David KnokeUniversity of Minnesota

Workshop on Clusters, Networks & Alliances

in the Telecommunication Sector

School of Management, University of Surrey

June 11, 2003

Mapping the Dynamics of Strategic Alliance Networks

in the Global Information Sector

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 SectorTo 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 AlliancesStrategic 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 INVESTMENTSR&D CONSORTIASTRATEGIC COOP. AGREEMENTSCARTELSFRANCHISINGLICENSINGSUBCONTRACTOR NETWORKSINDUSTRY STANDARDS GROUPSACTION 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 OrgsMy examples use the alliances among the 30 most-active GIS firms in 2000, from three continents and a dozen industries

Organization Primary SIC

America Online AOL Info retrieval

Apple APL Computer

AT&T ATT Telecomm

BellSouth BS Telecomm

Cisco CIS Communic equip

Compaq COM Computer

Hewlett-Packard HP Computer

IBM IBM Computer

Intel INT Semiconductor

Microsoft MS Software

Motorola MOT TV equip

Novell NOV Software

Oracle ORA Software

Sun Microsyst SUN Computer

Texas Instruments TI Semiconductor

Organization Primary SIC

British Telecomm BT Telecomm

Ericsson ERC Telecomm equip

France Telecomm FT Telecomm

Philips PHI TV equip

Siemens SIE Computer periph

Fujitsu FUJ Computer

Hitachi HIT Computer

Matsushita MAT AV equip

Mitsubishi MIT AV equip

NEC NEC Computer

NTT NTT Telecomm

Sony SON AV equip

Toshiba TOS AV equip

Bell Canada BCE Telecomm

Samsung SAM Semiconductor

1: Measuring DistancesThe 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

150 15 165

31 13 44

181 28 209

0

1

MICROSOF

Total

0 1

INTEL

Total

a=

d=b=

c=

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:

Others exclude the joint absences:

cb

daSneathandSokal

dcba

datCoefficienMatchingSimple

dcba

aRaoandRussell

:3

:

:

cb

aKulczynski

cba

aratiosimilarityJaccard

:

:)(

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 LocatingUsing 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 PositionsAnalysts 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 MapsThe 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 SMCMDS with Simple Matching Coefficients

2.52.01.51.0.50.0-.5-1.0-1.5

2.0

1.5

1.0

.5

0.0

-.5

-1.0

-1.5

-2.0

-2.5

TOS

TI

SUN

SONSIE

SAM

PHL

ORA

NTT

NOV

NEC

MOTMIT

MS

MAT

INT

IBM

HP

HIT

FUJ

FT

ERC

COM

CIS

BTBSBCE ATT

APLAOL

(UCINET; stress = 0.19)

Hclus SMCHierarchical Clusters for SMC

2.52.01.51.0.50.0-.5-1.0-1.5

2.0

1.5

1.0

.5

0.0

-.5

-1.0

-1.5

-2.0

-2.5

TOS

TI

SUN

SONSIE

SAM

PHL

ORA

NTT

NOV

NEC

MOTMIT

MS

MAT

INT

IBM

HP

HIT

FUJ

FT

ERC

COM

CIS

BTBSBCE ATT

APLAOL

(SPSS centroid clustering)

MDS with JaccardMDS with J accard Coefficients

1.51.0.50.0-.5-1.0-1.5-2.0

2.0

1.5

1.0

.5

0.0

-.5

-1.0

-1.5

TOS

TI

SUN

SON

SIE

SAM

PHL

ORA

NTT

NOV

NEC

MOT

MIT

MS

MAT

INT

IBM

HP HIT

FUJ

FT

ERC

COMCIS

BT

BS BCE

ATT

APL

AOL

(UCINET; stress = 0.17)

Hclus JaccardHierarchical Clusters for J accard

1.51.0.50.0-.5-1.0-1.5-2.0

2.0

1.5

1.0

.5

0.0

-.5

-1.0

-1.5

TOS

TI

SUN

SON

SIE

SAM

PHL

ORA

NTT

NOV

NEC

MOT

MIT

MS

MAT

INT

IBM

HP HIT

FUJ

FT

ERC

COMCIS

BT

BS BCE

ATT

APL

AOL

(SPSS centroid clustering)

The Art of Organizational CartographyConstructing 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.

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.

Martin Waldseemüller’s Carta Mariana, which named the southern continent after Amerigo Vespucci.