Change is one example. Together with the tacit-codified distinction, it has been increasingly recognized that the concept of knowledge is more complex and multifaceted than in its more simplified versions. Thus, different types and forms of knowledge are likely to exert quite different effects on the way economic activities are organized, on productivity and on the overall rates of technological and economic progress. © Oxford University Press 2000
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Knowledge, Innovative Activities and Industrial Evolution Along these lines, this paper start from a discussion of tacit and codified knowledge and then moves to identify other main dimensions of knowledge that are relevant for an understanding of firms’ innovation and growth and of industrial evolution. The starting point of the paper lies in the recognition that knowledge cannot simply be treated as information, and that processes of learning and innovation are hardly reducible to standard processes of acquisition of informtion. We claim that the distinction between tacit and codified knowledge focuses attention on one fundamental aspect of the process through which knowledge influences economic activities, namely the processes of diffusion. However, it tends to neglect other essential aspects of that process: how knowledge is then used to generate new knowledge, new products, processes and services (although, as is well known, it might be difficult in theory and practice to clearly distinguish and separate these two aspects). We claim that notions such as competencies or organizational capabilities, elusive as they might be, constitute the bridge between the two. The key point of the paper is that other dimensions of knowledge, coupled with the notion of learning processes and of competencies, are important for understanding the ways firms and industrial organization evolve. We recognize that tacit and codified knowledge are very important dimensions, but we claim that in order to have a deeper understanding of the relationship between knowledge and competencies on the one side, and the boundaries of firms, the sectoral organization of innovative activity and industrial evolution on the other, we need to consider other aspects of knowledge. In this paper, we discuss some of them. The first aspects refer to accessibility (the opportunity of gaining knowledge external to firms), cumulativeness (the extent to which new knowledge builds upon current knowledge) and technological regime (which includes opportunity, cumulativeness and appropriability, as well as other features of the knowledge base of innovative activities in an industry). The second ones refer to the domains of knowledge in terms of technology and in terms of demand and applications. The third ones refer to knowledge complementarities, and the related issues of coordination and integration of these complementarities. The consideration of these dimensions of knowledge greatly enriches an understanding of the determinants of the patterns and organization of innovative activities in an industry. The paper is organized in the following way. In section 2 we discuss the distinction between tacit and codified knowledge. In section 3 we propose a notion of competence somewhat related to knowledge: competencies keep together the various components of the stock of knowledge, identify and exploit complementarities between them, and act on the transformation 290
Knowledge, Innovative Activities and Industrial Evolution process finalized at the generation of new artifacts and knowledge. In section 4 we broaden the analysis by discussing examples of the relationship between knowledge, competencies, the sectoral organization of innovative activities and industrial evolution, looking at dimensions of knowledge such as accessibility, cumulativeness and technological regimes, the domains of knowledge and complementarities.
2. Some Preliminary Remarks on Tacit versus Codified Knowledge The debate about tacitness versus codification—firstly introduced by Nelson and Winter (1982)—has the important function of focusing the attention of economists on the fact that it may be profoundly misleading to treat knowledge as information. Rather, productive and innovative activities usually require the use of capabilities that are not adequately conceptualized as information, because they imply knowledge which is structured and ‘packaged’ in specific ways. Thus, two agents endowed with the same information may well end up doing different things (and/or doing the same thing in a better or worse way), because the cognitive structures of different individuals or groups are likely to be developed through experience, exposure to particular problems, etc., and hence their cognitive understanding of the information is different. The notion of tacitness has been used, developed and interpreted in a large variety of contexts, sometimes—as Cowan et al. (1998) correctly notice—even generating deeply conflicting implications in terms of both firms’ strategy and policy suggestions. In particular, the tacit versus codified knowledge distinction, as it contributes to define the degree to which knowledge is accessible to economic agents, has become very important for explaining the forms of organization of economic activities. In turn, the concept of tacit knowledge has been used to underpin the quest for a definition of the notions of competencies, core competencies, organizational capabilities and the like that have become in recent years so prominent in the management literature (Prahalad and Hamel 1990; Teece and Pisano, 1994) and in the theory of the firm (Nelson and Winter, 1982; Winter, 1987; Chandler, 1992) with important implications also for trade and growth theory. More recently, the issue has been revived starting from quite different grounds. First, it has been argued that new information technologies accelerate the degree of codification of knowledge. This is indeed a very important and interesting issue and—in essence—an empirical question. As such, it should be settled at the empirical level, although it might be very hard to find 291
Knowledge, Innovative Activities and Industrial Evolution adequate measures, especially if one wanted to measure the relative importance of tacit and codified knowledge.1 Second, even more interestingly, the questions have been asked what are the consequences of codification and what drives the processes of codification. From this perspective, in particular, the tacit versus codified nature of knowledge constitutes the basic bottom line for discriminating among alternative modes of organization of innovative and more generally economic activities. In principle, codified knowledge is likely to have the properties of a public good. Moreover, codification is likely to induce, ceteris paribus, division of labour, by reducing the transaction costs implied in the exchange of knowledge between different agents, by allowing the modularization of knowledge and, as a consequence, its separability and specialization in different phases or domains of the processes of knowledge production and use by different agents (Arora and Gambardella, 1996; Cowan and Foray, 1997; Cowan et al., 1998).2 Important as it is, however, the assumption of too sharp a distinction between tacit and codified knowledge may become misleading. Similarly, the properties of knowledge that are relevant for the analysis of the patterns of firms’ and industrial organization and change may not be simply reduced to the tacit-codified opposition. It has to be noted, for example, that codification does not necessarily coincide with ease of accessibility to knowledge. First, it is quite obvious that codified knowledge might be proprietary and tacit knowledge can be in the public domain. Second, an important difference relates to things that are hard to learn, even if they are codified and in the public domain (e.g. advanced mathematics). This might be the case when there is simply a lot to learn and the amount to be learned is always relative to the agent involved, i.e. to his knowledge.3 Similarly, codification does not necessarily entail division of labour. Again, 1 It should be noted that the degree of codification of knowledge is not equivalent to its importance. Even if more ‘data’ or information were generated by information technology, it is not clear that these greater volumes of data are in themselves of any economic significance. In addition, it is not at all clear that codification involves the ‘conversion’ of tacit knowledge into codified formats; may represent the creation of information that was not previously explicitly observed at all, as in the case of instrumentation of many physical processes. We owe this observation to Ed Steinmueller. 2 A stereotyped version of the relationship between knowledge, competencies and industrial dynamics focused on the tacit-codified distinction would run in the following way. Increasing codification of knowledge would allow for the possibility of exchanging knowledge among firms and therefore would allow for increasing specialization in knowledge generation, followed by exchange of it through modularities and open interfaces. See Arora and Gambardella (1996) for a much richer analysis of these kind of processes. 3 The bulk of this argumentation can be imperfectly summarized in the paradox: why is it that high-tech science is more difficult to learn that applied knowledge?
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Knowledge, Innovative Activities and Industrial Evolution the distinction tacit/codified is certainly important, but not sufficient nor perhaps necessary. The claim here is that as knowledge becomes codified, it becomes easier to transmit, use and separate. As a consequence, division of labour is likely to arise and to be beneficial to welfare. However, while it is most likely that codified knowledge is easier to transmit than tacit knowledge, this proposition applies strictly only to the ‘same’ knowledge. Once again there are things that are hard (easy) to transmit irrespective of their degree of codification. Moreover, even if there are certainly cases when tacit knowledge can be converted into codified knowledge without substantially altering the nature and the content of knowledge itself, in other cases the process of conversion is likely to produce codified knowledge which is simply uncommensurable with the ‘original’ tacit knowledge. In other words, the comparison between tacit and codified knowledge in terms of ease of transmission might be profoundly misleading and sometimes unwarranted, because one is comparing two totally different things. A similar argument applies to the ease of use. Clearly, the availability of handbooks and manuals in a library makes things easier; but this does not suffice for mastery in use. Again, there are things that are easy to use even if they are not codified. Finally, as we shall discuss at more length later, codification does not necessarily entail ‘separability and modularization’. There is an important distinction between codification and ‘embodiment’ of performances in artifacts/services: databases, washing machines, cars, chemical plants, mechanical tools, etc. Here, it is not codification per se that matters, but the fact that it was possible to separate provision of the knowledge from the use of the ‘embodied’ knowledge. This is clearly not necessarily linked to codification in any ordinary sense of the terminology. Another way to look at the limitations of an excessive simplification of the tacit/codified distinction might begin by noting that the relative emphasis on the tacit versus codified nature of knowledge underlies two different modes of conceptualizing the role of knowledge and its impact on industrial dynamics and growth. On the one hand, the more knowledge is conceptualized as codified, the more the role of knowledge is described as an input in the process of production of goods, services and other knowledge. Processes of codification make knowledge more easily available to economic agents and shift upwards the ‘production function’. How knowledge diffuses in the economy becomes the centre of the analysis. On the other hand, the more knowledge is conceptualized as tacit, the more the emphasis falls on the analysis of the processes through which individual agents use that knowledge to generate new products and processes that are initially difficult for rivals to reproduce and thus constitute competitive variety in the economic system. 293
Knowledge, Innovative Activities and Industrial Evolution That is to say, innovation and variety creation rather than diffusion constitutes the main object of inquiry. In the former approach, the main unit of analysis becomes the structure of connections among agents. In the latter, the description of the cognitive and organizational mechanisms which lead to innovation on the basis of the available knowledge acquires instead a central role in the analysis. As mentioned previously, a sharp distinction between the two aspects is difficult both in practice and in theory. Substantial evidence supports the idea that diffusion is likely to imply some important ‘creative’ activities on the part of the adopters. Conversely, it is quite obvious that knowledge diffusion, as defined by the nature of knowledge and by the structure of connections among agents, constitutes a primary force of technological advancement, and is a key aspect and a pre-condition for processes of innovation. In this respect, codification and diffusion are a fundamental part of learning processes, but ultimately only one part. Recent progress in the analysis of the distinction between codified versus tacit knowledge (Cowan et al., 1998) has gone far beyond these crude alternatives. The recognition of the fact that tacitness or codification are not simply a property of knowledge as such, but are at least partly endogenous, being influenced by economic incentives and other social/institutional processes, represents a major step forward. Similarly, in the latest and more sophisticated analysis of the notion of what codified knowledge is (Cowan et al., 1998), it is not only recognized that there are several ways through which codification may occur (including the routinization of certain activities). It is also suggested that between codification and tacitness there is a continuum rather than a sharp discontinuity. In this perspective, codification is essentially interpreted as the process of development of consensus and authority, and the boundaries between codified and tacit knowledge become somewhat blurred. More importantly, it reintroduces at the core of the analysis the view that the basic difference between the two is some sort of structure which is imposed by agents on the information they have and on what they are able to do, given the same (codified) knowledge. From this perspective, a key point of the question is, in our view, not so much the degree of codification of knowledge as such, but the fact that at least some part of the knowledge is and remains tacit. As we shall argue in the next section, a main point of interest is precisely how the codified and the tacit parts of knowledge interact. We shall also argue that the notion of competencies is closely related to this issue.
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3. From the Use of Knowledge to the Production of Knowledge and Artifacts: the Relevance of Competencies As mentioned previously, the debate on the role of knowledge and on the distinction between tacit and codified knowledge has spurred the emergence of concepts like competencies and organizational capabilities. These concepts underpin a substantial body of thinking in managerial theory and in the theory of the firm. These notions have resisted a precise definition and remain too elusive to be defined theoretically, let alone to be measured empirically. However, the first questions to ask are why such concepts may be useful, in which domains they pertain and, above all, why it is necessary to distinguish between knowledge (tacit and codified) and competencies. Here we shall only provide a few general comments on why the notion of competencies— as distinct from knowledge—is in our view useful and perhaps necessary for understanding the relationships between (tacit and codified) knowledge, innovative activities, and firms’ and industrial evolution. Basically, the concept of competencies tend to capture the ideas that: 1. Different agents (firms) know how to do different things in different ways (domains, levels of performance, etc.). As such, they constitute one of the major sources of both the heterogeneity among firms and their differential competitiveness. Indeed, the notion of competencies has been developed as an extension of the original ‘resource-based’ theory of the firm. 2. Competencies correspond to specific ways of packaging knowledge about different things. Therefore they have intrinsic organizational content and implications. There is a difference between having access to some knowledge and using that knowledge to produce products, services and/or new knowledge. The intuitive appeal of the notion of competencies is relatively straightforward in the extreme case when knowledge is equated to information and agents are assumed to have no cognitive limitations. In this sense, competencies might be defined simply as the information set controlled by any one agent. Put in this way, the notion of competencies can indeed be considered redundant. What is needed is just the notion of information, the hypothesis of perfect cognition and additional assumptions about the mechanisms that define the distribution of information among agents. The issue becomes more complex as soon as it is recognized that knowledge is different from information in that it also includes tacit knowledge. In this context, competencies might be thought of as that part of knowledge which 295
Knowledge, Innovative Activities and Industrial Evolution is not codified but is tacit. Even this formulation, though, is not satisfactory. Clearly, competencies embody not only tacit knowledge, but also information and—more generally—codified knowledge. Again, any notion of competencies may be considered redundant, insofar as what an agent can do is simply describable by the stock of knowledge he/she possesses. The need to distinguish between knowledge and competencies arises more clearly, in our view, when the issue of the cognitive abilities of agents and of the nature of such cognitive processes is explicitly considered. In particular, it arises with the recognition that: 1. There are different forms and degrees of codification, and that codification implies the imposition of a particular structure on available knowledge. 2. The knowledge stock of any one agent includes knowledge about different things (domains and functions) that have to be integrated for specific purposes. First of all, if an agent’s knowledge includes both codified and tacit knowledge, there must be something that links (even imperfectly) the two parts. A fortiori, this must be the case if codified knowledge is itself composed of different parts, which are codified in different ways and through different codes. At one level, the stock of knowledge of any one agent pertains to different objects, e.g. states of the world, properties of nature, the identities of other agents, procedures for doing things (Dosi et al., 1996). Or, as Lundvall points it, know-what, know-why, know-who and know-how (Lundvall and Johnson, 1994). At another level, knowledge is usually composed of various domains and content: technical and scientific disciplines, knowledge of specific markets, knowledge of the institutional environment, etc. Thus, the ‘stock of knowledge’ of any one agent is likely to be always composed of various parts, which can be integrated (or decomposed) in different ways and to different degrees, according to the specific situation the agent faces. Thus, even if knowledge were completely codified, but comprises knowledge about different domains and functions (see, for example, the distinction between bodies of understanding and bodies of practices), mechanisms for integrating those pieces are necessary. Such mechanisms are likely to be highly specific as a function of the specific ‘composition’ of the knowledge stock of any one agent, of the specific task the agent is facing, etc.—the more so, of course, if the stock of knowledge includes pieces of tacit knowledge and knowledge codified in different ways or degrees. In this respect, competencies may be thought of as that part of knowledge 296
Knowledge, Innovative Activities and Industrial Evolution that pertains to the linking together of the various bits of tacit and codified knowledge, that allows them to be mapped onto each other through codes, languages and practices.4 In other words, competencies are the ‘metastructure’—including representations of the world and problem solving processes like algorithms, heuristics and routines—that allows combining the necessarily different structures imposed on the codified and the tacit parts of the whole ‘stock of knowledge’, integrates its various subsets and enables the agent to ‘use’ the knowledge for specific purposes. This meta-structure is agent-specific and context specific, precisely because it links agent-specific knowledge to context-specific domains.5 Competencies could then be understood as the ways through which knowledge is elicited, used and applied to specific contexts and domains. A slightly different way to rephrase this idea consists in recognizing that there is a difference between having access to knowledge and using it. This is a difficult and subtle point. Indeed, knowledge may well be defined as including not only accessibility but also mastery. This is assumed in the standard discussion of the economics of information, where it is stated at the very beginning that one of the main properties of information is that it is costly to produce, but it is then indefinitely reusable at zero costs (and agents have no problem in distinguishing relevant information). However, it might still be useful to distinguish between two types of knowledge: knowledge as an input in the process of ‘doing something’ and knowledge as the ability to use those inputs in the transformation process finalized at the generation of new artifacts and knowledge. For example, in order to develop a new car, a manufacturer needs knowledge about various different technologies, production processes, markets, etc. These are the inputs. In order to actually develop a new car, a different form of knowledge is required: how to combine these inputs together and use the inputs in the transformation process aimed at the creation of a new car. A large part of the theory of diffusion points precisely to the point that the adoption of new technologies entails long and costly learning processes, adaptation to specific contexts (products, geographical areas, etc.), construction of complementary assets, infrastructures and institutions, etc. Moreover, the same evidence suggests that adoption often results in improvements, modifications and incremental innovations of the original technology which 4 In this sense, competencies are not simply reducible to ‘cognitive maps’, insofar as they also include specific problem solving processes. We owe this observation to Ed Steinmuller. 5 In some respects, the current discussion about the widening of the technological base of large firms coinciding with the narrowing of their degree of product diversification might be interpreted as suggesting precisely the relevance of these specific ‘integrative’ or ‘organizational’ capabilities (Gambardella and Torrisi, 1998; Grandstand and Pavitt, 1997; Pavitt, 1998).
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Knowledge, Innovative Activities and Industrial Evolution are very similar in nature to innovative activities. Similarly, knowledge about the physical properties of matter does not automatically translate into knowledge of the properties of artifacts that could be produced on the basis of that knowledge. What is at stake here is not whether such knowledge is codified or not; it may well be completely codified. The point is that these two types of knowledge are likely to entail different cognitive, organizational and more generally economic processes. As mentioned in the introduction, if knowledge is described as an input, then what matters is basically its diffusion; but there is more than this, empirically and theoretically. As we all know, there is a difference between teaching and doing research, between reading and writing an essay on the basis of what we have read. Different types of mental activities and institutional/organizational structures are involved and often mixed together. Often, in the economics of innovation this distinction is captured by contrasting diffusion and imitation on the one hand and innovation on the other. In this context, competencies can again be considered as that part of knowledge that allows the transformation of knowledge inputs into specific products. And it is also often stated that innovation (the process of creation of new knowledge) is intrinsically tacit. Again, we will not enter into this discussion. We simply do not know enough to be confident in making strong assertions on whether ‘creative activities’ can or cannot be codified in principle. Even a cursory glance at the current debate and research efforts in cognitive sciences and artificial intelligence, however, strongly suggests that it is precisely this kind of ‘procedural knowledge’ that appears to be more difficult to codify. This has to do with constructive processes of categorization, construction of mental models and representations, processes of decomposition and reintegration of problems. In any case, as discussed previously, the ease of use of some particular fragment of knowledge as an input certainly reduces (ceteris paribus) ‘production costs’, but as such it has very few immediate implications on how such knowledge is then integrated with other fragments of knowledge in the process of production, and the more so where the processes of creation of new knowledge are concerned. In other words, codified knowledge (like information) certainly reduces the costs of acquisition of particular fragments of knowledge, but its effective use may well entail substantial costs. Put another way, our proposition is simply that as the economics of codification has provided important contributions to our understanding of the processes through which knowledge becomes more accessible and is diffused throughout the economy, there is still another branch of the ‘economics of knowledge’ that needs to be developed which has to do with the processes 298
Knowledge, Innovative Activities and Industrial Evolution through which knowledge is used and created. Again, codification of procedural knowledge, modularization of production and research techniques, and introduction of general-purpose technologies are likely to have profound effects on the economy (Arora and Gambardella, 1994); but, as we shall discuss at more length in the following sections, these implications are not straightforward or simple. For the time being, it is worth noting that to the extent that codification or the introduction of general-purpose knowledge simplifies the communication of that knowledge (after the relevant codes have been learned), it necessarily changes the structure of the knowledge stock or redefines complementarities and interfaces. In this sense, codification may well increase the relevance of specific competencies, rather than reducing it. Summing up, we have tried to argue that there are dimensions of the concept of knowledge that are not simply reducible to the distinction tacit/ codified. Knowledge comprises information: codified knowledge to different degrees and in various forms; tacit knowledge, knowledge as an input and knowledge about how to integrate those inputs (which can still be partly tacit and partly codified). Competencies keep together the various components of the stock of knowledge, identify and exploit complementarities between them, and act on the transformation process finalized at the generation of new artifacts and knowledge.
4. The Relationship between Knowledge, Innovative Activities and Industrial Dynamics: a Theoretical-appreciative Discussion In this section we move from the previous discussion of competencies to some key aspects of knowledge (different from the tacit-codified dimension one) that we consider relevant for an analysis of the relationship between knowledge, competencies, the sectoral organization of innovative activities and industrial evolution. We will keep the discussion at a theoreticalappreciative level. The first dimensions of knowledge that we analyse refer to accessibility (i.e. the opportunity of gaining knowledge external to firms), cumulativeness (i.e. the extent to which new knowledge builds upon current knowledge) and the ones constituting a technological regime (which, in addition to cumulativeness, include also opportunity, appropriability and other features of the knowledge base of the innovative activities in an industry). We then move to the domains of knowledge, and discuss them in terms of technology and in terms of demand and applications. Finally we discuss the role of knowledge complementarities, and the related issues of coordination and integration of these complementarities. We show that the consideration of these dimensions of knowledge greatly enriches the 299
Knowledge, Innovative Activities and Industrial Evolution understanding of the determinants of the patterns and organization of innovative activities in an industry.
4.1 Accessibility and Cumulativeness Knowledge may have different degrees of accessibility and cumulativeness. With accessibility we refer to opportunities of gaining knowledge that are external to firms. This knowledge may be internal to the industry (thus favouring imitation within an industry) or external to it (thus affecting the availability of technological opportunities to incumbents and new firms). In both cases greater accessibility of knowledge decreases the concentration of an industry. Let us first examine accessibility of knowledge internal to the industry. Greater accessibility means lower appropriability: competitors may gain knowledge about new products and processes and, if competent, imitate those new products and processes. Given a rather uniform distribution of competencies, greater accessibility decreases the monopoly rents and market shares of innovators and reduces concentration in an industry. Most models of industrial dynamics show this result clearly, starting from the early models of Dasgupta and Stiglitz (1980) and Nelson and Winter (1982). Accessibility of knowledge external to the industry is related to scientific and technological opportunities, both in terms of level and in terms of sources. Here the external environment may affect firms through human capital with a certain level and type of knowledge or through scientific or technological knowledge developed in firms or other organizations such as universities or research laboratories. The sources of technological opportunities markedly differ among technologies and industries. As Freeman (1982), Rosenberg (1982) and Nelson (1982), among others, have shown, in some industries opportunity conditions are related to major scientific breakthroughs in universities. In other sectors, opportunities to innovate may often come from advancements in external R&D, equipment and instrumentation. In still other sectors, external sources of knowledge in terms of suppliers or users may play a crucial role. Not all external knowledge may be easily used and transformed into new artifacts. If external knowledge is easily accessible, easily transformable into new artifacts and exposed to a lot of actors (such as customers or suppliers), then innovative entry may take place (Winter, 1984). On the other hand, advanced integration capabilities are necessary among firms (Cohen and Levinthal, 1989), and the industry may be concentrated and formed by large, established firms. 300
Knowledge, Innovative Activities and Industrial Evolution Cumulativeness of knowledge means something different from accessibility: the degree by which the generation of new knowledge builds upon current knowledge. One can identify three different sources of cumulativeness. The first source is related to learning processes and dynamic increasing returns at the technology level. The cognitive nature of learning processes and past knowledge constrains current research, but also generates new questions and new knowledge. The second source is related to organizational capabilities. These capabilities are firm-specific and can be improved only gradually over time. They implicitly define what a firm learns and what it can hope to achieve in the future. The third source is related to feedback from the market. For instance, persistence may be simply the outcome of ‘success-breedssuccess’ processes like those used in Nelson and Winter’s (1982) models: innovative success yields profits that can be reinvested in R&D, thereby increasing the probability of further innovation. More generally, cumulativeness of knowledge generates specific trajectories of technological advancements that firms follow over time. From this discussion, it follows that cumulativeness may be observed at various levels of analysis. One is at the technological and the firm level. Here high cumulativeness implies an implicit mechanism leading to appropriability of innovations. In the case of low appropriability conditions and knowledge spillovers within an industry, however, it is also possible to observe cumulativeness at a more aggregate level, such as the sectoral level. Finally, cumulativeness may be present at the local level. In this case high cumulativeness within specific locations is more likely to be associated with low appropriability conditions and spatially localized knowledge spillovers. Cumulativeness at the technological and firm levels creates first-mover advantages and generates high concentration. Firms who have a head start develop new knowledge based on the current one and introduce continuous innovations of an incremental type. This is the case of most models of industrial dynamics in which cumulativeness at the firm level is at the base of firms’ increasing market shares (for an example see Nelson and Winter, 1982).
4.2 The Technological Regime Accessibility and cumulativeness are two key dimensions of the notion of technological regime, which is composed by opportunity and cumulativeness, as well as by appropriability and other basic features of the knowledge base underpinning innovative activities in a sector. The notion of the technological regime dates back to Nelson and Winter (1982) and provides a description of the knowledge environment in which firms operate. More generally, Malerba 301
Knowledge, Innovative Activities and Industrial Evolution and Orsenigo (1996, 1997a,b) have proposed that a technological regime is a particular combination of some fundamental properties of technologies: opportunity and appropriability conditions; degrees of cumulativeness of technological knowledge; and characteristics of the relevant knowledge base. Let us briefly discuss these basic dimensions, some of which have been already mentioned above: 1. Opportunity conditions reflect the abundance of knowledge external to an industry. They are related to the ease of innovating for any given amount of resources invested in search; 2. Cumulativeness conditions capture the properties that today’s knowledge forms the starting point for tomorrow’s knowledge advancements. This means that current innovative firms are more likely to innovate in the future in specific technologies and along specific trajectories than noninnovative firms. 3. Appropriability conditions summarize the possibilities of protecting innovations from imitation and of extracting profits from innovative activities. High appropriability means the existence of ways to successfully protect innovation from imitation. Low appropriability conditions denote an economic environment characterized by widespread knowledge externalities and spillovers. Moreover, firms utilize a variety of means in order to protect their innovations, ranging from patents, to secrecy, continuous innovations and the control of complementary assets (Teece, 1986; Levin et al., 1987). The effectiveness of these means of appropriability largely differ from industry to industry, thus affecting the level as well as the nature of knowledge externalities. 4. The knowledge base refers to the same key dimensions of knowledge considered relevant for the innovative activities of an industry. Tacit and codified are among the dimensions of the knowledge base that could be relevant for innovation. In addition, the means of knowledge transmission may vary greatly among sectors. One can argue that the more knowledge is ever-changing, tacit and part of a larger system, the more relevant are informal means of knowledge transmission, like ‘face-to-face’ talks, personal teaching and training, mobility of personnel and even acquisition of entire groups of people. Moreover, it should also be stressed that such means of knowledge transmission are extremely sensible to the distance among agents. On the other hand, the more knowledge is standardized, codified and modular, the more relevant are formal means of knowledge communication, such as publications, licenses and patents. In such circumstances, one can argue that geographical proximity does 302
Knowledge, Innovative Activities and Industrial Evolution not play a crucial role in facilitating the transmission of knowledge across agents. A fundamental implication of this argument is that the nature of knowledge strongly affects the way technological opportunities and knowledge externalities are transmitted among distant firms (Breschi and Malerba, 1997).6 More work needs to be done in order to go into this issue in depth at the empirical level. The notion of the technological regime also provides the basis for an explanation of the diversity in the patterns of innovation across sectors and technologies. It has been noted that the introduction of even rough proxies of opportunity and appropriability conditions significantly improves the performance of econometric tests on the relationships between market structure (e.g. firm size and degrees of concentration) and innovation (Cohen and Levin, 1989), and that the technological regimes allow to explain fundamental differences in the structure of innovative activities such as the one between Schumpeter Mark I and Schumpeter Mark II technologies. Schumpeter Mark I is characterized by ‘creative destruction’, with technological ease of entry, and a major role played by entrepreneurs and new firms in innovative activities. This model is likely to be characterized by high and wide opportunities, low cumulativeness, and a knowledge base external to the firms and rather accessible. In this model, many different firms explore the search space, which is too big and wide to be controllable by any single firm. New firms enter because the knowledge base may be new and requires to be learned. Independently of whether the new knowledge base is codi-fied or not, it is simply different and difficult to learn for incumbents. Firms’ technological advantages may be quite large, but innovations are neither kept proprietary nor lasting enough to generate a technological leadership. The limited levels of cumulativeness at the firm level soon make innovative advantages obsolete, thus leaving room for imitation and entry of new innovators. Lower opportunity conditions just reinforce the Schumpeter Mark I pattern. Notice, however, that the interplay between these variables may not be linear and may generate complex dynamic patterns. Schumpeter Mark II is characterized by ‘creative accumulation’, with the prevalence of large, established firms and the presence of relevant barriers to entry for new innovators. This regime is characterized by the dominance of a stable core of a few large firms with limited entry. Typically, in these industries knowledge and technological progress is strongly cumulative at the firm level 6 For example, Hicks (1995) shows that in the case of scientific research the strong geographical proximity found for citations to public research indicates the presence of both codified and tacit knowledge that is transmitted.
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Knowledge, Innovative Activities and Industrial Evolution and the opportunities for innovation are high. Again, the properties of the knowledge base and firms’ competencies affect significantly the organization of the industry. For example, firms may show different degrees of integration and the industry may show different mechanisms for coordinating complementarities, ranging from vertical integration (the mainframe industry in the 1960s), to tacit interfaces (Silicon Valley), to complex systems of subcontracting (auto, aerospace), to vertical division of labour regulated by market exchanges. Recent research (see Klepper, 1997) has clearly shown that during the evolution of industries changes may occur in Schumpeterian patterns of innovations. According to an industry life cycle view, a Schumpeter Mark I pattern of innovative activities may turn into a Schumpeter Mark II pattern. Early in the history of an industry, when knowledge is changing very rapidly, uncertainty is very high and barriers to entry very low, new firms are the major innovators and are the key elements in industrial dynamics. When the industry develops and eventually matures, and technological change follows well-defined trajectories, economies of scale, learning curves, barriers to entry and financial resources become important in the competitive process. Thus, large firms with monopolistic power come to the forefront of the innovation process (Utterback and Abernathy, 1975; Gort and Klepper, 1982; Klepper, 1996). On the other hand, in the presence of major knowledge, technological and market discontinuities, a Schumpeter Mark II pattern of innovative activities may be replaced by a Schumpeter Mark I pattern. In this case, a rather stable organization characterized by incumbents with monopolistic power is displaced by a more turbulent one with new firms using the new technology or focusing on the new demand (Henderson and Clark, 1990; Christensen and Rosenbloom, 1994). The empirical evidence (Malerba and Orsenigo, 1996) suggests the existence of differences across sectors in the patterns of innovative activities but similarities across countries in the patterns of innovation for a specific sector, thus providing support for the relevance of technological regimes in determining sectoral invariances in the patterns of innovative activities, as long as opportunity, appropriability and cumulativeness conditions are similar across countries. Empirical evidence on the similarity of some of the dimensions of appropriability and cumulativeness across advanced industrialized countries is now emerging. Malerba and Orsenigo (1990), Heimler et al. (1993), and the PACE and CIS surveys have shown that some dimensions of appropriability and cumulativeness are rather similar across several countries. The ability to generate and exploit opportunity conditions is however less similar across countries. This ability is related to the level and range of university research, 304
Knowledge, Innovative Activities and Industrial Evolution the presence and effectiveness of science–industry bridging mechanisms, vertical and horizontal links among local firms, user–producer interaction, and the types and level of firms’ innovative efforts (Nelson, 1993). A more direct attempt at trying to assess directly the relationships between the variables which define technological regimes on the one hand and the Schumpeterian patterns of innovation on the other has been provided by Breschi et al. (2000). The main results which emerge from such analysis show that variables related to technological regimes are individually significant at the conventional statistical level and have the expected sign. The relationship between technological regimes and Schumpeterian patterns of innovation, however, is mediated by the specific features of each national system of innovation. In sum, some essential features of the knowledge base of innovative activities within an industry (its degree of tacitness and codification, its accessibility, its means of knowledge transmission, and so on), as well as the learning conditions (in terms of technological opportunity, cumulativeness and appropriability), define a technological regime. Technological regimes explain the diversity across sectors of the patterns of innovative activities and the forces that, if not mediated by the specificities of the national or local systems of innovations, tend to make these patterns similar even across countries.
4.3 Knowledge Domains: Technology and Demand A second characteristic of knowledge is related to its domains. The domains of knowledge may differ drastically, thus affecting the type of competencies and the competition in an industry. Here a basic distinction between knowledge regarding technology and knowledge regarding demand may be introduced as an extreme example As far as knowledge about technologies is concerned, technologies may differ drastically in their knowledge bases. Thus firms develop competencies that are highly sector- and technology-specific (Pavitt, 1990; Grandstand, 1998). An additional point is that within the same industry the knowledge at the base of innovative activity changes over time. It could do this in two different ways: an evolution towards a dominant design and a drastic change. In the first case the growth of concentration and the rise of large dominant firms are highly possible. Thus concentration will increase (Utterback, 1994). In the second case, new types of competencies may be required for innovation. As a consequence, major industrial turbulence and entry of new firms take place 305
Knowledge, Innovative Activities and Industrial Evolution and industrial leadership has a major turnover (Jovanovich and McDonald, 1984; Tushman and Anderson, 1986; Henderson and Clark, 1990). A different type of domain of knowledge concerns applications, users and demand. Firms may learn the main characteristics of users over time and develop competencies that are related to the specific features of consumers and demand. A change in demand, users and applications represents a change in the context in which firms operate and may favour the entry of new firms rather than the success of established ones (Christensen and Rosenbloom, 1994; Levinthal, 1998). During the long-term evolution of an industry, major technological and demand discontinuities may take place which greatly affect market structure and the survival of established firms. A model of the long-term evolution of the computer industry has examined the dynamics of concentration in the presence of technological and demand discontinuities (Malerba et al., 1999). Technological discontinuities have been absorbed successfully by industry leaders much more than demand discontinuities. When a technological discontinuity takes place within an existing demand, incumbents are able to shelter the major change in the technology through the lock-in of existing customers. On the other hand, a major change in demand is also usually associated with changes in the related technologies, so that firms have to pass through several shifts in terms of knowledge, with major consequences for the entry and growth of new entrants. These results emphasize the need to examine the possible trade-offs and complementarities between knowledge about technologies and knowledge about demand (Malerba et al., 1998).
4.4 Knowledge Complementarities Finally, knowledge complementarities are the third aspect that we want to emphasize. When knowledge complementarities are present, various effects on the evolution of an industry are possible. Modularity and Division of Labour In a first approximation, if modularity is present, it is possible to have a division of labour among agents. Firms become specialized in the generation and production of standard pieces of knowledge or artifacts (modules) that are exchanged through the market. In upstream industries, specialized suppliers may enjoy economies of scale and learning from specialization, while downstream firms may concentrate on the use and the integration of different artifacts of pieces of knowledge (Arora et al., 1997). Thus, in this case of modularity, a simple and clear division of labour among agents is possible with respect to technology and demand. 306
Knowledge, Innovative Activities and Industrial Evolution Some firms may generate new technologies while others will apply it to different demand domains. However, this discussion of the increasing division of labour in presence of complementarities and modularities is too simple and does not take into consideration a variety of factors: coordination and integration advantages, the level and distribution of competencies in the industry, and more traditional issues related to transaction costs. We will discuss these issues in two cases: the organization of innovative activities and industrial dynamics in the case of a component and a system, and the role of design in the transformation process. Complementarities with Coordination When knowledge complementarities are present, organizational integration (and not specialization) may takes place because of the need to have better coordination and integration of different complementary capabilities in systemic innovations. In fact, a fundamental aspect of firms’ activities consists in the integration of different pieces of knowledge (Pavitt, 1998) through coordination and integration capabilities due to complementarities, particularly in the case of systemic innovations. If new components and subsystems have to be inserted into new systems, dynamic interdependencies and feedbacks may take place between components and systems, requiring continuous changes and adjustments in both (Langlois and Robertson, 1996). As a consequence, vertical integration in the development and production of both components and systems may be necessary. This is likely to be the case whenever the knowledge underpinning firms’ activities has a strong tacit component or there are high transaction costs in exchanging codified information. This is more so in the case of systemic innovations and rapid change (Teece, 1986): in this case, firms may decide to vertically integrate because ‘the cost of persuading, negotiating with, coordinating among and teaching outside suppliers in the face of economic change or innovation’ may be too high (Langlois-Robertson, 1996). The relationship between knowledge complementarities, competencies and industry dynamics has been examined by a model (Malerba et al., 1998) which refers to an upstream industry (a component industry such as semiconductors) and a downstream industry (a system industry such as computers). However, the argument is more general and may refer also to a system industry and a service (such as software) or a distribution industry. To these basic reasons for integration, others may be added. The first refers to the distribution of competencies within industries. Lack of already developed external capabilities in components or a clear superiority of internal competencies with respect to external ones may be a major reason for vertical 307
Knowledge, Innovative Activities and Industrial Evolution integration (Langlois and Robertson, 1996). Thus firms will (vertically) integrate in the development and production of upstream components if capabilities for the development and production of components are not available on the market. Similarly, even in the presence of external capabilities, firms will remain vertically integrated if internal capabilities are superior to external ones. In the first case, in a highly uncertain environment the lack of available external competencies in component development production pushes firms to develop internal capabilities, because it would take too much time, cost and effort to persuade external suppliers or distributors with no specific competencies in an activity to learn and build specific competencies in that activity. In the second case, superior internal capabilities with respect to external ones would push firms to remain vertically integrated into components. A second reason for integration is the presence of a large one-firm demand for components directed to many small component firms with similar capabilities in a relatively stable environment, with moderately growing rates of technological advance and competence-enhancing technological change in the upstream activity. Thus a firm may vertically integrate because of security of supply reasons: suppliers are too small and cannot grow quickly enough. In this case, a large downstream firm is likely to have the resources to (at least) maintain the pace of technical change, while suppliers cannot progress fast enough or produce at a sufficiently high scale. On the other hand, even in the presence of knowledge complementarities and coordination/integration advantages, disintegration and specialization may take place because of the working (in addition to modularity) of other mechanisms that more than outweigh the advantages of integration. First, there is a competence variety effect. Disintegration and specialization may take place because of too many different component firms with different capabilities being present in a highly innovative and turbulent environment. In the case of a fast rate of technological change, high uncertainty and a wide distribution of capabilities in the upstream industry, innovations may come from every quarter. Therefore a downstream firm may decide not to vertically integrate because it does not want to be locked into an inferior technology. This is particularly true in the first stages of an industry life cycle (often following major technological discontinuities) and/or in cases where barriers to entry are low in the upstream industry (a Schumpeter Mark I pattern). Thus a reason for vertical disintegration is related to a variety effect in knowledge, capabilities, visions and strategies. Second, there is a highly competent producer effect. Vertical disintegration and specialization may take place because of the emergence and establishment of a upstream highly capable industry leader. Over time, during the evolution 308
Knowledge, Innovative Activities and Industrial Evolution of an industry, a technological and market leader may get established as a result of competition and selection in the upstream industry, sometimes associated with the emergence of a dominant design (Klepper, 1996). This technological and market leader is extremely advanced in terms of competencies and research and production facilities, and has become the standard setter in the industry. Therefore a downstream firm may decide not to vertically integrate and instead purchase components from the market, because it will never be able to match the R&D efforts and the pace of technological advance of the competent supplier. The emergence of a capable industry leader is likely to be linked to the existence of various forms of static and dynamic increasing returns: typically, cumulative R&D and innovation, and large marketing expenditures generating brand-loyalty, but also more conventional variables such as static economies of scale in large markets. These factors constitute a powerful engine of specialization. Moreover, the same variables that induce the development of a strong upstream industry (and hence vertical disintegration) are also likely to generate the emergence of high degrees of concentration in that industry. In conclusion, the organization of innovative activities when knowledge complementarities among components and systems are present is the result of the dynamic interplay of knowledge, competencies and market structure, and more broadly of the coevolution of the upstream and downstream industries. These determinants should not be examined in isolation. Rather, more than one may be present in a specific situation, thus creating trade-offs between specialization and integration. Moreover, the determinants of vertical integration and specialization may change during the evolution of the upstream and downstream industries. Thus, in different moments of the evolution of an industry we may observe different reasons for vertical integration and disintegration. The time dimension is particularly important. In fact, while transaction costs-based explanations have a short-term dimension, capability-based explanations have a long-term view.7 In particular, it takes time to develop competencies. If a firm decides to exit from the development and production of components, and later on decides to re-enter through internal development and production (and not through the acquisition of an already exiting external supplier), it takes time and a lot of effort to do so.8 7 However, in the case of transaction costs that are persistently reproduced, it may take substantial time before a viable approach to their resolution is developed. Yet, this may involve another type of competence. 8 However, what is often also required is the effective transfer of currently accumulated knowledge by whatever means works. This may include licensing (such as Korean semiconductors), foreign expert teams (such as in chemical process industries) and so forth.
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Knowledge, Innovative Activities and Industrial Evolution Complementarities in the Transformation Process These considerations can be pushed even further by considering also the transformation process of knowledge into artifacts. In such a transformation process, a version of the discussion of modularity discussed above would claim that, in principle, a division of productive labour among specialist firms in design, production and distribution should emerge (Arora et al., 1997). This should hold also for artifacts of a system type in which different knowledge and component modules have to be integrated. In systems artifacts, with extreme modularity, system integrators are able to adjust to major changes in components and at the same time are themselves able to introduce completely new designs with existing component modules. For example, this has been the course of improvements in personal computer designs during much of the 1990s. However, this type of division of labour is possible only within a limited range of changes in system designs and component modules. Particularly when the environment is highly uncertain about what kind of system design will emerge, modularization in production does not imply organizational modularization and specialization in design. While remaining disintegrated at the production level, firms involved in design have to span over a wide range of knowledge domains along the transformation chain, from scientific advancements to users domains to rules and regulations for certifications (Grandstand, 1988; Brusoni and Prencipe, 1998; Pavitt, 1998). Thus the need for knowledge integration has created key actors characterized by a wide range of knowledge of different types. This is the case of the organization of design activities in industries such as aerospace, chemical engineering and telecommunication (Brusoni and Prencipe, 1998). In these industries the emergence of key actors with system integration capabilities has steered the rate and direction of technical advance. In sum, the role of knowledge complementarities may have quite different effects on industrial evolution in terms of specialization or organizational integration. In the case of modularities of these complementarities, a division of labour among agents is possible. Specialization may also take place because of the working of a competence variety effect: too many different component firms with different capabilities are present in a highly innovative and turbulent environment. On the other hand, in the case of systemic innovation, the need of better coordination and integration may lead to organizational integration, particularly if the distribution of competencies (within and across related industries) are asymmetrical. In addition, when the environment is highly uncertain about what kind of system design will emerge, modularization in production does not imply specialization in design.
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5. Concluding Remarks In this paper we have inserted the discussion of tacit and codified knowledge into a broader discussion of the relevant dimensions of knowledge that are necessary for an understanding of innovative activities at the sectoral level and their links with industrial evolution. We have argued that the distinction is indeed very important, but it constitutes only a part of the categorization of the dimensions of knowledge relevant for understanding innovative activities and firms’ and industrial evolution. We claimed that—irrespective of the degree of codification—knowledge always involves processes of categorization, frames or cognitive structures that integrate various fragments of knowledge and are likely to be highly specific—the more so as long as some part of knowledge, however ‘small’, is tacit. What remains interesting and important, therefore, is understanding the interaction between the various pieces of (tacit and codified) knowledge. Accordingly, we argued that the notion of competencies constitutes a key concept in this respect, precisely because it aims at capturing the ways through which agents structure their knowledge, and manages the interactions between differentiated fragments of information, knowledge codified in different codes and tacit knowledge. This is particularly important because an excessively simplified distinction between tacit and codified knowledge tends to generate too sharp a dichotomy between the two fundamental aspects of the process through which knowledge influences economic activities, namely the processes of diffusion on the one hand and the processes of use of knowledge to generate new knowledge, new products, processes and services on the other. Both in theory and in practice, the two processes are strictly intertwined and complementary. Again, what is more interesting and important is understanding how the two processes interact, rather than treating them as if they were separable. Finally, we argued that there are some further properties of knowledge, beyond the degree of tacitness and codification, and beyond competencies that have to be taken into account in order to understand the features of firms and industry organization. Particularly, we stressed the relevance of what we summarized in previous works as technological regimes: opportunities (how much there is to learn, how difficult it is, etc.), accessibility and cumulativeness. Technological regimes explain the diversity across sectors of the patterns of innovative activities and the forces that, if not mediated by the specificities of the national or local systems of innovations, tend to make these patterns similar even across countries. In addition, we have focused on the domains of knowledge and have introduced a basic distinction between knowledge regarding technology and knowledge regarding demand. Finally, we have 311
Knowledge, Innovative Activities and Industrial Evolution looked at the role of knowledge complementarities in terms of specialization or organizational integration. As an example, we have discussed how some properties of knowledge—some of which are not directly related to the tacitcodified distinction—can influence the patterns of division of labour. In sum, much remains to be done for establishing a better understanding of the relationships between the properties of knowledge and the patterns of firm and industrial organization and evolution. Tacitness and codification are certainly important properties of knowledge, but other dimensions are important too. More generally, we believe that the nature of knowledge (e.g. the ‘problem’ faced by agents), the way such knowledge evolves and the learning procedures that are used to increase agents’ knowledge are key determinants of the dynamics of firm and industry structure, even at a much more fine-grained level than the one discussed here.9 The identification of those properties of knowledge and learning, of how they differ and combine in different industries and how they influence the patterns of firms’ and industrial organization constitutes an exciting research area.
Acknowledgements We thank Ed Steinmueller for his careful editorial assistance and his deep comments on a preliminary draft. We also thank Patrick Llerena and an anonymous referee for very useful and extensive comments.
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