Porous and Fuzzy Boundaries:

A Network Approach to Corporate Diversification*

David Knoke

University of Minnesota

Universiteit van Tilburg

June 28, 2007

*Based on a research paper co-authored with Emanuela Todeva and Donka Keskinova. Sabbatical support provided by the University of Minnesota College of Liberal Arts.

Blurred BoundariesResearchers periodically not the “imprecision of industry definition and the ‘fuzziness’ of industry boundaries in economic environments characterized by product differentiation and technological change” (Venkatraman and Thomas 1988:546). “Industry and market boundaries are porous and ‘fuzzy’ especially where globalization is taking place” (McGee, Thomas and Pruett 1995:261).

A colorful example is Vivendi SA –active in music, video games, television, film, publishing, telecoms, and Internet – whose current incarnation involved 2000 merger of Seagram, Canal+, and Vivendi; a spin-off of original core water and waste companies; and sale of Universal Studios to NBC in 2006.

Corporate DiversificationCorporate diversification theories in finance economics and strategic management examine origins, trends, and financial consequences of diversified firms. An “age-old question”: does diversification – business units in different industries controlled by a single firm – create or destroy shareholder value compared to focused firms? (Martin & Sayrak 2003:38)

A diverse-focused dichotomy or count of the number of SIC industries obscures complex structural relations linking firms and industries, and fails to investigate whether particular combinations of industries differentially affect firm behaviors and performance outcomes.

Firms embedded within specific industrial network configurations may experience competitive advantages or disadvantages relative to firms located in alternative structural arrangements.

A Network Approach

We test two research hypotheses:

H1: The affiliation network reveals two discrete types of firm clusters,

(1) Diversified-industry firms operating in two or more industries

(2) Focused-industry firms concentrating on a single industry

H2: Diversified-industry clusters explain additional variation in firm financial performance above the additive effects of conventional industrial classifications.

To better understand blurred boundaries arising from corporate diversification in the Global Information Sector (GIS), we apply social network concepts and methods to reveal the structural relations among its industries and firms & to explain their effects.

Affiliation Networks

An affiliation network can be displayed either as a bipartite graph, or as a gxh

affiliation matrix (A) whose i,j entry indicators whether actor i participated in event j. Its hxg transpose matrix (A’) shows whether event j attracted actor i.

Formally, a pair of elementary sets connected by a (0-1 binary or ordinal) relation:Set N of g nodes (“actors”): N = {n1, n2, ..… ng}Set M of h nodes (“events”): M = {m1, m2, … mh}

L nondirected lines join the gxh ordered pairs of nodes <ni, mj>

An affiliation network consists of two-mode data, different sets connected by relations between but not within each set. If the two sets are “actors” and “events,” elements within each mode are indirectly tied, via common links to the other mode.

Familiar examples of affiliation networks include: persons belonging to voluntary associations; social movement activists participating in protest events; firms creating strategic alliances; nations signing trade and military treaties.

Duality of Persons & GroupsRonald Breiger’s (1974) classic article on the duality of persons and groups discussed: (1) actor-actor connections occurring through their co-membership or co-attendance at the same events; and (2) event-event connections via the overlap or interlocks with shared actors.

These two dual networks can be created by either pre- or post-multiplying an affiliation network and its transpose to create two one-mode matrices:

• AA’ is a gxg symmetrical matrix; its main diagonal entries show the number events in which an actor is affiliated; its off-diagonal elements are the number of events in which a row & column pair jointly participated.

• A’A is an hxh symmetrical matrix whose main diagonal entries show the number actors participating in the row event; its off-diagonal elements are the number of actors affiliated with a particular pair events.

Both dual matrices may be analyzed as one-mode networks, measuring such properties as size, density, reachability, and cohesion. Interpretations of co-memberships must recognize that entities are indirectly connected, and that the specific identities of those indirect paths cannot be known from the dual matrix (e.g., we know the number of events a pair attended but not which events).

The Global Information Sector

The GIS is based on the North American Industrial Classification System information sector (51) of firms producing & distributing info commodities

511 Publishing Industries (except Internet) 512 Motion Picture and Sound Recording Industries 515 Broadcasting (except Internet) 517 Telecommunications 518 Internet Service Providers, Web Search Portals, Data Proc Services 519 Other Information Services + 334 Computer and Electronic Product Manufacturing

Using 2005 Fortune and Forbes lists, we found 275 corporations active in at least one of the 33 five-digit GIS industries (median = 2.00, mean = 2.55). NAICS codes from Thomson and Datamonitor.

Firm revenues ranged from Pixar Studio’s $300 million to Nippon Telegraph & Telephone’s $101 billion.

NAICS Subsectors and Industries in the Global Information Sector___________________________________________________________________________________________

Code Industry Name Abbreviation N___________________________________________________________________________________________334 Computer and Electronic Product Manufacturing

33411 Computer and Peripheral Equipment Computer 5133421 Telephone Apparatus TeleApp 2333422 RadioTelevision Broadcasting and Wireless Broadcast 2033429 Other Communications Equipment Communic 2433431 Audio and Video Equipment AV 2133441 Semiconductor and Other Electronic Components Semicond 7333451 Navigational Measur, Electromedical & Control Inst. Navigat 1633461 Manufacturing Reproduc Magnetic & Optical Media Reprod 14

511 Publishing51111 Newspaper Publishers News 1751112 Periodical Publishers Period 2351113 Book Publishers Book 1651114 Directory and Mailing List Publishers Directory 1451119 Other Publishers OthPub 151121 Software Publishers Software 45

512 Motion Picture and Sound Recording51211 Motion Picture and Video Production Movie 1351212 Motion Picture and Video Distribution MovieDist 451213 Motion Picture and Video Exhibition MovieExh 251219 Postproduction Services and Other Industries PostProd 351222 Integrated Record Production Distribution Record 151223 Music Publishers Music 7

515 Broadcasting51511 Radio Broadcasting Radio 751512 Television Broadcasting TV 2751521 Cable and Other Subscription Programming Cable 21

517 Telecommunications51711 Wired Telecommunications Carriers Wired 5551721 Wireless Telecommun Carriers except Satellite Wireless 5851731 Telecommunications Resellers TCResell 2551741 Satellite Telecommunications Satellite 2751751 Cable and Other Program Distribution CableDist 651791 Other Telecommunications OtherTC 36

518 Internet Service Providers, Web Search Portals, and Data Processing Service51811 Internet Service Providers and Web Search Portals ISP 1951821 Data Processing Hosting and Related Services DataProc 29

519 Other Information Services51911 News Syndicates Syndic 351912 Libraries and Archives Library 1

___________________________________________________________________________________________

Measuring Similarity

Semiconductors1 0

1Wireless

Telecoms0

a3

b55

c70

d148

Newspapers1 0

1TV

0

a17

b17

c7

d242

In the two-mode 275 x 33 firms-by-industries binary matrix, a cell entry of 1 indicates a row firm operates in the column industry, and 0 indicates absence. For all pairs of columns we computed a 33 x 33 matrix of Jaccard similarity coefficients, the ratio between the size of an intersection to the size of a union for two industries.

The higher a Jaccard value, the greater the overlap among the firms in a pair of GIS industries:

Jaccard = (a / (a + b + c))

Jaccard = (17 / (17 + 17 + 7)) = 0.41 Jaccard = (3 / (3 + 55 + 70)) = 0.02

Clustering Industries

The next two figures display a hierarchical cluster analysis of the 33 GIS industry similarities (complete-link criterion) and a multidimensional scaling plot (stress = 0.24) with contiguity lines around the six diversified-industry clusters and three singletons.

Shown in the following two figures are cluster and MDS analyses of the dual 275 x 275 firm-by-firm matrix of Jaccard coefficients.

1. Bottom clusters are mostly equipment manufacturing (NAICS industries in subsector 334) and telecommunication industries (517), whose proximity implies stronger ties among these industries than to other parts of the Global Information Sector.

2. Presence of software industry (511) inside the cluster with computer manufacturing, navigational equipment, and reproducing media, and the presence of data processing (518) among the telecoms reveal some heterogeneity within those two diversified-industry clusters.

3. The three clusters at the top also exhibit substantial industry heterogeneity, which remains even if the large cluster of industries in the publishing, motion picture, and broadcasting subsectors were divided into two subclusters (dotted line).

Dendogram from Hierarchical Cluster Analysis of 33 Industries (Ordinal Scale)

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5

-1.0

-0.5

0.0

0.5

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Computer

TeleApp

Broadcast

Communic

AVSemicond

Navigat

Reprod

News Period

Book

Directory

OthPub

Software

Movie

MovieDist

MovieExh

PostProd

Record

Music

Radio

TV

Cable

Wired

Wireless

TCResell

Satellite

CableDist

OtherTC

ISP

DataProc

Syndic

Library

Multidimensional Scaling of Jaccard Coefficients among 33 GIS Industries

Clustering FirmsNext figure is a cluster analysis of 275 x 275 firm-by-firm matrix of Jaccards.

1. The 15 focused-firm clusters, labeled in boldface capitals, each have only a single dominant industry, with no other industry prevalent among least half its member firms.

2. The 11 diversified-firm clusters, labeled in hyphenated lower case letters, have between two and five additional industries in which half or more of their member firms participate.

The MDS plots intercluster proximities, calculated as weighted path lengths. (Cell counts are normalized within each matrix row to add to 1.00, then multiplied by the matrix transpose, producing a 24 x 24 cluster-by-cluster matrix. Higher values indicate greater similarity of a pair of firm clusters’ ties to all 33 industries.)

1. Four of 5 groups of firm clusters include focused and diversified industries.

2. Two groups at the upper left involve mixtures of publishing and mass media clusters, respectively.

3. Large heterogeneous group on the right side combines four focused-industry with four diversified-industry clusters of firms.

4. Also in the large group are both clusters of telecom apparatus-communication equipment manufacturers, separated from the adjacent group containing the telecom service-provider clusters.

Summary of Hierarchical Cluster Analysis of 275 GIS Firms __________________________________________________________________________________________________________________Firm Clusters’ Main Industries N Some Prominent Firms__________________________________________________________________________________________________________________

1. MOVIEEX 2 Regal Entertainment 2. AV 3 Maxtor, Philips 3. BROADCAST 8 Agilent, Matsushita, Qualcomm 4. news-tv 8 Daily Mail, Dow-Jones, Gannett, NY Times, Singapore Press 5. teleapp-commun -semicond 7 Alcatel, Cisco, Ericsson, Lucent, Nortel 6. TV 7 DirecTV, Fuji TV, Tokyo Broadcasting, Tribune 7. PERIOD 3 Primedia, VNU 8. WIRELESS 14 Comcast, EchoStar, Portugal Telecom, Sprint-Nextel, Telus 9. semiconductor-teleapp-communic 10 Intel, Nokia, Motorola, Sanyo, Siemens, Sumitomo10. SEMICONDUCTOR 39 Kyocera, Mitsubishi, Taiwan Semiconductor, Texas Instruments11. satellite-wireless-wired- othertc-tcresell-dataproc 17 Bell Canada, CBS, France Telecom, KDDI, NTT, Telecom Italia12. wired-othertc 17 China Unicom, Reuters, Telecom Indonesia, Telenor, Vodafone13. movie-tv 10 Disney, News Corporation, Time Warner, Viacom, Vivendi14. cable-tv 12 BSkyB, Liberty Global, ITV, Washington Post15. COMPUTER 19 Acer, Benq, Bull, Dell, Hewlett-Packard, Hitachi, SanDisk16. computer-semiconductor 12 Canon, LSI Logic, Nvidia, Oki, Samsung, Toshiba17. book-period 11 Axel Springer, McGraw-Hill, Pearson, Reader’s Digest18. satellite-tcresell-wired-wireless 6 AT&T, BellSouth, Hellenic Telecom, Qwest, Telstra, Verizon19. tcresell-wireless-wired 21 Alltel, Carso Global, China Netcom, Pakistan Telecom, Turkcell20. DATAPROCESS 12 Atos, EDS, First Data, NCR, Unisys, Xerox21. software-computer-reprod 14 Apple, Fujitsu, Microsoft, Oracle, SAP, Seagate, Sony, Sun22. SOFTWARE 17 Adobe, Autodesk, Avaya, CA, Infosys, Intuit, Siebel, VeriSign23. ISP 5 Belgacom, Google, Yahoo24. DIRECTORY 3 Dex, Dun & Bradstreet25. NAVIGATIONAL 7 Lexmark, Ricoh, Scientific-Atlanta 26. OTHPUB 1 Seat-Pagine__________________________________________________________________________________________________________________

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5

-1.0

-0.5

0.0

0.5

1.0

AV

BROADCASTnews-tv

teleapp-comm

TV

PERIOD

WIRELESS

semi-teleapp-commSEMICOND

sat-othertc-wireless

othertc-wired

movie-tv

cable-tv

COMPUTER

comp-semibook-period

sat-resell-wired

tcresell-wired

DATAP

soft-comp-reprod

SOFTWARE

ISP

DIRECT

NAVIGAT

Multidimensional Scaling of Weighted Path Distances among 24 Firm Clusters

Explaining Firm PerformanceNext two tables show ANCOVAs for 17 firm performance indicators, controlling for age, # employees, 32 NAICS industry dummies, and 12 diversified-industry firm clusters from the preceding cluster analysis.

1. Ten industrially diversified firm clusters have significantly effects in one or more equations. Relative to the focused firms, some diversified firms performed better (e.g., total assets, dividend per share), while others performed worse (e.g., net income, ROI).

2. Bottom panel reports F-ratios for tests of differences in R2s compared to equations without the 12 diversified-industry firm clusters. All show increased R2 of 1.5 - 8.3%. In seven instances they boosted the additive R2 by 20-59%. Thus diversified-industry clusters account for additional variation in firm financial performance beyond that attributable to additive effects of the NAICS industry classification.

Numerous opportunities to extend structural analysis: to other economic sectors, with longitudinal data, additional firm outcome measures, etc.

North American Product Classification System may soon allow three-mode networks of products-by-firms-by-industries. Then we can test not only whether boundaries are porous & fuzzy, but whether they’re also squishy!