http://oss.sagepub.com/ Organization Studies http://oss.sagepub.com/content/24/4/535 The online version of this article can be found at: DOI: 10.1177/0170840603024004002 2003 24: 535 Organization Studies Bat Batjargal Social Capital and Entrepreneurial Performance in Russia: A Longitudinal Study Published by: http://www.sagepublications.com On behalf of: European Group for Organizational Studies can be found at: Organization Studies Additional services and information for http://oss.sagepub.com/cgi/alerts Email Alerts: http://oss.sagepub.com/subscriptions Subscriptions: http://www.sagepub.com/journalsReprints.nav Reprints: http://www.sagepub.com/journalsPermissions.nav Permissions: http://oss.sagepub.com/content/24/4/535.refs.html Citations: What is This? - May 1, 2003 Version of Record >> at UNIV OF SOUTHERN CALIFORNIA on April 2, 2014 oss.sagepub.com Downloaded from at UNIV OF SOUTHERN CALIFORNIA on April 2, 2014 oss.sagepub.com Downloaded from
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http://oss.sagepub.com/Organization Studies
http://oss.sagepub.com/content/24/4/535The online version of this article can be found at:
DOI: 10.1177/0170840603024004002
2003 24: 535Organization StudiesBat Batjargal
Social Capital and Entrepreneurial Performance in Russia: A Longitudinal Study
Published by:
http://www.sagepublications.com
On behalf of:
European Group for Organizational Studies
can be found at:Organization StudiesAdditional services and information for
at UNIV OF SOUTHERN CALIFORNIA on April 2, 2014oss.sagepub.comDownloaded from at UNIV OF SOUTHERN CALIFORNIA on April 2, 2014oss.sagepub.comDownloaded from
Social Capital and EntrepreneurialPerformance in Russia: A Longitudinal StudyBat Batjargal
Abstract
Drawing on the social embeddedness perspective, this article examines the impact ofentrepreneurs’ social capital on their firm performance in post-Soviet Russia. Basedon face-to-face interviews with 75 Russian entrepreneurs in 1995 and follow-upinterviews in 1999, the study examines effects of structural embeddedness, relationalembeddedness and resource embeddedness on firm performance. The main findingis that relational embeddedness and resource embeddedness have direct positiveimpacts on firm performance, whereas structural embeddedness has no direct impactson performance.
Keywords: entrepreneurship, social capital, Russia
This article examines the way in which various aspects (structural, relationaland resource) of personal social networks of entrepreneurs affect financialperformance (revenue and profit margin) of their firms. The main argumentI would like to make is that in the entrepreneurial context, contact resourcesin parallel with structural and relational dimensions of networks will havesignificant impacts on firm performance. Resourceful contacts in terms offinancial resources, decision-making power, reputation, and social con-nections will be able to influence contractual decisions and terms (forexample, price) in favor of the entrepreneur, and the transactions based onthese decisions are likely to benefit firms’ financial indicators such as revenueand profitability. In this way, I incorporate empirically the resource aspect ofpersonal networks, and I argue that the resource dimension is as important asthe structural patterns and relational qualities of personal social networks.
Actions of economic agents are embedded in concrete, ongoing systems ofsocial relations and these relations facilitate and constrain agents’ profit andrent-seeking actions (Granovetter 1985). Social capital defined as networks ofrelationships and assets located in these networks (Bourdieu 1986; Burt 1997a;Coleman 1988; Lin 2001a) has positive effects on firm performance (Baker1990), product innovation (Tsai and Ghoshal 1998) and industry-wide networkformation (Walker et al. 1997). Similarly, the social capital of individualsfacilitates job and status attainment (Lin et al. 1981; Marsden and Hurlbert1988), enhances individuals’ power (Krackhardt 1990) and career mobility(Podolny and Baron 1997), and impacts CEO compensation (Belliveau et al.1996) and managerial performance (Galunic and Moran 1999).
Research on personal networks of entrepreneurs revealed that entrepreneursseek information, advice and social support from network alters (Aldrich andZimmer 1986; Birley 1985; Nohria 1992), control and manage exchange struc-tures through network dyads (Larson 1992), access financial capital (Shaneand Cable 2002; Uzzi 1999), and get endorsement from prestigious players toinfluence perceptions of the quality of their ventures (Stuart et al. 1999).
On the other hand, there is growing empirical evidence that the socialembeddedness has a negative aspect: tightly controlled relationships reinforcesocial obligations and expectations that may limit the freedom of economicagents to recognize and exploit new opportunities (Light and Isralowitz 1997;Podolny and Page 1998; Uzzi 1997). Previously instrumental relationshipsmay turn into ‘dark resources’ or social liabilities that constrain rent-seekingactivities of managers and entrepreneurs, affecting negatively theirperformance indicators (Bean and Bell-Rose 1999; Gargiulo and Benassi1999; Portes 1995).
Despite the growing research literature on positive and negative effects ofsocial capital on outcome variables, there is no study to date that examinesimpacts of various types of embeddedness on firm performance. Byexamining the effects of structural, relational and resource embeddedness ofpersonal networks of entrepreneurs on the financial performance of firms, Imake a contribution to social network theory, which until now has notincorporated systematically ‘resource embeddedness’ as a dimension of socialnetworks. By applying the embeddedness argument developed in the westernsocial and cultural environment to Russia, I expand the paradigm boundaryto formerly communist societies. In addition, by examining the effects ofsocial networks — a phenomenon deeply embedded in the Russian social,cultural and institutional environments — on entrepreneurial performance, Iclaim a contribution to the entrepreneurship literature.
The context of this study is a large transition economy — the RussianFederation. The rationale for the research site is threefold. First, instrumentalmobilization of social relations was and is a prevalent mode of getting thingsdone in Russia. The legacies of communal cultural traditions and the Sovieteconomic system, and the extreme economic chaos prevalent in contemporaryRussia make flows of goods and services through personal networks adominant mode of exchange among organizations and individuals (Berliner1957; Kryshtanovskaya and White 1996; Ledeneva 1998). There is researchevidence that extreme environments such as a hurricane or underclass povertyforce actors to mobilize social networks more than in stable environments(Hurlbert et al. 2001). Therefore, the Russian context is an appropriate siteto test hypotheses on the effects of various dimensions of social capital onoutcome variables. Second, social capital is an indigenous phenomenon thatis deeply embedded in local cultural and historical traditions. Examining therole of social capital in performance success in the Russian context is anattempt to verify hypotheses on the contingent value of social capital (Burt1997a). Third, Russia is a potential economic power in the Eurasian landmass,and therefore, it is important practically to understand the way in which firmsand individuals act and create wealth in that country.
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The resources of individuals defined as valuable assets possessed bypersons and embedded in a social network constitute the fundamental linkagebetween actors’ purposive actions and their outcomes, for example, entrepren-eurial entry and exit (Abell 1996). The initial resources of entrepreneurs havebeen found to have significant impacts upon venture performance (Brush andChaganti 1999; Cooper et al. 1994; Eisenhardt and Schoonhoven 1990). Thedistribution pattern of various resources among individuals at a given momentin time, however, is a function of social structure (Bourdieu 1986; Stinchcombe1965). Different resources or capitals are dispersed unevenly across hierarchicalas well as segmented groups in society (Anheier et al. 1995; Bourdieu 1986;Lin 2001a). The volume of resources possessed by an individual or group iscontingent upon the overall position of that individual or group in the socialspace. This differential distribution of resources across various levels ofstructural hierarchy is referred to as resource differential or heterogeneity ofsocial actors (Lin 2001a). The resource heterogeneity forms the set of con-straints that govern the functioning of society, in a durable way, determiningthe chances of success for instrumental actions by individuals, for example,entrepreneurs (Bourdieu 1986).
One form of resource heterogeneity is the inequality of social capital (Linet al. 2001) or social capital differential. In the entrepreneurial context, socialcapital differential refers to the uneven endowment of entrepreneurs withsocial resources in terms of network structure (Burt 1983b), relations andcontact resources (Bourdieu 1986; Burt 1992; Coleman 1990; Flap 1991; Laiet al. 1998; Lin 1982, 2001a). It is therefore assumed that heterogeneity inthe social capital of entrepreneurs will be reflected partially in varieties offirm performance because embedded relations influence the purchase and saledecisions of entrepreneurs in significant ways (Uzzi 1996).
Sociologists have conceptualized two dimensions of an individual’s socialcapital: structural embeddedness and relational embeddedness. Structuralembeddedness is the structure of the overall network of relations (Granovetter1990: 98). The structural properties of ego-centered networks such as networksize, density and diversity are dimensions of structural embeddedness.Relational embeddedness is the extent to which economic actions are affectedby the quality of actors’ personal relations (Granovetter 1990: 98). Suchdimensions as relational content (Burt 1983a, 1997b; Podolny and Baron1997), tie strength, for example, strong versus weak ties (Marsden andCampbell 1984), and relational trust (Galunic and Moran 1999; Tsai andGhoshal 1998) comprise the relational embeddedness of dyadic ties.
Although no writer has employed the term ‘resource embeddedness’, mostnetwork theorists have explicitly described the resource dimension ofindividual networks. In order to mobilize resources effectively, actors shouldbe aware of existing resources, and exchange partners should be motivatedto put resources at each other’s use. In addition, various resources should beavailable for instrumental mobilization (Granovetter 1982; Lin 2001b). Burt(1992: 12) defined social capital as ‘at once the resources contacts hold andthe structure of contacts in a network. The former describes whom you reachand the latter describes how you reach. In other words, the structural aspect
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and the resource aspect of networks are two different dimensions of personalsocial capital. Likewise, Bourdieu (1986: 249) interpreted social capital asthe aggregate of actual or potential resources, which are linked to possessionof a durable network of relationships, mutual acquaintances and recognition.Lin (2001a: 25) suggested a ‘resource-based definition’: social capital is theresources embedded in social networks accessed and used by actors foractions. Thus, social capitalists do capitalize on network contacts that occupystrategic network locations and significant organizational positions (Lin2001a).
Drawing on these definitions, I interpret resource embeddedness as thedegree to which network contacts possess valuable resources (Bourdieu 1986;Lai et al. 1998; Lin and Dumin 1986; Marsden and Hurlbert 1988). Resourceembeddedness, therefore, is a function of resource attributes of individualalters, for example, wealthy contact versus not-wealthy contact (Ibarra 1993;Lin 2001a). Consequently, I propose that heterogeneity in the structural,relational and resource aspects of networks leads to a variety of firmperformance.
Hypotheses
Structural Embeddedness
The empirical research on the effects of network structure on performancehas produced mixed evidence. Reese and Aldrich (1995) found no evidenceto suggest that the size of an entrepreneur’s network affects venture survival.On the contrary, Aldrich et al. (1987) documented that network density wasassociated with the profitability of new ventures, whereas network access-ibility positively influenced business founding. In a study of sales managersin a Fortune-100 firm, Galunic and Moran (1999) found that network sizeimpacts positively on revenue. In the Russian context, low-density networksof entrepreneurs facilitated better revenue growth in contrast to high-densitynetworks (Sedaitis 1998).
It is proposed that sizeable personal networks of entrepreneurs mayincrease the likelihood of locating clients and suppliers who are sociallybound. This may facilitate sales stabilization and eventual growth since theembeddedness provides room for negotiations that might allow entrepreneursto convert the social bonds into revenue growth and other tangible benefits.The personal chemistry between the entrepreneur and the supplier is likelyto enable the entrepreneur to purchase raw materials and other productioninputs at lower prices, and this might increase profit margin, boosting overallperformance. There is empirical evidence that personalized relations betweenentrepreneurs and their bankers lead to cheaper interest rates on loans (Uzzi1999), and that spin-off firms get favorable rates on equipment leasing fromtheir parent companies (Webster and Charap 1993). These arrangements mayimprove a firm’s financial performance ratios, such as return on assets.
Heterophilous contacts are conducive to the interactions of entrepreneurswith others of different attributes and resources. Bankers may be able to build
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a broad range of clients with differentiated needs across different industries.This may enable them to customize their products, building customer loyaltyand spreading the risk of defaults. In this way, bankers are likely to buildcustomers’ dependence, which may enhance their client retention. Tradingfirms may get better access to overdraft facilities, speedy cash managementand other services from embedded relationships with bankers, whereasproduction firms may deliver goods in time and be flexible toward ad hoccustomer demands. Network diversity may be crucial for manufacturing firmsin Russian conditions: the wide-spread phenomena of inter-enterprise loans,enterprise arrears for delivered products and barter exchanges that has plaguedthe Russian economy ever since the late 1980s (Dolgopyatova 1995) mayhave forced Russian manufacturers to diversify their networks in order tosurvive. Firms in transition economies enter and deliberately build a com-plicated web of interconnected firms where assets and liabilities are creativelydispersed in order to reduce the harmful effects of environmental uncertainties(Stark 1996). The simple production chain of resource firms makes them lessreliant on particularistic ties with managers of trading or manufacturing firms,although they are likely to gain benefits from bankers in services such asforeign exchange and international money transfers. Following theseassumptions, I propose:
H1: The greater the size of the initial networks of entrepreneurs, the betterthe firm’s performance.
H2: The greater the heterophily of the initial networks of entrepreneurs, thebetter the firm’s performance.
Relational Embeddedness
Constrained and facilitated by a history of interactions and consequent mutualexpectations, the actions of economic agents, such as price negotiation, arelargely a function of personal chemistry as much as of rational cost-benefitcalculations. For example, Baker (1984) found that the increase in number ofoption traders impeded communication among actors and that resulted inoption price volatility. This happened because as group size increased, thenumber of personalized trading relations that the average trader could sustaindid not. In this way, relational quality preconditioned option price stabilitythat affects directly firm or trader performance.
There is no empirical study to date that examines the effects of therelational aspect on entrepreneurial performance. There are, however, findingsthat friendship ties are more instrumental in finding better jobs (Lin andDumin 1986), and the relational trust enhances managerial sales andinnovation performance (Galunic and Moran 1999). Researchers have shownthat strong and weak ties do offer different advantages in different contexts(Krackhardt 1990; Hansen 1999; Podolny and Baron 1997).
Weak ties may be regarded as performance-boosting tools: vaguely definedrelationships provide a freedom to exploit new opportunities by bridging
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between disconnected contacts, and entrepreneurs with structural autonomiesare likely to gain most by not being bound by social expectations andobligations (Burt 1992).
Alternatively, strong ties with customers and suppliers may facilitaterevenue growth because these ties are more motivated to interact and typicallymore easily available for instrumental cooperation (Granovetter 1982). Strongties are described as enhancing firm performance directly through trustbuilding, information transfer and joint problem-solving arrangements (Uzzi1997). Therefore, I propose alternative hypotheses.
H3: The greater the number of weak ties in the initial networks of entre-preneurs, the better the firm’s performance.
H3 (alternative): The greater the number of strong ties in the initial networksof entrepreneurs, the better the firm’s performance.
Resource Embeddedness
Personal networks that are composed of resource-rich and powerful ties willproduce higher rates of return when they are mobilized. Burt (1992: 272)wrote: ‘a person with a poorly structured network that includes just one well-placed contact can do well through that contact’s sponsorship regardless ofhow well the person’s network as a whole is structured’. Connections withexecutives that manage large corporations and banks are conducive to greatervolumes of material resources and lucrative contracts. Powerful bureaucratswill be able to provide such ‘services’ as government contracts, tax privilegesand other tangible benefits for firms. Bureaucratic services are prevalent inRussia where political power has a definite market value (Kryshtanovskayaand White 1996). Empirical research on contact resources (that is, wealth,status and power) has found that both the availability of resources and theactual mobilization of resources play a crucial role in finding better jobs (Laiet al. 1998). It is therefore proposed that:
H4: The greater the volume of resources contacts possess, the better the firm’sperformance.
H5: The greater the resource mobilization, the better the firm’s performance.
Methodology
The Russian Context
The Russian societal context provides conditions where personal networksor particularistic relationships will have prevailing impacts on firm perfor-mance. The cultural inclinations of Russians to rely heavily on personalconnections, rather than on other alternative economic and legal means, hasmade them more relationship oriented (Bunin 1994; Ledeneva 1998). Blat —
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the system of informal contacts — served as an alternative mechanism forovercoming the rigidities in Soviet factory production and supply practices(Berliner 1957) and for obtaining consumer goods and services under therationing system (Ledeneva 1998). The volume of resources possessed bycontacts was contingent upon the political power of social actors (Shkaratanand Figatner 1992). The legacy of the Soviet economy of deficits, such aspersonal bargaining and barter trading, are still core features of Russianeconomic exchanges today, perpetuating the personalized nature of businessdealings. In common with other transition economies, economic actors inRussia use personal relationships as hedging safeguards against legal,institutional and environmental uncertainties (Sedaitis 1998; Stark 1996; Xinand Pearce 1996). Vladimir Yadov, a leading Russian sociologist, said in aninterview:
‘Russian entrepreneurship is embedded in neither markets nor hierarchy but in ahybrid form of the two. There exist therefore parallel structures where the sameamount of money and connections has different values in each. You make the samevolume of profit by spending different amounts of money. Succeeding in Russiancapitalism depends not only on your financial capability but also on many other factorssuch as informal networks, trust, etc.’ (Author’s interview, May 1995, Moscow).
Sample and Data Collection
The empirical data of the study is composed of face-to-face interviews with 75 Russian entrepreneurs during February–June 1995 (Table 1), and follow-up interviews with 56 original respondents in March–May 1999.Pilot interviews with six Moscow entrepreneurs were conducted in August1994. The sample includes firms in three Russian cities (that is, Moscow,Ekaterinburg and Petrozavodsk) and from four industries (that is, banking,trade, manufacturing and the resource sector). It also covers large, mediumand small firms, and new ventures versus privatized firms.
Batjargal: Social Capital and Entrepreneurs in Russia 541
The selection of Moscow, Ekaterinburg and Petrozavodsk is justified forthree reasons. First, Russia is composed of 89 regions and 11 economicdistricts. This geographic size and diversity makes national random samplesvirtually impossible. Second, the three cities represent Russia’s regionaleconomic diversities reasonably well. Moscow has a population of more than9 million and is located in the central economic district. Ekaterinburg is aformerly closed center of the military-industrial complex in the Urals and hasa population of 1.6 million. Petrozavodsk is the capital city of the Republicof Karelia, one of the 19 ethnic Republics, with a population of 300,000, andis located in the northern economic district. Third, surveys of the new firmcreation rate showed that Moscow is the most entrepreneurial city in Russia,while Ekaterinburg and Petrozavodsk were in the list of the top 10 and top40 entrepreneurial cities respectively (Alekseev et al. 1995).
I focused on banking, trade, manufacturing and the resource sector becausethese four industries have contrasting structural patterns, so that mostindustry-related particulars would be captured in the study. The four industriesalso differ sharply in terms of maturity and growth stage. Banking was thebest performing industry until the default in August 1998 (Glisin et al. 1995).Trade was and still is a growth industry (Shinkareva 1995). Manufacturing(especially heavy machinery) is a mature and declining industry (GoskomstatRossii 1994). The raw material extracting industry is the ‘cash cow’ in theeconomy. Although heavily dependent on world commodity prices, theresource industry is a stable earner of the country’s hard currency, and it islargely a mature industry. The resource sector (oil, gas, minerals, and timber)produces one-fifth of industrial output in the country (Goskomstat Rossii1994).
Accepting the notion that large and small enterprises are different, I controlfor firm size and included large, medium and small firms in the sample.Researchers have found that new firms and newly privatized firms in Russiahave substantially different ownership structures, patterns of control andbusiness strategies (Earle et al. 1996). Therefore, I included in the samplenew firms as well as newly privatized firms.
In 1995, I selected firms on the basis of a stratified random samplingprocedure in three cities. The computerized database of registered businessesof the Moscow City Committee of Statistics, the Business Assistance Centerof the Sverdlovsk Regional Administration in Ekaterinburg, and the StateCommittee of Statistics of the Republic of Karelia in Petrozavodsk were usedas sampling populations. I created 12 lists of firms (4 industries and 3 sizes)each of which contained the names of 20 firms.
Banks were classified as small (charter funds < US$50,000), medium(charter funds US$50,001–250,000), and large (charter funds > US$250,001).I base this classification on interviews with Russian experts and Central Bankofficials as well as previous studies of Russian banks (Lapidus and Van deWaal-Palms 1997). In manufacturing and the resource sector, firms weregrouped as small (< 100 employees), medium (101–500 employees), andlarge (> 501 employees). Trade firms were categorized as small (< 50employees), medium (51–200 employees), and large (> 201 employees). The
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classification is based on the definition of small firms in Russian law anddiscussions with Russian experts (Rossiiskaya federatsiya 1995).
Every second firm on these lists was selected. In 1995, 120 entrepreneurswere contacted and 82 agreed to be interviewed. The response rate was 68percent. Some seven respondents were discovered to be ineligible in the field,so that the final sample consisted of 75 entrepreneurs and directors. Therewere 50 new ventures and 25 privatized companies. In 1999, I re-interviewed56 original respondents (Table 2).
Financial performance data were collected from the firms as well as othersources, such as the Central Bank of Russia, the Association of RussianBanks, the Foundation for Small Business Development in three cities, andlocal tax offices. About half of the sample firms provided annual reports thatcontained accounting information. In addition, on most occasions, financialdirectors or chief accountants were interviewed on financial issues. Account-ing data in roubles have been deflated by the year’s average exchange rate ofUS dollars to Russian roubles published in The Economist. The reliability andconsistency of company financial statements still remains questionable inRussia, although significant progress has been made for the past few years tobring Russian accounting practices in line with western standards.
‘Location’ after 4 years
No Contact 9Murdered 2Committed suicide 1Left the country 2Hiding from criminal charges 2Unreachable 2
Not in Business 7Hired middle managers 2Civil servant 1Local politician 1Unemployed 2Retired 1
In Business 59Refusal 3Re-interviewed 56
Dependent Variable
The dependent variable is firm performance. Organizational performance maybe measured in various ways (March and Sutton 1997; Meyer 1994). In thisstudy, firm performance is measured by revenue growth, operating profitmargin and return on assets (Earle et al. 1996). Revenue growth for 1996,1997 and 1998 is expressed in percentages. I use revenue growth rather thansales figures because of the mixed sample of large, medium and small firmsas well as firms from four different industries. Operating profit margin for1995, 1996, 1997 and 1998, and return on assets for 1995, 1996, 1997 and1998 were expressed in percentages.
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Table 2Follow-upInterviews in 1999
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Independent variables are network size, network heterophily, strong ties,weak ties, network resourcefulness, and resource mobilization. There are twomethodologies commonly used to measure social networks: name generatorand position generator. The name generator has been used widely in thenetwork literature (Burt 1992, 1997a; Campbell et al. 1986). Although lesscommon than the first method, the position generator methodology has alsobeen used fruitfully (Angelusz and Tardos 1991; Belliveau et al. 1996; Linand Dumin 1986; Volker and Flap 1999).
Both methodologies have their own strengths and weaknesses. The namegenerator method allows one to measure the structural properties of networks(that is, density and structural holes) more thoroughly than the positiongenerator. In addition, the name generator enables one to track down changesin the personal networks of individuals over time (Burt 2002). However, thename generator method may be biased toward strong ties since people arelikely to remember better interactions with strong ties (Lin 2001a).
An advantage of the position generator method is that it captures occupa-tional or positional characteristics of network alters. The method also enablesone to collect data on strong and weak ties simultaneously (Lin 2001a). Thedownside of the position generator, however, are the limitations to conductinga thorough structural analysis of networks (for example, estimation ofstructural holes) and a potential bias of social desirability (that is, respondentsare likely to overstate the number of powerful and resourceful contacts theyhad).
In this study, I use the position generator method because the methodologyenables me to collect data on network structure, relational and resourcedimensions simultaneously (Lin et al. 2001).
Network size is measured by the number of ties indicated by respondents(Burt 1983b; Marsden 1990). I presented a table in which 12 types of occupa-tions (high-rank official in ministries and agencies, middle- and low-rankofficial in ministries and agencies, high-rank official in local governments,middle- and low-rank official in local governments, managers of large banks,managers of medium and small banks, managers of large manufacturingplants, managers of medium and small manufacturing plants, managers oflarge trade firms, managers of medium and small trade firms, managers of large resource-sector firms, and managers of medium and small resource-sector firms) were listed in rows, and two types of tie strength (friendship andacquaintance) were placed in columns (Lin and Dumin 1986; Lin 2001). Iasked the respondents to indicate how many people were in each cell.
Network heterophily measures the degree to which an ego-centered net-work contains diverse alters, for example, demographic characteristics oroccupation (Burt 1983b, Ibarra 1993; Marsden 1987; Renzulli et al. 1999).In this study, network heterophily measures the degree to which an egocentricnetwork contains alters from industries other than the respondent’s ownoccupational background. Heterophily captures the proportion of non-industry contacts.
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Friendship as a strong tie and acquaintance as a weak tie captured therelational aspect (Burt 1983a; Krackhardt 1992; Lin and Dumin 1986; Volkerand Flap 1999). Strong ties are the number of friends and weak ties are thenumber of acquaintances. I specified the meaning of friend (drug) as thosewith whom you have non-reciprocal and altruistic relationships, and themeaning of acquaintance (znakomyi) as those with whom you have reciprocalrelationships.
For resource embeddedness, I use two measurements: network resourceful-ness and resource mobilization. The former captures accessible contactresources, that is, resources possessed by contacts; whereas the lattermeasures mobilized contact resources, that is, resources that were actuallymobilized (Lin et al. 2001). This approach has been employed fruitfully in anumber of studies (Marsden and Hurlbert 1988; Lai et al. 1998; Lin andDumin 1986; Volker and Flap 1999). Lai et al. (1998) defined resourceembeddedness as a contact’s resource characteristics that are contingent uponoccupational status, authority position, and core versus peripheral sector. Ioperationalize resource embeddedness as the extent to which an egocentricnetwork is comprised of actors of high socio-economic position and the extentto which entrepreneurs have been able to marshal financial resources fromcontacts.
Network resourcefulness captures the number of high-rank officials inministries, high-rank officials in local governments, managers of large banks,managers of large manufacturing plants, managers of large trade firms, andmanagers of large resource sector firms. Lin (2001) used a similar classificationof positional hierarchy in the context of China, which has some similarities withRussia in terms of social transformation. Resource mobilization is a binaryvariable of one if financial resources were mobilized before the interview andzero if resources were not mobilized.
Batjargal: Social Capital and Entrepreneurs in Russia 547
Control Variables
Control variables (Table 3) are industry (banking, trade, manufacturing, andthe resource sector), firm size (large, medium and small), region (Moscow,Ekaterinburg, and Petrozavodsk) and firm origin (new venture versusprivatized). All controls were turned into dummies and included in theregression analysis.
Results
Firm Performance
Table 5 presents the results of the regression analysis predicting firm perfor-mance as a function of social capital. I focus my analysis on revenue growthand profit margin in 1998 for a number of reasons. First, Pearson’scorrelations (see Table 4) and multivariate regressions that included the otherperformance indicators did not show any meaningful relationships betweenindependent, dependent and control variables. Second, social capital as a formof social receivables and payables is accumulative (Bourdieu 1986; Lin 2001),and therefore, it is expected to influence outcome variables over time. Third,social capital is found to be more instrumental in extreme situations (Hurlbertet al. 2001). The year 1998 is the year when the Russian economic meltdownoccurred, and it is assumed that social capital will be prevalent in a time ofcrisis.
I do not present here the results of regressions on return on assets becauseROA was highly correlated with two other indicators (see Table 4) and theexclusion simplified models and strengthened their robustness. In addition,these regressions did not generate any meaningful results. I excluded fromthe regression analysis the resource sector dummy, Petrozavodsk dummy,medium dummy and privatized dummy, since the model specification and fitappeared much better without these controls. Likewise, network size wasexcluded in the models, which examine the effects of relational and resourceaspects, because it was highly correlated with other network variables (seeTable 4).
In Table 5, models 1–5 illustrate the effects of network variables on revenuegrowth in 1998. Model 1 is the baseline model that examines the impacts ofindustry, firm size, firm origin, region and firm age. None of the controlsappeared significant and the model is not significant (F = 1.47, R2 = 0.24).Model 2 demonstrates that network size (B = 0.001) has no effects on revenue,while heterophily (B = –1.34) is associated negatively with sales although itis not significant. The overall model is not significant (F = 1.69, R2 = 0.31).Model 3 examines the effects of strong and weak ties on revenue growth. Itdemonstrates that weak ties have positive and significant effects (B = 0.01, p< 0.05), while strong ties have no effects (B = –0.001). Model 3 is significant(F = 1.91, R2 = 0.34). Model 4 reports that while resource mobilization (B =0.36, p < 0.01) is associated positively and significantly with revenue growth,resourcefulness (B = 0.001) has no impact on performance. Model 4 is
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significant (F = 2.75, R2 = 0.43). Model 5 shows the effects of all control and independent variables except network size on revenue growth. TheEkaterinburg dummy (B = 0.34, p < 0.05) and resource mobilization (B =0.33, p = 0.01) predict positively and significantly the revenue growth offirms. The model is significant (F = 2.71, R2 = 0.50).
Models 6–10 examine the effects of predictor variables on profit marginin 1998. Model 6 is the baseline model and it shows that small firms (B =–0.93, p < 0.05) are likely to have smaller profit margins than other firms.The model is not significant (F = 1.31, R2 = 0.22). Model 7 shows that networksize and heterophily are not related to profit margin, and the model is notsignificant (F = 1.56, R2 = 0.31). Models 8 and 9 illustrate that there are noeffects of relational and resource dimensions on profit margin. Both modelsare insignificant (F = 1.43, R2 = 0.29, and F = 1.06, R2 = 0.23). Lastly, Model10 demonstrates that weak ties have positive impacts (B = 0.11, p < 0.05) onprofit margin. The model is not significant (F = 1.43, R2 = 0.36).
Models 2 and 7 suggest that hypothesis 1 (size) and hypothesis 2 (heterophily)are not supported. Models 3 and 10 reveal that hypothesis 3 (weak ties) isconfirmed, whereas hypothesis 3 alternative (strong ties) is not. The findingsin Models 4 and 5 suggest that hypothesis 4 (resourcefulness) is not con-firmed, while hypothesis 5 (resource mobilization) is supported strongly.
Discussion
Banking, trade and manufacturing firms did report negative revenue growthand profit margins in 1998, although coefficients are statistically insignificant(see Table 5). The finding suggests that resource sector firms (oil and timberexporters) did relatively better than other firms. The banking industry didworst of all, followed by trade and manufacturing firms. This is consistentwith my expectations that resource firms are more able to withstand externalshocks.
Large firms did slightly better than small firms in terms of revenues andprofit. This is due to the fact that bigger firms possess larger assets, whichenable them to be more flexible in product pricing. Flexible price policy mayinfluence favorably revenues and profits in a time of crisis. Likely explanationsfor the under-performance of small firms are twofold. First, smaller firms aremore vulnerable to economic crisis, since they have fewer financial and othertangible resources to mobilize. Second, small firms have less bargaining powerover suppliers and clients, since they do not offer the advantages of economyof scale or price discounts, and this negatively affects profit margins.
New ventures reported positive revenue growth and profit margins,although coefficients are statistically insignificant. In particular, profit margincoefficients are relatively high. This may indicate that new firms are moreentrepreneurial than privatized firms, which by nature have more inertia fromthe past.
Among regions, Ekaterinburg firms performed better for several reasons.The region is resource rich and produces the bulk of Russia’s oil, gas and
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other minerals outputs. The city is the home of the Russian military-industrialcomplex, which still possesses some of the best high-tech plants in thecountry. The administration of the oblast promoted free enterprise from theearly years of perestroika, and the city claims to have a strong historicaltradition of entrepreneurship in contrast to other regions of Russia (Admini-stratsiya Sverdlovskoi oblasti 1995; Zadorozhnyi et al. 1995). Moscow firmsdid badly because they are more integrated into the country’s unstablefinancial system, and they are more dependent on domestic market conditions.
The younger the firm, the worse the financial performance during theeconomic crisis. This is consistent with dominant theorizing that youngerfirms suffer considerably from a liability of newness (Stinchcombe 1965).
The differential in network size does not lead to negotiated buying andselling decisions that may favorably affect firms’ financial performance. Thefinding suggests that large networks may be more useful for informationtransfer than actual revenue growth. Since network size is correlated withother network variables, it may tentatively be suggested that size influencesindirectly economic actions. This is consistent with Granovetter’s (1990)theorizing that the structural aspect may have subtle and indirect impacts oneconomic transactions.
Although statistically insignificant, network heterophily hinders entrepren-eurial performance. The high proportion of non-industry ties in personalnetworks may redirect the attention, resources and time of entrepreneurs fromkey suppliers and customers. Relationship building and maintaining ties fromindustries other than her or his own harms firms’ financial performance. Thismay suggest that too diversified networks are ineffective or inefficient forrevenue and profit growth, at least in Russian conditions.
Structural autonomies found in weak relationships seem to provide greaterroom for flexible negotiations in buying and selling decisions, and thisfreedom for exploiting opportunities in structural holes (Burt 1992) may havefacilitated the revenue growth of Russian firms. Also weak ties are likely toreside in relatively distant or different network clusters, and this location mayincrease the likelihood of finding appropriate suppliers and customers. Onthe contrary, strong ties (friends) seem to affect performance negatively.Having many friends as your business partners leaves little room formaneuvering, and financial concessions to strong ties harm directly firms’revenues and profit margins. This is an example of how social capital mayturn into dark resources that affect outcome variables negatively (Light andIsralowitz 1997; Podolny and Page 1998; Uzzi 1997).
While having many high-position alters in personal networks does notincrease sales and profits, resource mobilization from contacts strongly andpositively predicts revenue growth. The ability of entrepreneurs to mobilizefinancial resources from network ties improves financial performance inseveral ways. Negotiated loans or equity capital reduces the cost of financialcapital, which will be reflected in lowered costs of output. The reduced costsof goods will make products more competitive on the market, facilitatingrevenue growth. The availability of cash makes firms more efficient inresponding to customer demand, which in its turn will increase sales. In
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addition, cash-richer firms are likely to have more room for price reductions,and this advantage may be reflected in more or less stable sales volumes in atime of crisis.
Conclusion
The study reveals that relational and resource embeddedness do affectentrepreneurial performance favorably and directly, whereas structuralembeddedness may not affect firms’ performance directly. Having manyweak ties and being able to mobilize financial resources from rich andpowerful contacts enables entrepreneurs to increase their revenues and profits.My overall argument in the study, that the resource aspect of networks is asimportant as the structural and relational dimensions, is tentatively supported.
Several limitations of the study should be discussed. Network measuresthat I have used are perceptual measures, and I was not able to validate themwith respondents’ friends and acquaintances. Therefore, there might be anissue of construct validity, although there is a similar study in a transitioneconomy context that used perceptual measures without validation and stillfound valid and interesting results (Peng and Luo 2000). The positiongenerator method does not let me estimate the structural properties ofnetworks, for example, density and structural holes. This may have affectedmy findings on structural embeddedness, although network size and hetero-phily are appropriate measures for network structure. Respondents are likelyto overstate the number of powerful and rich contacts, although I assume thatall the respondents overstated these numbers to the same extent, given thesingle country context of the study. The sample size is relatively small and,therefore, one should be cautious of over-generalization of the results. Theresearch site is Russia, which is going through simultaneous social, economicand political transformations, and this may impose some limitations on thegeneralizability of the findings to more stable western contexts. Companyperformance influencing variables such as strategy or entrepreneurial abilitywere not incorporated in the models, and therefore, the study may haveoveremphasized the effects of social capital.
I propose a number of research implications. Clearly, the resource aspectof networks should be further conceptualized and tested in different contextsthan entrepreneurial performance. For example, to what extent do contactresources (money, power, and connections) facilitate status attainment, suchas getting a job or being promoted? One direction for empirical research mightbe the question of what aspect of networks affects what performanceindicators, for example, product development or access to venture capital,and in what contexts. And, a promising, yet neglected, area of research is ‘thedark side’ of social capital: when social capital turns into social liabilities andhinders entrepreneurial performance.
The practical implication is that entrepreneurs should recruit moreresource-rich, weak ties into their personal social networks.
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I am grateful to the International Research and Exchange Board (IREX) for its financial supportfor this research. IREX is not responsible for the views expressed herein. I would like to thankMark Granovetter, Anne Tsui, two anonymous reviewers and seminar participants at HarvardUniversity, Stanford University, Peking University and London Business School forconstructive comments on earlier versions of this article. I am grateful to Tim Colton forsponsoring my visit at Harvard University and Mark Granovetter for sponsoring my visit atStanford University.
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Bat Batjargal is Visiting Scholar at Stanford University and Visiting AssistantProfessor at the Guanghua School of Management of Peking University, China. Heearned a Ph.D. from Oxford University, and previously, has held faculty and visitingappointments at Harvard University, London Business School, the Institute ofSociology of the Russian Academy of Sciences, and the United Nations Universityin Tokyo. His research interests are social networks, entrepreneurship, venture capitaland Internet industry in the transition economies of Russia and China, and SiliconValley, California.Address: Stanford University, Building 40, Main Quad, Stanford, CA 94305-2006,USA.E-mail: [email protected]
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