Transcript SOC 8311 Basic Social Statistics

Mapping the Dynamics of
Strategic Alliance Networks
in the Global Information Sector
David Knoke
University of Minnesota
Workshop on Clusters, Networks & Alliances
in the Telecommunication Sector
School of Management, University of Surrey
June 11, 2003
Lost in Space?
When constructing maps of social spaces for interorganizational
relations, analysts face crucial decisions about optimal procedures for:
1: Measuring distances or proximities between pairs of
organizations based on interactions (and avoidances?)
2: Locating organizations within multidimensional spaces
that accurately represent these distances/proximities
3: Identifying which organizations jointly occupy positions
(subgroups) based on equivalence/similarity of their
interorganizational relations
Proposed solutions combine methods drawn from
numerical taxonomy, classification, graph theory,
multidimensional scaling, and cluster analysis.
The Global Info Sector
To illustrate these choices, I will analyze data on the
announced strategic alliances among the world’s
largest info firms, the Global Information Sector:
 Five NAIC information subsectors (publishing; motion pictures &
sound recording; broadcasting & telecomms; info services & data
processing) plus the computer, telecommunications, and
semiconductor manufacturing industries
 145 organizations: 66% USA, 16% Europe, 15% Asia
 Alliances & ventures announced in general and business news
media from 1989 to 2000
 Total of 3,569 strategic alliances involving two or more orgs (many
collaborations also include additional partners)
Strategic Alliances
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)
SA governance forms vary in
the types of legal and social
mechanisms to coordinate &
safeguard alliance partners’
resource contributions,
administrative responsibilities,
divide rewards from their
collaboration (Todeva & Knoke 2003)
Hierarchical Relations
--------------------------------------------------------JOINT VENTURES
COOPERATIVES
EQUITY INVESTMENTS
R&D CONSORTIA
STRATEGIC COOP. AGREEMENTS
CARTELS
FRANCHISING
LICENSING
SUBCONTRACTOR NETWORKS
INDUSTRY STANDARDS GROUPS
ACTION SETS
--------------------------------------------------------Market Relations
Todeva, Emanuela and David Knoke. 2003. “Strategic Alliances and Corporate Social Capital.” Kölner
Zeitschrift für Sociologie und Socialpsychologie (Forthcoming)
Yoshino, Michael Y. and U. Srinivasa Rangan. 1995. Strategic Alliances: An Entrepreneurial Approach to
Globalization. Cambridge, MA: Harvard University Press.
30 Core GIS Orgs
My examples use the alliances among the 30 most-active GIS
firms in 2000, from three continents and a dozen industries
Organization
Primary SIC
Organization
Primary SIC
America Online AOL
Info retrieval
British Telecomm BT
Telecomm
Apple APL
Computer
Ericsson ERC
Telecomm equip
AT&T ATT
Telecomm
France Telecomm FT
Telecomm
BellSouth BS
Telecomm
Philips PHI
TV equip
Cisco CIS
Communic equip
Siemens SIE
Computer periph
Compaq COM
Computer
Fujitsu FUJ
Computer
Hewlett-Packard HP
Computer
Hitachi HIT
Computer
IBM IBM
Computer
Matsushita MAT
AV equip
Intel INT
Semiconductor
Mitsubishi MIT
AV equip
Microsoft MS
Software
NEC NEC
Computer
Motorola MOT
TV equip
NTT NTT
Telecomm
Novell NOV
Software
Sony SON
AV equip
Oracle ORA
Software
Toshiba TOS
AV equip
Sun Microsyst SUN
Computer
Bell Canada BCE
Telecomm
Texas Instruments TI
Semiconductor
Samsung SAM
Semiconductor
1: Measuring Distances
The social distance between a pair of actors varies with
their interaction frequency and/or strength. A strategic
alliance is an interaction event where orgs are present or
absent as partners. Across all alliances, a 2x2 table
displays the pattern of partnerships among each pair.
In 2000, the GIS orgs announced 452 alliances; 209 involved at
least two of the 30 core orgs. Here are the Microsoft-IBM counts,
with the four cell frequencies denoted by conventional letters:
MICROSOF * INTEL Crosstabulation
Count
INTEL
0
MICROSOF
Total
0
1
1
d= 150
c= 15
b= 31
a= 13
181
28
Total
165
44
209
What Really Counts?
For interval data, Euclidean distance and correlation are apt metrics.
But for binary counts, several other measures are more appropriate.
Some include both the joint presences and joint absences:
a
abcd
Ru sse llan d Rao :
S i m pleMatch i n gC oe ffi cietn:
ad
abcd
S ok alan d S n e ath3 :
ad
bc
Others exclude the joint absences:
Jaccard(sim ilarityratio) :
a
abc
Ku lcz yn sk:i
a
bc
A basic question is whether to include
or exclude the number of alliances
involving neither organization? (Akin
to the biological taxonomy decision to
count or ignore “absence of feathers”
when classifying fish species.)
Co-absence probably doesn’t indicate
mutual avoidance, because every org is
not a plausible partner for most
alliances. As all orgs participate in a
minority of events, cell “d” carries
heavy weight, which argues strongly for
using distance/similarity measures that
exclude a pair’s joint absences.
2: Plotting Locating
Using the distances/proximities among all organizations,
multidimensional scaling (MDS) programs can plot their
relative locations in 2-, 3-, or higher-dimensional spaces.
 Input is a square, symmetric matrix whose entries measure the
similarity/dissimilarity or equivalence between each row-and-column
pair; main diagonal entries are set to 0.
 An MDS program represents a pair of organizations that is more
proximate in the input data as located closer in the multidimensional
space; less-proximate pairs are located farther apart.
 MDS outputs estimated spatial coordinates for N-dimensions.
 Using these coordinates, org locations are displayed in a diagram.
 A stress value summarizes how well the estimated locations fit the
observed input data; lower stress (< .20) indicates a better fit.
Kruskal, J.B. and M. Wish. 1978. Multidimensional Scaling. Newbury Park, CA: Sage.
Schiffman, S.S., M.L. Reynolds and F.W. Young. 1981. Introduction to Multidimensional Scaling:
Theory, Methods, and Applications. New York: Academic Press.
3: Encircling Positions
Analysts can cluster analyze the distance/proximity matrix to
identify the organizations that jointly occupy positions (subgroups)
based on their similar/equivalent interorganizational relations. This
information is used to draw contiguity lines that encircle the
position members in a MDS diagram; with luck, the positions form
tight circles, not sprawling amoebas with tangled pseudopods.
Three general types of clustering algorithms, with multiple options for
specifying data, distances, and linkage procedures:
 Hierarchical cluster analysis: divisive or agglomerative methods
that find smaller organizational clusters nested inside larger ones;
dendograms (tree diagrams) reveal these hierarchical connections.
 Nonhierarchical cluster analysis: (k-means) divisive methods for
interval data that iteratively reallocate organizations among new sets
of nonnested clusters until the N user-specified clusters emerge
 Fuzzy cluster analysis: instead assignments to sharply separated
clusters, fuzzy methods specify a membership degree (from 0 and 1)
showing how likely an organization belongs to each cluster
Comparing Maps
The following two pairs of diagrams compare the MDS and
hierarchical cluster results for two contrasting measures of the
distances between the 30 core GIS organizations in 2000. The
resulting maps differ in several key features:
 For SMC distances (which count cell “d”): Organizations creating the
most alliances – Microsoft, IBM, and Japanese firms – are located at the
periphery, but firms with few partners fill a dense central “black hole”
 For Jaccard distances (which ignore cell “d”): The most active
American and Japanese organizations are nearer one another, and
Microsoft (which formed the most partnerships) is smack in the center
 Jointly occupied positions: The U.S. computer organizations have a
sprawling position in the SMC figure, but more tightly clustered in the
Jaccard plot. The Asian firms are split across two positions in SMC, but
altogether using Jaccard. Telecomms occupy distinct positions at the
top of the latter map.
MDS with Simple Matching Coefficients
MDS with SMC
2.0
1.5
1.0
.5
(UCINET; stress = 0.19)
HIT
MS
NEC
INT
TOS
MIT
FUJ
ERC
SAM
MAT
0.0
SIE
AOL
BT
TI
BS
PHL
FT
NOV
APL
BCE
SON
MOT
COM
ATT
ORA
-.5
NTT
SUN
CIS
HP
-1.0
-1.5
-2.0
IBM
-2.5
-1.5
-1.0
-.5
0.0
.5
1.0
1.5
2.0
2.5
Hierarchical Clusters for SMC
Hclus SMC
2.0
1.5
1.0
.5
(SPSS centroid clustering)
HIT
MS
NEC
INT
TOS
MIT
FUJ
ERC
SAM
MAT
0.0
SIE
AOL
BT
TI
BS
PHL
FT
NOV
APL
BCE
SON
MOT
COM
ATT
ORA
-.5
NTT
SUN
CIS
HP
-1.0
-1.5
-2.0
IBM
-2.5
-1.5
-1.0
-.5
0.0
.5
1.0
1.5
2.0
2.5
MDS with Jaccard Coefficients
MDS with Jaccard
2.0
(UCINET; stress = 0.17)
FT
1.5
TI
AOL
1.0
ATT
BT
.5
BS
BCE
ERC
MOT
NTT
MAT
SON
MS
PHL
0.0
HP
APL
SUN
CIS
TOS
IBM
HIT
NEC
COM
MIT
FUJ
-.5
INT
SAM
-1.0
ORA
NOV
SIE
-1.5
-2.0
-1.5
-1.0
-.5
0.0
.5
1.0
1.5
Hierarchical Clusters for Jaccard
Hclus Jaccard
2.0
(SPSS centroid clustering)
FT
1.5
TI
AOL
1.0
ATT
BT
.5
BS
BCE
ERC
MOT
NTT
MAT
SON
MS
PHL
0.0
HP
APL
SUN
CIS
TOS
IBM
HIT
NEC
COM
MIT
FUJ
-.5
INT
SAM
-1.0
ORA
NOV
SIE
-1.5
-2.0
-1.5
-1.0
-.5
0.0
.5
1.0
1.5
The Art of Organizational Cartography
Constructing maps of interorganizational and other
types of social relations involves as much art as
science. Researchers today enjoy an abundance of
concepts, measures, and computer graphics
programs. Unfortunately, no clearly superior
mapping method fits every purpose.
Decisions about which options to exercise rest on
assumptions and simplifications that can distort as
well as reveal underlying structures. Plotting and
clustering algorithms are susceptible to local
optima, which urges great caution and repetition of
analyses before drawing conclusions.
Martin Waldseemüller’s Carta
Mariana, which named the
southern continent after
Amerigo Vespucci.
Most importantly, organizational cartographers
should be guided by strong theoretical expectations
about the relationships they investigate. Greater
efforts must be undertaken to construct theories of
organizational space with testable propositions.
To do otherwise puts Descartes before des horse.