Porous and Fuzzy Boundaries: A Network Approach to Corporate Diversification* David Knoke

1. 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.

2. Blurred Boundaries Researchers 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.

3. Corporate Diversification Corporate 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.

4. A Network Approach 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. • We test two research hypotheses: • H1: The affiliation network reveals two discrete types of firm clusters, • Diversified-industry firmsoperating in two or more industries • Focused-industry firmsconcentrating on a single industry • H2: Diversified-industry clusters explain additional variation in firm financial performance above the additive effects of conventional industrial classifications.

5. Affiliation Networks 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. • 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 can be displayed either as a bipartite graph, or as a gxhaffiliation matrix (A) whose i,j entry indicators whether actor i participated in event j. Its hxgtranspose matrix (A’) shows whether event j attracted actor i.

6. Duality of Persons & Groups Ronald 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).

7. 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.

8. 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 51 33421 Telephone Apparatus TeleApp 23 33422 RadioTelevision Broadcasting and Wireless Broadcast 20 33429 Other Communications Equipment Communic 24 33431 Audio and Video Equipment AV 21 33441 Semiconductor and Other Electronic Components Semicond 73 33451 Navigational Measur, Electromedical & Control Inst. Navigat 16 33461 Manufacturing Reproduc Magnetic & Optical Media Reprod 14 511 Publishing 51111 Newspaper Publishers News 17 51112 Periodical Publishers Period 23 51113 Book Publishers Book 16 51114 Directory and Mailing List Publishers Directory 14 51119 Other Publishers OthPub 1 51121 Software Publishers Software 45 512 Motion Picture and Sound Recording 51211 Motion Picture and Video Production Movie 13 51212 Motion Picture and Video Distribution MovieDist 4 51213 Motion Picture and Video Exhibition MovieExh 2 51219 Postproduction Services and Other Industries PostProd 3 51222 Integrated Record Production Distribution Record 1 51223 Music Publishers Music 7 515 Broadcasting 51511 Radio Broadcasting Radio 7 51512 Television Broadcasting TV 27 51521 Cable and Other Subscription Programming Cable 21 517 Telecommunications 51711 Wired Telecommunications Carriers Wired 55 51721 Wireless Telecommun Carriers except Satellite Wireless 58 51731 Telecommunications Resellers TCResell 25 51741 Satellite Telecommunications Satellite 27 51751 Cable and Other Program Distribution CableDist 6 51791 Other Telecommunications OtherTC 36 518 Internet Service Providers, Web Search Portals, and Data Processing Service 51811 Internet Service Providers and Web Search Portals ISP 19 51821 Data Processing Hosting and Related Services DataProc 29 519 Other Information Services 51911 News Syndicates Syndic 3 51912 Libraries and Archives Library 1 ___________________________________________________________________________________________

9. Measuring Similarity 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

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

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

12. Multidimensional Scaling of Jaccard Coefficients among 33 GIS Industries

13. Clustering Firms Next figure is a cluster analysis of 275 x 275 firm-by-firm matrix of Jaccards. • 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. • 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.) • Four of 5 groups of firm clusters include focused and diversified industries. • Two groups at the upper left involve mixtures of publishing and mass media clusters, respectively. • Large heterogeneous group on the right side combines four focused-industry with four diversified-industry clusters of firms. • 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.

14. 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 -semicond7 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, Sumitomo 10. SEMICONDUCTOR 39 Kyocera, Mitsubishi, Taiwan Semiconductor, Texas Instruments 11. satellite-wireless-wired- othertc-tcresell-dataproc 17 Bell Canada, CBS, France Telecom, KDDI, NTT, Telecom Italia 12. wired-othertc 17 China Unicom, Reuters, Telecom Indonesia, Telenor, Vodafone 13. movie-tv 10 Disney, News Corporation, Time Warner, Viacom, Vivendi 14. cable-tv 12 BSkyB, Liberty Global, ITV, Washington Post 15. COMPUTER 19 Acer, Benq, Bull, Dell, Hewlett-Packard, Hitachi, SanDisk 16. computer-semiconductor 12 Canon, LSI Logic, Nvidia, Oki, Samsung, Toshiba 17. book-period 11 Axel Springer, McGraw-Hill, Pearson, Reader’s Digest 18. satellite-tcresell-wired-wireless 6 AT&T, BellSouth, Hellenic Telecom, Qwest, Telstra, Verizon 19. tcresell-wireless-wired 21 Alltel, Carso Global, China Netcom, Pakistan Telecom, Turkcell 20. DATAPROCESS 12 Atos, EDS, First Data, NCR, Unisys, Xerox 21. software-computer-reprod 14 Apple, Fujitsu, Microsoft, Oracle, SAP, Seagate, Sony, Sun 22. SOFTWARE 17 Adobe, Autodesk, Avaya, CA, Infosys, Intuit, Siebel, VeriSign 23. ISP 5 Belgacom, Google, Yahoo 24. DIRECTORY 3 Dex, Dun & Bradstreet 25. NAVIGATIONAL 7 Lexmark, Ricoh, Scientific-Atlanta 26. OTHPUB 1 Seat-Pagine __________________________________________________________________________________________________________________

15. Multidimensional Scaling of Weighted Path Distances among 24 Firm Clusters

16. Explaining Firm Performance Next 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. • 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). • 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!