Generating Business Referrals for SMEs: The Contingent Value of CEOs' Social Capital

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Generating Business Referrals for SMEs:The Contingent Value of CEOs’ Social Capitalby Barthelemy Chollet, Mickael Geraudel, and Caroline Mothe

We examine how small and medium-sized enterprise (SME) chief executive officers’ (CEOs)social capital (as measured by strength of ties and structural holes) can help them bring businessto their firms through the spread of positive referrals. Based on a sample of 408 French SME CEOs,we find a direct effect of social capital. Such effect is contingent on the CEO’s personality, withsocial capital being most beneficial to CEOs with low levels of conscientiousness. CEOs’ social tiesfacilitate the distortion of information, thereby leading personal contacts to give referrals to andendorse a focal CEO, even in the presence of negative signals, such as low conscientiousness.

IntroductionFirms receive a referral when a third party

recommends them to a previously unknownpotential customer, which may result in addi-tional business. Although business referrals arevaluable for all types of firms (Kumar, Petersen,and Leone 2010; Money, Gilly, and Graham1998; Provan 1984), small and medium-sizedenterprises (SMEs) should pay particular atten-tion to this way of gaining customers. First,SMEs usually have only limited resources todedicate to the search for new customers, andto marketing efforts in general. Positive wordof mouth and recommendations are particularlycost-effective (Trusov, Bucklin, and Pauwels2009; Villanueva, Yoo, and Hanssens 2008)because they can occur in the absence of anyeffort from the firm. Second, due to their smallsize and the limited scope of their activities,SMEs generally have lower profiles than largefirms, which makes reputation building difficult

(Goldberg, Cohen, and Fiegenbaum 2003). As aresult, sources of information about an SME arelimited, making it difficult for potential custom-ers to assess whether or not it would be prof-itable to do business with that firm. Opinionsand information circulated by third partiesincrease a firm’s prominence, thereby making itmore attractive as a trustworthy supplier (Leand Nguyen 2009; Seevers, Skinner, andDahlstrom 2010).

The marketing literature on referral behav-iors particularly focuses on current customersthat are satisfied with the product as the mostimportant source of referrals (Kumar, Petersen,and Leone 2010). Nevertheless, several studiesof small businesses suggest that another sourcemight contribute substantially, either directly orindirectly, to the generation of business refer-rals: the personal relationships of the SME chiefexecutive officer (CEO). These studies foundthat CEOs use their personal networks of rela-tionships to circulate favorable information in

Barthelemy Chollet is •• at the Grenoble Ecole de Management.Mickael Geraudel is •• at the GSCM-Montpellier Business School.Caroline Mothe is •• at the University of Savoy.Address correspondence to: Mickael Geraudel, GSCM-Montpellier Business School, Montpellier 34185,

France. E-mail: [email protected].

Toppan Best-set Premedia LimitedJournal Code: JSBM Proofreader: MonyArticle No: JSBM12034 Delivery date: 07 May 2013Page Extent: 23

Journal of Small Business Management 2013 ••(••), pp. ••–••

doi: 10.1111/jsbm.12034

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order to obtain more business for their firms(Jack 2005; Johannisson 1996; Uzzi 1997; Zhou,Wu, and Luo 2007). This particular contributionof social contacts owes to the fact that thecontacts hold first-hand information about theCEO’s reliability and may be motivated to trans-fer it to other individuals as a way of helping.Although these studies have made importantcontributions by highlighting the importance ofsocial ties in favoring referral behaviors, ques-tions that are crucial for business practiceremain unanswered: Why do some CEOs obtainmore business referrals than others throughtheir personal relationships? How can CEOsmaximize referrals and thereby ensure businessgrowth? The present study addressed thesequestions.

Our first objective was to examine whichconfigurations of CEO social capital induce themost business referrals. To this effect, we usean individual approach to social capital, whichfocuses on the potential that social relation-ships offer for the circulation of information(Adler and Kwon 2002; Burt 1992; Inkpen andTsang 2005). This approach can be used tocapture differences between CEOs in terms ofsocial capital and to determine how these dif-ferences may affect a CEO’s potential to obtainbusiness referrals. We argue that CEOs withstrong ties and structural holes in their personalnetworks will benefit from more favorableword of mouth and therefore more businessreferrals. The underlying rationale is that maxi-mizing business referrals through social tiesbasically requires two elements: an ability tocirculate information far beyond the set ofpersons that the CEO already knows (structuralholes) and the motivation to circulate this infor-mation (strong ties). By identifying the configu-rations of social capital that lead to morebusiness referrals, we contribute to a betterunderstanding of how SMEs may enhance theirbusiness performance.

The individual approach to social capitalhas already contributed to the study of SMEsby using similar variables to explain importantoutcomes, such as innovation, growth, orexport performance (Ellis 2000; Julien,Andriambeloson, and Ramangalahy 2004;Ozgen and Baron 2007; Zhou, Wu, and Luo2007). However, this approach has left thecomplexity of social capital underexplored.Apart from notable exceptions focusing onentrepreneurial ventures (Stam and Elfring2008; Vissa and Chacar 2009), the CEO’s

social capital has been considered anunequivocally beneficial factor, regardless ofboundary conditions. However, CEOs are indi-viduals who perceive, understand, and reactto their environment differently (Becherer andMaurer 1999; Ciavarella et al. 2004; Covinand Slevin 1989), which suggests that anybenefits they may obtain from their socialcapital will also vary according to their per-sonal characteristics.

Therefore, the second objective of our studywas to investigate the characteristics of SMECEOs as contingent factors of social capital. Indoing so, we respond to the call for furtherresearch into how actor-level characteristicsaffect the outcomes of social capital (Adler andKwon 2002; Zhou et al. 2009). In line withrecent work on the interaction between socialcapital and personality in organizational set-tings (Anderson 2008; Baer 2010; Zhou et al.2009), we consider personality traits as acrucial factor affecting the impact of socialcapital in the context of SMEs. Our researchrefers to theories of information circulationthrough social ties (Burt 2005; Ferrin, Dirks,and Shah 2006; Wong and Boh 2010), and weargue that during social interactions a CEO’ssocial contacts pick up behavioral cues indicat-ing positive or negative personality traits.These observations form the basis for judg-ments and assessments that will affect theirwillingness to recommend the CEO’s companyto other people and that will ultimately impactthe quality of information circulating alongsocial ties. As a result, the effect of socialcapital should be contingent on a CEO’spersonality.

In order to pursue these objectives, thepaper is structured as follows. We first examinethe mechanism of business referrals and theirimportance for SMEs. We then analyze CEOs’social capital in the light of the individualapproach to social capital. This leads to hypoth-eses about the impact of the key dimensions ofstrength of ties and structural holes on businessreferrals, and on its contingency to personalitytraits. After presenting the methodology anddata collection process, together with oursample of 408 CEOs of small and medium-sizedmanufacturing companies, we describe theresults of the survey. They offer new insightsabout how one particular trait of the CEO,conscientiousness, moderates the effect ofsocial capital in the circulation of favorableinformation: Rather than intensifying the

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positive outcomes of high conscientiousness,social capital attenuates the negative outcomesof lower conscientiousness. We conclude thepaper by discussing the implications and limi-tations of these findings.

Theoretical FrameworkBusiness Referrals in the Contextof SMEs

Because they can significantly help theprocess of customer acquisition, businessreferrals have received considerable attentionin the marketing literature. Although they areless controllable and manageable than market-ing actions (e.g., direct mail, broadcastmedia), referrals have some serious advan-tages (Chen, Wang, and Jinhong 2011; Kumar,Petersen, and Leone 2010; Trusov, Bucklin,and Pauwels 2009). First, their influence onattitudes and beliefs about a firm is muchstronger (Villanueva, Yoo, and Hanssens2008). Information about a product, a serviceor a firm is indeed considered more credibleby potential customers when it is transferredthrough referrals than when it comes fromthe firm itself (Anderson, Hakansson, andJohanson 1994; Seevers, Skinner, andDahlstrom 2010). Second, referrals contributeto customer acquisition at a much lower costthan marketing actions (Trusov, Bucklin, andPauwels 2009). Indeed, they often take placeas a result of spontaneous information circu-lation from one person to the other ratherthan because of a firm’s deliberate efforts.This argument is particularly crucial for SMEsas they tend to have limited resources to dedi-cate to gaining the attention of potential cus-tomers (Goldberg, Cohen, and Fiegenbaum2003).

Despite the great advantages of referrals,though, their impact on customer acquisitionmight vary with the type of purchasing deci-sion. The fact that someone recommends acompany to a potential customer does not nec-essarily lead the latter to become an actualcustomer. Business referrals seem to be espe-cially valuable when very first-hand informa-tion is needed before making purchasingdecisions (Anderson, Hakansson, and Johanson1994; Seevers, Skinner, and Dahlstrom 2010).This is particularly the case in situations ofbusiness-to-business relationships with highuncertainty due to product complexity or theneed for substantial mid- and long-term com-mitments (Bensaou and Anderson 1999; Mooi

and Ghosh 2010). In these situations, establish-ing a new business relationship on the solebasis of publicly available information aboutthe partner is risky (Podolny 1994). Screeningand selecting a new business partner throughthird parties seems much safer, as these thirdparties can provide important knowledge aboutthe trustworthiness and capabilities of the otherfirm (Li and Rowley 2002). Moreover, trustaccumulated over a long period between thefocal firm and some third party can simply betransferred to the newly formed dyad (Uzzi1997). Similarly, a firm can expect the potentialpartner to be more cooperative if there is athird party, as any opportunistic behavior intheir new relationship would create a seriousthreat of sanctions in the relationship it hasalready established with the third party(Podolny 1994).

In the context of SMEs, research has shownthat referrals are based on the circulation ofinformation about a firm’s CEO at least asmuch (if not more) as about the firm ingeneral. Studies of the specific case of entre-preneurial ventures are particularly enlighten-ing in this respect. As newcomers to business,entrepreneurs tend to leverage the personalties developed in earlier educational or pro-fessional situations (Hallen 2008). These indi-viduals know the entrepreneur well and theycan therefore compensate the lack of a trackrecord by serving as referrals to other compa-nies who would otherwise never consider thenewborn company (Harrison, Dibben, andMason 1997; Larson 1992; Shane and Cable2002; Stuart, Hoang, and Hybels 1999). As aresult, Jack found that the mobilization of anentrepreneur’s personal social ties is a keyfactor in obtaining orders through recommen-dations (Jack 2005). Similar mechanisms havealso been observed among established SMEs,such as in Uzzi’s (1997) study of the NewYork apparel industry, which showed that themaintenance of close personal relationshipsby CEOs leads to business referrals. He foundthat it is possible for two companies whoseCEOs do not know each other to quicklyestablish new commercial relationships if thetwo CEOs are engaged in a personal relation-ship with a third person who can recommendthem doing business together (Uzzi 1997).

All these findings suggest that the uniquecombination of social ties around a CEO canmake a serious difference by circulating favor-able information leading to business referrals.


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Yet they do not really tackle the issue of whysome CEOs have personal networks that lead tomore referrals than others. By relying on thewell-established concept of social capital, ourgoal is to conceptualize the key differencesacross CEOs in terms of their personal net-works so as to identify which configuration ofpersonal relationships favors the best outcomesin terms of referrals.

The Benefits of Individual Social CapitalIn the field of SMEs, the importance of per-

sonal relationships for business success hasbeen examined from a number of perspectives.Some authors have referred to embeddedness(Granovetter 1985) to designate situationswhere business decisions appear to be gov-erned by social framing and the structure of thenetwork of social ties (Uzzi 1997; Yli-Renkoand Autio 1998), whereas others have evokedsocial networks (BarNir and Smith 2002;Molina-Morales and Martinez-Fernandez 2010;Zhou, Wu, and Luo 2007) or social capital(Pirolo and Presutti 2010). In Asia, guanxi, asimilar notion, has been shown to be an impor-tant aspect of business life, with implicationsfor firm strategy (Carlisle and Flynn 2005; Chenand Chen 2004).

Adler and Kwon (2002) made a crucial con-tribution by showing how these approachesrelate to the broader concept of social capitaland contribute to two very different streams.One stream emphasizes the collective dimen-sion of social relationships, seeing socialcapital as “an attribute of a social unit, ratherthan an individual” (Inkpen and Tsang 2005,p. 150), a public good that is shared, availableto, and bringing benefits to all members of agroup (Inkpen and Tsang 2005). Our paperbuilds on the second stream, which considerssocial capital from an individual point ofview, as a concept that “helps explain the dif-ferential success of individuals and firms intheir competitive rivalry” (Adler and Kwon2002, p. 19). This stream sees social capitalmore as “a private good” (Inkpen and Tsang2005, p. 150), based on the notion that a con-figuration of social ties surrounding an actoris highly idiosyncratic and can therefore bringunique advantages to one actor over theothers.

This stream of research clearly establishedthat the ideal configuration of social capital hasto be analyzed in terms of the quality andstructure of the ties surrounding an actor rather

than their number (Adler and Kwon 2002).Both these qualitative and structural dimen-sions have been discussed, raising two theoreti-cal debates, one over the benefits of weakversus strong ties (Granovetter 1973; Hansen1999) and the other over the benefits of densenetworks of interconnected contacts versussparse networks of unrelated others (Burt1992).

The question of what level of tie strength andwhat type of structure bring the most positiveoutcomes has also been discussed in the field ofSMEs. Differences among CEOs on these dimen-sions have been reported to explain variance interms of firm growth (Stam and Elfring 2008;Vissa and Chacar 2009), innovation (Julien,Andriambeloson, and Ramangalahy 2004), andexport performance (Ellis 2000; Zhou, Wu, andLuo 2007). These studies argue that such resultsaccount for the ability of social capital to giveCEOs access to an important resource, namelyinformation. For example, strong personal con-tacts in the same business help provide CEOswith an accurate picture of their competitiveenvironment, making it easier for them to setprices (Ingram and Roberts 2000). Similarly,certain ties can facilitate the recognition of newbusiness opportunities by providing a CEO withtimely information about market changes (Ellis2000; Ozgen and Baron 2007), and some tiesmake it easier for SMEs to source externalknowledge (McEvily and Zaheer 1999). Allthese studies share the argument that personalcontacts have knowledge of the environmentwhich they can transfer to the CEO, and thatsome structures and levels of strength of aCEO’s ties are more effective than others in thisrespect.

However, social capital can also help infor-mation travel in the opposite direction. A CEO’spersonal contacts have information about theCEO that they can transfer to other individualswho are potential customers or who can them-selves circulate such information to potentialcustomers. Although some studies investigatingthe types of structure and the levels of tiestrength that give the best returns in terms ofcommunicating favorable information havebeen carried out in organizational settings (Burt2005; Ferrin, Dirks, and Shah 2006; Wong andBoh 2010), there has been no such investiga-tion with respect to SMEs. This void is all themore surprising given that such a study wouldhelp understand which configurations of socialcapital generate the most referrals for firms and


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therefore contribute to a better understandingof SME performance.

HypothesesSocial Capital and Business Referrals

The structure and strength of ties areimportant dimensions of social capital (Adlerand Kwon 2002). Intuitively, having thehighest number of direct contacts would beexpected to result in the best access toresources and to more referrals. However,Burt (1992) contradicted this intuitive view,arguing that the number of nonredundant con-tacts is more important than the total numberof contacts. This led him to introduce theconcept of “structural holes,” which he definedas gaps between nonredundant contacts. InFigure 1, Ego’s network contains several struc-tural holes. For example, Jack and Jane arenonredundant contacts: Because there are noties between them, they connect Ego to differ-ent others. On the other hand, Bob and Sueare redundant contacts: Because they knoweach other and belong to the same social“clique,” they indirectly connect Ego to thesame contacts.

An SME CEO whose network contains a lotof structural holes will be connected to manydifferent zones of the social structure, therebyguaranteeing that information about his/herfirm is disseminated to a maximum number ofpeople. By contrast, a CEO with a very densenetwork (in the extreme case, everyone knowseveryone else) will find it more difficult tospread information about his/her companybeyond his/her network of direct contacts.

Numerous empirical studies have beencarried out to test this theory, some of which

specifically link structural holes in the CEO’snetwork to SME performance. McEvily andZaheer (1999) found that structural holes had apositive effect on a firm’s acquisition of strategiccapabilities, in particular because low redun-dancy in the network offers access to broadersources of knowledge. Similarly, in a study ofIndian entrepreneurial ventures, Vissa andChacar (2009) found that firm growth washigher among entrepreneurial teams with struc-tural holes in their advice networks. Followingan analysis of ventures in the open sourcesoftware industry, Stam and Elfring (2008)reported that centrality, a measure that alsocaptures network structure, impacts firmgrowth.

Overall, these studies recognized that struc-tural holes have a positive impact on firm per-formance, but they were unable to determinewhether this impact was due to the ability ofstructural holes to provide a firm with access toinformation and resources or to the fact thatstructural holes promote broader disseminationof favorable information and recommenda-tions. As a result, it remains unclear whetherthe impact on firm performance is due to aninformation acquisition effect or an informationdiffusion effect. However, studies in the field ofreputation building at work have producedconvincing findings that structural holes favorthe diffusion of favorable information. Follow-ing a similar argument to that of structuralholes theory, Mehra et al. (2006) showed that amanager’s leadership reputation is positivelyinfluenced by his/her central position in friend-ship networks within his/her organization.Similarly, Wong and Boh (2010) foundthat non-overlapping contacts lead to broader

Figure 1Illustration of the Concept of Structural Holes

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diffusion of information about a focal manager,resulting in enhanced reputation.

H1: The higher the number of structural holesin an SME CEO’s network, the more businessreferrals he/she will obtain.

Another important dimension of SME CEOs’social capital is strength of ties, which isa function of interaction frequency, dura-tion, emotional intensity, and reciprocity(Granovetter 1973; Zhou et al. 2009). Granovet-ter’s argument is that if a CEO has strong tieswith two persons who do not know each other,it is highly probable that they will develop arelationship over time (Granovetter 1973).Applying this principle to all of a CEO’s rela-tionships leads to the conclusion that individu-als with strong ties tend to belong to ratherdense networks in which resources circulate “ina closed circuit.” Hence, it would be advanta-geous for an individual to create weak ties andto establish relationships with people he/shedoes not know and who belong to other socialgroups.

This argument suggests that structural holes,in Burt’s sense, are more likely to exist betweenweak ties than between strong ties. However,other authors point out other reasons for theimpact of strength of ties and provide argu-ments for a positive effect of strong ties(Ingram and Roberts 2000; Uzzi and Lancaster2003). According to these arguments, it is moreprobable that a member of a CEO’s networkwill recommend the CEO’s firm if the tie isstrong than if the tie is weak. First, a personwith a weak tie to a CEO is less likely to bemotivated to pass on information about theCEO’s firm, whereas a person with a strong tiewill generally be more motivated to support theCEO (Krackhardt 1992). Second, people withstrong ties to a CEO often know what kind ofresources and competences the CEO possesses(Borgatti and Cross 2003), increasing the prob-ability that they will spread information aboutthe CEO. In contrast, a weak tie implies lessmutual knowledge and, probably, a smalleramount of substantial information to spread.Third, a person with whom a CEO has a strongtie is more likely to introduce a “positive bias”when spreading information about the CEO orhis/her firm, relaying only more favorableaspects. This phenomenon is explained by peo-ple’s tendency to overestimate the qualities ofothers with whom they have strong ties

because of the emotional components associ-ated with such ties (Gershoff and Johar 2006).

These arguments may explain certain resultsreported in the literature. For example, in aqualitative study of 14 entrepreneurs, Jack(2005) reported that those who were able tobuild their firm’s reputation mostly relied onstrong ties based on family and friends. Simi-larly, in a study of medium-sized firms thatwere selecting partners for international jointventures, Wong and Ellis (2002) found thatstrong ties were more powerful than weak tiesin conveying information about potentialcontacts.

H2: The stronger the ties in an SME CEO’snetwork, the more business referrals he/shewill obtain.

Positive Personality andBusiness Referrals

By viewing networks as effective channelsfor spreading information, the individual per-spective of social capital provides a frameworkfor explaining how SMEs obtain business refer-rals. The better a CEO’s network, the better thediffusion of information. However, the impactof information that travels through the networkwill differ according to whether it is favorableor unfavorable (Burt 2005). Because theopinion of a person within a CEO’s networkwill depend on the CEO’s characteristics andbehavior, his/her personality traits should betaken into account alongside his/her personalnetworks (Baron and Markman 2000; Burt2005). Whether or not favorable information islikely to circulate within the social structurewill depend on these individual traits.

We consider personality traits to be funda-mental characteristics of CEOs and believe thatdifferences in personality traits between SMECEOs lead to differences in behavior. Severalstudies support this claim. For example, aCEO’s personality has been shown to influencehis/her company’s chances of survival(Ciavarella et al. 2004), and a CEO’s entrepre-neurial orientation to determine firm perfor-mance (Becherer and Maurer 1999; Covin andSlevin 1989). Consistently, the present studyfollows the idea that a CEO’s personality influ-ences behaviors that affect both his/her SMEand people’s opinions in the network—whichwill ultimately impact business referrals.

Of the many models that characterize per-sonality traits, we selected four traits from the


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“big five” model (Costa and McCrae 1992;Digman 1990; Zhao and Seibert 2006): agree-ableness, conscientiousness, extraversion, andopenness to experience. These traits, whichhave been extensively tested by psychologists,have the advantage of providing a clear distinc-tion between the personality aspects that arelikely to be interpreted either very negatively orvery positively by the people in contact with aCEO.1 Moreover, they have already been suc-cessfully applied to the context of entrepre-neurship (Zhao and Seibert 2006).

Based on Zhao and Seibert (2006), thesefour traits can be defined as follows. Agreeable-ness indicates whether a person is consideredtrustworthy, altruistic, and likely to take care ofothers, or, on the contrary, manipulative, self-centered, wary, and lacking compassion. Con-scientiousness indicates a person’s degree oforganization, his/her perseverance and motiva-tion to work. People with low conscientious-ness scores are disorganized and quicklydiscouraged. Extraversion describes the ten-dency to turn to the outside world. Extravertedpeople are dominant, energetic, active, talk-ative, and enthusiastic; they enjoy group lifeand seek stimulation through contact withothers. Introverted people prefer to spendmore time alone and are rather reserved andindependent. Openness to experience measurescuriosity and willingness to search for newexperiences and to explore original ideas.People with high scores on this dimension arecreative, innovative, imaginative, thoughtful,and nonconventional.

Several studies have addressed the impact ofthese personality dimensions on behavior (Lee,Ashton, and Shin 2005; Paunonen 2003), onsocial status (Anderson et al. 2001), and onperformance at work (Hurtz and Donovan 2000;Judge and Ilies 2002; Ones et al. 2007). Meta-analyses have shown that conscientiousness is aparticularly important explanatory factor (Judgeand Ilies 2002; Ones et al. 2007). When the otherfour dimensions operate, it is generally in asimilar direction: They correlate positively withindividual performance. However, most studieshave noted performance in terms of evaluations

made by supervisors (see the review by Oneset al. 2007). Thus, a person’s personality is likelyto affect his/her performance but may also influ-ence how he/she is judged by other people. Thenotion that performance does not exist “in itself”but only through subjective evaluations is par-ticularly relevant to our specific context of study:Because they lack objective information, poten-tial customers of a focal SME have to build anopinion based on their subjective perception ofpotential performance.

This consideration is consistent with anotherstream of research that shows that personalitytraits are subject to perception and stronglycontribute to the types of judgment othersmake. For example, in a study of the formationof impressions at work, Flynn, Chatman, andSpataro (2001) found that people who aredemographically different from their coworkersengendered more negative impressions.However, those who had high extraversion andself-monitoring scores engendered more posi-tive impressions than those with low scores forthese traits. Similarly, Scott and Judge (2009)found that high core self-evaluations (a higher-order trait combining traits such as self-efficacyand self-esteem) were a factor of popularityin the workplace. On the contrary, individualswith low core self-evaluations were appraisednegatively by others, resulting in lowerpopularity.

Taken together, these arguments suggesttwo complementary ideas: (1) personality traitsare subject to perceptions by others and theseperceptions partly drive their judgments; and(2) some traits are typically “positive personal-ity traits” that lead to more favorable judg-ments, resulting in the circulation of positiveinformation through personal relationships. Inthe case of SME CEOs, this should ultimatelyresult in more business referrals. Thus:

H3a: The more an SME CEO is agreeable, themore business referrals he/she will obtain.

H3b: The more an SME CEO is conscientious,the more business referrals he/she willobtain.

1We did not select emotional stability, the fifth dimension of the big five, as previous research on how othersmake judgments based on personality traits has shown that emotional stability is the least observable trait.This is because emotional stability does not produce clear behavioral manifestations that can be observed insocial interactions (Funder and Sneed 1993; Vazire 2010). Consequently, there is no theoretical foundation forassuming that this trait will be translated into favorable information diffusion.


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H3c: The more an SME CEO is extraverted, themore business referrals he/she will obtain.

H3d: The more an SME CEO is open to experi-ence, the more business referrals he/she willobtain.

Positive Personality as a ContingentFactor of Social Capital

A growing body of literature suggests thatthe effect of social capital is linked to the char-acteristics of individuals. Burt (1992) and Ibarra(1992) found that women get less advantagethan men from similar network positions. Inthe field of entrepreneurship, Stam and Elfring(2008) reported that centrality and the bridgingties connecting a founding team to other indus-tries have a significant effect on business per-formance, but the strength of this effectdepends on the founding team’s level of entre-preneurial orientation. Similarly, Anderson(2008) reported that the average tie strengthand the number of structural holes in a man-ager’s network have a positive impact on theamount and diversity of information themanager can obtain, but this impact is strongeramong managers with a high need for cogni-tion. Similarly, Baer (2010) and Zhou et al.(2009) found that the impact of weak ties wasmoderated by personality traits (openness toexperience and conformity, respectively).Extrapolating these findings to business refer-rals suggests that the amount of business refer-rals an SME CEO obtains from his/her networkwill depend on his/her personality traits.2

As already stated, some personality traits areperceived more positively than others (Scottand Judge 2009), and traits that are consideredpositive are more likely to lead to favorableinformation about a CEO being communicatedalong social ties. Thus, a social network thatensures good diffusion of information (struc-tural holes and strong ties) may provide evengreater benefits if the CEO at the hub of thisnetwork has personality traits that are per-ceived as positive. However, networks are not

neutral vehicles for diffusing information, asthe information they transmit tends to beattenuated or distorted during the diffusionprocess.

This attenuation and distortion are influencedby both structural holes and strong ties. Nega-tive aspects of ego’s personality circulate moreeasily and are more likely to become known byall the people linked to ego when ego is at thecenter of a dense network. In addition, the“echo” phenomenon (Burt 2005) leads to nega-tive opinions being amplified and exaggeratedduring the circulation process. In Figure 1, thetie between Bob and Sue makes it possible fornegative information to circulate from one to theother and to become amplified during conser-vations. On the contrary, the absence of a tiebetween Jane and Jack may be beneficial to ego.For example, a CEO with very positive person-ality traits will benefit from the lack of a tiebetween Jane and Jack because they will spreadinformation through different parts of the socialstructure. However, a CEO with very negativepersonality traits will benefit even more fromthis situation because the lack of a tie betweenJane and Jack will attenuate the negative signalgiven by negative personality, as it cannotbecome a topic of conversation in the CEO’snetwork. There can be no contagion from Janeto Jack, and no possibility for amplificationthrough “echo” effects, as they do not knoweach other. Hence, it may be more beneficial forCEOs with negative personality traits to havenumerous structural holes in their networks.

H4a: The positive relationship between struc-tural holes and business referrals is strongerwhen an SME CEO is low on agreeableness.

H4b: The positive relationship betweenstructural holes and business referrals isstronger when an SME CEO is low onconscientiousness.

H4c: The positive relationship between struc-tural holes and business referrals is

2Some authors focus on personality as an antecedent of social capital rather than as a moderator (Kalish andRobbins 2006; Kim and Kim 2007; Klein et al. 2004; Mehra, Kilduff, and Brass 2001; Oh and Kilduff 2008;Sasovova et al. 2010). Positioning personality as a moderator or an antecedent seems to depend on the exacttrait being considered. Self-monitoring (“the extent to which individuals are willing and able to monitor andcontrol their self-expressions in social situations,” Mehra, Kilduff, and Brass 2001, p. 124) was found to bean antecedent in five of the six previously cited studies, but other traits have received very limited attentionas antecedents.


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stronger when an SME CEO is low onextraversion.

H4d: The positive relationship between struc-tural holes and business referrals is strongerwhen an SME CEO is low on openness toexperience.

As already stated, a strong tie creates a delib-erate or unconscious tendency for a contact tooverestimate the qualities of a focal actor and todistort information (Gershoff and Johar 2006).Similarly, a strong tie may enhance the motiva-tion for a contact to provide support and helpto ego, regardless of what it may cost (energy,time, or legitimacy). In such a situation, a CEOwith very positive personality traits mightbenefit from strong ties, as they will enhancethe diffusion of positive information andthereby reinforce an already positive signal.However, CEOs with very negative personalitytraits might benefit even more because contactswith strong ties will tend to ignore negativesignals and filter and distort informationthrough a positively biased schema beforepassing it on to other contacts. As a result, theywill attenuate the negativity of the signal. Onthe contrary, negative information that travelsalong weak ties is more likely to be transferred“as is,” leading to poor outcomes in terms ofbusiness referrals. Consequently, strength ofties is likely to have the biggest effect on refer-rals for CEOs with low scores on positive per-sonality traits.

H5a: The positive relationship between strengthof ties and business referrals is strongerwhen an SME CEO is low on agreeableness.

H5b: The positive relationship between strengthof ties and business referrals is strongerwhen an SME CEO is low onconscientiousness.

H5c: The positive relationship between strengthof ties and business referrals is strongerwhen an SME CEO is low on extraversion.

H5d: The positive relationship between strengthof ties and business referrals is strongerwhen an SME CEO is low on openness toexperience.

MethodologyData

We tested our hypotheses on a sample ofCEOs of manufacturing SMEs3 in Haute-Savoie,France. Restricting a study’s scope to one geo-graphical area is common practice in the field(e.g., Camisón and Villar-López 2010;Madrid-Guijarro, Garcia, and Van Auken 2009;Niskanen and Niskanen 2010; Van Auken,Kaufmann, and Herrmann 2009) because itfacilitates the data collection process. Moreimportantly, it ensures relatively homogeneousenvironmental conditions, thereby minimizingthe role of extraneous variables. This aspect isparticularly important in studies of socialcapital (Aarstad, Haugland, and Greve 2010;McEvily and Zaheer 1999; Molina-Morales andMartinez-Fernandez 2010), as “the patterns ofsocial capital are strongly conditioned by thesocial context where business partners areembedded” (Pirolo and Presutti 2010, p. 205).

The area we selected has one predominantcluster, the Arve Valley, which has a highdensity of small subcontracting firms and thelargest concentration of precision engineeringcompanies in Europe. These firms operate in abusiness-to-business environment, manufactur-ing mostly nonstandard products and respond-ing to the specific needs of corporatecustomers, such as original equipment manu-facturers in the automotive or aerospace indus-tries. In this type of environment, purchasingdecisions are often quite complex (Shao et al.2008) and businesses need more refined andreliable information than what is publicly avail-able. These decisions are also often risky due tohigh uncertainty, which generates a need forparticularly trustworthy sources of information(Uzzi 1997). As a result, business referrals inthis context should be of particular importancefor customer acquisition. Another feature ofthis area is that it has been described as a“Marshallian district,” with a long tradition ofinterpersonal relationships acting as cement forinterfirm collaboration (Courlet, Pecqueur, andSoulage 1993). It has also received regularfinancial support from national, regional, andlocal authorities in order to promote coopera-tion. These characteristics should clearly facili-tate information circulation and encouragerelying on informal sources to assess the reli-ability of other firms.

3We used the European Union’s definition of an SME as a firm with fewer than 250 employees.


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In December 2007, we sent an invitation toparticipate in the study to the CEOs of the 1,581manufacturing SMEs listed in the databases ofthe Haute-Savoie Chamber of Commerce andThésame, an Arve Valley organization that pro-vides support to local firms in the metal prod-ucts, mechanical engineering, and electronicsindustries. The invitation e-mail included acover letter explaining that the study was sup-ported by the Chamber of Commerce andThésame. After two follow-up e-mails, wereceived 535 completed questionnaires, 427 ofwhich were completed by respondents whoidentified themselves as the CEOs of their firm.We removed a further 19 questionnaires fromthe sample due to missing data, which left uswith a database of 408 CEOs. This gave a finalresponse rate of 25.81%, which is quite satis-factory compared with standards in the field forthis type of study (Bartholomew and Smith2006; Baruch and Hotlom 2008).

As shown in Table 1, most of the respon-dents had a graduate degree (47.79%), weremale (79.90 percent), and had had long tenurewith their company (more than 10 years for57.11% of them). There were 44.12 percent ofthe firms in the sample that had fewer than 10employees, 39.22 percent had between 10 and49 employees, and 16.67 percent had between50 and 249 employees. Most of the firms oper-ated in the metal products (25.25 percent) orelectronics industries (24.02 percent), followedby the chemical (18.38 percent) and industrialmachinery (13.48 percent) industries. A com-parison between the composition of the finalsample and the parent population did not showany statistically significant differences in termsof firm size and industry.

MeasuresBusiness Referrals. We applied a newly devel-oped scale that uses respondents’ reports to

Table 1Sample Characteristics

Number Percentage

IndustryMetal products 103 25.25Industrial machinery 55 13.48Electronic and electrical equipment 98 24.02Chemicals, rubber & plastic products 75 18.38Other manufacturing industries 77 18.87

Firm size (employees)Fewer than 10 180 44.1210 to 50 160 39.2250 to 250 68 16.67

CEO educationGraduate degree 195 47.79Undergraduate degree 117 28.68No undergraduate degree 96 23.53

CEO genderWomen 82 20.10Men 326 79.90

CEO tenure (number of years with the company)Less than 2 19 4.662 to 5 63 15.445 to 10 93 22.79More than 10 233 57.11

Mean 12.618Standard deviation 9.2741

CEO, chief executive officer.


1

2

3

4

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30

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32

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capture the degree to which customer acquisi-tion relies on referrals. We pretested an initiallist of items with eight researchers in manage-ment and with a group of 10 SME CEOs takenfrom the parent population. Purification of theinitial set resulted in a three-item scale (trans-lated from French): “People recommend mycompany to customers,” “People stronglyadvise other firms to do business with mycompany,” “My company obtains contractsthanks to favorable word of mouth.” Respon-dents rated how much they agreed with eachitem on a six-point Likert scale ranging from“strongly disagree” to “strongly agree.” For thedata collected, the scale had a satisfactory Cron-bach’s alpha of 0.779.4 To the best of ourknowledge, the only previous study to havemeasured self-assessed levels of business refer-rals is Seevers, Skinner, and Dahlstrom (2010),which was published after we had collected ourdata. Excepting the specific wording for theirtarget population (retail buyers in the golfindustry), Seevers et al.’s items are very similarto ours.

Name Generators. We used name generatorsto build the variables relating to the respon-dents’ networks. This method requires respon-dents to identify the people with whom theyhave contact on various levels (e.g., friendshipor advice). In line with previous studies (Burt1992; Rodan and Galunic 2004), we used fivename generators. Respondents were asked togive the names or initials of people they havecontact with for (1) obtaining advice beforemaking important decisions, (2) exchanginginformation on business trends and competi-tion, (3) recruiting employees, and (4) findingsolutions to technical problems. The fifth gen-erator was a more open heading: “anybody youconsider important for the management of yourbusiness and who did not fall into the previouscategories.” Each respondent could enter up to18 names, and for each name the respondentwas expected to answer a number of questions.

Structural Holes. Structural holes can be mea-sured in several ways; however, the mostwidely used measurement is aggregate con-straint. It indicates the extent to which therelationships in a focal actor’s network lead,directly or indirectly, to the same people (Burt

1992, pp. 54–55). In other words, it expressesthe extent to which a focal actor is surroundedby individuals who have connections withother people in the network. In this respect, itis strongly correlated with network density.

Burt (1992) defined the constraint exertedby an alter j on a focal actor i as:

c p p p q i jq

ij ij id qj= +⎛⎝⎜

⎞⎠⎟ ≠∑

2

, ,

where pij is the proportion of all relations thatcontact j represents in i’s network. Sq piq pqj isthe portion of i’s relations with other contactswho are in turn connected to j. It gives ameasure of the importance of j in i’s network.If this sum is very high, it means that thepresence of j in i’s network considerablyreduces the number of structural holes. Theaggregate constraint is obtained by summing allthe constraints exerted by each individual alterin ego’s network:

c c i jj

i ij= ≠∑ ,

In order to compute cij, we asked eachrespondent to indicate whether a pair of his/her contacts was connected, and to do this forevery pair of contacts. These data were thenconverted into constraint values using UCINETVI software (Borgatti, Everett, and Freeman2002). If structural holes positively impact busi-ness referrals (as postulated in H1), then con-straint should negatively impact this variable.Because constraint can range between 0 and 1,and in order to facilitate interpretation, we used1—constraint to directly measure structuralholes. This is in line with previous research(McEvily and Zaheer 1999; Rodan and Galunic2004).

Tie Strength. Of the many measures that havebeen devised to assess tie strength (Marsdenand Campbell 1984), the most commonly usedare frequency of interactions and emotionalcloseness. However, Marsden and Campbell(1984) showed that emotional closeness giveshigher validity than frequency of interactionsbecause this latter variable is often a correlate

4See Appendices for the principal components analysis of business referrals.


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of elements that are not connected with tiestrength (e.g., geographical proximity). There-fore, we used emotional closeness in thepresent study. Our data collection tool askedrespondents to position each of their listedcontacts on a Likert scale ranging from “distant”to “especially close” (Burt 1992). A respon-dent’s “strength of ties” score was the averageof the scores obtained for all the contactshe/she listed.

Personality Traits. The “big five” scales havebeen frequently tested and validated. The Inter-national Personality Item Pool website containstranslations of the most frequently used items in10 languages (Goldberg 1999). We selected sixitems for each dimension, so as to avoid clutter-ing the questionnaire and to maximize theresponse rate. Our pretests made it possible toensure that all the items were well understood.Thus, we retained four personality variables:agreeableness (a = 0.812), conscientiousness(a = 0.774), extraversion (a = 0.760), and open-ness to experience (a = 0.757).

Controls. We controlled for several variablescapturing key differences across SMEs. Firmsize was measured in terms of number ofemployees, with the SMEs being divided intothree categories: fewer than 10 employees,from 10 to 50 employees, and more than 50employees. Two dichotomous variables werecreated: “fewer than 10 employees” and “from10 to 50 employees.” We also controlled forindustry, using NES5 codes. We created adichotomous variable for each category ofindustries mentioned in Table 1 (except “othermanufacturing industries”). Other items on thequestionnaire were used to measure character-istics of the CEOs, such as gender, tenure(number of years with the company), and edu-cation, for which we distinguished three cat-egories (graduate degree, undergraduatedegree, no degree). We used “graduate degree”and “undergraduate degree” as two dichoto-mous control variables.

ResultsThe summary statistics and correlation

matrix for all the variables are presented inTable 2. The hypotheses were tested using hier-archical regressions.6

The Direct Effects of Social Capitaland Personality

With the stepwise introduction of the vari-ables according to a hierarchical logic of regres-sion, adding network variables (Model 2,Table 3) and then personality variables (Model3) significantly enhanced the explanatorypower of the model.

Our results support H1 and H2 (Model 2)with both stronger ties and larger numbers ofstructural holes in a CEO’s network leading tomore business referrals. This second result is inline with Burt (1992). H3 is also supported, asAgreeableness (H3a), Conscientiousness (H3b),Extraversion (H3c), and Openness to Experi-ence (H3d) have a significant impact on busi-ness referrals. Overall, business referralsdepend on the extent to which informationabout a CEO spreads through his/her personalnetwork and on the nature of this information.

Personality Moderating the Effect ofSocial Capital

Model 4 (Table 3) includes all the variablesthat were introduced in the previous models,together with the interactions between thepersonality and network variables.7 One per-sonality trait—conscientiousness—significantlymoderates the effect on business referrals ofboth structural holes and strength of ties. Thisparticularity of conscientiousness is consistentwith previous research. Of the “big five” traits,conscientiousness has been found to be themost important factor in individual perfor-mance (Judge and Ilies 2002; Ones et al. 2007).This trait signals reliability, motivation to fulfillcommitments, and willingness to pay attentionto detail, and these aspects may be more impor-tant in the context of business relations thanthe other traits (agreeableness, extraversion,and openness to experience).

5NES codes are a standard classification used by the French National Institute of Statistics and EconomicStudies (INSEE 2010).6In line with Jaccard and Turrisi (2003), we mean centered network and personality variables beforeprocessing the data, in order to avoid multicollinearity problems.7Correlations between network variables and personality traits are extremely weak (-11.2 percent for thestrongest correlation). These results provide additional evidence that personality traits should not beconsidered as antecedents. This is line with recent works (Anderson 2008; Baer 2010; Zhou et al. 2009).


12345678910

1112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869

70

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10bs_bs_query


Tab

le2

Mea

ns,

Sta

ndar

dD

evia

tions,

and

Corr

elat

ions

12

34

56

78

910

11

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14

15

16

17

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nes

sre

ferr

als

12

Gen

der

(1=

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221

3Ten

ure

(ln)

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45-0

.060

14

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duat

edeg

ree

-0.0

13-0

.048

-0.1

90**

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nder

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ree

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060.

091*

0.04

7-0

.620

***

16

Met

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duct

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164*

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0.02

11

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ery

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nic

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114

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840

840

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Tab

le3

Hie

rarc

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ssio

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ysis

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nes

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ndar

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odel

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4

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der

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(-0.

239)

-0.0

11(-

0.24

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(-0.

033)

Ten

ure

-0.0

55(-

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(-1.

048)

-0.0

09(-

0.18

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(-0.

392)

Gra

duat

edeg

ree

-0.0

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0.58

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(-0.

746)

-0.0

54(-

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(-1.

110)

Under

grad

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ree

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(-0.

613)

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(-0.

411)

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duct

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Figure 2 depicts the interaction effectsgraphically (based on Aiken and West 1991).These graphs use the nonstandardized coeffi-cients to establish the regression slope, consid-ering three cases: a high value for themoderating variable (one standard deviation[S.D.] above the mean), a low value for themoderating variable (one S.D. below themean), and a value equal to the mean forthe moderating variable.

The overall positive effect on business refer-rals of both structural holes and strength of tieswas stronger among CEOs with lower consci-entiousness. Hence, conscientiousness miti-gates the positive effect of social capital, whichindicates that networks are a source of infor-mation distortion and attenuation, as well as asource of information diffusion. CEOs with lowconscientiousness (a signal that is negative forbusiness) will get more business referrals ifthey have a network that is rich in structuralholes because structural holes dampen the dif-fusion of negative signals. As the individuals ina CEO’s network do not know each other, thenegative signal cannot become a topic of con-versation and will therefore lose its intensity.

On the contrary, a low level of structuralholes (high density and redundancy in thenetwork) leads to intense circulation of thenegative signal. The fact that information circu-lates very rapidly in a dense network will resultin negative aspects being widely known bypeople connected to the CEO. Moreover, as wellas spreading negative opinions, talk amplifies

and exaggerates them (Burt 2005). In contrast, avery conscientious CEO will benefit from theamplification and exaggeration of opinionsthrough a dense network (see the slightly nega-tive slope for very conscientious individuals)because positive personality signals will betransmitted and amplified via conversationsbetween individuals who know the CEO as wellas each other.

We also found support for an interactionbetween strength of ties and conscientiousness.The impact of strength of ties was very highamong CEOs with low conscientiousness andabsent among CEOs with high conscientious-ness. CEOs with low conscientiousness obtainedmore business referrals when they had strongties (on the right in Figure 3) and fewer businessreferrals when they had mainly weak ties. Onthe contrary, very conscientious CEOs obtainedsimilar levels of business referrals no matterhow strong (or weak) the ties in their networks.This is consistent with the notion that lowconscientiousness is a negative signal that limitsthe diffusion of favorable information. Whensuch negative information is transmitted alongweak ties, it is likely to be transferred “as is.” Onthe other hand, when it travels along strong ties,it is likely to be filtered and positively biased.

Table 4 summarizes the empirical supportfound for our hypotheses.

DiscussionThis study examined how a CEO’s social

capital and personality favor business referrals.

Figure 2Interaction Effect between Structural Holes and Conscientiousness

-0,3

-0,1

0,1

0,3

0,5

0,7

Bu

sin

ess

Ref

erra

ls

Structural Holes

Low Consciensciousness

High Consciensciousness

Mean


1

23

4

5

6

789

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In line with the individual approach to socialcapital (Adler and Kwon 2002; Burt 1992), weview social ties as important vehicles forspreading first-hand information about the CEOto potential customers (Uzzi 1997). Our find-ings offer several contributions. From a theo-retical point of view, they highlight therelationship between information diffusion andnetworks that are rich in structural holes,whereas previous research focused on the roleof this variable for SMEs in terms of informationacquisition (McEvily and Zaheer 1999). Simi-larly, although strength of ties has been studiedas an important variable affecting informationacquisition (Julien, Andriambeloson, andRamangalahy 2004; McEvily and Zaheer 1999),we provide evidence that it plays an importantrole in information diffusion. Thus, our studyprovides an original contribution to the debateover the “strength of strong ties” (Hansen 1999)versus the “strength of weak ties” (Granovetter1973).

Our research also contributes to currentefforts to move beyond a “universal” theory ofsocial capital. In line with a very recent streamof research (Anderson 2008; Baer 2010; Zhouet al. 2009), we argue that the value of socialcapital is contingent on personality. Buildingon research into the perception of personalitytraits (Flynn, Chatman, and Spataro 2001; Scottand Judge 2009), we tested a model in which aCEO’s personality is a signal that is interpretedand referred to by contacts in his/her personalnetwork. We found that positive personality

traits have a direct positive effect on referrals.In addition, one of these personality traits, con-scientiousness, moderates the impact of socialcapital. Conscientiousness signals that a focalactor is a reliable and hard-working person,and therefore a good job performer (Judge andIlies 2002; Ones et al. 2007). Thus, it is notsurprising that the conscientiousness of a CEOprovides potential customers with a particularlyvaluable indication of his/her expected reliabil-ity in business.

A much more insightful finding lies in theway conscientiousness moderates the effect ofsocial capital. Social capital (strong ties andstructural holes) appears to be very beneficialfor CEOs with low conscientiousness butalmost neutral for CEOs with high conscien-tiousness. In other words, rather than intensi-fying the benefits accruing from highconscientiousness, social capital compensatesfor the negative reputation effect that low con-scientiousness could create. Strong ties seem toinvolve a certain “bias” in the spread of infor-mation, with contacts disseminating favorableinformation and endorsing a CEO even whenthe initial signal is negative. Similarly, structuralholes between contacts can prevent negativesignals being propagated contagiously fromone group of contacts to another, therebyreducing the likelihood of “echo” effects. Thisis very beneficial for CEOs with low conscien-tiousness, but not particularly advantageous forCEOs with high conscientiousness. Takentogether, these results indicate that social ties

Figure 3Interaction Effect between Strength of Ties and Conscientiousness

-0,3

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0,1

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0,7

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Strength of Ties

Low Conscienciousness

High Conscienciousness

Mean


1

23

4

5

6

789

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must be seen as channels that alter informationas it travels through them, thereby offering amore complex view of the individual approachto social capital.

These findings on personality as a contin-gent factor open new research avenues aboutthe relationships between social capital andpersonality. Some studies have taken a differ-ent approach from ours, focusing on person-ality as an antecedent of social capital, rather

than as a moderator. They found that traitssuch as self-monitoring (e.g., Kim and Kim2007; Mehra, Kilduff, and Brass 2001; Oh andKilduff 2008; Sasovova et al. 2010) or neuroti-cism (Kalish and Robbins 2006; Klein et al.2004) lead to specific structures of personalnetworks. The coexistence of these studieswith approaches positing other traits as mod-erators (Anderson 2008; Baer 2010; Zhouet al. 2009) reveals a need for theoretical

Table 4Synopsis of Results

Direct effect of social capital on business referrals

Direct effect of social capital on business referralsH1: The higher the number of structural holes in an SME CEO’s network,

the more business referrals he/she will obtain.Supported

H2: The stronger the ties in an SME CEO’s network, the more businessreferrals he/she will obtain.

Supported

Direct effect of positive personality on business referralsH3a: The more an SME CEO is agreeable, the more business referrals

he/she will obtain.Supported

H3b: The more an SME CEO is conscientious, the more business referralshe/she will obtain.

Supported

H3c: The more an SME CEO is extraverted, the more business referralshe/she will obtain.

Supported

H3d: The more an SME CEO is open to experience, the more businessreferrals he/she will obtain.

Supported

Positive personality moderating the effect of social capitalStructural holes

H4a: The positive relationship between structural holes and businessreferrals is stronger when an SME CEO is low on agreeableness.

Not supported

H4b: The positive relationship between structural holes and businessreferrals is stronger when an SME CEO is low on conscientiousness.

Supported

H4c: The positive relationship between structural holes and businessreferrals is stronger when an SME CEO is low on extraversion.

Not supported

H4d: The positive relationship between structural holes and businessreferrals is stronger when an SME CEO is low on openness toexperience.

Not supported

Strength of tiesH5a: The positive relationship between strength of ties and business

referrals is stronger when an SME CEO is low on agreeableness.Not supported

H5b: The positive relationship between strength of ties and businessreferrals is stronger when an SME CEO is low on conscientiousness.

Supported

H5c: The positive relationship between strength of ties and businessreferrals is stronger when an SME CEO is low on extraversion.

Not supported

H5d: The positive relationship between strength of ties and businessreferrals is stronger when an SME CEO is low on openness toexperience.

Not supported

CEO, chief executive officer; SME, small and medium-sized enterprise.


1

2

3

4

5

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41

42

43

44454647484950515253


clarification. Future research should considermodels including some traits as antecedents ofsocial capital (in particular, those capturingskills for or orientation toward socialization,e.g., self-monitoring) and other traits as mod-erators (those that are independent fromsocialization and more relevant to task-relateddimensions, e.g., openness to experience orconscientiousness).

Future research should also examine theprocesses through which a person’s opinionmakes him/her more or less likely to recom-mend a CEO. This could be done by collectinginformation from CEOs’ social contacts, ratherthan from CEOs themselves. Such studieswould also address one of the limitations of thepresent study and therefore strengthen thevalidity of our results. Although relying on theself-evaluation of CEOs to assess business refer-rals is now a well-established approach(Seevers, Skinner, and Dahlstrom 2010), itentails some risk of bias due to differences inperceptions.

Finally, further research is needed beforeour results can be generalized. As in anysurvey, a limited response rate entails a risk ofpoor fit between the sample and the parentpopulation. Although our response rate wassatisfactory compared with standards in thefield (Bartholomew and Smith 2006; Baruchand Hotlom 2008), we checked for possibledifferences between respondent and nonre-spondent firms. Our analyses revealed no sig-nificant differences in terms of industry andfirm size, suggesting that our sample is repre-sentative of the parent population. The gener-alizability of the results from this population toother contexts is less clear. We studied manu-facturing SMEs operating in a business-to-business environment, where informal sourcesof information about a firm are particularlyvaluable in the process of making purchasingdecisions. Similar studies in the context ofmuch simpler purchasing decisions may lead tovery different results.

In a similar vein, the benefits of individualsocial capital are highly dependent on thesocial context at a broader level (Adler andKwon 2002) and our findings are based on ageographically restricted industrial cluster,as is the case for many other studies of socialcapital among SMEs (Molina-Morales andMartinez-Fernandez 2010; Pirolo and Presutti2010). Our results should therefore be inter-preted in the light of the particular social

context of our study. In clusters, which arecharacterized by higher mutual trust and coop-eration (Chetty and Agndal 2008; Cooke,Clifton, and Oleaga 2005), the observed ben-efits of individual social capital (our researchquestion) may be fueled by the preexistinghigh level of collective social capital (ourcontext). Moreover, local institutions usuallyplay an important part in promoting collabora-tion and provide resources to make this pos-sible (Fromhold-Eisebith 2005; Gilly and Wallet2001). Another aspect that makes clustersfavorable environments for business referralsthrough social ties is that physical proximityand collocation make it easier for CEOs to havefrequent face-to-face interactions (Chetty andAgndal 2008). Therefore, it would be interest-ing to replicate our study in areas with a muchlower concentration of SMEs, less specializa-tion, and less active local institutions. In suchcontexts, it would be reasonable to hypothesizethat individual social capital would have alower impact on business referrals due to theabsence of collective social capital. A compari-son of two areas would contribute to a betterunderstanding of how levels of social capital(individual and collective) interact, an aspectthat was pinpointed by Ibarra, Kilduff, and Tsai(2005) as one of the future challenges in thefield.

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Appendix

Quality of Representation for Business Referrals

Retained items Cos2 Var a

“People recommend my company to customers” 0.780 70% 0.779“People strongly advise other firms to do business with my company” 0.747“My company obtains contracts thanks to favorable word of mouth” 0.580

Quality of Representation for Agreeableness

Retained item label Cos2 Var a

“I am interested in people” 0.569 57% 0.812“I sympathize with other people’s feelings” 0.561“I make time for others” 0.624“I feel others’ emotions” 0.591“I make people feel at ease” 0.514

Quality of Representation for Conscientiousness

Retained item label Cos2 Var a

“I usually put things back in their proper place” 0.744 69% 0.774“I pay attention to detail” 0.598“I like order” 0.733

Quality of Representation for Extraversion

Item label Cos2 Var a

“I do not talk a lot” 0.636 58% 0.760“I keep in the background” 0.610“I start conversations” 0.532“I talk to a lot of different people at parties” 0.554

Quality of Representation for Openness to Experience

Item label Cos2 Var a

“I have a vivid imagination” 0.715 68% 0.757“I have excellent ideas” 0.689“I am quick to understand things” 0.639



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Generating Business Referrals for SMEs: The Contingent Value of CEOs' Social Capital

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