Presentazione Soda - Networks And Innovation

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Presentazione Soda - Networks And Innovation

  1. 1. Networks and innovation Giuseppe Soda Claudio Dematté Research Division Prepared for Area Tec. Seminar, September 2009 Copyright SDA Bocconi 2005
  2. 2. Key Notion of Network (relational) paradigm The principal idea: Economic action and organizational performance are influenced by the nature and the structure of the relations between and among organizational actors
  3. 3. Key Notion of Network (relational) paradigm • Three perspectives: – Network Analysis (social) as a Prism: networks as an analytical device for illuminating relations, not necessarily social, whether inside a firm, in the inter-organizational ties that link firms, or in the environments of organizations; – Ties and Structures of Ties as Explanatory concepts: network theory (structural) argue that behaviors, actions, decisions, are best discovered by examining relations among actors rather than just their attributes; – Networks as Org. Forms: it views networks as a kind of organizing logic, a way of governing relations among economic actors.
  4. 4. What is a network? • A set of nodes (e.g. individuals, groups, organizations) linked by a set of relationships (Laumann et al., 1978) • Relationships or ties can be – Directed (one-directional) – Undirected (as in being physically proximate) – Dichotomous (present or absent) – Valued (considering the strength)
  5. 5. Network perspective in the management literature • Network perspective builds on the general notion that economic action does not take place in a solitary social context but it is embedded within networks of interconnected relationships providing opportunities and constraints on behaviour (Granovetter, 1973). • Network perspective analyzes how the structure of relationships influences performance and gives to the actors, such as individuals, teams and organizations, the ability to acquire information, expertise and other resources (Laumann et al., 1978; Fombrun, 1982; Stinchcombe, 1990; Krackhardt, 1990; Burt, 1992; Ancona and Caldwell, 1992; Brass and Burkhardt, 1993; Mizruchi, 1994; Powell and Smith-Doerr, 1994; Ibarra, 1995; Podolny and Baron, 1997; Brass, Galaskiewicz, Greve and Tsai, 2004; Rodan and Galunic, 2004). • Network ties transmit information and are thought to be especially influential information conduits because they provide salient and trusted information that is likely to affect behaviour (Podolny, 2001).
  6. 6. Determinants of innovation (Ahuja et al., 2008)
  7. 7. Role of networks in innovation • Through network ties and network structures organizations can pool or exchange resources and develop new ideas giving access to more diverse sources of information and capabilities (1994; Powell, Koput and Smith-Doerr, 1996; Obstfeld, 2005, Soda et al. 2004; Soda and Zaheer, 2009). • Networks constitute a critical locus for knowledge creation and innovation providing access to information, resources and support necessary for new ideas (Powell and Brantley, 1992; Ibarra, 1993; Shan, Walker, and Kogut).
  8. 8. Networks and level of analysis • Influence of network structures on innovation has been investigated at – Individual, team and organizational levels (Krackhardt, 1990; Burt, 1992; Ancona and Caldwell, 1992; Brass and Burkhardt, 1993; Ibarra, 1995; Podolny and Baron, 1997; Rodan and Galunic, 2004) – Internal relations within teams and organizations (Gulati, 1998; Rowley et al., 2000; Hansen, 1999, 2002; Tsai, 2001; Tsai and Ghoshal, 2002; Gupta and Govindarajan, 2000; Levin and Cross, 2004) – External relations among individuals, teams and organizations (Ancona and Caldwell, 1992; Podolny, Stuart, Hannan, 1996).
  9. 9. Mixed empirical results on the relationship between network structure and innovation • Empirical evidence deriving from brokerage versus closure argument on innovation is mixed – Ahuja, (2000); Rowley et al., (2000); Obstfeld, (2005); Uzzi and Spiro, (2005), Soda (2008) found at different levels of analysis a positive impact of network closure on innovation – Baum et al., (2000); Ruef, (2002); Rodan and Galunic, (2004); Zaheer and Bell, (2005); Fleming et al., (2007), found a positive impact of brokerage on innovation
  10. 10. Exploiting structural position rents: Generating innovation through strategic alliances networks Giuseppe Soda Copyright SDA Bocconi 2005
  11. 11. Strategic alliances and firm performance Alliance is a voluntary, interorganizational, formal or informal agreement of cooperation or coordination including exchange, sharing and joint development of idiosyncratic assets, knowledge, and resources/ capabilities among the partners (Dyer, Singh 1998).
  12. 12. How does network position matter? Usually managers: • do not look at the alliance portfolio and whole network of alliances in which they are embedded; • they underestimate the impact of the positioning strategy in the industry network of alliances This research investigates the impact of network position on firm performance. More specifically we investigate to which extent product and process innovation capacity can be affected by the position in a network of strategic alliances
  13. 13. An example: Germany
  14. 14. Research questions • Does network centrality enhance innovation outcomes? • How network centrality makes influences on innovation processes? • Brokerage or closure?
  15. 15. Brokerage and Structural holes illustrated Disconnections between neighbors and hence “entrepreneurial opportunities for information access, timing, referrals, and control.” Figure 1 - Illustration of Weak Ties and Structural Holes 1 Strong Ties Weak Ties 1’s Ties to 2 and 3 Bridge Structural Hole Structural Hole 2 3
  16. 16. The classic controversy between cohesion and brokerage • Coleman (1988): “closed • Granovetter (1973) social structure engenders “ties within closed networks trust because it enables are strong and connect you collective sanctions” to similar others while weak • Trust enables greater ties connect you to sharing of resources, risk dissimilar others and fresh and information info” • Redundant ties facilitate • Cohesive networks recycle information flow redundant information • Easier coordination and • Brokerage provides power mobilization and control advantages
  17. 17. Alliances in the automotive industry (years 2001-02-03)
  18. 18. Alliances in the automotive industry (years 2002-03-04)
  19. 19. Alliances in the automotive industry (years 2003-04-05)
  20. 20. Data • 359 JVs 2001-2006 • 592 firms, 1136 links Figura 4: finalità delle Joint Venture • Contents of Jvs: 60 – Assembling 50 – Commercial 40 30 – R&D 20 – Services 10 Production Services Commercial – Production 0 2001 R&D 2002 Assembling 2003 2004 2005 2006 Assembling R&D Commercial Services Production
  21. 21. Data • Patents (2001-2006) for all sampled firms. Figura 9: numero brevetti e nuove alleanze 90 100 70 80 50 60 30 40 10 20 -10 0 2003 2004 2005 2006 Brevetti (migliaia) Nuove alleanze
  22. 22. Variables and analysis Variables Dependent variable: Number of patents presented by the companies to the USPTO in the years 2004-2005-2006. Although patents could not perfectly represent firms’ innovative capacity, they are a proxy used in several studies (Powell, Koput, Smith-Doerr 1996; Hagedoorn 2002,; Ahuja 2000). We considered presented and not registered patents in order to guarantee data homogeneity because the time windows for the patents registration could be different for the several patents. Independent variables Cohesion is operationalized using the Density measure. Density is equal to (D = 2k / n*(n-1)), where k is the number of existing ties among all the partners in the network and n is the number of partners in the network. Brokerage is operationalized using the Effective Size measure (Burt, 1992) that is equal to the number of partners to which focal firm is connected minus the mean of partners belonging to the ego-network . Control variables Country is a dummy variable for the geographic area (Asia Pacific, Europe e North America) Industry - Sic Code for the Production of complete vehicles (3711) and Production of components. Analysis We used Generalized Linear Model (GLM) selecting the Negative Binomial option to cope with the overdispersion of the dependent variable (variance greater than mean)
  23. 23. Results Patents 04 _____________________ b Std. Err. Ego Density 01-03 -.01 .00 Structural Holes 01-03 .66 *** .15 Country Asia Pacific .95 *** .19 Country .88 ** .28 Sic 3711 -.04 .19 _cons 3.60 .20 Patents 05 _____________________ b Std. Err. Ego Density 02-04 -.01 * .00 Structural Holes 02-04 .69 *** .12 Country Asia Pacific .92 *** .24 Country 1.55 *** .34 Sic 3711 .72 ** .22 _cons 3.22 .27 Patents 06 _____________________ b Std. Err. Ego Density 03-05 -.02 *** .00 Structural Holes 03-05 .67 ** .22 Country Asia Pacific -.34 .29 Country Europe .34 .28 * p < .05 Sic 3714 .35 .20 ** p < .01 _cons 4.29 .31 *** p < .001
  24. 24. Conclusions • Network position matters Figure 1 - Illustration of Weak Ties and Structural Holes • Bridging structural holes enhance innovation 1 Strong Ties • Network closure is Weak Ties detrimental for innovation 1’s Ties to 2 and 3 Bridge Structural Hole Structural Hole 2 3

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