Evolution of the Strategic Alliance Network in the Global Information Sector
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Evolution of the Strategic Alliance Network in the Global Information Sector David Knoke & Xi Zhu University of Minnesota SIENA Workshop Groningen University January 8-11, 2007

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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.


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Corporate Social Capital Information Sector

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)


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CSC through SANs Information Sector

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.


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Strategic Alliance Networks Information Sector

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).


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Global Information Sector 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)


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Research Hypotheses Information Sector

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.


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Rising Alliance Rates Information Sector


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Diverse Purposes Information Sector


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Closeness Centrality Information Sector

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


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Betweenness Centrality Information Sector

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


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Organization Information Sector

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.


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SAMSUNG Information Sector


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Evolution Analysis Information Sector

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].


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GIS Core Firm Alliances Information Sector

SANs among 26 GIS firms 1998-99-00 (binarized at 2+ per year).

Here is the 2000 matrix, density = 0.618:


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Evolution of the GIS Core Information Sector

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


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Issues in SAN Evolution Information Sector

♠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?


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Further Steps Information Sector

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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References Information Sector

Burt, Ronald S. 1992. Structural Holes: The Social Structure of Competition. Cambridge, MA: Harvard University Press.

Coleman, James S. 1990. “Social Capital.” Pp. 300-321 in Foundations of Social Theory. Cambridge, MA: Harvard University Press.

Kenis, Patrick and David Knoke. 2002. “How Organizational Field Networks Shape Interorganizational Tie-Formation Rates.” Academy of Management Review 27:275-293.

Knoke, David. 2001. Changing Organizations: Business Networks in the New Political Economy. Boulder, CO: Westview.

Leenders, Roger Th. A. J. and Shaul M. Gabbay (eds.). 1999. Corporate Social Capital and Liability. Boston: Kluwer Academic Publishers.

Lin, Nan. 2001. Social Capital: A Theory of Social Structure and Action. New York: Cambridge University Press.

Snijders, Tom A.B. 2005. “Models for Longitudinal Network Data.” Pp. 215-247 in Models and Methods in Social Network Analysis, edited by Peter J. Carrington, John Scott and Stanley Wasserman. New York: Cambridge University Press.

Todeva, Emanuela and David Knoke. 2002. “Strategische Allianzen und Sozialkapital von Unternehmen.” (“Strategic Alliances and Corporate Social Capital”) Kölner Zeitschrift für Sociologie und Sozialpsychologie. Sonderheft 42:345-380.

Yoshino, Michael Y. and U. Srinivasa Rangan. 1995. Strategic Alliances: An Entrepreneurial Approach to Globalization. Cambridge, MA: Harvard University Press.


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