Knowledge Driven Capitalization of Knowledge

Riccardo Viale

doi:10.1007/978-3-642-40216-6_14

Methodological Cognitivism pp 345-365 | Cite as

Knowledge Driven Capitalization of Knowledge

Authors
Authors and affiliations

Riccardo Viale

Chapter

First Online: 21 September 2013

518 Downloads

Abstract

Capitalization of knowledge happens when knowledge generates an economic added value. The generation of economic value can be said to be direct when one sells the knowledge for some financial, material or behavioral good. The generation of economic value is considered indirect when it allows the production of some material or service goods that are sold on the market. The direct mode comprises the sale of personal know-how, such as in the case of a plumber or of a sports instructor. It also comprises the sale of intellectual property as in the case of patents, copyrights or teaching. The indirect mode comprises the ways with which organizational, declarative and procedural knowledge is embodied in goods or services. The economic return in both cases can be financial (for example cash), material (for example the exchange of consumer goods) or behavioral (for example the exchange of personal services). In ancient times, the direct and indirect capitalization of knowledge was based mainly on procedural knowledge. Artisans, craftsmen, doctors, and engineers sold their know-how in direct or indirect ways within a market or outside of it. Up to the first industrial revolution, the knowledge that could be capitalized remained mainly procedural. Few were the inventors that sold their designs and blueprints for the construction of military or civil machines and mechanisms. There were some exceptions, as in the case of Leonardo da Vinci and several of his inventions, but, since technological knowledge remained essentially tacit, it drove a capitalization based primarily on the direct collaboration and involvement of the inventors in the construction of machines and in the direct training of apprentices.

Keywords

Background Knowledge Cognitive Style Industrial Revolution Technical Norm Intellectual Property Right

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, to check access.

Appendix: Is Capitalization of Knowledge a Threat to Academic Life?

Academic communities are fearful that the capitalization could pollute the university goal of knowledge production per se. This fear seems to be—linked to a traditional image of the division of labor in universities. Curiosity-driven research is separated from technology-driven research. Therefore, if a university focuses on the latter, it handicaps and weakens the former. On the contrary, in my opinion, in many technological fields knowledge production simultaneously encompasses various aspects of research. The theory of polyvalent knowledge (Viale and Etzkowitz 2010b) implies that, contrary to the division of knowledge into divergent spheres—applied, fundamental, technological—or into mode 1 (disciplinary knowledge) and mode 2 (applied knowledge) (Stokes 1997; Gibbons et al. 1994), a unified approach to knowledge is gradually becoming established. In frontier areas such as nanotechnologies and life sciences, in particular, practical knowledge is often generated in the context of theorizing and fundamental research. And, on the other hand, new scientific questions, ideas and insight s often come from the industrial development of a patent and the interaction of basic researchers and industrial labs. The polyvalence of knowledge encourages the multiple roles of academics and their involvement in technology firms, and vice versa of industrial researchers in academic labs.

One way of sustaining the reliability of this theory is to verify whether or not there is any complementarity between scientific and technological activities, measured by the number of publications and patent s respectively. In the case of polyvalent knowledge , the same type of knowledge is able to generate both scientific output and technological output. Since the scientific knowledge contained in a publication generates technological applications represented by patents, and technological exploitation generates scientific questions and answers, we should expect to see some complementarity between publishing and patenting. Researchers who take out patents should show greater scientific output and a great capacity to impact on the scientific community, measured by the impact factor or citation index.

In other words, increasing integration between basic science and technology implies that there is no rivalry between scientific and technological output. The rivalry hypothesis holds that there is a crowding-out effect between publication activities and patenting. The substitution phenomenon between publications and patents stems from the inclusion of market-related incentives into the reward structure of scientists (Dasgupta and David 1985; Stephan and Levin 1996). Scientists increasingly choose to allocate their time to consulting activities and research agreements with industrial partners. They spend time locating licensees for their patents or working with the licensee to transfer the technology. Time spent doing research may be compromised. These market goals substitute peer review judgment and favor short-term research trajectories and lower quality research (David 1998). Moreover, the lure of economic rewards encourages scientists to seek IP protection for their research results. They may postpone or neglect publication and therefore public disclosure. Industry funding, commercial goals and contract requirements may lead researchers to increase secrecy with regards to research methodology and results (Blumenthal et al. 1986; Campbell et al. 2002). Both these mechanisms may reduce the quantity and the quality of scientific production. This behavior supports the thesis of a trade-off between scientific research and industrial applications.

On the contrary, a non-rivalry hypothesis between publishing and patenting is based on complementarity between the two activities. The decision of whether or not to patent is something that happens at the end of research and not before the selection of scientific problems (Agrawal and Henderson 2002). Moreover, relations with the licensee and the difficulties arising from the development of patent innovation can generate new ideas and suggestions that point to new research questions (Mansfield 1995). In a study 65 % of researchers reported that interaction with industry had positive effects on their research. A scientist said: ‘There is no doubt that working with industry scientists has made me a better researcher. They help me to refine my experiments and sometimes have a different perspective on a problem that sparks my own ideas’ (Siegel et al. 1999).

On the other hand, the opposition between basic and technological research seems to have been overcome in many fields. In particular, in the area of key technologies such as nanotechnology, biotechnology, ICT, new materials and cognitive technologies, there is continuous interaction between curiosity-driven activities and control of the technological consequences of the research results. This is also borne out by the epistemological debate. The Baconian ideal of a science that has its raison d’être in practical application is becoming popular once again after years of oblivion. And the technological application of a scientific hypothesis, for example regarding a causal link between two classes of phenomena, represents an empirical verification. An attempt at technological application can reveal anomalies and incongruence that make it possible to define initial conditions and supplementary hypotheses more clearly.

In short, the technological ‘check’ of a hypothesis acts as a ‘positive heuristic’ (Lakatos 1970) to develop a ‘positive research programme’ and extend the empirical field of the hypothesis. These epistemological reasons are sustained by other social and economic reasons. In many universities, scientists wish to increase the visibility and weight of their scientific work by patenting. Collaboration with business and licensing revenues can bring additional funds for new researchers and new equipment, as well as meeting general research expenses. This in turn makes it possible to carry out new experiments and to produce new publications. In fact Jensen and Thursby (2003) suggest that a changing reward structure may not alter the research agenda of faculty specializing in basic research.

The presence of a complementary effect or the substitution of publishing and patenting has been studied empirically in recent years. Agrawal and Henderson (2002) have explored whether at the Departments of Mechanical and Electrical Engineering of MIT patenting acts as a substitute or a complement to the process of fundamental research. Their results suggest that while patents counts are not a good predictor of publication counts, they are a reasonable predictor of the “importance” of a professor’s publications as measured by citations. Professors who patent more write papers that are more highly cited and thus patenting volume may be correlated with research impact. These results offer some evidence that, at least at the two departments of MIT, patenting is not substituting for more fundamental research, and it might even be a complementary activity.

Stephan et al. (2007) used the Survey of Doctorate Recipients to examine the question of who is patenting in US universities. They found patents to be positively and significantly correlated to the number of publications. When they broke the analysis down to specific fields, they found that the patent-publishing results persisted in the life sciences and in the physical/engineering sciences. The complementarity between publishing and patenting in life sciences has been studied by Azoulay et al. (2005). They examined the individual, contextual and institutional determinants of academic patenting in a panel data set of 3,884 academic life scientists. Patenting is often accompanied by a flurry of publication activity in the year preceding the patent application. A flurry of scientific output occurs when a scientist unearths a productive domain of research. If patenting is a by-product of a surge of productivity, it is reasonable to conclude that a patent is often an opportunistic response to the discovery of a promising area.

In the past senior scientists and scientists with the most stellar academic credentials were usually also the most likely to be involved in commercial endeavors. But a feature of the Second Academic Revolution and the birth and diffusion of entrepreneurial universities is that the academic system is evolving in a way that accommodates deviations from traditional scientific norms of openness and communitarism (Etzkowitz 2000). In fact, the data of Azoulay et al. (2005) indicate that now many patenting events take place in the early years of scientists’ careers and the slope of the patent experience curve has become steeper with more recent cohorts of scientists. Patents are becoming legitimate forms of research output in promotion decision. Azoulay et al. (2005) shows that patents and papers encode similar pieces of knowledge and correspond to two types of output that have more in common than previously believed.

The study that makes the most extensive analysis of the complementarity between patenting and publishing is by Fabrizio and Di Minin (2008). It uses a broad sample drawn from the population of university inventors across all fields and universities in the US, with a data set covering 21 years. Table 14.1 provides the annual and total summary statistics for the entire sample and by inventor status. A difference of mean test for the number of publications per year for inventors and non-inventors suggests that those researchers holding a patent applied for between 1975 and 1995 generate significantly more publications per year than non-inventors. The inventors in their sample are more prolific in terms of annual publications, to the order of 20–50 % more publications than their non-inventor colleagues. The results suggest also that there is not a significant positive relationship between patenting and citations to a faculty member’s publications.

Table. 14.1

Patenting and publishing summary statistics for inventors and non-inventors

	Inventors		Non-investors		All
	Mean	St. Dev	Mean	St. Dev.	Mean	St. Dev.
Annual pubs	3.99	5.18	2.24	2.96	3.12	4.32
Annual pats	0.56	1.55	0	0	0.28	1.14
Total pubs	79.93	84.78	43.71	47.72	62.00	71.30
Total pats	11.02	16.21	0	0	5.57	12.77

Nor was evidence of a negative trade-off between publishing and patenting found in Europe. Van Looy et al. (2004) compared the publishing output of a sample of researchers in the contract research unit at the Catholic University of Leuven in Belgium with a control sample from the same university. The researchers involved in contract research published more than the colleagues in the control sample. Univalent single sourced formats are less productive than the polyvalent research groups at the Catholic University of Louvain that, ‘(…) have developed a record of applied publications without affecting their basic research publications and, rather than differentiating between applied and basic research publications, it is the combination of basic and applied publications of a specific academic group that consolidates the groups R&D potential’ (Ranga et al. 2003). This highly integrated format of knowledge production evolved from two divergent sources: industrial knowledge gained from production experience and scientific knowledge derived from theory and experimentation.

In Italy an empirical analysis of the consequences of academic patenting on scientific publishing has been made by Calderini and Franzoni (2004), in a panel of 1,323 researchers working in the fields of engineering chemistry and nanotechnologies for new materials over 30 years. The impact of patents is positive in the quantity of publications. Development activities are likely to generate additional results that are suitable for subsequent publications, although there might be 1 or 2 years of lag. Moreover, quality of research measured by the impact factor is likely to increase with the number of patents filed in the period following the publication. Scientific performance increases in the proximity of a patent event. This phenomenon can be explained in both ways. Top quality scientific output generates knowledge than can be exploited technologically. And technological exploitation is likely to generate questions and problems that produce further insights and, consequently, additional publications. The same kind of results are found by Breschi et al. (2007) in a study made on a sample of 592 Italian academic inventors.

References

Agrawal, A., & Henderson, R. (2002). Putting patents in context: Exploring knowledge transfer from MIT. Management Science, 48(1), 44–60.CrossRefGoogle Scholar
Anderson, J. R. (1983). The architecture of cognition. Cambridge, MA: Harvard University Press.Google Scholar
Anderson, J. R. (1996). ACT: A simple theory of complex cognition. American Psychologist, 51, 355–365.CrossRefGoogle Scholar
Azoulay, P., Ding, W., & Stuart, T. (2005). The determinants of faculty patenting behavior: Demographics or opportunities? (NBER Working Paper 11348). Paper presented at the NBER Conference on Academic Science and Entrepreneurship, Santa Fe, NM.Google Scholar
Balconi, M., Pozzali, A., & Viale, R. (2007). The ‘codification debate’ revisited: A conceptual framework to analyze the role of tacit knowledge in economics. Industrial and Corporate Change, 16(5), 823–849.CrossRefGoogle Scholar
Barsalou, L. W. (2000). Concepts: Structure. In A. E. Kazdin (Ed.), Encyclopedia of psychology (Vol. 2, pp. 245–248). New York: Oxford University Press (American Psychological Association).Google Scholar
Beth, E., & Piaget, J. (1961). Etudes d’Epistemologie Genetique, XIV: Epistemologie Mathematique et Psichologie, Paris: PUF.Google Scholar
Blumenthal, D., Gluck, M., Lewis, K., Stoto, M., & Wise, D. (1986). University-industry relations in biotechnology: Implications for the university. Science, 232(4756), 1361–1366.CrossRefGoogle Scholar
Braine, M. D. S. (1978). On the relation between the natural logic of reasoning and standard logic. Psychological Review, 85, 1–21.CrossRefGoogle Scholar
Breschi, S., Lissoni, F., & Montobbio, F. (2007). The scientific productivity of academic inventors: New evidence from Italian data. Economics of Innovation and New Technology, 16(2), 101–118.CrossRefGoogle Scholar
Calderini, M., & Franzoni, C. (2004). Is Academic patenting detrimental to high quality research? An empirical analysis of the relationship between scientific careers and patent applications (CESPRI Working Paper no. 162). Milan: Università Bocconi.Google Scholar
Campbell, E. G., Clarridge, B. R., Gokhale, M., Birenbaum, L., Hilgartner, S., Holtzman, N. A., et al. (2002). Data withholding in academic genetics. JAMA: The Journal of the American Medical Association, 287(4), 473–480.CrossRefGoogle Scholar
Carruthers, P., Stich, S., & Siegal, M. (Eds.). (2002). The cognitive basis of science. Cambridge, MA: Cambridge University Press.Google Scholar
Cheng, P. W., & Holyoak, K. J. (1985a). Pragmatic reasoning schemas. Cognitive Psychology, 17, 391–416.CrossRefGoogle Scholar
Cheng, P. W., & Holyoak, K. J. (1985b). Pragmatic versus syntactic approaches to training deductive reasoning. Cognitive Psychology, 17, 391–416.CrossRefGoogle Scholar
Cheng, P. W., & Holyoak, K. J. (1989). On the natural selection of reasoning theories. Cognition, 33, 285–313.CrossRefGoogle Scholar
Cheng, P. W., & Nisbett, R. (1993). Pragmatic constraints on causal deduction. In R. Nisbett (Ed.), Rules for reasoning. Hillsdale, NJ: Erlbaum.Google Scholar
Clark, H. H. (1996). Using language. Cambridge, MA: Cambridge University Press.CrossRefGoogle Scholar
Cleeremans A. (1995). Implicit learning in the presence of multiple cues. Proceedings of the 17th Annual Conference of the Cognitive Science Society, Hillsdale, NJ: Erlbaum.Google Scholar
Cleeremans, A., Destrebecqz, A., & Boyer, M. (1998). Implicit learning: News from the front. Trends in Cognitive Science, 2, 406–416.CrossRefGoogle Scholar
Collins, A. M., & Quillian, M. R. (1969). Retrieval time from semantic memory. Journal of Verbal Learning and Verbal Behaviour, 8, 240–248.CrossRefGoogle Scholar
Cowan, R., David, P., & Foray, D. (2000). The explicit economics of codification and tacitness. Industrial and Corporate Change, 9, 211–253.CrossRefGoogle Scholar
Dasgupta, P., & David P. (1985). Information disclosure and the economics of science and technology (CEPR Discussion Papers 73). London: Centre for Economic Policy Research.Google Scholar
David, P. A. (1998). Common agency contracting and the emergence of “open science” institutions. The American Economic Review, 88(2), 15–21.Google Scholar
Etzkowitz, H. (2000). Bridging the gap: The evolution of industry-university links in the United States. In L. Branscomb et al. (Eds.), Industrializing knowledge. Cambridge, MA: MIT.Google Scholar
Etzkowitz, H. (2008). The triple helix: University-industry-government innovation in action. London: Routledge.CrossRefGoogle Scholar
Fabrizio, K. R., & Di Minin, A. (2008). Commercializing the laboratory: Faculty patenting and the open science environment. Research Policy, 37(5), 914–931.CrossRefGoogle Scholar
Fondazione Rosselli. (2008). Different cognitive styles between academic and industrial researchers: A pilot study. Retrieved from http://www.fondazionerosselli.it
Gibbons, M., Limoges, C., Nowotny, H., Schwartzman, S., Scott, P., & Trow, M. (1994). The New production of knowledge: The dynamics of science and research in contemporary societies. London: Sage.Google Scholar
Giere, R. N. (1988). Explaining science. Chicago, IL: Chicago University Press.CrossRefGoogle Scholar
Grossman, M., Smith, E. E., Koenig, P., Glosser, G., Rhee, J., & Dennis, K. (2003). Categorization of object descriptions in Alzheimer’s disease and frontal temporal demential: Limitation in rule-based processing. Cognitive Affective and Behavioural Neuroscience, 3(2), 120–132.CrossRefGoogle Scholar
Jackendoff, R. (2007). Language, consciuosness, culture. Cambridge, MA: MIT.Google Scholar
Jensen, R., & Thursby, M. (2003, May). The academic effects of patentable research. Paper presented at the NBER Higher Education Meeting, Cambrigde, MA.Google Scholar
Johnson-Laird, P. N. (1993). Human and machine thinking. Hillsdale, NJ: Lawrence Erlbaum Associates (It. Translation (1994). Deduzione, Induzione, Creatività. Bologna, Italy: Il Mulino).Google Scholar
Johnson-Laird, P. (2008). How we reason. Oxford: Oxford University Press.CrossRefGoogle Scholar
Lakatos, I. (1970). Falsification and the methodology of scientific research programmes. In I. Lakatos & A. Musgrave (Eds.), Criticism and the Growth of Knowledge. Cambridge, MA: Cambridge University Press.Google Scholar
Mansfield, E. (1995). Academic research underlying industrial innovation: Sources, characteristics, and financing. Review of Economics and Statistics, 77(1), 55–65.CrossRefGoogle Scholar
March, J., & Simon, H. A. (1993). Organizations (2nd ed.). Cambridge, MA: Blackwell Publishers.Google Scholar
Markman, A. B., & Gentner, D. (2001). Thinking. Annual Review of Psychology, 52, 223–247.CrossRefGoogle Scholar
Merton, R. (1973). The sociology of science. Theoretical and empirical investigations. Chicago, IL: University of Chicago Press.Google Scholar
Mokyr, J. (2002a). The gifts of Athena: Historical origins of the knowledge economy. Princeton, NJ: Princeton University Press (It. Translation (2004). I doni di Atena, Bologna: il Mulino).Google Scholar
Mokyr, J. (2002b). Innovations in an historical perspective: Tales of technology and evolution. In B. Steil, G. Victor, & R. Nelson (Eds.), Technological innovation and economic performance. Princeton, NJ: Princeton University Press.Google Scholar
Moore, G. E. (1922). The Nature of Moral Philosophy. Philosophical Studies. London: Routledge & Kegan Paul.Google Scholar
Mowery, D., & Rosenberg, N. (1989). Technology and the pursuit of economic growth. Cambridge, MA: Cambridge University Press.CrossRefGoogle Scholar
National Science Foundation. (2002). Converging technologies for improving human performance. Washington, DC: National Science Foundation.Google Scholar
Nisbett, R. E. (2003). The geography of thought: How Asians and Westerners think differently and why. New York, NY: The Free.Google Scholar
North, D.C. (2005). Understanding the Process of Economic Change. Princeton, NJ: Princeton University Press (It. Translation (2006). Capire il processo di cambiamento economico, Bologna: il Mulino).Google Scholar
Politzer, G. (1986). Laws of language use and formal logic. Journal of Psycholinguistic Research, 15, 47–92.CrossRefGoogle Scholar
Politzer, G., & Nguyen-Xuan, A. (1992). Reasoning about promises and warnings: Darwinian algorithms, mental models, relevance judgements or pragmatic schemas? Quarterly Journal of Experimental Psychology, 44, 401–421.CrossRefGoogle Scholar
Pozzali, A., & Viale, R. (2007). Cognition, Types of ‘Tacit Knowledge’ and Technology Transfer. In R. Topol & B. Walliser (Eds.), Cognitive Economics. New Trends. Oxford: Elsevier.Google Scholar
Ranga, L. M., Debackere, K., & von Tunzelman, N. (2003). Entrepreneurial universities and the dynamics of academic knowledge production: A case of basic vs. applied research in Belgium. Scientometrics, 58(2), 301–320.CrossRefGoogle Scholar
Reber, A. S. (1993). Implicit learning and tacit knowledge. An essay on the cognitive unconscious. Oxford: Oxford University Press.Google Scholar
Rosenberg, N., & Birdzell, L. E. (1986). How the west grew rich. the economic transformation of the economic world. New York, NY: Basic Books (It. Translation (1988). Come l’Occidente è diventato ricco, Bologna: il Mulino).Google Scholar
Rosenberg, N., & Mowery, D. (1998). Paths of innovation. Technological change in 20th-century America. Cambridge, MA: Cambridge University Press (It. Translation (2001). Il Secolo dell’Innovazione, Milano: EGEA).Google Scholar
Ross, B. H. (1996). Category learning as problem solving. In D. L. Medin (Ed.), The psychology of learning and motivation: Advances in research and theory (Vol. 35, pp. 165–192). New York, NY: Academic Press.Google Scholar
Rumain, B., Connell, J., & Braine, M. D. S. (1983). Conversational comprehension processes are responsible for fallacies in children as well as in adults: If is not the biconditional. Developmental Psychology, 19, 471–481.CrossRefGoogle Scholar
Searle, J. (1995). The construction of social reality. New York, NY: Free.Google Scholar
Searle, J. (2008). Philosophy in a new century. Cambridge, MA: Cambridge University Press.CrossRefGoogle Scholar
Siegel, D., Waldman, D., & Link, A. (1999). Assessing the impact of organizational practices on the productivity of university technology transfer offices: An exploratory study (NBER Working Paper#7256). Cambridge, MA: National Bureau of Economic Research.Google Scholar
Smith, E. E., & Kosslyn, S. M. (2007). Cognitive psychology. Mind and brain. Upper Saddle River, NJ: Pearson-Prentice Hall.Google Scholar
Shinn, T. (1982). Scientific disciplines and organisational specificity: The social and cognitive configuration of laboratory activities. Sociology of the Sciences, IV, 239–264.CrossRefGoogle Scholar
Stephan, P. E., Gurmu, S., Sumell, A. J., & Black, G. (2007). Who’s patenting in the university? Evidence from the survey of doctorate recipients. Economics of Innovation and New Technology, 16(2), 71–99.CrossRefGoogle Scholar
Stephan, P. E., & Levin, S. G. (1996). Property rights and entrepreneurship in science. Small Business Economics, 8(3), 177–188.CrossRefGoogle Scholar
Sternberg, R. J. (2009). Cognitive psychology. Belmont: Wadsworth.Google Scholar
Stokes, D. E. (1997). Pasteur’s quadrant: Basic science and technological innovation. Washington, DC: Brookings Institution Press.Google Scholar
Thaler, R. H., & Sunstein, C. R. (2008). Nudge. Improving decisions about health, wealth, and happiness. New Haven, CT: Yale University Press.Google Scholar
Van Looy, B., Ranga, L. M., Callaert, J., Debackere, K., & Zimmerman, E. (2004). Balancing entrepreneurial and scientific activities: Feasible or mission impossible? An examination of the performance of university research groups at K. U. Leuven, Belgium. Research Policy, 33(3), 443–454.CrossRefGoogle Scholar
Viale, R. (1991). Metodo e società nella scienza. Milano: Franco Angeli.Google Scholar
Viale, R. (2008a). Origini storiche dell’innovazione permanente. In R. Viale (a cura di). La cultura dell’innovazione. Milano: Editrice Il Sole 24 Ore.Google Scholar
Viale, R. (2009). Cognitive styles in academic and industrial research. New York, NY: Columbia University. http://www.italianacademy.columbia.edu/publications_working.html#0809
Viale, R., & Etzkowitz, H. (2010b). Anticyclic triple helix. In R. Viale & H. Etzkowitz (Eds.), The capitalization of knowledge: A triple helix of university-industry-government (p. 2010). Cheltenham: Edward Elgar.Google Scholar
Von Wright, G. H. (1963). Norms and action. A logical enquiry. London: Routledge.Google Scholar

Copyright information

Authors and Affiliations

Riccardo Viale
- 1

1.Rosselli FoundationTorinoItaly

Knowledge Driven Capitalization of Knowledge

Abstract

Keywords

References

Copyright information

Authors and Affiliations

Personalised recommendations

Cite chapter