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Global gatekeeping, representation, and network structure: a longitudinal analysis of regional and global knowledge-diffusion networks

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Abstract

This paper argues that structural characteristics of knowledge-diffusion networks, such as density levels, centralization levels, and the presence of global knowledge brokers, contribute to the emergence of dominant designs and the competitiveness of countries' firms and industries. It further suggests that national institutional structures and firm-specific attributes influence the development of these knowledge-diffusion networks. Six propositions, developed from examination of one industry's networks and previous scholarly literature, specify these arguments.

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Notes

  1. In Figure 1 and 2, and in later regressions, a firm was identified as entering and exiting the industry based on the first and last years in which it published an article, presented a conference paper, received an FPD patent, was mentioned in a newspaper article or press release, or was listed as an industry participant in an academic article on the FPD industry.

  2. Data were normalized based on the size of countries' industries and weighted to discount a firm's score when more than one company acted as a broker between a given pair of firms.

  3. Regression results remain largely the same when European firms are included. All variance inflation factors for both regressions fall under 3.

  4. Production data from Borrus and Hart (1994).

  5. Although several European countries may be characterized as corporatist, few corporatist institutions existed on a Europe-wide level prior to 1989.

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Acknowledgements

I gratefully acknowledge funding from the Carnegie Bosch Institute for Applied Studies in International Management, the Alfred P Sloan Foundation, and George Washington University's Center for the Study of Globalization.

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Authors and Affiliations

  1. Department of International Business, George Washington University, Washington, DC, USA

    J W Spencer

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Correspondence to J W Spencer.

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Accepted by Thomas Brewer, outgoing Editor, 7 February 2003.

Appendix A

Appendix A

Network measures

Density

Density reflects the number of ties in a network divided by the total number of possible ties (Knoke and Kuklinski, 1982). Dichotomous measures identify only the presence of a tie between two firms (at least one citation occurs). Valued measures give more weight to ties when they are repeated (multiple citations occur). In Figure A1, the network is denser in Country B (9 of 30 potential ties completed) than Country A (5 of 30 ties completed).

figure 4

Figure A1

Centralization

Centralization reflects the degree to which a small number of firms hold prominent positions in a network. A centralization index takes its greatest value when a single firm displays high centrality by maintaining ties to all other firms and these other firms have no ties to each other. Centralization is lowest when every firm has the same level of centrality (Wasserman and Faust, 1994). In Figure A1, centralization is higher in Country A than Country B, with Firm F displaying high centrality, and all other companies exhibiting low centrality. Firm-level centrality is measured in several ways:

  1. 1)

    Degree centrality identifies firms as most central when they maintain ties to many other actors. (Firm F forged ties with all other firms in its country.)

  2. 2)

    Betweenness centrality labels firms as central when they lie on geodesic paths that link other firms. A geodesic represents the path of shortest distance between any two points in the network. (Firm F lies on a geodesic between all other pairs of firms in its country.)

  3. 3)

    Closeness centrality identifies a firm as most central when its geodesics to all other firms are of minimum length. (Firm F's geodesics to all firms in Country A are of length 1.)

Euclidean distance

Multidimensional scaling calculates proximities among actors in a network, using x–y coordinates to reveal which actors are ‘close’ to one another based on the number of ties that connect them (Wasserman and Faust, 1994). Euclidean distances based on multidimensional scaling range from zero to one.

Adjacency

Two firms are adjacent if at least one tie links them (Firms G and K are adjacent). For each firm, I calculated the percentage of home-region and foreign firms adjacent.

Reachability

A firm is reachable by another firm if a path of citation relationships can be constructed to link them. (In Figure A1, all firms are reachable by all other firms.) For each firm, I calculated the percentage of home-region and foreign firms that were reachable.

Knowledge brokers

Knowledge brokers bridge a gap between two groups of firms. (In Figure A1, Firms C and J act as knowledge brokers.) Gatekeepers absorb information from actors outside their group, and convey information to members of their group. Representatives absorb information from actors within their group, and convey information to actors outside their group. A firm's score as a global gatekeeper reflects the number of times it lies on a cross-national geodesic, citing a foreign firm, and being cited by a home-region firm. A firm's score as a global representative reflects the number of times it lies on the geodesic, citing a home-region firm, and being cited by a foreign firm. Both scores were normalized and weighted to allocate higher scores when a firm was the only link between other firms and a lower score when other alternative paths were available.

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Spencer, J. Global gatekeeping, representation, and network structure: a longitudinal analysis of regional and global knowledge-diffusion networks. J Int Bus Stud 34, 428–442 (2003). https://doi.org/10.1057/palgrave.jibs.8400039

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