Evolution of the Strategic Alliance Network in the Global Information SectorDavid Knoke & Xi Zhu University of Minnesota SIENA Workshop Groningen University January 8-11, 2007 Thanks to the Ford Foundation, Digital Media Forum, and University of Minnesota for funding and to Anne Genereux, Song Yang, and Francisco J. Granadosfor research assistance.
Corporate Social Capital Social Capital Resources accruing to an ego actor through direct and indirect relations with its alters that facilitate ego’s attainment of its expressive or instrumental goals. Diverse conceptualizations of an actor’s social capital: • “inheres in the structure of relations between persons and among persons” (Coleman 1990:302) • “at once the resources contacts hold and the structure of contacts in the network” (Burt 1992:12) • “resources embedded in a social structure which are accessed and/or mobilized in purposive action” (Lin 2001:12) Corporate Social Capital (CSC)Social relations embedded in work-related organizational roles (e.g., workers, teams, executives, owners), not in their personal networks. “Corporate social capital, then, refers to: The set of resources, tangible or virtual, that accrue to a corporate player through the player’s social relationships, facilitating the attainment of goals.”(Leenders & Gabbay 1999:3)
CSC through SANs A firm’s ties to organizations in a strategic alliance network increases its probability of accessing and using the valuable CSC resources held by the firm’s partners, including their: • Financial resources, credit extensions • Knowledge, information, technologies/patents • Marketing expertise, country/culture penetration • Org’l statuses, corporate/brand reputations • Trustworthiness and low risk (moral hazards) Organizations aware of such CSC advantages may act strategically in pursuing new alliances, partnering with firms that maximize its CSC portfolio. At the field-net level, an evolving strategic alliance network comprises a collective CSC structure which simultaneously facilitates and constrains the opportunities for its member firms.
Strategic Alliance Networks Corporate social capital relations span multiple levels of analysis from individuals, to workteams, to firms, and organizational field network (Kenis & Knoke 2002). At the IOR level, repeated alliances generate a strategic alliance network form of CSC. Strategic alliance: at least two partner firms that (1) remain legally independent; (2) share benefits, managerial control over performance of assigned tasks; (3) make contributions in strategic areas, e.g., technology or products(Yoshino & Rangan 1995). Strategic alliance network “The set of organizations connected through their overlapping partnerships in different strategic alliances” (Knoke 2001:128; Todeva & Knoke 2002). Firms are closely tied to one another through many direct alliances or many indirect ties through third firms (i.e., partners-of-partners).
Global Information Sector Basic CSC concepts could help to explain the evolution of the strategic alliance network in the Global Information Sector (GIS). This sector increased collaborative agreements exponentially 1989-2000, creating a complex web of overlapping partnerships. • Five NAICS info subsectors (publishing; motion pictures & sound recording; broadcasting & telecomms; info services & data processing) plus computer, telecomm, semiconductor manufacturing industries • 145 multinational corporations: 66% USA, 16% Europe, 15% Asia • Alliance & venture announcements in general & business news media from 1989 to 2000 • Total of 3,569 alliances involving two or more GIS organizations (some alliances include noncore partners)
Research Hypotheses Three types of H’s about network evolution involve changes in global structure, partner choice, and organizational performances. H1: Network Structural Change:The GIS SAN evolved from a fragmented small world of specialized cliques toward preferential attachments to key producers, and then to structurally cohesive connectivity. H2a: Transitivity:Firms are more likely to form new alliances with other organizations that result in transitivity. H2b: Balance:Firms with a specific number of partners are more likely to form new alliances with other orgs having an identical or very similar N of partners. H2c: Indirect Relations:Firms are more likely to form new alliances with other organizations to which they are linked by numerous indirect connections. H2d: Similarity / Interdependence:Firms are more likely to form new alliances with other organizations that having similar / complementary attributes.
Closeness Centrality CENTRALITY: ORGS INVOLVED WITH MANY PARTNERS DEGREE= Number of ties directly connecting focal org to other orgs (in- or out-degrees) CLOSENESS= Inverse of sum of distances to other orgs (geodesics = shortest paths) NETWORK CENTRALIZATION: Extent to which one actor has high centrality and others low CLOSENESS 1991: AT&T 1995: IBM; Sun; Intel 2000: Microsoft; IBM; Sun; HP
Betweenness Centrality CENTRALITY: ORGS INVOLVED WITH MANY PARTNERS BETWEENNESS= Number of times an org occurs on a geodesic between other pairs of orgs NETWORK CENTRALIZATION: Extent to which one actor has high centrality and others low BETWEENESS 1991: AT&T; Time Warner 1995: AT&T; Intel; IBM 2000: Microsoft; IBM
Organization Organization Primary SIC Primary SIC America Online AOL British Telecomm BT Info retrieval Telecomm Ericsson Telecomm equip Apple Computer France Telecomm FT Telecomm AT&T Telecomm Philips TV equip BellSouth BS Telecomm Siemens Computer periph Cisco Communic equip Fujitsu Computer Compaq Computer Hitachi Computer Hewlett-Packard HP Computer Matsushita AV equip IBM Computer Mitsubishi AV equip Intel Semiconductor NEC Computer Microsoft Software NTT Telecomm Motorola TV equip Sony AV equip Novell Software Toshiba AV equip Oracle Software Bell Canada BCE Telecomm Sun Microsystems Computer Samsung (Korea) Semiconductor Texas Instruments TI Semiconductor MAPPING The GIS CORE Hierarchical cluster & multidimensional scaling analyses to identify positions and spatial proximities among 30 most-active GIS firms (1991, 1995, 2000). Similarity = N of partnerships per dyad.
Evolution Analysis The macro-evolution of GIS alliance network, under dynamic constraints of network properties, assumes methodological individualism (actor-oriented model) SIENA(Simulation Investigation for Empirical Network Analysis; Snijders 2005)models the changing network connections as outcomes of org’l decisions to add or drop ties, assuming that orgs seek to maximize various “objective function” elements (e.g., preferences for increased network transitivity, reciprocity, balance, alliances with popular and active partners, etc.) SIENA estimates effects using two or more observed matrices of dichotomous ties. It applies the method of moments, implemented as a continuous-time Markov chain Monte Carlo simulation (MCMC) [i.e., actors know network’s current structure, but not its earlier states].
GIS Core Firm Alliances SANs among 26 GIS firms 1998-99-00 (binarized at 2+ per year). Here is the 2000 matrix, density = 0.618:
Evolution of the GIS Core SIENA analysis of strategic alliances (dichotomized at 2+ per year) among the 26 most-active GIS firms for 1998-1999-2000. Results consistent with all H2’s except transitivity hypothesis. *p < .05 ** p < .01 ***p < .001
Issues in SAN Evolution ♠What substantive interpretations can we make about the SIENA parameters? How robust for the larger GIS network and longer evolutionary span? ♦ Which, if any, tie-formation processes in interorganizational relations are functionally equivalent to interpersonal choices? ♥ Do balance and transitivity have the same meanings in organizational partnering and social psychological affiliation? ♣Are different theoretical concepts, principles, and propositions necessary to explain interorganizational network dynamics? If so, what are they?
Further Steps GIS orgs built up extensive corporate social capital by rapidly expanding the worldwide strategic alliance network. Structural cohesion seems increasing important for collective actions and individual firm outcomes. By expanding the GIS dataset to cover 1986-2005, I hope to track transformations in structures and processes from the Sector’s origins to well beyond the bursting of the Dot.com Bubble in Spring 2000. Using data on firm profits, growth, patent innovations, I will test the third set of hypothesis about organizational performance: Are structurally equivalent or socially cohesive clusters of collaborating organizations better able to use the structural advantages of jointly occupied network positions to access valuable information, obtain scarce resources, and improve their members’ performances? By helping to provide policymakers with a deeper understanding of the types of alliance networks that affect firm innovations, subsequently modified legislative, regulatory, and trade association policies might be crafted to foster the development of interorganizational connections with optimal structural characteristics.
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