Abstract
Knowledge, its use and production, is seen as the central resource within societies and organizations today. Modern societies are no longer characterized as industrial societies but as knowledge societies; modern economies are knowledge-based economies. Social and economic development and performance depend on the capacity of individuals and organizations to continuously search for and exploit new knowledge (i.e., their capacity for innovation).
This is the revised version of a paper that I presented at the research conference on “International Competitiveness and Innovative Capacity in Universities and Research Organizations—New Modes of Governance” in May 2001 at the FÖV, Speyer. This became the starting point for building a network of researchers with the aim of analyzing the reforms of the German research system and their consequences for research conditions and performance.
A research stay at the Interuniversity Consortium on Social Science Theory and Methodology at the University of Groningen, NL, afforded me the time to return to this unfinished manuscript. I acknowledge funding from the National Science Organization of the Netherlands and thank my hosts at the ICS Groningen for providing such a stimulating research environment.
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Notes
- 1.
A knowledge-based economy is characterized by the fact that the competitive edge of firms has changed from price competition to continuous innovation and improvement (OECD 1998). Besides rapid changes in goods and services, it involves the creation and management of change becoming a mission in itself and knowledge transactions becoming more important and more numerous (Maskell 2000; Weingart 2001).
- 2.
Information is defined as data (signs + syntax) that are put into a context of a problem at hand. Knowledge is the goal-oriented integration of several informational elements (Steinmüller 1993: 236).
- 3.
Knowledge, and particularly fundamental scientific knowledge, used to be thought of as a pure public good. Since Arrow (1970), it is widely held that fundamental knowledge is not marketable since nobody can be excluded from its use. Once you know the “product,” you can use this knowledge “for free” without having to pay for it. Moreover, knowledge does not diminish when it is used; the consumption of knowledge does not bear the characteristic of rivalry in consumption. This state of affairs used to legitimize public financing of basic research since otherwise basic knowledge would be undersupplied. But this is not the whole story. Even knowledge as a public good cannot really be taken from the shelf and put to new applications without further investments by the user. And its potential value can only be recognized if a firm is involved in the knowledge process and knowledge community producing this new knowledge. This is one of the causes of the increasing importance of non-market processes. The necessity to ensure this absorptive capacity to some extent lowers the temptation for free-riding on the knowledge investments of other actors. To make use of their knowledge, you must invest in its “absorption” and thereby you (inadvertently) contribute to the collective knowledge pool. This underlies the growing activities of industry in basic research.
- 4.
- 5.
Combining knowledge of a codified type but also using tacit knowledge will yield concatenated knowledge, which is typical of scientific instrumentation and technical engineering. This type of knowledge is produced in application contexts (transdisciplinary knowledge). This type of research is of a more applied type and can build bridges between basic and applied research (Jansen 1995a; de Solla Price 1984). It is typical of the so-called mode 2 of knowledge production (Gibbons et al. 1995).
- 6.
See Buskens and Yamaguchi (1999: 303) on the longer diffusion times in transit models compared to contagion models. In transit models, a resource actually travels within a network (e.g., a scientist with particular tacit knowledge), whereas in contagion models, a resource is duplicated by any transfer that stays with the transmitter and the receiver (such as explicit knowledge). Also the effect of local density (i.e., social closure) on trapping information in a special corner of the network is more severe in the transit model.
- 7.
Thus, Cook and Brown redefined the spiral of knowledge by insisting that knowledge is not converted but new knowledge created.
- 8.
- 9.
- 10.
Innovation used to be defined as the first marketing of a new product or the first introduction of a new process. It was preceded by the stage of “invention” (i.e., making a design for a product/process by applying several types of knowledge) and followed by the diffusion of the new product/process. Nowadays, these stages are no longer neatly differentiated. Many feedback loops have to be acknowledged (Kline and Rosenberg 1986; Brooks 1994; OECD Oslo Manual 1992; Schumpeter 1934).
- 11.
von Hippel (1987, 1988), Tushman and Nadler (1986), Blackler et al. (1998), Gibbons et al. (1995), Knorr et al. (1980), Lemaine et al. (1976), Mulkay (1972), Schumpeter (1934, 1946), Tushman and Anderson (1986), Henderson and Clark (1990), Henderson and Cockburn (1994), Nahapiet and Ghoshal (1998), Alchian and Demsetz (1972) on co-specialized assets, Crane (1972) on invisible colleges.
- 12.
Networks in a methodological sense consist of a set of nodes (actors, events, ideas) and the edges/relations that they define (e.g., information flow, influence, membership). From a technical point of view, a market is thus a special kind of “network.” Nevertheless, the term network as used in the literature usually denotes an entity different from a market. It starts from a different conception of man as embedded in social structures and thereby less under-socialized than homo economicus and ends in a critique of equilibrium theory, which assumes that prices tell us all we need to know and that transactions with each and every market partner are a viable option for any market participant irrespective of constraints in time, place, matching opportunities, etc. (Baker 1984; Granovetter 1985; White 1988; Podolny 2001). Part of the arguments of new institutional economics and its analysis of networks as hybrid governance structures (Williamson 1991) are therefore matched by sociological network theory (for the differences, see Jansen 1996, 2002a). At the same time, social network analysis provides an analytical tool that is able to tackle all sorts of structures. Thus we need not presuppose that a structure is hierarchical (organizational structure) or atomistic (pure market). Rather, this issue can be dealt with as an empirical question in the analysis.
- 13.
See the idea of solutions looking for problems in Cohen et al. (1972) on the garbage can model of decision making, which seems to me an inspiring organizational model for highly creative organizations.
- 14.
Innovativeness of a patent is measured as non-overlap in patent citations with citers of previous patents of the firm. Technological clusters are established via patent-citation analysis.
- 15.
Other mechanisms are of a more symbolic kind. Action and especially risk-taking action and learning depend upon building bridges between heterogeneous organizations and their “solutions.” Successful innovation needs visions of new solutions and confidence in their viability. Representations of collective innovation goals and strategies can thus have a coordinating effect. They focus the awareness of the collaborating partners and turn a confusing variety of opportunities into clearer paths to follow. Even misrepresentations of opportunities can have positive consequences in that they help overcome puzzlement and helplessness when confronted with a turbulent and overly complex environment (Levinthal and Warglien 1999; Lundvall 1992).
- 16.
See Uzzi (1996, 1997) on embedded strong ties in supplier–contractor networks and their positive effect on stability and knowledge transfer; Talmud and Mesch (1997) on the positive effect of local cluster cohesion on the stability of an industry (i.e., turnover in the top ten of the industry); Ingram and Roberts (2000) on the positive effect of cohesion and friendship between competitors on the occupancy rates and profitability in the Sydney hotel industry. See Uzzi (1999) on better access to capital as well as lower interest rates for small and medium-sized firms via strong ties and Baker (1990) on the prevalent strategy of embedding ties to banks; see Hansen (1999) on the benefits of strong ties for the transfer of complex and tacit knowledge and R&D project progress within an organization. Most of these studies analyzed local networks around a focal actor (ego-network analysis).
- 17.
Although networks and positions in networks do become visible, the network of an organization is largely an intangible resource. It comprises formal and informal ties, indirect ties, network positions, and characteristics of the whole network at the organizational as well as the member level. Networks are difficult to describe, imitate, or substitute, and thus networking capacity becomes a strategic asset of an organization in global competition (Galaskiewicz and Zaheer 1999; Nahapiet and Ghoshal 1998; Maskell 2000).
- 18.
Centrality of an actor is an aggregate measure of the actor’s embeddedness in symmetric ties. The simplest index counts the number of ties of an actor (degree or outdegree) in relation to possible ties. More sophisticated indices will also take into account indirect ties and weight them with their path distance. An open question is how long a path may be in order to effectively transfer public or tacit knowledge, social influence, or goods and services. The most sophisticated type of centrality index weights the impact of each actor who is connected to ego with her or his own centrality in the overall network (Bonacich centrality).
Prestige of an actor is an aggregate index of her or his embeddedness in asymmetric ties. Prestige or prominence is based on the ties pointing to an actor. Centrality in asymmetric networks is defined on the basis of the outward ties of an actor. The most simple prestige index is a count of the ties to an actor (indegree). There are also several ways to weight the impact of direct and indirect ties for the calculation of an actor’s prestige (Jansen 2003, Chaps. 6 and 7). Prestige scores, and particularly their deviation from equivalent centrality scores, measure the influence and legitimacy of an actor (provided the relation is positive).
- 19.
High prestige in patent-citation networks had a positive influence on growth rates, too. This effect was larger for firms with a small technological overlap to their competitors than for those with a large overlap.
- 20.
Betweenness-centrality in the research network has a larger effect on performance than in contact networks (beta = 0.86 versus 0.51). Betweenness measures the bridging capacity of an actor between two actors who are not directly connected. Indegree prestige is the best predictor in the contact network (0.65) and only slightly worse than betweenness in the research network (0.73). The Bonacich power index, which was constructed for negatively connected networks of competition for ties, also shows a significantly higher effect on performance for the research network than for the contact network (0.70 versus 0.56). In this index, the power of ego is larger the less power those who are ego’s contacts have. To take this one step further, ego’s power is enhanced if his or her contacts face other strong actors. The rationale behind negatively connected networks is that a resource can be given to just one actor (e.g., confidential information, a job, a marital relationship, or a partner position in a research project). This produces competition for ties. See Cook et al. (1983), Cook and Yamagishi (1992), Markovsky et al. (1988), Szmatka and Willer (1995) on power in negatively connected networks.
- 21.
Evidence of a positive effect of structural holes mostly comes from studies of managers’ networks (Burt 1992; Burt et al. 2000; Gargiulo and Benassi 2000; Podolny and Baron 1997; Gabbay and Zuckerman 1998). Perhaps structural holes are more profitable for them than for organizations or groups. Collectivities are more dependent on the establishment of their collective identity, whereas the identity of individuals seems to be given by their body and its clear boundaries. And the need for a legitimate identity is better dealt with by a trust-building strategy than by a brokerage strategy.
- 22.
An analysis at the level of whole industries showed that the stability of an industry is positively related to the existence of structural holes and local cohesive cliques. But an increase in local densities leads to corporate instability (Talmud and Mesch 1997). It increases competition, devalues existing collaborations, and increases the incentives to search for new opportunities.
- 23.
See Gulati and Gargiulo’s (1999) study of industrial networks (automotive, industrial automation, and new materials) and Stuart (1999) on alliance formation in semiconductors across prestige differences in patent-citation networks. For typical patterns found in science networks, see Jansen (1995c), Shrum and Wuthnow (1988), Hargens et al. (1980) and Mullins et al. (1977).
- 24.
In the British case study mentioned above (Jansen 1995c, 2000a), central positions in the emerging center–periphery structure tended to support high-quality research. High-performing blocks in that structure were characterized by the combination of several disciplines in the research groups and by organizational heterogeneity in the composition of their positions. Their exposure to different norm systems via relations to different peripheries did not harm the scientific performance of the members of the center position. But it did harm their forcefulness in science-policy terms. This has been explained by their lower internal centralization and larger heterogeneity (Jansen 1995c). Overlapping and contradictory Simmelian ties may be a concurring explanation.
- 25.
Density in the sense of niche theory is not the same as network density! Membership in the same technological niche is not derived from dense ties between actors but from the structural equivalence of actors (i.e., identical or similar relations to other actors, such as in a production chain).
- 26.
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Jansen, D. (2017). Networks, Social Capital, and Knowledge Production. In: Hollstein, B., Matiaske, W., Schnapp, KU. (eds) Networked Governance. Springer, Cham. https://doi.org/10.1007/978-3-319-50386-8_2
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