Resource Search,Interpersonal Similarity,and Network Tie Valuation in Nascent Entrepreneurs’ Emerging Networks

Abstract

This article contributes to the entrepreneurship and network literatures by addressing the fundamental research question of how a new venture’s initial network ties are formed. The authors focus on the broad network search evinced by nascent entrepreneurs at the very earliest stages of venture and network creation and examine some of the instrumental and interpersonal mechanisms driving nascent entrepreneurs’ value attributions about the contacts met during this network search. The authors utilize a unique empirical data set of 1,407 entrepreneur–contact dyads collected during a 6-month period of real-time nascent venture activity. Their results suggest a view of new venture network construction in which the content benefits conferred through anticipated or real resource acquisition form a clear basis for entrepreneurs’ assessments of value, with the process benefits of interpersonal age and gender similarity playing an amplifying role. Contrary to their expectations, the authors did not observe direct interpersonal similarity effects. Their findings shed light on some of the very early decision processes that underlie an entrepreneur’s network search and thus are critical to network formation.

Keywords

new ventures networks interpersonal similarity resources nascent entrepreneurs

Although the network literature has grown exponentially over the past several decades (Borgatti & Foster, 2003), researchers have only recently begun to give attention to issues of network construction and evolution (e.g., Hallen, 2008; Ozcan & Eisenhardt, 2009; Stuart & Sorenson, 2007). To the extent that this topic has been explored, researchers have tended to focus on (1) an organization’s placement within a comparatively dense or comparatively diffuse interorganizational network structure (Burt, 1982, 2001; Coleman, 1988), and/or (2) the obligations, dependencies, and trust created through an organization’s existing network relationships (e.g., Gulati & Gargiulo, 1999; Podolny & Page, 1998; Portes & Sensenbrenner, 1993). While the mechanisms of tie choice vary across these two views, the foundational argument remains the same—and has proven robust across established companies: An organization’s new tie opportunities and consequent network evolution are facilitated or constrained as based on preexisting network characteristics (i.e., past ties shape future ties). Most nascent ventures, however, lack the prior connections seen with established companies and are initiated with what approximates a network “clean slate” (Milanov & Fernhaber, 2009: 47). A research gap thus remains. The extant literature continues to be largely mute with respect to addressing how a network’s initial ties are formed by those seeking to launch brand new ventures (e.g., Aldrich & Kim, 2007; Hallen, 2008).

The entrepreneurship literature has long given voice to the importance of networks in the new venture process; ideas are often spawned, opportunities are often recognized or exploited, and funding is often provided either directly or indirectly through personal contacts (Dubini & Aldrich, 1991; Larson, 1991; Shane & Cable, 2002; Zhang, Soh, & Wong, 2010). The strength of this literature, however, lies primarily in explaining the why of a new venture network’s emergence rather than the how of its emergence—emphasizing the organizational benefits conferred through a young organization’s partner set (Birley, 1985; Larson & Starr, 1993) or network location (Aldrich, Reese, & Dubini, 1990; Knoke, 1990). Although some more recent work has explored processes of early network emergence—capturing the ways in which network relationships strengthen and become embedded over time (Hite, 2005; Hite & Hesterly, 2001; Larson, 1992) plus the ways that young firms develop their early investor ties (Hallen, 2008) and alliance portfolios (Ozcan & Eisenhardt, 2009)—much remains to be uncovered regarding the mechanisms underlying the initial formation of a new venture’s network. Given that a company’s network ties tend to originate with the company’s founder or founders (Baker, Miner, & Eesley, 2003; Hallen, 2008), we seek to understand how nascent entrepreneurs effect that origination.¹

In this article, we address recent calls for a better understanding of the “process through which ties are selected for entrepreneurship” (Jack, 2010: 133). We do so by examining the broad network search evinced by entrepreneurs at the very earliest stages of venture and network creation—stages that predate the selection and cultivation of those individuals at the longer-term heart of the new venture network and the formation of a management team. More specifically, we examine some of the mechanisms by which nascent entrepreneurs identify those individuals perceived as most valuable to their new venture creation efforts and, thus, those dyads most likely to merit ongoing cultivation. Of note, we view organizational nascency as distinct from organizational youth (i.e., that which has been studied in prior work on “young” firms’ network evolution). Nascency connotes a state in which ventures do not yet exhibit the collectivity of features representative of emerged organizations (i.e., intentionality, boundaries, resources, and ongoing exchange; Katz & Gartner, 1988); nascency, in effect, describes a preorganizational state, while youth describes an organizational state.

Thus conceptualized, we are researching entrepreneurs at a point where they must assess the utility of their network contacts in the absence of an objective and quantifiable basis for that assessment. Given the inherent uncertainty associated with correctly predicting a venture’s short- and long-term needs, we contend that entrepreneurs are likely to pursue those who they perceive as most likely to be of use in transitioning the venture out of nascency. This view aligns with research that has described perception as a primary driver of entrepreneurial decisions and actions (Forbes, 1999; Hill & Levenhagen, 1995; Penrose, 1959). We contend that entrepreneurs are likely to cultivate relationships with individuals who they perceive to deliver new venture value (with value defined as the extent to which entrepreneurs perceive contacts as providing a meaningful contribution to new venture emergence) and, conversely, to minimize relationships with individuals perceived to be negative or neutral contributors. We thus seek to understand what determines the value attributions made by nascent entrepreneurs about the individuals met during their new venture network searches.

Recent work on entrepreneurial team formation has identified both instrumental and social psychological factors as relevant to entrepreneurs’ decision making (Aldrich & Kim, 2007; Forbes, Borchert, Zellmer-Bruhn, & Sapienza, 2006). Taking a similar approach, we seek to generate new insight about the emergence of new venture networks by integrating traditional theories of instrumental resource pursuit with social psychological theories regarding individuals’ tendency to interact with others of shared personal characteristics. Within our discussion of social psychology, we draw on interpersonal similarity and attraction theory (e.g., Byrne, 1971; Heider, 1958) and construal theories of psychological distance—theories that clarify the mechanisms by which social, spatial, and temporal closeness shape individuals’ interpersonal judgments (Liberman, Trope, & Stephan, 2007; Liviatan, Trope, & Liberman, 2008; Trope & Liberman, 2003; Trope, Liberman, & Wakslak, 2007). At its core, our article seeks to address the following question: How do resource search and interpersonal similarity impact the ways in which nascent entrepreneurs attribute value to those who they meet in their quest to build new venture networks and launch new ventures?

The current work makes several contributions to the entrepreneurship and network literatures. First, and most important, by focusing on nascent entrepreneurs’ value attributions, we elucidate some of the very early decision processes that underlie an entrepreneur’s network search and thus are critical to network formation. Examining networks at this nascent stage of a firm’s life allows us to assess entrepreneurs in that period of time when they are known to be actively meeting and interacting with those individuals who might emerge as long-term network contacts and form the core of the venture’s resource network (Greve & Salaff, 2003). It also uniquely positions us to explore emerging organizations at a time of particular network dynamism and organizational ambiguity, without the potentially confounding influence of the extant structures often seen to shape ongoing firm decisions (e.g., board interlocks, strategic alliances, investor relationships).

In studying a venture’s first founder, we further secure a unique window into the managerial mind, without the organizational constraints that emerge over time. Insofar as a founder and his or her firm are synonymous at the earliest stages of a venture, the current study sheds light on how managers (and hence organizations) navigate uncertainty and create new relationships in pursuit of the resources essential to growth. Insofar as a founder is not yet part of a management team and acts as the new venture’s sole decision maker, the current study also sheds light on some ways in which social psychology can influence processes of network formation. This article thus provides an integrated theoretical lens through which to understand the independent and joint effects of instrumental resource search and interpersonal similarity on value attributions in nascent venture network development. Uniquely, it develops this lens using empirical data collected from nascent entrepreneurs as they were building their networks, rather than after the fact.

Theory and Hypotheses

We develop our conceptual model in the following sections—first examining the strategic, resource-driven view of network tie valuation, then drawing on social psychological theory to discuss the relationship between interpersonal similarity and tie valuation, and finally integrating these perspectives to examine the interaction effects between the instrumental and interpersonal drivers of nascent entrepreneurs’ value perceptions in network construction.

Resources

Within organizational theory, it has long been held that an established firm’s competitive advantage is integrally linked to its resource endowments and the capabilities rendered through those endowments (e.g., Barney, 1991; Penrose, 1959). More recent work has elaborated this view to (1) articulate a critical relationship between firm resources and the network in which a firm resides and (2) identify some network characteristics central to resource access (e.g., Arya & Lin, 2007; Lavie, 2006). In aggregate, for our current purposes, the work suggests that a firm’s instrumental resource requirements are likely to manifest in strategic network-building efforts—with actors establishing ties to secure those resources that they view as most critical to firm success (Jarillo, 1987, 1989; Miles & Snow, 1984; Thorelli, 1986).

Although the resource needs of a new venture might be more rudimentary than those typically discussed with established firms (e.g., there is a need for fundamental resources such as money and people before the combined resources of the two together can emerge), their overall significance is similar. The satisfaction of resource needs determines the new venture’s likelihood of successful emergence, growth, and survival. Also, the satisfaction of resource needs relies to a significant degree on the establishment of effective networks (e.g., Dubini & Aldrich, 1991; Larson, 1991; Raz & Gloor, 2007; Shane & Cable, 2002). Entrepreneurs act as resource mobilizers (Greene, Brush, & Brown, 1997; Starr & MacMillan, 1990) and bricoleurs (Baker et al., 2003; Garud & Karnøe, 2003)—proactively pursuing the relationships through which resources are anticipated to derive.

Arguably, the utility of resources constitutes a truism. The challenge in understanding resources thus lies not in proving that they are useful in general but in identifying how and under what circumstances their usefulness can be realized. More specifically, as has been noted by both proponents and critics of the resource-based view, the mere possession of resources constitutes an insufficient condition for their effective use (Lippman & Rumelt, 2003; Penrose, 1959; Sirmon, Hitt, & Ireland, 2007). Resource-driven value creation within traditional organizations is dependent in part on the rarity, inimitability, and nonsubstitutability of the resources in question (Barney, 1991; Sirmon et al., 2007). Resource management is also fundamental to an organization’s ability to extract value from its resources. As defined by Sirmon et al., resource management is a deep and dynamic process that encompasses a host of components: the (1) structuring of a resource portfolio (i.e., the acquisition, accumulation, and divestiture of resources); (2) bundling of resources within the portfolio (i.e., the stabilization, enrichment, and pioneering of resource combinations); and (3) leveraging of resource bundles (i.e., the mobilization, coordination, and deployment of resources).

Within the very uncertain environment of organizational nascency, however, resource management is itself nascent (i.e., the quest for resource acquisition will have been initiated, but limited credible attention can have been given to long-term issues of resource structuring, bundling, and leveraging). Entrepreneurs thus face a remarkably difficult challenge: They must initiate resource management in the absence of resources. More specifically, they must pursue the acquisition of resources without precise knowledge about which particular resources will be best suited to the organization over time and how those resources might be combined and deployed to support successful organizational emergence and growth. Even in instances where some specific awareness of resource requirements is apparent from the outset, entrepreneurs will be challenged by the fact that these requirements will change over time (Delmar & Shane, 2004; Gartner, 1985).

When the network–resource linkage is further factored into the nascent picture, the entrepreneurial challenges are compounded. Not only must entrepreneurs initiate effective resource management without a satisfactory awareness of resource needs, but they must do so by identifying and cultivating relationships with often comparatively unknown individuals who might (or might not) be able to deliver what is perceived as essential. Entrepreneurs must, in effect, build their networks without any certainty that those within the network will ultimately be able or willing to deliver appropriate resources as and when they are needed. Under these circumstances, the question emerges: On what resource basis (if any) might an entrepreneur assess a network contact as offering value to the nascent venture? While the provision of resources to the entrepreneur might by itself represent one objective basis for attributing value to the providing node, the other resource attributes noted above are likely to remain unidentified in the short term. It also, and critically, remains unclear how value should be attributed in situations where resources have been discussed (or even promised) but not yet delivered. In nascency, decisions regarding which network relationships to pursue often predate resource receipt.

Given both the wide-ranging needs and the uncertainty that firm emergence and growth necessarily imply, we expect nascent founders to seek out network contacts who are perceived to offer potential access to the widest variety of resources. With variety comes versatility—the increased likelihood of (1) securing the foundational blocks for valuable resource combinations and (2) realizing “economies of scope” in one’s network (i.e., reducing network management costs because fewer relationships can be used to access what is needed in a timely manner). Variety is also consistent with resource diversity—a feature that is associated with competitive advantage in the context of established firms (Barney, 1991). We thus expect founders to judge the overall utility of a network contact based on the extent to which he or she offers resource multiplexity, which we here define as the simultaneous, prospective availability of multiple resources, such as capital, people, sales, and information.

As noted by Mishina, Pollock, and Porac (2004), this “more is better” perspective has been cited repeatedly in research that explores the link between established organizations and firm expansion. It is a perspective, however, that they and others have challenged as assumptive and flawed. In elaborating this point, they cite Penrose’s (1959) view that too much resource accumulation can be inefficient; Stevenson, Roberts, and Grousbeck’s (1994) view that new venture success need not be dependent on controlled resources; and, relatedly, Stevenson and Gumpert’s (1985) view that new venture success can be achieved through the parsimonious and effective use of an only very limited resource allocation.

We believe that, just as there is a fundamental difference between possessing and using resources, there is a fundamental difference between anticipated resources and realized resources. In effect, we believe that while less might be better than more in the context of an operating organization, some is better than none for the nascent venture—a view that is consistent with concepts of traditional economic rationality (i.e., profit as output cannot be achieved without input) and bounded rationality (Simon, 1957). In the context of bounded rationality, we expect that time and certainty constraints will lead nascent entrepreneurs to satisfice through acceptance of resource multiplexity as a proxy measure for future resource acquisition. While entrepreneurs will likely acknowledge that breadth does not, by definition, connote value, we expect they will still see the network contact who claims possession of multiple resources as, on a probabilistic basis, most likely to deliver value over the long term.

Larson and Starr (1993) have argued that relationships in entrepreneurship become multidimensional and complex as they develop and mature. Our perspective is aligned: We expect entrepreneurs, in the absence of knowing which relationships will yield the greatest long-term benefits, to invest in those relationships perceived to have the potential for greatest breadth and hence longevity. In sum, we expect the perceived value of a network contact to increase in parallel with the number of resources perceived to be available through that contact.

Hypothesis 1: The resource multiplexity offered by a network contact will be positively associated with the perceived value of that contact.

Interpersonal Similarity

Social psychological research has repeatedly affirmed the primacy of interpersonal similarity as a mechanism that attracts individuals to one another, enhancing the likelihood of their interacting and developing personal or professional relationships (i.e., interpersonal similarity or attraction theory). This tendency to choose like others is driven cognitively by the benefits perceived to derive from likeness—benefits that include enhanced interpersonal trust, ease of communication, and reciprocity (Blau, 1977; Byrne, 1971). That this theory is robust is evidenced not solely by its psychological tradition but also by the long-standing presence of a similar theory in sociology—where it is labeled homophily (Lazarsfeld & Merton, 1954; McPherson, Smith-Lovin, & Cook, 2001) and broadly construed as driven both by the individual cognitions noted above and by social structure (McPherson et al., 2001). In social structural terms, individuals’ tendency to choose like others is driven by preexisting homophily within communities, families, and organizations (Ibarra, 1992; Kalmijn, 1998; Lawrence, 2006; Verbrugge, 1977): Because individuals invest most of their waking hours within these largely homogeneous social structures, individuals are more likely to interact with those like themselves.

Within an organizational context, similarity effects have been shown to be particularly strong—influencing individual connections and thus shaping informal networks, mentoring relationships, and broader reference groups (Ibarra, 1992; Lawrence, 2006; Thomas, 1990). Although one might expect entrepreneurs in the process of new venture creation to be proactive in seeking new and diverse contacts, there is evidence to suggest that similarity effects remain strong during organizational nascency. First, research has affirmed that entrepreneurs tend to draw on preexisting relationships (e.g., friends, family) in the earliest stages of venture start-up (Aldrich & Carter, 2004; Bhide, 1999). Since one’s prior contacts tend toward homophily, as confirmed by the general sociological literature (see McPherson et al., 2001, for a review), it follows that entrepreneurial dependence on and trust in prior contacts might yield an enduring tendency toward homophilous selection. Second, research has confirmed the commonality of homophily in founding teams (Forbes et al., 2006; Ruef, Aldrich, & Carter, 2003). This phenomenon implies a selection process wherein individual founders are attracted, despite the probable existence of heterogeneous options, to like others as cofounders. The perceived benefits of similarity seem operative here, as it is otherwise unclear why founders would not seek to realize the oft-touted benefits of group and top-management-team diversity (e.g., Milliken & Martins, 1996; Watson, Kumar, & Michaelsen, 1993; Wiersema & Bantel, 1992).

In the following, we discuss two fundamental types of interpersonal similarity—age and gender—that are likely to drive nascent entrepreneurs’ value attributions. Our prioritization of these two similarity dimensions was motivated in significant part by their comparatively easy external identification (i.e., entrepreneurs are likely to be fairly accurate with their assessments of both, even at the earliest and most superficial stage of dyadic contact). Further, age and gender have been widely studied in both the interpersonal similarity and entrepreneurship literatures, providing a strong empirical basis for examining this issue in our setting.

Age similarity

Those of the same age are raised in the same historical socioeconomic context (Elder, 1999; Giele & Elder, 1998) and thus develop similar values, skills, affiliations, and networks (Elder, 1999). Age has been shown to be among the strongest predictors of close friendships and one’s more general circle of friends (Verbrugge, 1977). Age homophilous relationships are often closer and longer lasting—and tend to evince more exchange—than those characterized by age heterophily (Fischer, 1982; McPherson et al., 2001). Further, within organizations, age has been shown to drive recognition of shared experiences and the initiation of unplanned conversations (Zenger & Lawrence, 1989). Although age differences are viewed as relevant in the context of mentoring relationships (Hunt & Michael, 1983; Levinson, Darrow, Klein, Levinson, & McKee, 1978)—and mentoring is arguably of particular use to nascent entrepreneurs—these differences are not as pronounced as might be expected; mentors tend not to be much older than their protégés, as too significant an age differential can impair the functional efficiency of a relationship, hindering communication and creating an uncomfortable “parent–child” dynamic (Levinson et al., 1978).

We expect nascent entrepreneurs’ value attributions to be in line with these findings. Given that age similarity inclines individuals toward greater levels of interpersonal attraction and understanding, we expect that nascent entrepreneurs will more easily and efficiently approach, communicate with, and trust others of similar age. Age homophilous others should be more likely to provide nascent entrepreneurs with feedback and assistance to support new venture goals. Conversely, a significant age differential should diminish the perceived value of a contact on the basis of a much younger contact’s comparative inexperience or a much older contact’s threat to entrepreneurial autonomy. In sum, we believe that facility of interaction and cognitive alignment will yield a higher likelihood of meaningful interactions between those who are similar in age and that this heightened likelihood will drive higher value attributions by nascent entrepreneurs.

Hypothesis 2a: Age similarity with a network contact will be positively associated with the perceived value of that contact.

Gender similarity

Time and again, gender similarity has been recognized as central to interactions generally (McPherson et al., 2001) and entrepreneurship specifically (Ruef et al., 2003). Gender homophily has been observed in a variety of network and group settings, including friendship networks (Verbrugge, 1977), political discussion networks (Huckfeldt & Sprague, 1995), organizational networks (Brass, 1985), and MBA student projects (Mehra, Kilduff, & Brass, 1998). Gender homogeneous founding teams have been assessed as five times likelier than heterogeneous teams (after excluding couples; Ruef et al., 2003). Male entrepreneurs have been found to possess predominantly male networks (Aldrich, 1989). And although female entrepreneurs have also been found to possess predominantly male networks (Aldrich, 1989), this compositional tendency is likely driven by the disproportionate representation of men in entrepreneurship rather than the inherent interactional interests of women. Female entrepreneurs are, in fact, more likely to seek and attain information from other women (Smeltzer & Fann, 1989), and women’s business support groups are often formed in reaction to male representation in entrepreneurship (Aldrich, 1989).

Underlying this tendency toward gender similarity are the gender-based values, beliefs, and communication patterns that individuals develop through socialization (Merton, 1963). Abundant evidence has shown that women, for example, are socialized to show their emotions, to be caring toward others, and to be listeners (Bass & Avolio, 1994; Brunner, 1998; Pounder & Coleman, 2002). Female managers’ behaviors have been found to differ from those of male managers, with comparatively more attention given to multitasking, supporting, nurturing, developing personal relationships, and teamwork (Priola, 2004). Further, gender differences in language patterns have been shown to impact organizational decision making and social interactions (Sheridan, 2007; Tannen, 1995). Consistent with these findings, we expect gender similarity to play a role in nascent entrepreneurs’ valuations of network contacts. We anticipate that shared gender-based values and communication patterns will foster benefits (e.g., interpersonal trust and reciprocity) that are viewed as supportive of new venture development. Conversely, we anticipate that gender differences will hinder communication (Sheridan, 2007), diminishing the perception of new venture value. As a result, we believe that nascent entrepreneurs will attribute higher value to same-gender network contacts than to different-gender contacts.

Hypothesis 2b: Gender similarity with a network contact will be positively associated with the perceived value of that contact.

Interaction Effects

The mechanisms through which resource multiplexity and interpersonal similarity impact perceived relationship value are fundamentally different in nature. Resource multiplexity delivers what might be labeled content benefits to a new venture, increasing the perceived scope of the tangible and intangible resources available to an entrepreneur. In contrast, interpersonal similarity yields what might be termed process benefits, increasing the perceived efficiency of a dyad (as manifest in higher levels of interpersonal trust and affection and better or more frequent communication). In the following, we suggest that resource multiplexity and interpersonal similarity may interact to produce effects that explain further variance in value attributions.

To develop a theoretical link between resources, interpersonal similarity, and perceived value, we draw on construal level theory (CLT). CLT provides a robust theoretical basis for recognizing perception as deriving in significant part from a tendency toward the use of heuristics in uncertain and interactive contexts. It is thus particularly applicable to our setting of nascent entrepreneurs’ early network search. Perhaps more significantly, CLT clarifies a gap left unaddressed by arguments that explore resources or interpersonal similarity independently of one another. Specifically, it identifies the cognitive mechanisms underlying an individual’s perception of dyadic similarity as indicating an enhanced likelihood of securing more than just “intangibles” such as interpersonal trust and affection (notwithstanding the inherent value of those intangibles). In other words, it identifies the mechanism underlying our belief that process benefits are likely to support the realization of content benefits.

CLT finds its roots in Heider’s (1958) work on interpersonal perception and the now entrenched notion that interpersonal similarity can be understood as an instantiation of psychological distance (Liviatan et al., 2008; Trope & Liberman, 2003). It also draws on extant work that recognizes interpersonal perceptions as substantively impacted by individuals’ psychological distance from one another (e.g., Andersen & Cole, 1990; Andersen, Glassman, & Gold, 1998; Chen-Idson & Mischel, 2001; Prentice, 1990). According to CLT, individuals (1) develop differential representations of others as a function of the dyadic psychological closeness communicated via interpersonal similarity and (2) assess others’ actions as based on those differential representations (Liberman et al., 2007; Liviatan et al., 2008; Trope & Liberman, 2003; Trope et al., 2007).

At the heart of CLT is the notion, as per its name, that psychological closeness has a direct effect on the level at which individuals construe others, with closeness viewed in more concrete or detailed terms (lower level construals) and distance viewed in more general or abstract terms (higher level construals; e.g., Andersen & Cole, 1990; Andersen et al., 1998; Chen-Idson & Mischel, 2001; Prentice, 1990)—even when the close and distant objects can be identified as equivalent (see Trope et al., 2007, for a review). For our purposes, these construal levels are relevant because they shape how actors assess others’ contributions to outcomes: Feasibility (“what is likely to happen”) is seen as characteristic of relationships with similar others, while desirability (“what I’d like to happen”) is seen as characteristic of relationships with those who are dissimilar (Liberman & Trope, 1998; Liviatan et al., 2008; Wakslak, Trope, Liberman, & Alony, 2006). Of note, CLT (as supported by prospect theory) also links feasibility to short time horizons and desirability to long time horizons (Wakslak et al., 2006)—a fact that is significant insofar as individuals are known to prefer immediate results over delayed ones (even when delayed results might prove to be better; Kahneman & Tversky, 1979; Liberman & Trope, 1998).

Individuals in uncertain situations are often faced with a basic question: Do I choose the option that is available now or the better option that might become available later? In the context of new venture network development, a similar dilemma is present: Should the entrepreneur cultivate the network contact perceived as most likely to deliver real resources in the short term or the one anticipated to provide more useful resources down the road? We expect entrepreneurs to choose the “yes and now” over the “perhaps and eventually.” In the lexicon of CLT, we believe founders will exhibit a systematic cognitive tendency to perceive resources as more likely to be realized in the short term through similar (i.e., psychologically close) others, rendering those others more valuable within the environment of organizational emergence.

We thus propose that when broader relationship content in the form of resource multiplexity is combined with interpersonal similarity, there will be an amplified effect on value attribution. Following the logic of CLT, nascent entrepreneurs will have a more tangible and detailed understanding of the nature and applicability of the resources available through similar others. Further, the increased interpersonal trust, affection, and ease of communication deriving from similarity (Brass, 1985; Heider, 1958) are likely to result in an improvement in the dyad partner’s willingness to provide and speed in providing new venture assistance. The resources of similar others will thus be perceived by nascent entrepreneurs as more accessible and as offering a heightened likelihood of delivering real business value in the short term.

In sum, we argue that the positive effect of high resource multiplexity on perceived relationship value will be even more pronounced when it is accompanied by age- or gender-based interpersonal similarity—as the “what,” the “if,” and the “when” of the network relationship are all improved. Conversely, when the scope of resources is narrowed, a lack of interpersonal similarity (or an increase in psychological distance) will further dampen the perceived value of the relationship.

Hypothesis 3a: Age-based interpersonal similarity and resource multiplexity will have a positive interaction effect on the perceived value of a network contact.

Hypothesis 3b: Gender-based interpersonal similarity and resource multiplexity will have a positive interaction effect on the perceived value of a network contact.

Method

Sample and Data

The data used in this article were taken from a larger, exploratory research effort conducted by the first author. Data were collected from early-stage entrepreneurs over 6 months of nascent venture activity and related network search between March 2003 and March 2004. The decision to study individual founders reflected a desire to avoid the “multiple networks” challenge presented by start-up teams, wherein mapping the new venture network involves aggregating simultaneous individual networks (containing overlapping and proprietary contacts). Also, a study of individual founders at the earliest stages of a venture predates and thus sheds light on the emergence of what tends, within the research, to be retrospectively labeled a “founding team.”

After soliciting interest for the study in consultation with or through several hundred individuals and organizations across multiple channels (e.g., e-mail lists, message boards), 49 entrepreneurs were identified as satisfying the eligibility criteria and agreed to participate. The eligibility criteria were developed via extensive definitional discussions with entrepreneurship experts about the attributes of a “nascent venture” and were refined via a month-long, 4-participant pilot test. Each participant, at the study’s outset, (1) was the sole manager and founder of a California-based venture; (2) spent at least 10 hours per week on the venture; (3) had been working on the venture for less than 24 months; and (4) had received less than $100,000 in private funding. For reference, these criteria were consistent with, but stricter than, those used in prior work (e.g., the Panel Study of Entrepreneurial Dynamics defined nascent entrepreneurs as committing 160 hours to a venture over a 12-month period; Reynolds & Curtin, 2008). The more stringent criteria were here applied to minimize significant variation across the sample; participating entrepreneurs were all actively engaged in developing truly nascent ventures and initiated their ventures with similar ex ante resource endowments.

Of the initial 49 participants, 32 remained in the study for the full 6 months and provided information on a total of 1,407 network dyads. Our level of analysis is the entrepreneur–contact dyad. Twelve respondents dropped out within 2 weeks of study entry—a rapid attrition rate that suggested concern about time commitment or lack of sustained interest in the research. At various points during the study, 4 additional respondents provided notification that they had ceased new venture operations for reasons of business failure or inadequate business success. One individual stopped at an interim point without any accompanying explanation. The 32 entrepreneurs (23 men and 9 women) who remained in the study represented a wide range of venture types (e.g., service and product, online and brick-and-mortar, technology and nontechnology) as well as different types of individuals (e.g., in terms of age and education). Tests revealed that geographic location represented the only demographic difference between those who dropped out of the study and those who remained for 6 months; there appeared to be a relationship between respondent attrition and physical distance from the researcher.

The study commenced with a 1½-hour, semistructured interview in which entrepreneurs described the development of their individual business concepts, detailed their current resource positions, and mapped the set of contacts perceived to support the ventures. As is common in egocentric network research, a name generator approach was used to solicit the names of contacts. Entrepreneurs were asked to elaborate on the content and nature of each dyad, as well as to provide demographic information (e.g., age, sex, location) about themselves and all contacts. Thereafter, roughly every 2 weeks over the next 6 months, entrepreneurs completed periodic surveys regarding existing and new network ties—the latter identified by entrepreneurs as individuals with whom they had, since the prior survey, engaged in meaningful business interactions (“An interaction was meaningful if it advanced or retains the potential to advance the business or your thinking about the business.”). Questions for new contacts encompassed demographics and the resources expected or realized through the dyad. Questions for existing contacts addressed ongoing business interactions and relationships with other network members. At the end of the 6-month study period, entrepreneurs completed a final survey in which they assessed the contribution of each network contact to the new venture’s development.²

Initial interview data were collected from each entrepreneur via an in-person meeting that was recorded and transcribed. All subsequent surveys were completed by the entrepreneurs on a website created for this research. The site software, an advanced web application custom developed by the first author and a programmer, supported data input encompassing all aspects of the research. Of note, the online system was designed with advanced error checking to ensure the coverage and quality of responses. Items were left unanswered in very few instances, when a respondent explicitly requested (via a bottom-of-screen check box) that the survey advance with a question left blank; no systematic patterns were seen among these requested blanks. Figure 1 provides a view of the unique dynamic data captured via this online method.

Figure 1

Dynamic Network Data Visualization Using Study Data

In an effort to ensure that common method or source problems would not exist in the data set (Phillips, 1981), there was a time lag between measurement of the independent variables (in the initial and periodic surveys) and the dependent variable (at the end of the 6-month study period). To affirm that the time lag was effective in combating common method bias, we conducted Harman’s one-factor test (Podsakoff & Organ, 1986). Common method variance was not identified: Three factors accounted for 56% of the variance, with the first factor accounting for just 19.5% of the total.

Dependent Variable

The dependent variable captures the perceived value of each dyad in an entrepreneur’s network. This variable was measured as based on responses to the following survey question, administered at the end of each entrepreneur’s 6-month study participation:

Assume that you’ve been given $1,000, with the condition that you need to give ALL of the money away to those in your network. Specifically, you have to reward each person in your network based on his or her value to your new business. (There are no rules regarding equity. It is okay to reward $0 to individuals. Alternatively, you could give all of the money to one individual.)

Follow-up survey questions were used to assess the rationale underlying bonus assignments and, importantly, to confirm respondents’ interpretations of the word value. For each rewarded individual, the entrepreneur was asked for an open-ended explanation of his or her decision. Answers provided ample evidence that each entrepreneur’s understanding of value was consistent with that described throughout this article (i.e., that entrepreneurs saw value to a business as involving a meaningful contribution to new venture emergence). Perceptions of venture emergence were checked against entrepreneurs’ reports, on each survey, of milestone achievements—with an elaborated set of objective outcomes provided for response (e.g., made a product sale, received financing). The $1,000 constraint was designed to encourage thoughtful consideration on the part of respondents, forcing them to make some evaluative trade-offs (rather than, for example, allowing high valuations across the entire network). We explicitly account for this artificial constraint in our statistical model.

The decision to use value as an outcome measure was driven by the unique challenges associated with organizational nascency. Many nascent ventures exist and operate for a period of time prior to appearing in such traditional business listings as state sales tax files (Aldrich, Kalleberg, Marsden, & Cassell, 1989; Busenitz & Murphy, 1996). These ventures may not have filed for a business license or incorporated and are not likely to have emerged to the point at which they would exhibit, with any reliability, traditional outcome measures (e.g., economic value of exchange, firm survival, profits). Truly nascent ventures (such as those studied here) have not progressed to the point at which objective, external measures of network utility are available. Further, prior organizational research has noted that while archival measures of objective environmental conditions are appropriate for examining an organization’s external constraints, perceptual measures of the environment are more appropriate for studying managerial and organizational action (Boyd, Dess, & Rasheed, 1993). Given these challenges and our particular interest in the network-building actions of those founding truly nascent ventures, a perceptual measure is used.

In an effort to secure a fine-grained sense of the differences across the individual nodes within a given entrepreneur’s network, the value question allowed a broad range of answers, from 0 to 1,000. The average number of total contacts for each entrepreneur was 45 (ranging from 2 to 116), and the average number of valued contacts was 16 (ranging from 1 to 68). This distribution confirmed the theoretical expectations underlying the research design (i.e., that entrepreneurs meet numerous people prior to identifying that small subset which is of particular use; Larson & Starr, 1993); awards were distributed nonnormally, with 68% of all contacts securing a bonus of 0 and 98% of all bonuses falling between 0 and 200. We account in our model formulation for the significant number of 0 values (as described in the Model section). See Table 1 for descriptive statistics and correlations.

Table 1

Descriptive Statistics and Correlations of the Variables in the Model

Variable		1	2	3	4	5	6
1	Relationship value
2	Age similarity	−.02
3	Gender similarity	.02	.01
4	Resource multiplexity	.11***	.08**	.07**
5	Geographic proximity	.01	.03	−.08**	.09***
6	Relationship length	.09***	−.08**	−.01	−.07**	−.03
	Mean	22.62	0.80	0.67	1.25	1.92	30.23
	Standard deviation	71.61	0.40	0.47	0.74	1.32	57.22
	Maximum	1,000.00	1.00	1.00	4.00	3.00	251.00
	Minimum	0.00	0.00	0.00	0.00	0.00	0.00

Note: Unnormalized values of the dependent variable (relationship value) are used for descriptives and correlations.

p < .01. ***p < .001.

Independent Variables

Resource multiplexity was measured as the number of different resource categories anticipated from or provided by each dyad partner. Entrepreneurs selected from the following preset list of resource categories: strategy, hiring, capital, customers, and other. Resource categories were similar to those in extant research (e.g., Birley, 1985; Brush, Greene, & Hart, 2001; Starr & MacMillan, 1990) but simplified in response to pilot test data. Because “other” responses were uncommon and often overlapped with one of the primary categories, we reallocated these (or, in rare instances, excluded them). Responses ranged from 0 to 4, with a mean of 1.25.

Age similarity was measured using a binary variable that indicated whether or not the entrepreneur and the contact were fewer than 10 years apart in age. The decisions to utilize a binary variable and a 10-year range were motivated by (1) the inexactness inherent to having founders assess network contacts’ ages (i.e., social norms and legal concerns preclude individuals from asking others for age information) and (2) the assumption, based on prior life course perspective (Elder, 1999) and mentoring (Levinson et al., 1978) research, that age similarity effects would be driven primarily by whether or not the founder and the contact were in roughly the same age cohort. Gender similarity was measured using a binary variable that indicated whether or not the dyad partners were of the same gender. Of the 1,407 network contacts, 982 (70%) were men, and the average age of a contact was 39.1 years.

As control variables, we include (1) the duration of the relationship between the founder and the contact and (2) a measure of the dyad’s geographic proximity. We include relationship duration because prior research has shown that longer relationships differ from shorter ones in terms of, for example, trust, commitment, confidence in partner assessment, and relationship performance (e.g., Hite, 2005; Swann & Gill, 1997); the variable also allows us to control for any potential tendency to recency bias in nascent entrepreneurs’ valuations of their contacts. We include geographic proximity because prior research has shown it to enable face-to-face contact, the development of strong relational ties (Gordon & McCann, 2000; Saxenian, 1994), and information exchange (Adams & Jaffe, 1996; Borgatti & Cross, 2003; Sorenson & Audia, 2000). Relationship length was measured as the number of months the entrepreneur had known each contact. We observe a bimodal distribution for this variable; dyads tended to either come from the founder’s prior personal network or represent new acquaintances. Geographic proximity was measured as a categorical variable that described a contact as located in the same California county as the entrepreneur, elsewhere in California, elsewhere in the United States, or outside of the United States. Higher values of the variable indicate greater geographic proximity.

Model

As noted earlier, the dependent variable in the current study is a nascent entrepreneur’s valuation of each contact in his or her network. There are two specific empirical issues with the dependent variable that need to be taken into account in model formulation. First, consistent with our focus on nascent entrepreneurs’ broad network search, the respondents assigned zero values to a large number of contacts. Second, as all respondents were required to distribute a total of $1,000 across their contacts, the contact valuations were relative (i.e., they depended, in part, on the total number of valued contacts an entrepreneur had and the values assigned those contacts). Below, we discuss how we handle these issues in our empirical model.

Data with a large number of values clustered at zero are common in a number of contexts, including health care economics (e.g., Berk & Lachenbruch, 2002; Tooze, Grunwald, & Jones, 2002). To account for a scenario with excessive zeros, prior researchers have adopted a mixed-distribution model formulation. Such models typically comprise a mixture of two parts: The first part assesses whether or not the dependent variable of interest is zero, and the second part measures the effects of variables of interest on the dependent variable in its nonzero domain. In cases like ours, where the dependent variable is continuous, the mixed-distribution model is essentially a Tobit model that accounts for left censoring.³ Following this precedent, our model assumes that entrepreneurs first decide whether or not each contact provides new venture value and then assign a value amount to those deemed to be making a nonzero contribution. The first part of our model thus predicts the probability that an entrepreneur will value a contact at more than zero dollars; the second part predicts the level of the value, conditional on the contact receiving a nonzero value.

The $1,000 constraint and the relative nature of contact valuations are handled in the second part of our model. Specifically, we normalize the values assigned by the entrepreneurs as follows: If Value_ij is the raw value assigned by entrepreneur i to contact j, and if there are a total of n_i contacts to whom the entrepreneur i assigned value, then the normalized value y_ij is

y_{i j} = \frac{\frac{{Value}_{i j}}{\sum_{j} {Value}_{i j}} - \frac{1}{n_{i}}}{1 - \frac{1}{n_{i}}} .

${Value}_{i j} / \sum_{j} {Value}_{i j}$ represents the share of value dollars received by contact j from founder i and varies from 0 to 1. Thus, y_ij varies from $\frac{1}{1 - n_{i}}$ to 1 and $\sum_{j} y_{i j} = 0$ for each entrepreneur (i.e., the sum of the normalized, relative valuations across an entrepreneur’s total contacts must be 0).

In our model, in addition to including our focal variables of interest and control variables, we also account for unobserved heterogeneity across the entrepreneur–contact dyads. We present the details of our model specification and estimation in the appendix.

Results

The results of our analyses are presented in Table 2. Model 1 includes the direct effects of the hypothesized variables and the control variables. Model 2 includes all hypothesized variables, the interaction terms, and the control variables. When estimating Model 2, we first centered the interaction variables to reduce multicollinearity (Aiken & West, 1991). The overall fit of our hypothesized Model 2 is good with a statistically significant chi-square (p < .01). The likelihood ratio test rejects Model 1 in favor of Model 2; the calculated χ² = 16.64, while the critical χ²(4, 0.01) = 13.28. We describe the results of the hypothesis tests based on the better fitting Model 2, discussing the second part of our model only (as it tests the hypothesized effects that are the focus of the study).

Table 2

Parameter Estimates of the Two-Part Model

Variable	Model 1	Model 2
Binary part
Resource multiplexity	.09***	.08***
Age similarity	−.06	−.09
Gender similarity	.06	.07
Resource Multiplexity × Age Similarity		.02
Resource Multiplexity × Gender Similarity		.04
Geographic proximity	.03	.04
Relationship length	.11***	.12***
Intercept	−.04	−.03
Linear part
Hypothesis 1: Resource multiplexity	.27***	.24***
Hypothesis 2a: Age similarity	.13	.14
Hypothesis 2b: Gender similarity	.12	.12
Hypothesis 3a: Resource Multiplexity × Age Similarity		.02*
Hypothesis 3b: Resource Multiplexity × Gender Similarity		.05*
Geographic proximity	.04	.05
Relationship length	.11**	.12**
Intercept	−.59**	−.63**
Log-likelihood	−722.26	−713.94

Note: Model 1 includes main effects only. Model 2 includes main and interaction effects.

p < .05. **p < .01. ***p < .001.

Hypothesis 1 suggested a positive relationship between resource multiplexity and the perceived value of a contact. The results of the model (γ = .24, p < .001) support this hypothesis. In Hypotheses 2a and 2b, we expected a positive relationship between (a) age similarity and value and (b) gender similarity and value. These direct effect hypotheses are not empirically supported in our analyses. In Hypotheses 3a and 3b, we argued that interpersonal similarity and resource multiplexity would together have an amplified effect on a contact’s perceived value. The positive interaction effects between (a) resource multiplexity and age similarity (γ = .02, p < .05) and (b) resource multiplexity and gender similarity (γ = .05, p < .05) on contact value provide support for Hypotheses 3a and 3b. These results indicate that interpersonal similarity serves as an amplifier for resource multiplexity rather than a direct determinant of value. Figure 2 illustrates the two interaction effects. In control variable effects, we find a significant positive effect of relationship length on the perceived value of the contact (γ = .12, p < .01).

Figure 2

Interaction Effects of (a) Resource Multiplexity and Age Similarity and (b) Resource Multiplexity and Gender Similarity on Relative Value of a Nascent Entrepreneur’s Contact

Discussion

In this article, we have attempted to address a research question left largely unaddressed in the entrepreneurship and network literatures to date—namely, how does an entrepreneur create the ties used to construct a new venture’s initial network (Aldrich & Kim, 2007; Hallen, 2008)? More specifically, we have attempted to contribute to the literature on new venture network emergence by shedding light on some of the mechanisms driving nascent entrepreneurs’ value attributions about the contacts met during the entrepreneurs’ initial, broad network searches. Our results suggest a view of new venture network construction in which the content benefits conferred through anticipated or real resource acquisition form a clear basis for entrepreneurs’ assessments of value, with the process benefits of interpersonal similarity playing an amplifying role. That these resource-based and interpersonal factors work together, absent issues of network structure, to impact tie valuation, suggests that new venture network construction might be better understood if conceptualized as the integrated consequence of instrumental and social psychological forces.

Our research model drew on insights from the entrepreneurship, social networks, and social psychological literatures. While prior research has examined the role of networks as a critical source of resources for young organizations (e.g., Aldrich et al., 1989; Birley, 1985; Jarillo, 1989; Knoke, 1990) and has confirmed entrepreneurs’ tendency to surround themselves with like others (e.g., Aldrich, 1989; Ruef et al., 2003), it has not tended to explore these factors’ influence on nascent network search and construction. While the similarity–attraction dynamic has been well documented in social psychological research (Byrne, 1971; Heider, 1958)—confirming a widespread tendency toward interpersonal homogeneity within organizational interaction networks, mentoring relationships, and broader reference groups (Ibarra, 1992; Lawrence, 2006; Thomas, 1990)—prior work has not delineated the mechanisms by which similarity preferences might drive the enhanced valuation of like others. Further, the extant literatures have not examined how instrumental resource considerations and interpersonal similarity might together affect such valuations. To our knowledge, the current study represents the first attempt to directly examine the comparative and interactive roles of resources and interpersonal similarity in the context of nascent entrepreneurs’ emerging new venture networks. Further, it is the first to directly examine new venture network search as it happens, rather than after the fact. Our results thus make several theoretical and empirical contributions to the extant literature.

Theoretical and Empirical Contributions

Our results confirmed that resource multiplexity increases perceived relationship value—both affirming the salience of resource search to the nascent entrepreneur and suggesting that multiplexity is used as a proxy for projecting the likelihood of useful resource realization over the long term. While prior research has often assumed that resource plenty is likely to deliver value, the literature has not been consistent on this point. Our results suggest that while multiplexity may be just one of many relevant resource attributes, it ranks as one of the only available attributes that can be inferred at the most nascent stages of an organization; thus, our results suggest that multiplexity is often understood as an indicator of usefulness. Our results further suggest a broader interpretation of resources than is usual—suggesting that they serve as both antecedents to and outcomes of new venture networks. Resource multiplexity impacts network development, which then drives future resource outcomes for the entrepreneur. Thus conceptualized, the nascent entrepreneur’s challenge can be described as not just the acquisition of needed resources (Lichtenstein & Brush, 2001) but also and integrally the ability to identify that limited subset of network contacts most likely to provide those resources.

Contrary to our prediction, interpersonal age and gender similarity did not have significant direct effects on perceived value. This finding surprised us given the extant empirical support for homophilous entrepreneurial networks (Aldrich, 1989; Smeltzer & Fann, 1989) and founding teams (Ruef et al., 2003), but it need not be wholly inconsistent with prior research. Given the primacy of resources in the extant strategy literature and the primacy of structure in the extant networks literature, the current research could be interpreted to suggest that interpersonal similarity has, at best, a marginal role in an actor’s network construction efforts. This perspective is supported by research on the benefits of diversity in groups and top management teams (e.g., Milliken & Martins, 1996; Watson et al., 1993; Wiersema & Bantel, 1992). The management team literature has found mixed results on the link between homophily and team performance, suggesting that it may be contingent upon the level of uncertainty in the environment or the time horizon taken (Harrison, Price, & Bell, 1998; Webber & Donahue, 2001); it could be that these same contingencies affect the value that interpersonal similarity brings to the nascent entrepreneur.

The results for our interaction hypotheses suggest a different interpretation. When we conceptualized interpersonal similarity as a modifier, rather than a direct determinant, of value, we found that it amplified the effects of resource multiplexity. We believe this reflects the empirical enactment of CLT—where a relationship manifesting greater degrees of similarity and thus psychological closeness will be one in which the nascent entrepreneur perceives a heightened likelihood of accessing resources in the short term (through mechanisms such as concrete construals, greater interpersonal trust, and enhanced communication). More specifically, a relationship manifesting greater degrees of psychological closeness (as communicated via like age and like gender) is one in which greater value is ascribed—even in those instances where it is objectively recognized that dissimilarity or diversity might be conducive to better long-term results.

In essence, interpersonal similarity was not valued independently but was valued when accompanied by the content effects perceived to accrue through resources (here, resource multiplexity). In fact, interpersonal similarity and resource multiplexity together were more highly valued than resource multiplexity alone. Given this finding, our research affirms similarity’s persistence as a central mechanism in network emergence. It provides support for the notion that resources might become more accessible by virtue of similar contacts’ shared motivations for and greater efficiency in exchange. Of note, to the extent that interpersonal similarity and resource multiplexity are jointly perceived as offering the ability to enhance the likelihood of realizing valued outcomes, the selection pattern evidenced here might be expected to result in the eventual formation of homophilous networks and teams. Arguably, our findings provide a new explanation for why researchers so often find high levels of emergent homophily at the earliest stages of an organization (Forbes et al., 2006; Ruef et al., 2003), this despite the general awareness of diversity’s upside.

Taken together, our results broaden existing theory on “gestational” (Van de Ven, 1980) organizational processes within the field of entrepreneurship. Empirical research on the nascent network search preceding an entrepreneur’s identification of useful individuals (i.e., his or her decisions with respect to network inclusion and exclusion) has, to date, been limited at best. Our work addresses this gap, shedding light on some antecedents to entrepreneurs’ contact selection and deselection decisions. Insofar as perceived value can be rightly argued as predictive of ties that will be cultivated over the long term, our work also sheds light on the impact of such decisions with respect to both network development and organizational emergence. Because our findings suggest that value assessments are shaped by both instrumental and interpersonal considerations, we further demonstrate the potential of explanations that integrate traditional strategic and social psychological theories.

Of note, our study is among the first to deploy CLT in a nonexperimental, empirical setting. By using CLT, we developed the “missing link” between interpersonal similarity, resources, and tie valuation. We thus integrated social psychological influences into the structural and instrumental model often found in strategy and network research. By focusing on perceived value as an immediate outcome variable and by examining detailed network search as it happens rather than after the fact, we were able to observe important influences that might be confounded by other factors or rationalized away by participants in a retrospective study. By situating the research within the arena of nascent entrepreneurship, we bridged individual and organizational levels of analysis and developed an empirical lens through which to interpret resource-based and social psychological mechanisms of tie valuation—a lens that might well be effective when deployed in a more general environment of organizational uncertainty and resource deficiency.

Limitations and Future Directions

Several limitations in the current study provide avenues for future research. First, while our unique empirical setting and methodology enabled us to study network construction without the confounding influence of extant structures, in what approximated real time (rather than retrospectively), they could be perceived as offering too limited a view of the phenomena in question. We expect our theory and results to hold in other organizational settings characterized by dynamism, ambiguity, and comparative resource scarcity; future studies in different empirical settings should be conducted to affirm this expectation.

Second, the present investigation focused on resource multiplexity, rather than on the magnitude, quality, or timing of individual resources. Where previous research has examined the importance of particular resource types and the sequence in which they are acquired during the venture creation process (Delmar & Shane, 2004; Lichtenstein & Brush, 2001), our goal was to capture the aggregate resource breadth available through network dyads. We also restricted our analysis to two particular operationalizations of interpersonal similarity (age and gender). While extant research and our theory would not lead us to expect vastly different results using other measures of interpersonal similarity, future work could be expanded to include other measures such as educational background or professional experience.

Third, our dependent variable was based on a perceptual measure of value. Although perceptions have been shown to be strong predictors of organizational and entrepreneurial action (Boyd et al., 1993; Forbes, 1999; Hill & Levenhagen, 1995), future longitudinal studies of longer duration could encompass both nascency and organizational or network emergence. Such studies, while difficult and time consuming to execute, would facilitate the examination of links between perceived value and traditional outcome measures—at the level of the dyad, the network, and the organization. Such studies would also enhance our understanding of resource realization and the relationship between value and network selection. Our dependent variable also utilized an artificial ($1,000) constraint. Future studies could experiment with different value operationalizations, such as the rank ordering of ties or valuation based on a different (e.g., nonmonetary) scale.

In conclusion, this study aimed at providing new insight into the phenomenon of new venture network emergence by integrating resource-driven and social psychological perspectives to elucidate the antecedents of nascent entrepreneurs’ value attributions in network search. We attempted to provide both conceptual and empirical bases for further investigation of this important topic and hope that our results will prompt further research in the area.

Footnotes

Appendix

Acknowledgements

This article was accepted under the editorship of Talya N. Bauer. We would like to thank Howard Aldrich, Gary Dushnitsky, Harry Sapienza, and Olav Sorenson for providing feedback on earlier versions of this article. A portion of this research was supported by a grant from the Harold Price Center for Entrepreneurial Studies at the University of California, Los Angeles, Anderson School of Management.

Notes

References

Adams

Jaffe

1996. Bounding the effects of R&D: An investigation using matched establishment-firm data. RAND Journal of Economics, 27: 700-721.

Aiken

L. S.

West

S. G.

1991. Multiple regression: Testing and interpreting interactions. Newbury Park, CA: Sage.

Aldrich

H. E.

1989. Networking among women entrepreneurs. In Hagan

Rivchun

Sexton

D. L.

(Eds.), Women-owned businesses: 102-132. New York: Praeger.

Aldrich

H. E.

Carter

N. M.

2004. Social networks. In Gartner

W. B.

Shaver

K. G.

Carter

N. M.

Reynolds

P. D.

(Eds.), Handbook of entrepreneurial dynamics: The process of business creation: 324-335. Thousand Oaks, CA: Sage.

Aldrich

H. E.

Kalleberg

Marsden

Cassell

1989. In pursuit of evidence: Sampling procedures for locating new business. Journal of Business Venturing, 4: 367-386.

Aldrich

H. E.

Kim

P. H.

2007. Small worlds, infinite possibilities? How social networks affect entrepreneurial team formation and search. Strategic Entrepreneurship Journal, 1: 147-165.

Aldrich

H. E.

Reese

P. R.

Dubini

1990. The go-between: Brokers’ roles in entrepreneurial networks. In Frontiers of entrepreneurship research: 554-555. Wellesley, MA: Center for Entrepreneurial Studies, Babson College.

Amemiya

1985. Advanced econometrics. Cambridge, MA: Harvard University Press.

Andersen

S. M.

Cole

S. W.

1990. “Do I know you?” The role of significant others in general social perception. Journal of Personality and Social Psychology, 46: 294-307.

10.

Andersen

S. M.

Glassman

N. S.

Gold

S. W.

1998. Mental representations of the self, significant others, and nonsignificant others: Structure and processing of private and public aspects. Journal of Personality and Social Psychology, 75: 845-861.

11.

Arya

Lin

Z.(J).

2007. Understanding collaboration outcomes from an extended resource-based view perspective: The roles of organizational characteristics, partner attributes, and network structures. Journal of Management, 33: 697-723.

12.

Baker

Miner

A. S.

Eesley

D. T.

2003. Improvising firms: Bricolage, account giving and improvisational competencies in the founding process. Research Policy, 32: 255-276.

13.

Barney

J. B.

1991. Firm resources and sustained competitive advantage. Journal of Management, 17: 99-120.

14.

Bass

B. M.

Avolio

B. J.

1994. Shatter the glass ceiling: Women may make better managers. Human Resource Management, 33: 549-560.

15.

Berk

K. N.

Lachenbruch

P. A.

2002. Repeated measures with zeros. Statistical Methods in Medical Research, 11: 303-316.

16.

Bhide

1999. The origin and evolution of new businesses. Oxford, UK: Oxford University Press.

17.

Birley

1985. The role of networks in the entrepreneurial process. Journal of Business Venturing, 1: 107-117.

18.

Blau

P. M.

1977. Inequality and heterogeneity. New York: Free Press.

19.

Borgatti

S. P.

Cross

2003. A relational view of information seeking and learning in social networks. Management Science, 49: 432-445.

20.

Borgatti

S. P.

Foster

P. C.

2003. The network paradigm in organizational research: A review and typology. Journal of Management, 29: 991-1013.

21.

Boyd

B. K.

Dess

G. D.

Rasheed

A. M. A.

1993. Divergence between archival and perceptual measures of the environment: Causes and consequences. Academy of Management Review, 18: 204-226.

22.

Brass

D. J.

1985. Men’s and women’s networks: A study of interaction patterns and influence in an organization. Academy of Management Journal, 28: 327-343.

23.

Brunner

C. C.

1998. Women superintendents: Strategies for success. Journal of Educational Administration, 36: 160-182.

24.

Brush

C. G.

Greene

P. G.

Hart

M. M.

2001. From initial idea to unique advantage: The entrepreneurial challenge of constructing a resource base. Academy of Management Executive, 15: 64-78.

25.

Burt

R. S.

1982. Toward a structural theory of action. New York: Academic Press.

26.

Burt

R. S.

2001. Structural holes versus network closure as social capital. In Lin

Cook

Burt

R. S.

(Eds.), Social capital: Theory and research: 31-56. New York: Aldine de Gruyter.

27.

Busenitz

Murphy

G. B.

1996. New evidence in the pursuit of locating new businesses. Journal of Business Venturing, 11: 221-231.

28.

Byrne

1971. The attraction paradigm. New York: New York.

29.

Chen-Idson

Mischel

2001. The personality of familiar and significant people: The lay perceiver as a social cognitive theorist. Journal of Personality and Social Psychology, 80: 585-596.

30.

Coleman

J. S.

1988. Social capital in the creation of human capital. American Journal of Sociology, 94: S95-S120.

31.

Delmar

Shane

2004. Legitimating first: Organizing activities and the survival of new ventures. Journal of Business Venturing, 19: 385-410.

32.

Dubini

Aldrich

1991. Personal and extended networks are central to the entrepreneurial process. Journal of Business Venturing, 6: 305-313.

33.

Elder

G. H.

1999. Children of the great depression: Social change in life experiences: 301-343. Boulder, CO: Westview.

34.

Fischer

C. S.

1982. To dwell among friends. Chicago: University of Chicago Press.

35.

Forbes

D. P.

1999. Cognitive approaches to new venture creation. International Journal of Management Reviews, 1: 415-440.

36.

Forbes

D. P.

Borchert

P. S.

Zellmer-Bruhn

M. E.

Sapienza

H. J.

2006. Entrepreneurial team formation: An exploration of new member addition. Entrepreneurship Theory and Practice, 31: 225-248.

37.

Gartner

1985. A conceptual framework for describing the phenomenon of new venture creation. Academy of Management Review, 10: 696-706.

38.

Garud

Karnøe

2003. Bricolage versus breakthrough: Distributed and embedded agency in technology entrepreneurship. Research Policy, 32: 277-300.

39.

Giele

J. Z.

Elder

G. H.

Jr.

1998. Methods of life course research: Qualitative and quantitative approaches. Thousand Oaks, CA: Sage.

40.

Gordon

I. R.

McCann

2000. Industrial clusters: Complexes, agglomeration and/or social networks. Urban Studies, 37: 513-532.

41.

Greene

P. G.

Brush

C. G.

Brown

1997. Resources in new firms: An exploratory study. Journal of Small Business Strategy, 8: 25-40.

42.

Greene

W. H.

2007. Econometric analysis. Upper Saddle River, NJ: Prentice Hall.

43.

Greve

Salaff

J. W.

2003. Social networks and entrepreneurship. Entrepreneurship Theory and Practice, Fall: 1-22.

44.

Gulati

Gargiulo

1999. Where do interorganizational networks come from? American Journal of Sociology, 194: 1439-1493.

45.

Hallen

2008. The causes and consequences of the initial network positions of new organizations: From whom do entrepreneurs receive investments? Administrative Science Quarterly, 53: 685-718.

46.

Harrison

D. A.

Price

K. H.

Bell

M. P.

1998. Beyond rational demography: Time and the effects of surface- and deep-level diversity on work group cohesion. Academy of Management Journal, 41: 96-107.

47.

Heider

1958. The psychology of interpersonal relations. Oxford, UK: John Wiley.

48.

Hill

R. C.

Levenhagen

1995. Metaphors and mental models: Sensemaking and sensegiving in innovative and entrepreneurial activities. Journal of Management, 21: 1057-1074.

49.

Hite

2005. Evolutionary processes and paths of relationally embedded network ties in emerging entrepreneurial firms. Entrepreneurship Theory and Practice, 29: 113-144.

50.

Hite

Hesterly

W. S.

2001. The evolution of firm networks: From emergence to early growth of the firm. Strategic Management Journal, 22: 275-286.

51.

Hsiao

1986. Analysis of panel data. Cambridge, UK: Cambridge University Press.

52.

Huckfeldt

Sprague

1995. Citizens, politics, and social communication: Information and influence in an election campaign. New York: Cambridge University Press.

53.

Hunt

D. M.

Michael

1983. Mentorship: A career training and development tool. Academy of Management Review, 8: 475-485.

54.

Ibarra

1992. Homophily and differential returns: Sex differences in network structure and access in an advertising firm. Administrative Science Quarterly, 27: 422-447.

55.

Jack

2010. Approaches to studying networks: Implications and outcomes. Journal of Business Venturing, 25: 120-137.

56.

Jarillo

J. C.

1987. On strategic networks. Strategic Management Journal, 9: 31-41.

57.

Jarillo

J. C.

1989. Entrepreneurship and growth: The strategic use of external resources. Journal of Business Venturing, 4: 133-147.

58.

Kahneman

Tversky

1979. Prospect theory: An analysis of decision under risk. Econometrica, 47: 263-291.

59.

Kalmijn

1998. Intermarriage and homogamy: Causes, patterns and trends. Annual Review of Sociology, 24: 395-421.

60.

Katz

Gartner

1988. Properties of emerging organizations. Academy of Management Review, 13: 429-441.

61.

Knoke

1990. Community power structures. In Knoke

(Ed.), Political networks: 173-215. London: Sage.

62.

Lambert

1992. Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics, 34: 1-14.

63.

Larson

1991. Partner networks: Leveraging external ties to improve entrepreneurial performance. Journal of Business Venturing, 6: 173-188.

64.

Larson

1992. Network dyads in entrepreneurial settings: A study of governance of exchange relationships. Administrative Science Quarterly, 37: 76-104.

65.

Larson

Starr

J. A.

1993. A network model of organization formation. Entrepreneurship: Theory and Practice, Winter: 5-15.

66.

Lavie

2006. The competitive advantage of interconnected firms: An extension of the resource-based view. Academy of Management Review, 31: 638-658.

67.

Lawrence

B. S.

2006. Organizational reference groups: A missing perspective on social context. Organization Science, 17: 80-100.

68.

Lazarsfeld

P. F.

Merton

R. K.

1954. Friendship as social process: A substantive and methodological analysis. In Berger

Able

Page

(Eds.), Freedom and control in modern society: 18-66. New York: Octagon.

69.

Levinson

D. J.

Darrow

C. M.

Klein

E. G.

Levinson

M. H.

McKee

1978. The seasons of a man’s life. New York: Knopf.

70.

Liberman

Trope

1998. The role of feasibility and desirability considerations in near and distant future decisions: A test of temporal construal theory. Journal of Personality and Social Psychology, 75: 5-18.

71.

Liberman

Trope

Stephan

2007. Psychological distance. In Kruglanski

A. W.

Higgins

E. T.

(Eds.), Social psychology: Handbook of basic principles, vol. 5: 353-383. New York: Guilford.

72.

Lichtenstein

B. M. B.

Brush

C. G.

2001. How do “resource bundles” develop and change in new ventures? A dynamic model and longitudinal exploration. Entrepreneurship Theory and Practice, 25: 37-58.

73.

Lippman

S. A.

Rumelt

R. P.

2003. A bargaining perspective on resource advantage. Strategic Management Journal, 24: 1069-1086.

74.

Liviatan

Trope

Liberman

2008. Interpersonal similarity as a social distance dimension: Implications for perception of others’ actions. Journal of Experimental Social Psychology, 5: 1256-1269.

75.

McFadden

Train

2000. Mixed MNL models of discrete response. Journal of Applied Econometrics, 15: 447-470.

76.

McPherson

Smith-Lovin

Cook

J. M.

2001. Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27: 415-444.

77.

Mehra

Kilduff

Brass

D. J.

1998. At the margins: A distinctiveness approach to the social identity and social networks of underrepresented groups. Academy of Management Journal, 41: 441-452.

78.

Merton

R. K.

1963. Social theory and social structure. New York: Free Press.

79.

Milanov

Fernhaber

S. A.

2009. The impact of early imprinting on the evolution of new venture networks. Journal of Business Venturing, 24: 46-61.

80.

Miles

R. E.

Snow

C. C.

1984. Fit, failure and the hall of fame. California Management Review, 26: 10-28.

81.

Milliken

F. J.

Martins

L. L.

1996. Searching for common threads: Understanding the multiple effects of diversity in organizational groups. Academy of Management Review, 21: 402-433.

82.

Mishina

Pollock

T. G.

Porac

J. F.

2004. Are more resources always better for growth? Resource stickiness in market and product expansion. Strategic Management Journal, 25: 1179-1197.

83.

Ozcan

Eisenhardt

K. M.

2009. Origin of alliance portfolios: Entrepreneurs, network strategies, and firm performance. Academy of Management Journal, 52: 246-279.

84.

Penrose

E. T.

1959. The theory of the growth of the firm. New York: John Wiley.

85.

Phillips

L. W.

1981. Assessing measurement error in key informant responses: A methodological note on organizational analysis in marketing. Journal of Marketing Research, 18: 395-415.

86.

Podolny

J. M.

Page

K. L.

1998. Network forms of organization. Annual Review of Sociology, 98: 1320-1350.

87.

Podsakoff

P. M.

Organ

D. W.

1986. Self-reports in organizational research: Problems and prospects. Journal of Management, 12: 531-544.

88.

Portes

Sensenbrenner

1993. Embeddedness and immigration: Notes on the social determinants of economic action. American Journal of Sociology, 98: 57-76.

89.

Pounder

J. S.

Coleman

2002. Women: Better leaders than men? In general and educational management it still all depends. Leadership and Organization Development Journal, 23: 122-33.

90.

Prentice

D. A.

1990. Familiarity and differences in self- and other-representations. Journal of Personality and Social Psychology, 59: 369-383.

91.

Priola

2004. Gender and feminine identities: Women as managers in a UK academic institution. Women in Management Review, 19: 421-430.

92.

Raz

Gloor

P. A.

2007. Size really matters: New insights for start-ups’ survival. Management Science, 53: 169-177.

93.

Reynolds

P. D.

Curtin

R. T.

2008. Business creation in the United States: Panel Study of Entrepreneurial Dynamics II initial assessment. Foundations and Trends in Entrepreneurship, 4: 155-307.

94.

Ruef

Aldrich

H. E.

Carter

2003. The structure of founding teams: Homophily, strong ties, and isolation among U.S. entrepreneurs. American Sociological Review, 68: 195-225.

95.

Saxenian

1994. Regional advantage: Culture and competition in Silicon Valley and Route 128. Cambridge, MA: Harvard University Press.

96.

Shane

Cable

2002. Network ties, reputation, and the financing of new ventures. Management Science, 48: 364-382.

97.

Sheridan

2007. Gender, language, and the workplace: An exploratory study. Women in Management Review, 22: 319-336.

98.

Simon

1957. Administrative behavior. New York: Macmillan.

99.

Sirmon

D. G.

Hitt

M. A.

Ireland

R. D.

2007. Managing firm resources in dynamic environments to create value: Looking inside the black box. Academy of Management Review, 32: 273-292.

100.

Smeltzer

L. R.

Fann

G. L.

1989. Gender differences in external networks of small business owner/managers. Journal of Small Business Management, 27: 25-32.

101.

Sorenson

Audia

2000. The social structure of entrepreneurial activity: Geographic concentration of footwear production in the United States, 1940-1989. American Journal of Sociology, 106: 224-262.

102.

Starr

MacMillan

1990. Resource cooptation via social contracting: Resource acquisition strategies for new ventures. Strategic Management Journal, 11: 79-92.

103.

Stevenson

H. H.

Gumpert

D. E.

1985. The heart of entrepreneurship. Harvard Business Review, 63: 85-94.

104.

Stevenson

H. H.

Roberts

M. J.

Grousbeck

H. I.

1994. New business ventures and the entrepreneur (4th ed.). Burr Ridge, IL: Irwin.

105.

Stuart

T. E.

Sorenson

2007. Strategic networks and entrepreneurial ventures. Strategic Entrepreneurship Journal, 1: 211-227.

106.

Swann

W. B.

Gill

M. J.

1997. Confidence in person perception: Do we know what we think about our relationship partners? Journal of Personality and Social Psychology, 73: 747-757.

107.

Tannen

1995. The power of talk: Who gets heard and why. Harvard Business Review, 73: 138-148.

108.

Thomas

D. A.

1990. The impact of race on managers’ experiences of developmental relationships. Journal of Organizational Behavior, 11: 479-492.

109.

Thorelli

H. B.

1986. Networks: Between markets and hierarchies. Strategic Management Journal, 7: 37-51.

110.

Tooze

J. A.

Grunwald

G. K.

Jones

R. H.

2002. Analysis of repeated measures data with clumping at zero. Statistical Methods in Medical Research, 11: 341-355.

111.

Train

2003. Discrete choice methods with simulation. New York: Cambridge University Press.

112.

Trope

Liberman

2003. Temporal construal. Psychological Review, 110: 403-421.

113.

Trope

Liberman

Wakslak

2007. Construal levels and psychological distance: Effects on representation, prediction, evaluation, and behavior. Journal of Consumer Psychology, 17: 83-95.

114.

Van de Ven

A. H.

1980. Early planning, implementation, and performance of new organizations. In Kimberly

J. R.

Miles

R. H.

(Eds.), The organizational life cycle: 83-133. San Francisco: Jossey Bass.

115.

Verbrugge

L. M.

1977. The structure of adult friendship choices. Social Forces, 56: 576-597.

116.

Wakslak

C. J.

Trope

Liberman

Alony

2006. Seeing the forest when entry is unlikely: Probability and the mental representation of events. Journal of Experimental Psychology, 135: 641-653.

117.

Watson

W. E.

Kumar

Michaelsen

L. K.

1993. Cultural diversity’s impact on interaction process and performance: Comparing homogeneous and diverse task groups. Academy of Management Journal, 36: 590-602.

118.

Webber

S. S.

Donahue

L. M.

2001. Impact of highly and less job-related diversity on work group cohesion and performance: A meta-analysis. Journal of Management, 27: 141-162.

119.

Wiersema

M. F.

Bantel

K. A.

1992. Top management team demography and corporate strategic change. Academy of Management Journal, 35: 91-121.

120.

Zenger

T. R.

Lawrence

B. S.

1989. Organizational demography: The differential effects of age and tenure distributions on technical communication. Academy of Management Journal, 32: 353-376.

121.

Zhang

Soh

Wong

2010. Entrepreneurial resource acquisition through indirect ties: Compensatory effects on prior knowledge. Journal of Management, 36: 511-536.