corporate social capital and strategic isomorphism - CiteSeerX

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CORPORATE SOCIAL CAPITAL AND STRATEGIC ISOMORPHISM: THE CASE OF THE ISRAELI SOFTWARE INDUSTRY Shaul M. Gabbay, Ilan Talmud and Ornit Raz

ABSTRACT Corporate Social Capital has been receiving increasing attention in recent study of organizations. In this paper we focus our attention on strategic orientation of firms and reveal the ways they are affected by the social structure in which they are embedded. We focus on the way strategic orientation is socially determined and diffused. Our empirical application analyses 100 Israeli software firms, operating in four industrial districts. We reveal five generic business orientations. Applying corporate social capital as a framework, we find that similarity in business orientation is significantly associated with a firm’s position in the inter-organizational network and with a firm’s geographic location. Both network position and geographic location serve as a pool of social resources for adopting firm strategic style, deem successful in a highly uncertain sector thus creating corporate social capital.

Social Capital of Organizations, Volume 18, pages 135–150. Copyright © 2001 by Elsevier Science Ltd. All rights of reproduction in any form reserved. ISBN: 0-7623-0770-6

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INTRODUCTION Recent global developments in world economy, such as growing global competition, higher customer demands, and investor pressures on firms to improve their short- and long-term performance, have pressured organizations to change their short and long term strategic capabilities (Flaanery et al., 1996; Useem, 1990; 1996). As competition and uncertainty increasingly characterize organizational environments, organizational decision-makers are faced with the growing importance of choosing the “correct” strategy for their organization. In this paper we reveal the social structural determinants which influence this decision making process. As opposed to the rational based conceptualization of strategic choice we show how in severe uncertainty, firms adopt strategic preferences which are socially and geographically defused in the social structure in which they are embedded. We also argue that the “strategy of choosing a strategy”, in some instances, confers performance benefits for organizations thus creating corporate social capital. Our study uses the link between social capital and institutional perspectives as the theoretical springboard. We argue that this theoretical combination is of relevance for inter-connected market players. It is particularly useful for analyzing firms, which are operating in severely unpredictable organizational environments. We focus our attention on the social structure of firm’s interconnectedness and ask how the social structure confers advantages for these firms. Our analysis is a type D corporate social capital (Gabbay & Leenders, 2001). Namely we show how the inter-firm social structure confers social capital at the firm level. Moreover, this study inquires about the way in which strategic orientation is socially diffused within 100 Israeli software firms, which are operating in four different geographic districts. We will demonstrate how in this highly globalized, uncertain industry, the two elements of social structure – network position and geographical proximity – provide a dual gain of corporate social capital. The first gain is recognition; the second global bridging capacity. Our paper outlines two possible routes of acquiring corporate social capital by using the case of the inter-organizational network of Israeli software firms. The first is by occupying a central position in the industry network; the other path for creating corporate social capital is feasible by geographic proximity to other, similarly situated software firms. These two routes affect the organizations in two valuable ways: (1) learning how to deal with strategic interactions in an erratic, hyper-competitive world, and (2) obtaining recognition from other players in the field. We argue that the latter creates a symbolic resource. We apply two social structural constructs as our empirical application – network centrality and network cohesion. We begin with reviewing the

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relevant literature of network centrality and cohesion. We then move to discuss the theoretical postulates regarding business operation in severely uncertain environments. Next, we specify hypotheses, review the case studied, and describe our empirical findings. We conclude with the theoretical and empirical applications of our findings.

NETWORK POSITION AND SIMILARITY IN ATTITUDES Organizational success is linked to the position corporate actors occupy in a given social space. Voluminous research has documented that corporate profitability (Burt, 1992; Burt et al., 1994; Talmud, 1994), performance (Harrington, this volume), organizational survival (Han, 1993; Talmud & Mesch, 1997; Wells et al., this volume), innovation (Greve & Salaff, this volume), reputation (Galaskiewicz, 1985; Podolny, 1993), favorable loan terms (Uzzi & Gillespie, 1999), and the ability to operate under severe secrecy and uncertainty (Podolny 1993; Faulkner, 1983; Faulkner & Anderson, 1987; Baker & Faulkner, 1993) are all significantly linked to position in inter-organizational networks (see also Mizruchi & Potts, 1998). Gabbay and Leenders have recently developed a framework for understanding positive and negative outcomes, which arise from the social structure in which an organization is embedded. Corporate social capital is defined as “the set of resources, tangible or virtual, that accrue to a corporate player through the player’s social relationship, facilitating the attainment of goals.” (1999: 3). In this paper we refer to two social structural constructs for characterizing the network in which firms are embedded – network centrality and network cohesion. Both were found as a key sources for corporate leverage in creating and sustaining advantages for organizations, thus creating corporate social capital (Freeman, 1999). Concepts such as “betweenness” (Freeman, 1977, 1979) and “structural autonomy” (Burt, 1992) are just two examples of local network centrality; systematically benefiting market players at the expense of others. On the other hand, network cohesion was also found to be valuable for organizations (Useem, 1996; Knoke, 1999). If we add up these two social structural characteristics (network centrality and network cohesion), we can presume that corporate membership in a cohesive clique, being located at the very center of an inter-organizational business network, is valuable corporate social capital. According to this perspective, a key position in a central clique of an inter-organizational network provides the corporate actor with a rent-seeking capacity. Corporate social capital enables a business organization to extent its profitability, or to accrue valuable resources (financial or symbolic) which are necessary for corporate success. 137

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Opinions and preferences of social actors are affected by preferences and opinions of significant others. This has been found both at the individual level (Coleman & Menzel, 1966; Rogers, 1995) and at the organizational level (Baum & Oliver, 1992; Stuart, 1999; Zuckerman, 1999; Podolny & Castellucci, 1999). In formulating an opinion, then, corporate actors take into account the possible responses of other significant corporations on their decision. Additionally, similarly positioned actors have similar resources. Hence, we will expect central actors to act similar to one other. In the same way, we would expect closely related actors, constituting a clique, to have similar resources and to share their opinions (Burt, 1987, 1992; Burt et al., 1994). Yet, network centrality and cohesion are not the only available routes for corporate social capital. In this paper we extend the relevant discourse of social structure as corporate social capital to include the proximity to industrial clusters (Porter, 1998). We argue that geographic proximity is also a form of corporate social capital because it opens an ecological avenue for acquiring close contacts, which can be used as valuable resources, mostly informational and symbolic.

UNCERTAINTY MANAGEMENT AND INSTITUTIONAL ISOMORPHISM WITHIN INDUSTRIAL CLUSTERS Geographic proximity enables an organization to imitate “successful” patterns of decision-making and strategy formulation at a minimum cost. Business reality and organizational environment are rarely objective. As business organizations are created and run by human actors, their boundaries, problem setting and solutions are contrived as well (Mayer & Rowan, 1977). Corporate problems, organizational solutions and business strategic orientations are socially constructed and socially diffused. The structural mechanism behind business construction of strategic orientation is social imitation. Still, this process does not operate at randomly and/or in a social vacuum. For example, according to institutional theory, “modeling . . . is a response to uncertainty” (DiMaggio & Powell, 1983). When relations between means and ends are poorly understood, when business environment cannot be predicted, when even the symbolic closure of an organization field is constantly and unpredictably changing, organizations tend to imitate other organizations. According to this perspective, the modeled organizations may not be necessarily “successful” in an objective sense. Yet to the extent that a player becomes “a model” for success, the player is considered successful and legitimate and the organizational field becomes more homogeneous. This process of “institutional isomorphism” comes to be in spite of the strategic intent of executives to plan and invent original solutions for their problems. Over and above these intentions of originality, business

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managers use their social resources to learn what a corporate solution is, and more important, what a corporate problem is. Furthermore, corporate executives consult their business associates and consultants (who are often used as overlap contacts), and eventually narrow down the range of possible and acceptable solutions, resulting in a limited set of options and options (DiMaggio & Powell, 1983). Numerous network studies have demonstrated how uncertainty is managed via tangible social ties (Podolny, 1993, 1994; Podolny & Feldman, 1997; Podolny & Castelluci, 1999; Uzzi & Gillespie, 1999; Mizruchi & Fenn, 1999). Still, corporate social horizons are not unlimited. While some corporate players have a higher proportion of distant contacts than others do, most organizations learn to deal with their organizational routines from their immediate surrounding. A significant closure to this social scope is what has been suggested as an industrial cluster (Porter, 1998). An industrial cluster is defined as geographically concentrated linkages and organizational complementary capabilities (Porter, 1998). Industrial clusters become then a flexible spatial setting, where competitors and cooperating firms learn from one another generic ways to deal with the environment at minimum cost (Asheim, 1994; Humphrey, 1995).

ISRAELI SOFTWARE INDUSTRY: UNCERTAINTY AND CORPORATE STEERING IN THE “NEW ECONOMY” The software development industry has become an increasingly important sector in the Israeli economy. Investments are primarily in human resources in the software industry. Thus, the relatively high scholastic capabilities of Israelis convey a strong relative advantage to the software industry in the Israeli economy. Over the last ten years, it is estimated that revenues based on software exports and developments have enjoyed a 2550% increase. Several large buyout events have characterized the Israeli software sector mostly by U.S. Firms. (e. g. AOL $407 million acquisition of Mirabilis, a two -year-old Israeli software developer of ICQ, as well as a $4.7 billion acquisition of another Israeli firm – Chromatis Networks – by Lucent Technologies). As the software industry becomes globalized and operates under environmental conditions of severe uncertainty, for a firm to be identified as a part of the generic strategic style is of extreme importance in this sector. The software industry operates under the “new economy” rules. In this industry, the old rules of financing, human resources management, raising venture capital and investor relations, seem to be working under different and new codes. Moreover, many standards of infrastructure, research and development, and related computer products are open to “interpretative flexibility” of their applications and the 139

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ways which are accepted to mold a product and to approach a capital market (Pinch & Bijker, 1987; Darr & Talmud, 2000). More specifically, the uncertainty characterizing the software industry is strongly related to targeting ways and means of specific knowledge regarding other applications of complementary and competing applications. Additionally, the software industry operates under global hyper-competition. Many products are developed in a parallel fashion by various competitors, who usually are targeting similar, sometimesidentical user markets. Moreover, the comparative advantage of a software firm is not entirely dependent on its software quality. Many times it also depends on its reputation and in the ways its relations with other firms are managed. A software firm has to deal intensively with an additional variety of players: cooperating firms which sub-contract some parts of the production, other firms who try to access and recruit its employees, venture capital funds, and market analysts. A software firm has to deal at the same time with capricious institutional investors, private venture capitalists (“angels”), brokerage firms, and potential users. More specifically, a typical Israeli software firm experiences extremely high workforce turnover rate. Hence, uncertainty reduction mechanisms are frequently performed through the fostering of corporate strategic capabilities. For example, some software developers reduce the instability they face by using strategic alliances or vertical integration with large multinational communication or computer corporations at relatively early developmental stages. Another example is personnel recruitment and human resources management. Both are affected in this sector by the firm’s reputation (Fiegenbaum et al., 2000). Returning to the theoretical basis we discussed earlier – according to institutional theory, then, as there is no solid way in this segment of “new economy” to evaluate corporate performance, strategic isomorphism is a necessary tool for navigating any meaningful mental model for purposive action. Strategic isomorphism is also a key factor for recognition as a legitimate player among equivalent firms.

HYPOTHESES DERIVATION AND SPECIFICATION Following the literature of social capital, we argue that similarity in strategic style will be predicted by the social structure in which the firm is embedded. More specifically – social resources available to players are a function of their positions in the inter-organizational network. We thus hypothesize: Hypothesis 1: Central actors in the corporate network will differ from less central players in their strategic orientation.

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Following institutional theory and industrial cluster literature, we hypothesize that geographic location is a source of corporate social capital: corporate actors will learn strategic orientations within the confines of a geographical location. We therefore hypothesize: Hypothesis 2: Geographic location and corporate strategic orientation will be associated.

DATA COLLECTION We used eight different sources to trace 350 Israeli software corporations. These sources included Dun and Bradstreet, The Israeli Software Association, The Israeli Export Association, popular media citations, The High-Tech Information Booklet, as well as “snow ball” method. Firms were operating in various areas of specialty including information systems (28%), multimedia (20%), communication (18%), information security (5%), sub-contracting projects (10%) and a variety of others (19%). We specifically examined 100 Israeli firms (out of 227 attempts). The study uses two questionnaires. The first inquires into CEO characteristics and firm’s attributes. The second questionnaire focuses on corporate social network, exposing the position that the organization holds in the industry network. The latter was based on a full network list. Moreover, we also provided respondents with the option of adding other connections to firms, which did not appear on the given (full network) list. The population seems to be quite homogeneous. Firm age varies between a year to eleven years. The average number of employees is 42. Seventy percent of the studied software firms employ up to 30 people. Demographics of the CEO is highly invariant (mostly married male, age 40, most of them are highly educated in fields such as – computers, electronics or business. Most managers were found to be from Western family background).

FINDINGS Strategic Orientation and Centrality in Overlapping Cliques Strategic Orientation Each software firm’s CEO filled out 32 questions, set up as dichotomous generic alternatives, where the respondent has to locate himself on a Likkert scale. The questions aim at indicating the strategic style of the company. Principal 141

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Component Analysis reveals five generic strategic styles, which were composed into five scales (see Fiegenbaum et al., 1996): (1) (2) (3) (4) (5)

Time to market Sensor-environment Infrastructure investment Reliability Conservatism

Each generic style indicates different strategic orientation of the firm. For example, in “Time to market,” the management emphasizes fast delivery of products to the marketplace (even at the expense of slight quality control problems). In “Sensor-environment,” the firm is keenly aware of its competitors. The third style, “Infrastructure investment,” reveals a long-term calculation, in development, even at the expense of immediate financial losses. The fourth style, “Reliability,” marks the strategic significance that the firm attributes to its relations and exposure towards customers. Lastly, “Conservatism,” indicates a legal-formal basis for relations with corporate clients, rather than general trust and acquiescence. Centrality in Overlapping Cliques We argue that corporate software managers who belong to the same clique will adopt similar strategic orientation. Clique analysis is a concise tool to uncover cohesive subgroups in a given population (Mokken, 1979). In a sparse population, there are only a few overlapping sub-groups. By contrast, the existence of highly overlapping social circles indicates a dense population (Kadushin, 1968). A simple unconstrained clique analysis, (using UCINET 5), reveals 84 overlapping cliques, composed of three to seven members. The industry network is comprised of many – small and highly overlapping – cliques. This finding clearly indicates the frequency in which each actor appears, in various combinations, in a clique. To estimate actors’ centrality in these cliques, we test to what extent frequent overlap of key players exist. We performed this analysis because social references and isomorphic tendencies operate through brokerage. Accordingly, we narrowed down our analysis using N-Cliques procedure in UCINET 5. The procedure N-Cliques examines the maximal sub-graph, in which every pair of actors by a path of length 3 or less. The routine then provides an analysis of the overlapping structure of the n-cliques. This analysis gives information on the number of times each pair of actors are in the same n-clique and gives a single link hierarchical clustering based upon this information.1 From 32 actors appearing in 7 subgroups, 18 actors are permanent; they appear in any single clique. We refer to these 18 actors as “the leading group” of

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Table 1. Strategic Differences Between the Leading Group and Non-Members. Generic Strategic Style

Identity

N

Mean

Std. Deviation

T value *p < 0.05

Time to Market

Leading Subgroup Non-Members

17 82

2.470 2.502

564. 631.

284.

Sensor Environment

Leading Subgroup Non-Members

17 82

1.850 1.942

757. 490.

199.

Infrastructure Investment in the Present

Leading Subgroup Non-Members

17 82

2.557 3.011

825. 596.

*4.697

Reliability

Leading Subgroup Non-Members

17 82

3.809 4.462

1.26 2.058

005.

Conservatism

Leading Subgroup Non-Members

17 82

2.105 2.433

667. 632.

1.063

N = 99

the Israeli software industry, and we further examined whether their central location in overlapping dense cliques is linked to their strategic tendencies. Strategic Orientation and Network Position To test the extent, to which membership in the central sub-group is associated with generic corporate style, we ran several t-tests, comparing each strategic orientation scale between members to non-members in the leading group. The results are presented in Table 1. Table 1 illuminates that only in one strategic style there is a sharp difference between the leading group of the Israeli software industry and the rest of the population – investment in infrastructure. Although this finding is also linked to private ownership (private firms also tend to embrace this style in comparison with public ownership; p < 0.05), one clearly can find evidence that this pro-growth inclination is a distinct mark of the leading Israeli software firms. Stronger evidence regarding geographic cluster and strategic inclination can be found in Table 2. Geographic Location and Similarity in Strategic Orientation Table 2 shows the results of the GLM Multivariate procedure, which provides the analysis of variance for multiple dependent variables by one or more factor 143

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Table 2. Geographic Location and Strategic Style. (GLM – Multivariate Analysis).

Geographic Location Tel-Aviv

Strategic Orientation

F *P < 0.05 1 ** P < 0.00

Time to Market1 Sensor Environment Infra-Structure Investment Reliability Conservation

029. 075. 10.629 2.316 2.624

Jerusalem

Time to Market Sensor Environment Infra-Structure Investment Reliability Conservation

6.670 29.625 065. 001. 2.682

Herzalia

Time to Market Sensor Environment Infra-Structure Investment Reliability Conservation

1.138. 641. 3.238 826. 074.

Haifa

Time to Market Sensor Environment Infra-Structure Investment Reliability Conservation

053. 176. 302. 271. 655.

*

* **

variables. Using this general linear-model procedure, we test the hypotheses about the effects of five strategic style variables (factors) on the means of five groups (geographic location) of a joint distribution of the five dependent variables. Further, we performed a Discriminant Functional Analysis to position each place in a typical relative location across all generic strategies. The analysis is presented in Table 3. Table 3 shows that a single cannonical function was revealed, explaining 83% of the total variance in the five strategic styles. More important, looking at the group centroids, it seems that the most dramatic difference in strategic orientation is between software firms located in Tel Aviv and those located in Jerusalem. The analysis also shows that Herzlia and Haifa are located in between, and that that Herzlia is closer to Tel-Aviv and Haifa than to Jerusalem. We found no evidence for other rival possible explanations to link geographic

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Table 3. Geographic Location and Strategic Orientation. (Discriminant Functional Analysis). Standardized Canonical Discriminant Function Coefficients Strategic Orientation

Canonical Function 0.33

Time to Market Sensor Environment Infra-Structure Investment Reliability Conservatism

Explained Variance (%)

Canonical Correlation

Wilks’ Lambda

83.6

608.

565.

919. 130. 049. 235.

Chi-Square df

p < 0.001

15 55.164**

Group Centroids Tel-Aviv Herzlia 0.612 0.328

Haifa

Jerusalem

0.0025 1.350

location and similarity in strategic styles. We find that there is no association between – firm age, size, business domain – and geographic location. We therefore can conclude that our findings point to the fact that only strategic orientation is linked with geographic location. How geographic location linked with strategic orientation? Why, different Israeli software firms, from a variety of founding years, business domain and size do tend to adopt the strategic style of firms in proximity to them? We refer to our theoretical springboard, which we delineated at the earlier stage of this paper for an explanation. According to DiMaggio and Powell (1983), high level of uncertainty in an organizational field leads to mimetic isomorphism, wherein organizations are inclined to adopt those strategic models that they deem successful. Because almost any operation in the software industry is shaped by severe uncertainty, firms tend to imitate strategies of other firms. However, this does not happen randomly and in a social structural vacuum. The tendency is to mimic strategic orientations of other firms, which are viewed as triumphant models in the relevant business environment. A lack of a clear idea regarding the “order of things” in the so-called – “new economy” – brings about the modal in which the Israeli software firm must borrow other existing models from the firm’s immediate surrounding. This is why quite heterogeneous firms in a similar geographic area tend to adopt the same mental models of business attitudes. 145

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The research implication is that the meaning of an “industrial cluster” is extended to also provide “facilitating proximity.” This implies that software firms will be capable of adopting relevant business attitudes. Of course, it is beyond the scope of this paper to evaluate whether the adoption of these strategy models is “rational.” Still, the fact that geographic location is associated with similarity in business models, coupled with a lack of association between a firm’s other attributes to geographic location, alludes to our speculation – derived from the “New Institutional Theory.” We argue that the purpose of this adoption is twofold. The first is obtaining a “mental model” serves as a corporate “steering mechanism” under uncertainty. The second is achieving legitimacy among other significant players in the “industrial cluster”.

CONCLUSION The Two Meanings of “Industrial Cluster” We found partial evidence that Israeli software firms occupying a central position in tightly inter-connected industrial networks are different than their Israeli software firms. The central firms put more weight on strategies, aiming to increase corporate infrastructure, even at the expense of other generic strategic emphasis (e.g. reliability toward customers, time to market). We also know that these 18 corporations are more connected to overseas operations and global marketing. These key players indeed are the linking players of Israeli software firms with global capital and marketing markets. They serve as key identifiers for other firms of worthwhile investment and technological solutions in this highly uncertain market, operating according to the new rules of the “new economy.” A key position in the central clique thus provides corporate social capital. Even in the age of the virtual revolution, local space seems to be important (White, 1992). In many ways, social networks are still confined to a certain extent by physical barriers. We found that each industrial cluster is characterized by a similar generic strategic orientation. These two main findings offer several implications for corporate social capital as a framework. First, corporate social capital is also a function of social space. Network models of social structure serve as a heuristic tool to diagnose the ways in which corporations enjoy economic benefits from their position in a social network. Second, physical space also confers corporate social capital. Geographical proximity seems to impact the way corporate actors learn subtle models assisting them to make sense of their uncertain environment.

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Porter’s original concept of “industrial cluster” (1998) was geographic, referring to the spatial nexus of ties between competitors and cooperating firms. Yet one can conclude from the findings of this study that the industrial cluster is therefore both social and geographic in features. Some elements of industrial cluster are trans-local, while other components are clearly local. Corporations use both geographic distance and close ties as corporate social capital. Most of the recent studies of corporate social capital (Gabbay & Leenders, 1999) have dealt with tangible assets. Yet this study has demonstrated that the benefits of corporate social capital may be symbolic, not only narrowly economic. This argument extends the relevant literature on strategic alliances, which are a legitimizing signal to the organizational environment. For example, Baum and Oliver (1992) show that potential customers legitimate day-care organizations if they possess a tie to some prominent organization in the community. Stuart et al. (1999) show how the investment community is more receptive to biotechnology firms that possess an affiliation to a prominent partner. Zuckerman (1999) illustrates how the larger investment community evaluates the exchange that a firm possesses with financial analyst’s affects how favorably that firm is viewed. In this paper we have demonstrated how the adoption of strategies, which are diffused by key leaders in the industry, affects the signaling capabilities of these firms in a highly uncertain environment. This phenomenon is based to the two – structural proximity and the physical proximity that the firms have – to key leaders in the industry – social and geographical. Still, the way geographic proximity was measured limits the current study. Also, the dependent variable was operationalized in terms of similarity in attitudes. Future studies should tackle more precisely the ways social networks and geographical proximity interact, as well as the external conditions affecting these relations. Another related factor to be investigated is the degree to which geographic distance is a barrier to the formation and maintenance of beneficial corporate ties. Additionally, future research should focus more attention on the symbolic benefits of corporate social structure.

ACKNOWLEDGMENTS We are grateful for the comments of two anonymous reviewers. We thank Gilat Cohen for her valuable participation in the data collection. We thank Haya Shnap for her research assistance.

NOTE 1. These are found using an adapted version of the Bron and Kerbosch (1973) algorithm in UCINET 5. 147

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