Abstract
Technological knowledge can be understood as a collective good when it is the outcome of the integration between internal to the firm investments in R&D and learning and the absorption of competencies and technologies provided by external organizations (such as, other firms, universities, R&D centers). Technological communication is a crucial strategy in such dynamic interaction between the firm and the system. Only under effective conditions of technological communication the private and social benefits derived from the exploitation of spillovers are higher than the private losses due to partial inappropriability. The article presents a simple microeconomic framework to understand knowledge production and distribution, integrating the effects and conditions of technological communication within a knowledge production function. The interaction between internal investments in R&D and learning, partial inappropriability, the conditions for the access to external knowledge and the exploitation of spillovers explains increasing returns in the production of knowledge.
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Notes
This is for instance the case of large firms obtaining important scale and scope economies in R&D (Chandler 1990).
Henkel and Von Hippel (2005) recently stretched this main line of analysis to include the effects of freely revealed innovations (p. 81) for user-driven innovation and the related welfare. Although this article does not address explicitly the role of users and consumers in the innovation process, it can be easily seen that implications for intermediary users are relevant, as far as firms need to access, use and adapt external technological competencies, often embodied in technologies and artifacts, in order to generate new knowledge and to innovate.
Instead, the framework presented here is more consistent with Arrow (1969).
The amount of external knowledge available in the system and potentially accessible by the firms is modeled as a function of the conditions of infrastructures and institutions enabling the diffusion of existing knowledge, such as technology transfer offices, standards, generic technologies and infra-technologies (Tassey 2000, 2005b). Here, g′ > 0, g″ > 0. In more formal terms, the effects of structural and institutional characteristics of the system on the availability and diffusion of external knowledge can be usefully appreciated with the percolation methodology. Percolation methodology can account for the variety of economic actors involved in knowledge and innovation dynamics, the connectivity and interactions between such actors and the way in which previous knowledge and innovation are diffused within the system in order to generate further technological knowledge and innovations. Here, it is possible to specify the different elements that enter in the percolation process in terms of the mixed percolation probability PP, as follows: PPZ = P(pf, pt, G, E). The mixed percolation probability PP is a non-decreasing function of pf and pt, respectively the receptivity and connectivity probabilities of a given network Z, of G, the number of firms in such a network and, finally, of E, the extent to which innovation opportunities, generic technology and external scientific and technological breakthroughs are able to impact the knowledge production in the system. For the formal exposition of percolation methodology see David and Foray (1994).
In order to fully account for the strong complementarities at place between internal and external knowledge as inputs into the production of new knowledge, the relationship between these inputs has been modeled as multiplicative, along the lines of the models developed by Kremer (1993) and Caves (2000). Thus, the parameter θ for the amount of external knowledge the firm can absorb varies between 0 and 1.
In this article I will consider the amount of external knowledge accessible by the firm and viable in the knowledge generation process of the firm as a given property of the system in which the firm plays. I will not articulate the case of proactive firms that are able to creatively react to the characteristics of the system in terms of, for instance, the relative input prices or poor connective structures. Hence, I will not articulate the implications of different relative prices for the way in which firms decide their investments in knowledge production. For this complementary analysis, see Patrucco (2009).
Tassey’s article and this one share a departure point, that is the appreciation of the gap between social and private rates of return of innovation investments. Tassey then develops a microeconomic policy model to understand and react to the problem of underinvestment in innovation. In this article I leave aside specific policy issues and focus on the conditions of effective interaction between the firm and its environment—although policy implications could be derived from the present analysis, for instance in terms of investments in technological infrastructures and channels of technological communication. These deserve separate and careful treatment.
The effect of the complementarity between internal and external knowledge on increasing returns is similar to that between generic and specific knowledge in Romer (1986, 1990), although the general working of the knowledge production process developed in this article is different from EGT models. Moreover, the complementarity between internal and external knowledge only accounts for potential increasing returns. When for instance γ2 is equal to zero, then standards decreasing returns do take place. Increasing returns are possible only when a minimum amount of internal and external knowledge is available in the firm and the system, along the lines, for instance, of the analysis developed by Cohen and Levinthal (1989).
In the following, I will develop a qualitative analysis of the interactions between appropriability conditions and availability of external knowledge. For instance, with very strong IPRs regimes ensuring high levels of appropriation of the results of internal investments in R&D&L, there is limited scope for the exploitation of knowledge spillovers and the diffusion of external knowledge.
Clearly, in Fig. 1a, \(\gamma_{{2}({\rm N}^{\prime})} < \gamma_{{2}({\rm N}^{\prime\prime})} < \gamma_{{2}({\rm N}^{\prime\prime\prime})} < \gamma_{{2}({\rm N}^{n})}\).
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Acknowledgments
Cristiano Antonelli, Davide Consoli, Stan Metcalfe, Francesco Quatraro, Ed Steinmueller and two anonymous referees of this Journal provided useful suggestions and comments. I acknowledge the financial support of the Collegio Carlo Alberto of the University of Torino through the funding of BRICK - Bureau of Research on Innovation, Complexity and Knowledge.
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Patrucco, P.P. The economics of collective knowledge and technological communication. J Technol Transfer 33, 579–599 (2008). https://doi.org/10.1007/s10961-008-9085-z
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DOI: https://doi.org/10.1007/s10961-008-9085-z
Keywords
- Collective knowledge
- Increasing returns
- Knowledge diffusion
- Knowledge spillovers
- Technological communication