Transcript SOC 8311 Basic Social Statistics

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
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.
Rising Alliance Rates
GIS Strategic Alliances 1989-2000
5
4
Total (100s)
3
Mean per Org
2
1
0
1988
1990
1992
1994
YEAR
1996
1998
2000
Diverse Purposes
GIS Types of Alliances
100
90
80
Equity Investment
70
Product Adaptation
60
Research & Develop
50
Marketing
40
Production
30
Contract
20
License
10
Standards
0
Legal-Political
1988
1990
1992
1994
1996
YEAR
1998
2000
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
GIS Closeness 1989-2000
CLOSENESS
6.0
5.0
1991: AT&T
Mean
1995: IBM; Sun; Intel
4.0
2000: Microsoft; IBM; Sun; HP
3.0
2.0
Network
1.0
0.0
1988
1990
1992
1994
YEAR
1996
1998
2000
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
GIS Betweenness 1989-2000
BETWEENESS
100.0
1991: AT&T; Time Warner
80.0
Mean
1995: AT&T; Intel; IBM
2000: Microsoft; IBM
60.0
40.0
20.0
Network
0.0
1988
1990
1992
1994
YEAR
1996
1998
2000
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.
Organization
Primary SIC
Organization
Primary SIC
America Online AOL
Info retrieval
British Telecomm BT
Telecomm
Apple
Computer
Ericsson
Telecomm equip
AT&T
Telecomm
France Telecomm FT
Telecomm
BellSouth BS
Telecomm
Philips
TV equip
Cisco
Communic equip
Siemens
Computer periph
Compaq
Computer
Fujitsu
Computer
Hewlett-Packard HP
Computer
Hitachi
Computer
IBM
Computer
Matsushita
AV equip
Intel
Semiconductor
Mitsubishi
AV equip
Microsoft
Software
NEC
Computer
Motorola
TV equip
NTT
Telecomm
Novell
Software
Sony
AV equip
Oracle
Software
Toshiba
AV equip
Sun Microsystems
Computer
Bell Canada BCE
Telecomm
Texas Instruments TI
Semiconductor
Samsung (Korea)
Semiconductor
GIS Core Alliances in the Triad
6
5
4
3
2
YR91
1
YR95
YR00
0
USA
Europe
Japan
Europe-USA
USA-Japan
Japan-Europe
1991 GIS (MDS Stress = 0.102)
1.0
SAMSUNG
ERICSSON
TI
BS
BCE
INTEL
BT
SUN
HP
0.0
NECSIEMENS
CISCO
NOVELL
HITACHI
ATT
IBM
COMPAQ MICROSOFT
FUJITSU
MOTOROLA
ORACLE
APPLE
MITSUBISHI
TOSHIBA
NTT
MATSUSHITA
FT
PHILIPS
SONY
-1.0
-1.5
-.5
.5
1.5
1995 GIS (MDS stress = 0.142)
1.5
PHILIPS
1.0
BCE
BT
CISCO
.5
SIEMENS
TI
ERICSSON
FUJITSU
HP
SUN
HITACHI
MITSUBISHI
COMPAQ
0.0
TOSHIBA
NEC
IBM
ATT
NOVELL
MATSUSHITA
SAMSUNG
MICROSOFT
MOTOROLA
INTEL
-.5
AOL
APPLE
SONY
FT
ORACLE
-1.0
BS
NTT
-1.5
-2.3
-2.0
-1.8
-1.5
-1.3
-1.0
-.8
-.5
-.3
.0
.3
.5
.8
1.0
1.3
1.5
1.8
2000 GIS (MDS stress = 0.137)
2.0
BS
1.5
BT
1.0
TI
ATT
FT
BCE
.5
MOTOROLA
AOL
ERICSSON
PHILIPS
0.0
NOVELL
SUN CISCO
MATSUSHITA
MICROSOFT
IBM
NTT
HP
TOSHIBA
SONY
INTEL
HITACHI
-.5
FUJITSU
ORACLE
COMPAQ
APPLE
NEC
MITSUBISHI
-1.0
SIEMENS
SAMSUNG
-1.5
-2.0
-1.5
-1.0
-.5
0.0
.5
1.0
1.5
2.0
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.
OBJECTIVE
FUNCTION
Parameter Stnd error t-ratio
Rate (1998-1999)
11.82
2.67
4.43***
Rate (1999-2000)
8.41
1.95
4.31***
Density (degree)
0.71
0.12
5.92***
Transitivity
0.01
0.07
0.14
Balance
1.34
0.31
4.32***
Indirect Relations
0.69
0.19
3.63***
Geographic Similarity
0.47
0.13
3.62***
Industry Similarity
0.21
0.19
1.11
*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.
References
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.