NICHE AND PERFORMANCE: THE MODERATING ROLE OF NETWORK EMBEDDEDNESS

Strategic Management JournalStrat. Mgmt. J., 26: 219–238 (2005)

Published online 22 December 2004 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/smj.443

NICHE AND PERFORMANCE: THE MODERATINGROLE OF NETWORK EMBEDDEDNESS

ANN ECHOLS* and WENPIN TSAISmeal College of Business Administration, Pennsylvania State University, UniversityPark, Pennsylvania, U.S.A.

What is the relationship between niche and performance? We identify two types of nichepositions—product niche and process niche—defined by the extent to which a firm offersdistinctive products and has distinctive operational processes, respectively. We argue that theeffect of each niche on firm performance is contingent upon network embeddedness—the extentto which a firm is involved in a network of interconnected inter-firm relationships. Using datacovering the period 1995–98 pertaining to venture capital firms and their holdings in initialpublic offerings (IPOs), we show that both product niche and process niche interact with networkembeddedness to determine firm performance. Our findings suggest that the extent to which a firmoffers distinctive products or processes will be more positively associated with firm performancewhen network embeddedness is high. Copyright 2004 John Wiley & Sons, Ltd.

A key to understanding inter-firm competition isthe concept of niche. A niche represents a firm’sdistinctiveness relative to other players in the com-petitive arena. As such, a niche describes not onlya firm’s competitive environment, but also howit competes with others (Carroll, 1985). A nicheallows a firm to establish differences by offer-ing a product or set of products that few, if any,other firms offer (Porter, 1980; Stuart, 1998), orby doing business using operational processes thatfew, if any, other firms practice (Baum and Oliver,1996; Carroll, 1984, 1985).1 Firms in a niche

Keywords: network embeddedness; product niche; pro-cess niche; venture capital∗ Correspondence to: Ann Echols, Smeal College of BusinessAdministration, Pennsylvania State University, 403 BusinessAdministration Building, University Park, PA 16801, U.S.A.E-mail: [email protected] Obviously, a firm could maintain a number of different niches,including both a product and process niche, but for the purposesof this paper our study is limited to the concept of firms in asingle niche—either a product or process niche.

(niche-firms) thus create value by way of offer-ing products or practicing processes that differ insome significant way from those of rivals.

The concept of niche has attracted considerableinterest in management research. Population ecol-ogists have examined properties of a firm’s niche(Hannan and Freeman, 1977; McPherson, 1983)and the impact of a niche position on competitivedynamics. For example, in a study of the world-wide semiconductor industry, Podolny, Stuart, andHannan (1996) have shown that niche-firms haveenhanced firm survival rates. Strategic manage-ment scholars have also identified the niche posi-tion as a way of competing in the marketplace.For example, in studying strategic groups, Harri-gan (1985) investigated the heterogeneity of strate-gic positions among firms and emphasized thevalue of holding a distinctive position in an indus-try. Drawing extensively from industrial organiza-tion economics, Porter (1980, 1996) has analyzedcompetitive positioning of firms and argued thatniche-firms can justify charging higher prices. A

Copyright 2004 John Wiley & Sons, Ltd. Received 30 July 2001Final revision received 23 July 2004

220 A. Echols and W. Tsai

niche-firm gains economic profit because it worksin a relatively uncontested market. Being in a nichemeans the firm is not under pressure to reduceprices, because the more distinctive the firm’sproducts or processes, the less competition the firmfaces.

Although the convergence of organizationalecology and strategic management research hashighlighted the importance of the niche conceptin understanding different aspects of competitivedynamics, it has not clarified the performanceeffect of a firm’s niche. Several scholars haveidentified advantages as well as disadvantages thataccrue to niche-firms (e.g., Carroll, 1984). Forexample, a niche may have negative consequenceswhen a market dries up, rendering a firm’sproducts or practices obsolete (Liles, 1977). Inthis study, we test the performance implicationsof a firm’s niche, where a niche is a continuumof relative distinctiveness describing what productsthe firm offers or how the firm does business.

To understand how niche is associated withdifferent performance outcomes, we use a con-tingency perspective and argue that the effect ofniche on performance is contingent upon networkstructure. The network structure describing withwhom firms interact is an important contingencyfactor, because firms do not make key decisionsabout what product(s) to offer and how to dobusiness in a social vacuum without consideringother firms (Burt, 1992; Granovetter, 1985). Thus,the niche–performance relationship can be betterunderstood by simultaneously examining a firm’sniche and network structure. By considering afirm’s network structure, we are able to specificallyanswer our research question: Under what con-ditions does the distinctiveness of a firm’s nicheprovide performance benefit to the firm?

The central tenet of our argument is that howwell a niche-firm performs is contingent upon theextent to which it is embedded in an inter-firm net-work. To examine this, we focused on the U.S.venture capital industry as our research setting.The U.S. venture capital industry provides an idealsetting because this is an industry with high com-petitive pressure motivating many firms to be nicheplayers. In addition, this is an industry in whichfirms frequently form networks of inter-firm work-ing relationships. By simultaneously investigatinga firm’s niche position and network embeddednessin the venture capital industry, our paper advancesa theory of competitive positioning in strategic

management and provides a more refined under-standing of how venture capital firms compete.

THEORY AND HYPOTHESES

A niche implies a ‘way of earning a living’ (Elton,1927: 63–64): an explanation from animal ecol-ogy, whereby niches are identified by looking atthe key attributes of earning a living in terms ofwhat the animal eats and how it preys (Ricklefs,1979). Similarly, in the business context, a firm’sniche can be studied by investigating how a firmdiffers from its rivals in terms of what productsit offers and how it does business (e.g., the opera-tional processes it chooses to practice) (Day, 1981;Porter, 1996). As such, a niche is defined by theextent to which a firm’s product offering or oper-ational processes are unlike what rivals offer orpractice, respectively. In this research, we focuson two kinds of niches or ways a firm ‘earns itsliving’ distinctively: its product offerings or oper-ational processes.

Product niche

The concept of product niche describes a firm’scompetitive position based on an analysis of thecompetitive intensity surrounding what productsthe firm offers. A firm in a product niche positionoffers products that—because they differ consid-erably in ways that are economically meaningfulfrom those of its rivals—create value. By offer-ing a product or set of products that differ fromthose of competitors, a firm can reduce its compet-itive pressures, increasing its likelihood of gaininga competitive advantage (Porter, 1980). There aremany examples of distinctive product offerings. Inthe automobile industry, Audi TT, an all-wheel-drive coupe, is a distinctive product, since veryfew automobile manufacturers offer a vehicle thatcombines a sports-look coupe body with all-wheel-drive capability. In the vision care industry, John-son & Johnson’s 1-day ACUVUE is a distinctiveproduct, since it is the only UV-blocking daily dis-posable lens in the marketplace. These examplesare of single products, which are easy to explain;yet multi-product firms can also create a nicheby offering a distinctive product mix identified byaggregating the distinctiveness of individual prod-uct offerings to the firm level. By examining therelative distinctiveness of each product in a firm’s

Copyright 2004 John Wiley & Sons, Ltd. Strat. Mgmt. J., 26: 219–238 (2005)

Niche and Performance 221

product portfolio and aggregating this information,we can determine the firm’s overall product niche.

Process niche

The concept of process niche describes a firm’scompetitive position based on an analysis of howthe firm operates its business. A firm in a pro-cess niche position engages in business operationsthat—because they differ considerably in waysthat are economically meaningful from those ofits rivals—create value. A process niche rests onthe distinctiveness of the firm’s operating know-how or ways of practicing business. Whether ornot it offers distinctive products, a process niche-firm practices a different way of operating itsbusiness, including managing the processes relatedto its value chain activities in an innovative way(Chatterjee, 1998). For example, some online retailstores are classified as process niche-firms whenthey provide a distinctive online ordering pro-cess that allows customers to perform real-timeinventory checking and shipment tracking in aconvenient, reliable, and timely fashion. Althoughoffering the same name-brand products as competi-tors do, these online stores differentiate themselveswith a distinctive operational process that attractscustomers. The distinctive operational process hasresulted in a proprietary business model that showsthe importance of a process niche. The idea ofprocess niche is also illustrated in a recent articleby Markides (1998), showing how a small Dan-ish bank called Lan & Spar altered its operationalprocesses to employ a unique, real-time system,enabling it to conduct business differently and,hence, become a niche-firm.

Network embeddedness as a moderator of theniche–performance relationship

How does a firm’s niche position affect its perfor-mance? The above discussions on product nicheand process niche describe a firm’s decisions con-cerning what to offer and how to operate. How-ever, a firm’s performance is not simply a functionof these decisions. Instead, these decisions and thefirm’s resulting performance are contingent uponanother important factor: the firm’s social structurethat describes who connects to whom in the firm’snetwork. A firm’s competitive position, includingdecisions regarding whether or not to be a prod-uct or process niche-firm, is not formed in a social

vacuum. When making decisions about what tooffer and how to operate, a firm must, at thesame time, take into account the social context inwhich it interacts with other firms. Through col-laborations with other firms in the industry, a firminvolves itself in an inter-firm network that con-tains useful information and resource flows. Thestructure of such a network is a critical factor indetermining the success of any firm’s niche posi-tion.

Research in economic sociology has highlightedthe importance of social networks in economicactions (Granovetter, 1985; Coleman, 1990). The‘performance of firms can be more fully under-stood by examining the network of relationshipsin which they are embedded’ (Gulati, Nohria, andZaheer, 2000: 203). Networks of inter-firm rela-tionships provide channels for sharing valuableinformation and resources. A firm can use its net-work channels to search for advice and gain accessto key resources needed to deal with its compet-itive challenges. As many scholars have argued,network relationships are an important aspect ofthe social capital that determines a firm’s abil-ity to create value or to achieve economic goals(e.g., Coleman, 1990; Tsai and Ghoshal, 1998;Tsai, 2000). Drawing on social network theory, weuse the concept of network embeddedness to exam-ine the impact of a niche-firm’s relationships on itsability to perform well.

Network embeddedness describes the structureof a firm’s relationship with other firms—specific-ally, the extent to which a firm is connected toother firms and how interconnected those firmsare, in turn, to each other (Granovetter, 1992;Nahapiet and Ghoshal, 1998). Network embedded-ness means the extent to which a firm is surroundedby other firms in such a way that its network struc-ture is redundant or not. At one extreme, highnetwork embeddedness means that a firm belongsto a dense network among other firms, many ofwhich are tightly connected with each other. Insuch a dense network, firms tend to know eachother well through recurring interactions and inter-connected ties that engender familiarity and trust(Gulati, 1995; Gulati et al., 2000). At the otherextreme, low network embeddedness means thata firm belongs to a sparse network in which fewof its contacts are not connected to each other.In such a sparse network, firms may instrumen-tally work with a variety of other firms that are



not fully aware of each other’s working relation-ships. Figure 1 provides an illustration of the con-cept of network embeddedness based on inter-firmrelationships in the venture capital industry. Asshown in this figure, Crosspoint Venture Partnershas a redundant network (e.g., high embeddedness)because it is connected to firms that work witheach other. In contrast, New Enterprise Associateshas a non-redundant network (e.g., low embedded-ness) because it is connected to firms that do notwork with each other. Given that a firm’s networkstructure significantly affects the firm’s businessopportunities and performance (Burt, 1992; Gra-novetter, 1985; Powell, 1990; Tsai, 2001; Uzzi,1996), a firm has to pay close attention to thestructure of its network, optimizing not just a sin-gle relationship, but also the firm’s entire networkof relationships (Dyer and Nobeoka, 2000; Gulatiet al., 2000).

We examine how network embeddedness mod-erates the performance effects of product andprocess niche. To accomplish this objective, weintegrate the network embeddedness concept witha contingency approach (which suggests two ormore predictor variables with interactive effectson the value of the criterion variable; e.g., Aikenand West, 1991). Thus, we analyze how networkembeddedness and niche position interact to affectfirm performance.

We argue that network embeddedness bringsbenefits to both product and process niches—though in two different ways. While firms in aproduct niche require fine-grained product or mar-ket information, firms in a process niche require

protection of process knowledge, all else heldequal. Because firms in a product niche and firmsin a process niche require different kinds of infor-mation and knowledge to succeed, we discusshow network embeddedness moderates each niche,accordingly.

Interaction between product niche andnetwork embeddedness

We argue that the impact of product niche onfirm performance is contingent upon the extent towhich the firm is embedded in a network withinwhich it can obtain specific information needed todevelop and market its products. The more dis-tinctive a firm’s product offerings, the more itrequires specific market information to succeed.Product niche-firms usually need specific infor-mation regarding the detailed profiles of certaincustomers, the specifications of certain productparts, and the quality of certain suppliers. Obtain-ing exact information is critical to firms that needto stay abreast of complex and often rapidly chang-ing information. These firms have little if anytolerance for information noise (i.e., informationthat is unreliable or unsubstantiated). To reduceinformation noise, a firm can make use of ahighly embedded network, since such networksprovide multiple and repetitive sources of infor-mation, enabling constant reassessment of infor-mation accuracy. As Rowley, Behrens, and Krack-hardt have argued, ‘the ability to triangulate amongmultiple sources allows firms to evaluate the infor-mation obtained and gain a richer understanding’(Rowley, Behrens, and Krackhardt, 2000: 375).

CrosspointVenturePartners

KleinerPerkinsCaufieldBuyers

JAFCO

CharterGrowthCapital

RhoManagement

BayviewFund:RobertsonStephens

High Network Embeddedness

NewEnterpriseAssociates

GreylockManagement

KingsburyAssociates

RoserVentures

NeedhamCapital

Chemicals &MaterialsEnterpriseAssociates

InstitutionalVenturePartners

Low Network Embeddedness

Figure 1. Network embeddedness



High network embeddedness describes a networkwith enduring, interconnected ties that are usefulsources of detailed and fine-grained information(Gulati, 1998; Uzzi, 1996). Such information canbe transferred within a network in which moreopportunities exist to clarify possible ambigui-ties and abstractions (Jones et al., 1998). Actorsstrongly tied in a network tend to have developeda relation-specific heuristic for processing complexand fine-grained information (Hansen, 1999).

Thus, when network embeddedness is high, aproduct niche is likely to be positively associatedwith firm performance. This is because intensiveinteractions in an embedded network allow theniche player to perform better by gaining and act-ing on fine-grained, reliable information essentialfor developing and marketing distinctive or evenunique products. In contrast, when network embed-dedness is low, the product niche–performancerelationship is likely to be less positive or evennegative. This is because the less connected struc-ture of a low-embeddedness network may offercoarse, unreliable information—information thatmay distract the firm from operating effectivelyin its niche position, thus resulting in poor perfor-mance.

Hypothesis 1: The interaction between productniche and network embeddedness has a posi-tive impact on firm performance (i.e., the extentto which a firm offers distinctive products willbe more positively associated with firm perfor-mance when network embeddedness is high).

Interaction between process niche and networkembeddedness

The impact of process niche on firm performanceis contingent upon the extent to which the firm isembedded in a network that safeguards the processknowledge needed to effectively secure its ways ofdoing business. A process niche-firm adopts dis-tinctive ways of doing business and has specialoperational knowledge. The more a firm relies ondistinctive processes to compete with other firms,the more the firm needs to protect its special oper-ational knowledge to succeed. Compared to firmsin a product niche, firms in a process niche havedistinctive processes but may not have distinc-tive products. These firms will emphasize secur-ing the distinctive process knowledge they already

have rather than emphasize gathering fine-grainedproduct or market information.

A firm in a process niche has distinctive knowl-edge that it considers top secret. Protecting thisknowledge from other companies is essential tothe firm’s success. Research on secret disclosurehas suggested secret flows in a ‘clique communica-tion model’ that involves only interconnected tiesamong clique members (Steele, 1989). Because theinterconnected ties of a highly embedded networkcan provide multiple channels to facilitate commu-nications within partnerships and prevent revealingspecial knowledge to unwanted parties, a processniche-firm benefits from a highly embedded net-work structure. Such a firm is better off maintain-ing a highly embedded network in which the firmswith whom it connects are also tightly connectedto each other. Given that distinctive knowledge caninfluence the processes and outcomes of a firm’soperations, a firm will share such knowledge onlywith trusted others.

High network embeddedness promotes the devel-opment of trust (e.g., Krackhardt, 1992; Nelson,1989). A highly embedded network permits actorsto know one another, alleviates appropriation con-cerns, and allows actors to consider one anothertrustworthy. Actors who are highly interconnectedare likely to develop a shared understanding ofbehavioral norms within their social system andinfluence one another to conform to such norms(Coleman, Katz, and Menzel, 1966). The pres-ence of such behavioral norms encourages firmsin an embedded network to rely on each other fordiscretion and security when dealing with specialknow-how in their partnerships.

In sum, the relationship between process nicheand firm performance is likely to be contingentupon the level of network embeddedness. Whennetwork embeddedness is high, a process niche islikely to result in high firm performance. This isbecause interconnected ties provide such a firmwith the kind of highly reliable and trustworthypartnerships needed to protect and advance its spe-cial knowledge. In contrast, when network embed-dedness is low, the process niche–performancerelationship is likely to be less positive or evennegative as it is difficult to safeguard distinctiveknowledge with partners that are not tightly con-nected. Even though a firm may be willing to trustothers in a low-embeddedness network, communi-cation problems are likely to be rife; the lack ofmultiple channels for communications requires a



firm to expend significant extra effort, time, andresources to clarify the details of its special oper-ations when working with other firms. Therefore:

Hypothesis 2: The interaction between processniche and network embeddedness has a posi-tive impact on firm performance (i.e., the extentto which a firm has distinctive operational pro-cesses will be more positively associated withfirm performance when network embeddednessis high).

METHODS

The U.S. venture capital industry

The empirical setting of this research is the U.S.venture capital industry (see Lerner, 1994a, fora history of this industry). Venture capital firmsraise funds and pool investors’ money to purchaseequity primarily in private start-up companies. Bytaking a start-up through its initial public offering(IPO: the event whereby a firm places its stockfor public trade for the first time), venture capitalfirms can generate substantial investment returnson the open market (Gompers and Lerner, 1999).Without IPO investments, venture capital firmsare severely constrained both in realizing gains(Huntsman and Hoban, 1980; Tyebjee and Bruno,1984a, 1984b) and in retaining an investor pool(Black and Gilson, 1999; Freeman, 1999).

When guiding a start-up toward its IPO, aventure capital firm, especially one sitting onthe start-up’s board of directors, can significantlyinfluence the company’s business operations andmanagerial decisions. Venture capital firms canalso add value to start-ups through offering indus-try-specific expertise, such as knowledge about andconnections with suppliers, distributors, and cus-tomers (MacMillan, Kulow, and Khoylian,1989; see also Fried and Hisrich, 1995). Venturecapital firms can also take care of different needsassociated with the developmental stages of a start-up’s life (e.g., Carter and Van Auken, 1994; Gor-man and Sahlman, 1989; Ruhnka and Young, 1987,1991).

Although venture capital firms compete witheach other in IPO investments, they also col-laborate with each other through co-investment:one venture capital firm takes the lead by cre-ating a syndicate using multiple venture capital

firms’ funds to co-invest in a start-up firm (Lerner,1994b). Venture capital firms create syndicates notonly to increase the amount of money providedto a start-up (Steier and Greenwood, 1995), butto share tasks associated with selecting, managing,and advising start-ups. Venture capital firms withinthe same syndicate tend to interact with each otherto find the best way to increase the value of theircommon investment. A venture capital firm mayhave more than one fund and each fund may beinvolved with several investments simultaneously,enabling it to be a member of many syndicates. Inthis way, the venture capital firm maintains multi-ple inter-firm relationships with its co-investors asit strives to increase its relevant expertise and capa-bilities (Gompers and Lerner, 1999). In addition toa network of co-investment relationships, venturecapital firms also maintain relationships with ser-vice providers in other industries to meet the start-up’s needs as they arise (e.g., relationships withconsulting, auditing, law, and investment bankingfirms).

Sample and data collection

We selected our sample of U.S. venture capitalfirms based on two criteria. First, we selected ven-ture capital firms that were autonomous in offer-ing private equity financing to start-up enterprisesfor the purpose of earning high rates of return.By autonomous, we mean independently managedfirms that function at arm’s length from their fund-ing sources. Thus, our sample includes corporatesubsidiaries engaged in providing private equitysuch as St. Paul Venture Capital, Inc., a self-sustaining unit that makes investment decisionsseparately from the operations of its parent, St.Paul Fire and Marine Insurance Company. Oursample does not include corporate-directed devel-opment capital, such as Hallmark Cards’ Devel-opment Capital Division or Microsoft’s Invest-ment Division, both of which were established forthe purpose of satisfying diverse corporate needsrather than simply investing for high rates of finan-cial return.

Second, we selected venture capital firms withinfluence over start-ups in which they invested.The venture capital firms we selected had requiredas a condition for receiving venture capital fund-ing that the start-up either allow them a board seator grant at least one board seat to at least one ofthe syndicate partners effective as of the time the



start-up held its IPO. By way of board represen-tation, venture capitalists monitor the start-up andhelp it to reach the performance goals needed forit to achieve a successful IPO (Barry et al., 1990;see also Jain, 2001). There can be no doubt thatventure capital firms can significantly influence themanagement of any company in which they investby placing representatives on its board of direc-tors. Therefore, we assume that the venture capitalfirms selected for our sample exercised such boardinfluence, thereby contributing significantly to thesuccess of their IPO investments.

Our research model uses a temporal sequence:Time1 from January 1, 1995, through December31, 1996; and Time2 from January 1, 1997, throughDecember 31, 1998. We measured our independentvariables—product niche, process niche, and net-work embeddedness—during Time1. We regressedthese variables on firm performance measured dur-ing Time2. This temporal sequence allows us tosee the effects of our independent variables on ourdependent variable, but not vice versa. To identifywhich venture capital firms were associated withwhich IPOs as well as the attributes of these IPOs,we used multiple archival sources, including Secu-rities Data Company’s New Issues data, Venture-One’s IPO Reports, Venture Economics’ Ventur-eXpert data, and Center for Research in SecuritiesPrices (CRSP) stock price data. We also identifiedventure capital firms’ attributes using Galante’sVenture Capital and Private Equity Directories andPratt’s Guides to Venture Capital Sources. We val-idated data across sources and remedied the fewdata discrepancies that existed by bringing theseinconsistencies to the attention of the publisherswho originated the data. In all cases, appropri-ate representatives working for the data publishingcompanies conceded the errors or omissions, andclarifications were made on a case-by-case basis.We also made several phone calls to venture capitalfirms to fill in missing data.

Our final sample includes 80 venture capi-tal firms active in both Time1 and Time2 thatheld investments in a total of 369 IPOs. Toensure that the IPOs were comparable, we includedonly IPOs with firm commitment underwritingarrangements, and excluded unit issues, financialindustries (SIC codes 6000–6999), spin-offs, pri-vatizations, foreign tranches, and American Depos-itory Receipts/American Depository Shares, assuggested by Ritter (1991) and Megginson andWeiss (1991).

Measures

Dependent variable

Firm performance. We operationalized venturecapital firm performance using the number of suc-cessful IPOs in each venture capital firm’s Time2

portfolio.2 The majority of the profits accrued toventure capital firms are generated by only a fewsuccessful IPOs (Black and Gilson, 1999; Gom-pers, 1994; Huntsman and Hoban, 1980; Lerner,1994c; Sahlman, 1990). Identifying successfulIPOs is critical to understanding a venture cap-ital firm’s performance, as these few successfulIPOs are considered to be ‘home runs’ (Schilit,1991), providing tremendous benefit to venturecapital firms. Not only does a venture capital firm’spotential to earn high returns increase when itsinvestment can be traded on a public market, butsuccessful IPOs offer valuable non-monetary ben-efits through image enhancement that attracts fur-ther investor and entrepreneur attention (Lerner,1994c). The most successful IPOs receive intensemedia hype and attention. Such acclaim oftenshines a strong light onto the venture capital firmsthat supported them.

We identify a successful IPO using a buy-and-hold strategy as suggested by Ritter (1991): theIPO must have a compounded daily return greaterthan the return generated by the correspondingdaily NASDAQ Composite Index, documented asof the end of each month (where a month is definedas a successive 21-day trading period relative to theIPO offering date), cumulatively, for the IPO’s first12 consecutive months in the aftermarket.3 Thealgorithm we used is mathematically presented inAppendix 1.

Independent variables

Product niche. A product niche describes theextent to which a firm’s product lines differ fromthose of its competitors. In this research, a venturecapital firm’s product line is defined by the IPOsin which it invests, where an IPO is defined by

2 We control for the total number of IPOs in venture capitalfirms’ Time2 portfolio via the ‘exposure’ variable in our Poissonregression model.3 The first 252-day period is equivalent to an IPO’s first yearon the market, where a month is defined as a successive 21-daytrading period, following Ritter (1991).



its primary 3-digit Standard Industrial Classifica-tion (SIC) code.4 Any two venture capital firms, ifnot co-investing, are considered to have compet-ing products if their IPOs are in the same 3-digitSIC code. The more a venture capital firm’s prod-uct portfolio is like the portfolios of other venturecapital firms (having many IPOs in the same 3-digit SIC code as other firms’ IPOs), the less theventure capital firm has a product niche.

We measured a venture capital firm’s productniche by first calculating for each focal venturecapital firm the proportion of its IPOs sharing thesame 3-digit SIC code with the competing IPOsof every other venture capital firm in our sample.Since there are 80 firms in our sample, we com-pared each focal firm with the other 79 firms. Wethen subtracted each proportion from 1 to arriveat a distinctiveness score (which ranged from 0 to1.0). We then aggregated the distinctiveness scoreof each firm with every other firm in the sam-ple; this measure is the product niche score. Theidea of our product niche score calculation is sim-ilar to Stuart’s (1998) operationalization of nicheposition. A high product niche score indicates thatthe venture capital firm’s product portfolio wasrelatively distinctive compared to the product port-folios of other firms. A low product niche scoreindicates that the venture capital firm is very sim-ilar to its competitors in terms of the investmentproducts offered.

Process niche. A process niche captures theextent to which a firm differentiates itself by oper-ating its business using a distinctive process orprocesses. Venture capital firms operate differentlyin terms of whether they are involved with invest-ments at different stages. Each specific stage ofa start-up’s development requires correspondinglyspecific know-how on the part of the venture capi-tal firm (Carter and Van Auken, 1994; Ruhnka andYoung, 1987). For example, a venture capital firminvolved in early-stage investing must capitalize onknowledge about product commercialization, set-ting up a business, and hiring quality managers,as well as targeting and selling to an appropriatemarket. In contrast, a venture capital firm involved

4 We focus on a 3-digit SIC level instead of a 4-digit SIC levelin this research, because there were too many unique industrycategories at the 4-digit SIC level, and our dataset containedenough observations for each industry category in order for usto capture overlap among firms.

in later-stage investing must capitalize on knowl-edge about establishing internal control mecha-nisms, managing public relations, and expandingthe business, which may include finding diversifi-cation opportunities. By being involved in certainstages of investment, a venture capital firm createsa profile of its operational knowledge.

Thus, a venture capital firm creates a processniche by signaling to potential customers that itwill conduct business in a certain investment stageor set of stages that differ from those of its rivalsin ways that create value. Likewise, entrepreneurslook for venture capital firms that have expertisein the investment stage most appropriate to theircurrent requirements. A venture capital firm withexpertise in a distinctive set of investment stageshas a knowledge profile that enables it to operatedifferently from other venture capital firms. Thisis similar to the way firms in the daycare industrydifferentiate different stages for their operations, asreported by Baum and Oliver (1996). Therefore,using data from Galante’s Directories, we appliedBaum and Oliver’s (1996) niche measure to indi-cate the extent to which a venture capital firm usesa distinctive set of investment stage knowledge.Appendix 2 shows the computational detail for theprocess niche measure. A venture capital firm witha high process niche score used processes that weredifferent compared to the processes used by otherfirms. A venture capital firm with a low processniche score was very similar to its competitors interms of its operational processes.

Network embeddedness. Based on Burt’s (1992)network redundancy measure, we operationalizednetwork embeddedness to represent the intercon-nectedness of each venture capital firm’s contactswithin our dataset. Each venture capital firm’snetwork included a set of inter-firm relationshipsdefined by the number of times each firm co-invested in IPOs with partner venture capital firmsduring Time1.

Because the network redundancy measure canbe biased by network size, we cannot compare itacross networks of different sizes, unless we con-trol for network size (Scott, 2000). The collinearitybetween the redundancy measure and network size(i.e., the larger a focal firm’s network, the lesslikely it is to have a densely embedded network)presents a limitation: we cannot simultaneouslyinclude both network redundancy and network sizeas two separate variables in our model. Instead, we



divided the network redundancy measure by net-work size, an appropriate way to deal with thissituation as suggested by Pedhazur and Schmelkin(1991).

By dividing network redundancy by networksize, the measure results in a very small score. Toadjust this measure to a scale comparable to thatof other variables, we multiplied it by 100. Theresult of this adjustment reflects the proportionalredundancy in a focal firm’s network. A high mea-sure indicates that a focal firm’s network consistsof tightly connected others relative to its networksize. A low measure indicates that a focal firm’snetwork consists of unconnected or loosely con-nected others relative to its network size.

Control variables

We controlled for venture capital firm-level char-acteristics including firm size, firm age, firm type,focus, proximity to IPO, and competitive intensity.We operationalized firm size as the logarithm ofthe amount of capital (in millions of dollars) undermanagement associated with each venture capitalfirm as of the end of Time1. We operationalizedfirm age as the number of years from the foundingyear to the year 1996. We operationalized firm typeby distinguishing between limited partnerships andother organizational forms (e.g., independent sub-sidiaries of banks, public corporations, or privatecorporations). We used a dichotomous variable,coding 1 for venture capital firms organized aslimited partnerships, and 0 for all others.5 We oper-ationalized focus as the number of 3-digit SICcodes in the venture capital firm’s Time1 portfolio.We operationalized proximity to IPO as the meandistance in statute miles between the zip code asso-ciated with each of the venture capital firm’s IPOholdings in Time1 and the zip code associated withits closest office to that IPO.6 The closer the ven-ture capital firm is located to its investments, the

5 In this research, we found differences at the 0.05 level of sig-nificance between limited partnerships and other venture capitalfirms in terms of firm age, location, and whether or not theycharged fees for their services. We found no evidence that thesetwo types of firms differed in terms of our independent vari-ables: firm size, network size, total number of IPO investments,and number of successful IPOs. Nor did we find evidence thatthey differed in regard to several other attributes. We includedvariables reflecting the differences between firm types as con-trols in additional analyses. These differences did not influencethe interaction effects we hypothesized.6 We chose to focus on the venture capital firm’s closest officeto the IPO for the sake of parsimony, since some of the venture

more intense its monitoring of and influence onthat investment is likely to be (Lerner, 1995). Weoperationalized competitive intensity by countingthe number of venture capital firms located withina 120-mile radius of a focal venture capital firm’smain office as of the end of Time1, where distancewas computed in statute miles between office zipcodes. We chose a 120-mile radius based on infer-ences from Lerner’s (1995) findings.

In addition to the above firm-level attributes,we controlled for one investment-level variable:investment size. Investment size may affect per-formance since larger investments are typicallyassociated with more mature companies, and henceare more likely to be associated with higher stockprice performance than smaller investments. Weoperationalized investment size as the total annualrevenues (in millions of dollars) of each venture inwhich a venture capital firm invested. We aggre-gated investment size to the venture capital firmlevel by taking the mean revenue for all invest-ments in each venture capital firm’s Time2 portfo-lio.

Assessment of content validity

Content validity refers to the extent to which mea-sures reflect a specific domain of content (Bollen,1989). Our approach to content validity focuseson qualitative definitions to specify the scope ofcontent coverage that can be achieved by our mea-sures (Hoskisson et al., 1993). We clarify howeach of our measures—firm performance, prod-uct niche, process niche, and network embedded-ness—appropriately reflect its purported content.

The definition of firm performance includes twodomains of interest: financial returns and marketsuccess. Whereas financial returns are often mea-sured by profitability, such as the return on aninvestment, market success is often measured bythe performance of an existing product, a newproduct introduction, or an IPO, for example. Thedomains are interrelated, since a firm with a highlevel of market success is likely to have increasingfinancial returns. However, it is possible that a firmwith an initially successful product will not sus-tain high profitability over time; market leadership,

capital firms had up to three branch offices in addition toheadquarters, as stated in the Galante’s Directory applicableto the year of observation. For all computations of distancesbetween zip codes in this study, we used Bridger Systems’ZIPFind Deluxe 3.0 software program.



after all, may be ephemeral. To meet content valid-ity, performance measures must seek to addressboth domains of interest. Our firm performancemeasure, the number of successful IPOs, appearsto emphasize market success. However, severalstudies on venture capital firms have shown thatsuccessful IPOs represent the majority of a ven-ture capital firm’s earnings (Huntsman and Hoban,1980), and more than 60 percent of their profit(Norton and Tenenbaum, 1993). Also, a success-ful IPO can generate much recognition for a ven-ture capital firm, garnering the attention needed toattract investors to venture capital funds (Lerner,1994c). As such, the content validity of our firmperformance measure rests on the claims and find-ings about the importance of successful IPOs inthe venture capital context.

Product and process niches are defined as theextent to which a firm has a distinctive set ofproducts or processes, respectively. In the ven-ture capital industry, the major product lines forventure capital firms are the IPO investments indifferent industries, and the major processes arethe different investment stages involved (Fried andHisrich, 1995; Gupta and Sapienza, 1988, 1992).We have satisfactorily covered the product andprocess niche constructs by measuring the distinc-tiveness of venture capital firms’ IPO investmentsand investment stages.

The domain of interest with respect to networkembeddedness includes the idea of contact redun-dancy (otherwise known as network interconnect-edness). As described in the Methods section, weoperationalize network embeddedness using Burt’s(1992) network redundancy measure, which takesinto account this domain.

Assessment of face validity

Face validity addresses whether a measure ‘on itsface’ seems like a good translation of the con-struct of interest. Several scholars have consideredface validity a weak form of construct validity,as it requires subjective judgment throughout theresearch process (Lacity and Jansen, 1994). How-ever, face validity can be improved if it relieson the judgment of a carefully selected panelof experts (Robinson and O’Leary-Kelly, 1998).Thus, in our assessment of face validity, we soughtindustry experts’ assessments regarding our mea-sures’ representation of particular constructs. Weconsulted five experts: a founder/chief executive

officer of a venture capital-backed start-up thatwent through its IPO, two venture capitalists fromdifferent top venture capital firms, and two analysts(professionals paid to analyze and report on ven-ture capital activity), each employed by a differentorganization. We specifically asked each personif the way we measured firm performance, prod-uct and process niche, and network embeddednesswere legitimate for the venture capital context. Allfive experts approved and so established the facevalidity of our measures.

RESULTS

Table 1 provides descriptive statistics and a cor-relation matrix for the variables used in thisstudy. Of the 80 venture capital firms in ourdataset, 68 percent were limited partnerships and50 percent were founded after 1980. The meancapital under management reported by the ven-ture capital firms as of the end of Time1 was$573.85 million. Over both time periods analyzed,the average number of IPOs supported by a venturecapital firm was 11.66. Regarding performance,25 percent of the venture capital firms had one ormore successful IPOs in their Time2 portfolio.

As shown in Table 1, the correlations among ourindependent variables are low. To further examinepotential collinearity among the variables, we cal-culated variance inflation factors (VIFs) associatedwith each of the predictors in our model. The valueof VIFs ranged from 1.18 to 1.63, with a mean of1.30, suggesting no problem with collinearity.

We also conducted a skewness test and outlieranalyses for all of our independent variables, andfound that one variable, network embeddedness,presented a distribution that differed from normal-ity (the Kolmogorov–Smirnov Z-value was 3.777,p = 0.000, 2-tailed). The result is not unexpected,given that network embeddedness measured usingBurt’s (1992) formulation of network redundancytends to show a skewed distribution. To see thepotential impact of skewness, we winsorized thenetwork embeddedness variable at the extremes of1 percent and 5 percent, and ran our analysis withthe variable winsorized and unwinsorized, respec-tively (Kendall and Stuart, 1979, for the proceduresfor winsorizing outlier observations). The patternof our statistical results is the same, whether or not



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we winsorize the network embeddedness variable.Thus, we report unwinsorized results here.

We relied on Poisson regression to test ourhypotheses, because this method is suitable forestimating the number of discrete occurrences ofsome events (Lindsey, 1995; STATA, 1997), suchas the number of successful IPOs in which a ven-ture capital firm invests. We used a chi-squaregoodness-of-fit test to assess whether our depen-dent variable fitted a Poisson distribution. Theresults indicate that we cannot reject the nullhypothesis that our dependent variable fits a Pois-son distribution (χ2 = 1.609; p = 0.657; d.f. = 3).

Table 2 shows the results of our Poisson regres-sion analysis. Four models were estimated. Model1 is the baseline model including only the con-trol variables. Model 2 adds to the control vari-ables the set of independent variables: productniche, process niche, and network embeddedness.Models 3 and 4 test our two hypotheses regard-ing the interaction effects between niche andnetwork embeddedness. To test these interactioneffects, we mean centered all three independentvariables, created a separate multiplicative termbetween each niche variable and network embed-dedness, and entered each multiplicative term into

separate models accordingly. As Aiken and West(1991) have suggested, mean centering reducesdistortion resulting from a high correlation betweenthe interaction term and its components.

Hypothesis 1 states that the interaction betweenproduct niche and high network embeddedness hasa positive impact on firm performance. As shownin Model 3 in Table 2, the coefficient of the inter-action term between product niche and networkembeddedness is positive and statistically signifi-cant (p ≤ 0.05), suggesting that product niche ismore positively associated with firm performancewhen network embeddedness is high. Model 3is statistically significant (χ2 = 23.79; p ≤ 0.05)and shows a substantial improvement over Model2 (LL-change = 3.73). Hence, we support ourhypothesis regarding the positive interaction effectbetween product niche and network embeddednesson firm performance.

Hypothesis 2 states that the interaction betweenprocess niche and high network embeddedness hasa positive impact on firm performance. As shownin Model 4 in Table 2, the coefficient of the inter-action term between process niche and networkembeddedness is positive and statistically signifi-cant (p ≤ 0.05), suggesting that process niche is

Table 2. Effects of product niche, operational niche, and network embeddedness on firm performance

Model 1 Model 2 Model 3 Model 4

Intercept −7.731∗∗ −6.061∗ −9.331∗∗ −8.803∗∗

Control variablesFirm size 0.483 0.465 0.505 0.699∗

Firm age 0.043∗∗ 0.041∗ 0.045∗ 0.047∗∗

Firm type 1.636+ 1.487 2.007+ 1.712+

Focus −0.106 −0.046 −0.038 −0.148Proximity to IPO −0.001 −0.001 −0.001 −0.002+

Competitive intensity 0.019 0.032 0.055 0.071Investment size 0.016∗∗ 0.017+ 0.029∗ 0.025∗∗

Independent variablesProduct niche −0.044 −0.033 −0.026Process niche 0.001 0.002 0.011Network embeddedness 0.008 −0.635∗ 0.108∗∗

Product niche × Network embeddedness 0.010∗

Process niche × Network embeddedness 0.003∗

Model statisticsLog likelihood −42.796 −41.473 −39.068 −39.002Change in log likelihood from Model 1 3.728 3.794Pseudo-R2 16.520 19.10 23.790 23.920Model χ 2 16.940∗ 19.580∗ 24.400∗ 24.530∗

N 80 80 80 80

Two-tailed tests: +p ≤ 0.10; ∗ p ≤ 0.05; ∗∗ p ≤ 0.01.The beta coefficients shown are non-standardized.



more positively associated with firm performancewhen network embeddedness is high. Model 4is statistically significant (χ2 = 24.53; p ≤ 0.05)and shows a substantial improvement over Model2 (LL-change = 3.79). Hence, we support ourhypothesis regarding the positive interaction effectbetween process niche and network embeddednesson firm performance.

To see the pattern of the interaction effects,we plotted the trend showing the relationshipbetween niche and firm performance at both highand low levels of network embeddedness. We

define high- and low-level network embeddednessbased on one standard deviation above and belowthe mean of the network embeddedness variable,respectively. Figures 2 and 3 present the interac-tion plots, showing that the niche–performancerelationship varies depending upon the level ofnetwork embeddedness. The plots show that whennetwork embeddedness is high the niche–perfor-mance relationship is positive, and when networkembeddedness is low the niche–performance rela-tionship is negative. In each figure, the slope ofthe line that describes the positive relationship

Per

form

ance

Product Niche



Figure 2. The moderating effect of network embeddedness on the product niche–firm performance relationship

Process Niche

Per

form

ance



Figure 3. The moderating effect of network embeddedness on the process niche–firm performance relationship



between niche and performance for firms with highnetwork embeddedness is significantly differentfrom the slope of the line that describes the neg-ative relationship between niche and performancefor firms with low network embeddedness.

Since we focused on only the 1995–98 time-frame, readers may think that we only capturedthe market volatility attributable to the irrationalhype associated with the Internet bubble, and failedto capture the subsequent Internet IPO meltdown.To address this, we used a performance bench-mark—the NASDAQ Composite Index—to mea-sure daily IPO performance relative to the market’sdaily performance. Thus, in our study we incorpo-rated the relative strength of each IPO, and not itsabsolute performance, which is likely to be influ-enced by overall market activity during a certainperiod of time.

We also tried to take into account market volatil-ity by controlling for investment uncertainty (mea-sured as whether or not an invested venture madea profit before it went public), and market hype(measured as the extent to which the market priceexceeded the prospectus price of the new stock).The statistical significance of our independent vari-ables did not change when these two control vari-ables were included in the Poisson model. Givenour concern with limited power due to our smallsample, we did not include these controls in ourfinal model.

DISCUSSION

The purpose of this research is to investigate how afirm’s niche may affect its performance under dif-ferent levels of network embeddedness. We iden-tify two niche types and show that for each therelationship between niche and performance sig-nificantly varies when the level of network embed-dedness changes. We find that the extent to whicha firm is distinctive or in a niche has a morepositive impact on its performance when networkembeddedness is high. These findings support ourhypotheses regarding the network contingency ofthe niche–performance relationship.

We distinguish between two niche types—product and process—based on what products thefirm offers, and how the firm operates its busi-ness. By identifying two different niche types, ourresearch contributes to a better understanding of

ways in which a firm can be distinctive or differ-ent from its competitors. Previous research tendsto focus on one niche type, mainly product niche,without acknowledging the fact that a firm canbe distinctive and gain a competitive advantage inother ways. Studying two niche types provides amore comprehensive understanding of competitivepositioning. By identifying different niche types,our research opens new avenues for scholars toexamine niche strategies from different viewpoints.

We investigate the niche–performance relation-ship to understand how firms may obtain a com-petitive advantage. Findings in previous research(Brush and Chaganti, 1999; Galbraith and Schen-del, 1983; Lawless and Anderson, 1996) suggestthat the niche–performance relationship is notalways straightforward. For example, traditionalstrategy models of competitive positioning sug-gest that a firm can benefit by being in a niche(e.g., Harrigan, 1985; Morrison and Roth, 1992;Porter, 1980); yet our research shows that theniche–performance relationship is not always pos-itive. Negative or positive performance outcomesmay accrue depending upon the distinctivenessof the product offering (e.g., extent of its prod-uct niche) or operational processes practiced (e.g.,extent of its process niche) coupled with the extentto which each firm’s inter-firm network is struc-turally embedded.

Implications for management research

That niche and network embeddedness interact toexplain performance is a significant finding; com-petitive positioning research could, therefore, ben-efit from investigating the network structure ofinter-firm cooperative relationships. Most studieson competitive positioning are silent about theimportance of inter-firm cooperative relationships,even though firms tend to compete and coop-erate with each other at the same time. Giventhat competition and cooperation tend to be flipsides of the same coin (Tsai, 2002), the extent towhich a blending between competitive position-ing and cooperative strategies takes place shouldreceive more attention in future strategic manage-ment research.

Further, when examining the economic bene-fits of competitive positioning, researchers shouldpay close attention to how a network of rela-tionships among firms functions. The network offirms is social in nature. And when we refer to



the social context we are in fact pointing to thesocial characteristics of firm networks. Althoughprior research has highlighted the importance ofcompetitive positioning in the marketplace, veryfew studies have discussed the role of competitivepositioning in a social context. A firm does notdo everything alone, nor does it make decisionsabout what to do without considering what otherfirms are doing. Given that firm behavior and per-formance are shaped by social relationships (Gra-novetter, 1985), a firm’s competitive positioningcannot be meaningfully analyzed without consid-ering its relational position in a social context. Ourresearch suggests that scholars can better under-stand how firms gain a competitive advantage byinvestigating the structure of inter-firm networkscharacterizing the social context.

Our study shows how the structure of a firm’sinter-firm network may serve as a contingencyfactor affecting the benefits of a firm’s niche posi-tion. The social network literature has documentedboth the pros and cons of maintaining embeddedvs. sparse network structures. Some scholars haveshown how a sparse network structure leads to bet-ter economic outcomes than does a dense networkstructure, due to the sparse network’s ability toprovide flexibility, autonomy, control, and access(Burt, 1992). Other studies have shown how adense network structure contributes to better eco-nomic outcomes than does a sparse network struc-ture, due to the dense network’s ability to facilitatethe sharing of in-depth knowledge and tacit know-how, as well as safeguard customized exchanges(Podolny, 1994; Uzzi, 1996, 1997). Studies sepa-rately considering the benefits of either sparse ordense network structures are prevalent in the liter-ature, yet these studies are inconclusive in deter-mining which type of network structure is better orworse for firms. One reason for a lack of consistentfindings, we believe, is that firms differ greatly rel-ative to each other in terms of their product lines,and the way they operate. Such differences havenot yet been considered in the network literature.Yet, these differences imply that firms pursuingdifferent strategies (whether or not they strategi-cally opt to pursue a product or process niche)need different levels of network embeddedness tosucceed. Both types of structure may be benefi-cial depending upon the firm’s strategy, as sug-gested by the literature setting forth the advantagesand disadvantages of sparse vs. highly embed-ded networks (Baker and Obstfeld, 1999; Burt,

1992; Coleman, 1990, 1994; Granovetter, 1973;Lin, 1999; Podolny and Baron, 1997). However,without considering firm-level differences in termsof major strategic decisions, research on inter-firmnetworks is incomplete. In our study, we simulta-neously consider relative firm-level differences andrelative network structure, showing the value ofboth low embeddedness and high embeddedness,as one way to resolve the debate.

Our research relied on both the management andfinance literatures to inform us about venture cap-ital activities. Both the management and financedisciplines seek to better understand venture cap-ital as an industry, the impact of venture capitalon IPO performance, and, more specifically, theoperational processes of venture capital firms. Ourresearch offers a preliminary step toward under-standing the venture capital industry. Although wefocus on the effect of venture capital firms’ nicheon performance, our results provide an impetusfor future research to more fully explore the pro-cesses related to various investment activities andcompetitive–cooperative dynamics in the venturecapital industry. Future research might focus onexploring the processes whereby a venture capitalfirm selects its investment portfolio, structures itsnetworks, and allocates its resources (time, exper-tise, and money). Many opportunities exist for col-laboration between research in management andfinance, especially when it comes to furthering ourunderstanding of venture capital firms’ activities.

Implications for management practice

Understanding the determinants of firm perfor-mance is the most important managerial issue forevery firm, including venture capital firms. Ourfindings provide insight as to how venture capi-tal firms might gain performance benefits by beingin a niche position. Although not hypothesized, ourresults showed that the relationship between nicheposition and firm performance can be either posi-tive or negative, depending upon the network con-ditions in which a firm is embedded. Our resultssuggest that a venture capital firm pursuing a moredistinctive position needs to be involved in a highlyembedded network to achieve high performance,and that a venture capital firm pursuing a less dis-tinctive position needs to be involved in a sparselyconnected network to achieve high performance.

Our findings also encourage managers to thinkmore broadly about the competitive positioning



of their firms. To successfully position a firmin a competitive environment, we suggest thatmerely selecting a distinctive set of products orprocesses is not enough. Competitive positioningalso involves a consideration of complex interac-tions between firms in a social context where firmsare more or less embedded. Such a social contextexists within a network of inter-firm relationshipsthat provide critical information and knowledge forbusiness operations. Understanding the social con-text is an important task for managers who try toposition their firms strategically in a competitivearena.

Limitations and extensions

Several limitations pertain to this study. Althoughwe tried to conduct a longitudinal investigation,the timeframe of this research (1995–98) is shortowing to the lack of reliable archival informationon specific venture capital firm activity prior to1995 (Gompers and Lerner, 1999) and the diffi-culty of obtaining complete venture capital firmboard-of-director-backed IPO information after1998. Given that we have only 4 years of data, wesplit the data into two periods of 2 years each, andused niche and network data in the first 2-year timeperiod to predict venture capital firm performanceoutcomes in the second 2-year time period. It iscertainly possible that alternative models could becreated. For example, there may be a reverse cau-sation model, whereby a venture capital firm’s per-formance may affect its niche position as well asits network structure. However, given the limiteddata available, we are unable to test all possiblealternative causal models. We chose our currentresearch design because we are interested in pre-dicting performance outcomes. As many scholarshave suggested, IPO performance is a fundamentaloutcome to predict as venture capital firms heavilyrely on IPOs to succeed (e.g., Black and Gilson,1999; Gompers and Lerner, 1999; Huntsman andHoban, 1980; Schilit, 1991). Our research does notrule out the possibility of other alternative causalmodels, but is an initial step toward understandingthe complexity of intertwined relationships.

Another concern related to the short timeframeof this research is that possible idiosyncratic mar-ket conditions during this period may limit thegeneralizability of our findings to other time peri-ods. To better understand the effects of recentdynamics on the venture capital industry, we spoke

with several industry experts. According to theseexperts, the key success factors required in the ven-ture capital industry did not dramatically changebetween the mid-1990s and the year 2000. Anec-dotal evidence, as provided by the majority of ourinterviewees, leads us to believe that the way inwhich venture capital firms conduct business and,thus, may differentiate themselves, especially interms of product selection, has likewise not dra-matically changed in terms of firms’ ability tobe relatively common or unusual. Thus, our inde-pendent variables do not seem to be affected byidiosyncratic market conditions during the time-frame of our research. With respect to the poten-tial impact of market conditions on our dependentvariable, we benchmarked IPO stock price to theNASDAQ Composite Index, and controlled forinvestment sentiment and uncertainty in our addi-tional analyses. However, these controls are by nomeans comprehensive given the range of macroe-conomic conditions that may have impacted ourfindings.

Another limitation is that we tested our theoryand hypotheses in a specific industry, which mayrestrict external generalizability to other industries.Future studies that replicate our model using alarger sample covering similar project-based indus-tries, such as construction, film, law, or music, aswell as non-project-based industries, may enhanceexternal validity. When exploring other industries,researchers may obtain enlightening results byexamining resource distribution within the popula-tion, changes in concentration, and environmentalconditions—characteristics that affect niche firmsand their outcomes. Furthermore, we constructeda network of inter-firm relationships based on IPOdata. Venture capital firms have other networks thatour study does not measure, and the importance ofthese networks needs to be investigated. Other net-works could be more or less meaningful to the ven-ture capital firm compared to their co-investmentrelationships in IPOs. To study other venture capi-tal firm networks such as referral, advice seeking,or friendship resources requires that we gather pri-mary data and ensure a complete response fromevery single firm.

Future research may advance our understandingof the niche concept by examining how nichesare formed and sustained. Certain resources andcapabilities are required for firms to strategicallyobtain a niche position. Perhaps the resource-based or dynamic capabilities view of the firm



can provide a useful lens for researchers studyingniche formation and sustainability. Researchersmay examine whether the formation of a processniche requires more tacit knowledge than the for-mation of a product niche (Reed and DeFillippi,1990) to explain why a process niche may sustainlonger than a product niche. Researchers may alsoexamine which kinds of firms are more likely tooccupy certain types of niche.

To further extend our understanding of theniche–network–performance relationship, futureresearch might explore how a niche may changeover time, how niche changes may affect firm per-formance, and how a firm’s niche and networkco-evolve. Moreover, future research may exploreantecedents of why certain social structures areformed, discover additional moderators or medi-ators to niche theory, and apply other theoreticalperspectives to understanding firm performance.Many opportunities exist to deepen our under-standing regarding how firms weigh alternatives,determine how to conduct business, and decidewhen to change how they conduct business.

CONCLUSION

This research enhances our understanding of howfirms use niche and network structure to improvetheir performance. Based on data from the venturecapital industry, the results show that a venturecapital firm is more likely to reap economic gainsfrom doing business in a niche when surrounded byother venture capital firms that are tightly linked inembedded relationships than when surrounded byother venture capital firms that are sparsely con-nected. The more a firm differentiates itself fromother firms in terms of what product it offers, orhow it operates, the more embedded its inter-firmnetwork needs to be to positively affect its perfor-mance. The results of this study contribute to theorganizational ecology and strategic managementliteratures by identifying different niche types andclarifying the niche–performance relationship. Theresults also contribute to social network researchby showing the value (and the possible down-side) of network embeddedness. Certainly, nicheand network structure should be studied simulta-neously; establishing a product or process nichealone is not enough to achieve superior perfor-mance. Managers are wise to thoughtfully consider

their firm’s position in an inter-firm network whencrafting a niche strategy.

ACKNOWLEDGEMENTS

We would like to thank Don Bergh, Steve Borgatti,Denny Gioia, Don Hambrick, David Harrison,Martin Kilduff, Yasemin Kor, David Krackhardt,Chris Muscarella, Linda Tegarden, Linda Trevino,Klaus Weber, David Wagstaff, and two anonymousreviewers for their helpful comments and sugges-tions on earlier drafts of this manuscript. We aregrateful to Ilya Lipkovich for helping with math-ematical computations, Raman Kumar for assist-ing in programming and downloading CRSP data,and Nicola McCarthy for editing our manuscript.This research was partially supported by the FarrellCenter for Entrepreneurship in the Smeal Collegeof Business Administration at Penn State.

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APPENDIX 1: FORMULA USED TOCOMPUTE THE DEPENDENTVARIABLE: FIRM PERFORMANCE

Firm performance = Number of successful IPOsSuccess = 1, when Benchmarked return i,12 > 0where:

Benchmarked return i,t

={[

T t∏τ=T 0

(1 + Ri,τ )

]}−

{[T t∏

τ=T 0

(1 + Rm,τ )

]}

Benchmarked return i,t is the abnormal stock priceperformance of the new issue, i, from the offerdate to the end of month t

i is the index of stockst is the index of months from month 1 to 12, where

a month is a successive 21-trading-day periodfollowing Ritter (1991)

τ (tau) is the index of daysTo is the first aftermarket return day in CRSP

within 6 days after the offering dateTt is the last day of month t (i.e., days 22, 43, 64,

85, 106, 127, 148, 169, 190, 211, 232, 253)Ri,τ = defined by ‘RET()’ in the CRSP

tapes = (Pi,τ − Pi,τ−1 + fi,τ + DIVi,τ )/Pi,τ−1

fi,τ = price adjustment factor for stock i

Rm,τ = (NASDAQIndexi,τ − NASDAQ-Indexi,τ−1)/NASDAQIndexi,τ−1

P = stock priceDIV = stock dividend

APPENDIX 2: FORMULA USED TOCOMPUTE INDEPENDENT VARIABLE:OPERATIONAL NICHE

Based on the non-overlap intensity measure usedby Baum and Oliver (1996), a venture capital

firm’s operational niche is computed as fi = n −di

where:

f is a vector of non-overlap intensities for aventure capital firm

Each venture capital firm Fi (I = 1, . . . , n) isassociated with its profile of m binary indi-cators xij = {0, 1}, j = 1, . . . , m, where a 1means the venture capital firm operates in oneof the following stages, and a 0 means itdoes not: Research and Development, Seed,Start-up, First Stage, Second Stage, Mezzanine,Bridge, Acquisition, Leveraged Buy-out, Man-agement Buy-out, Recapitalization, and/or Spe-cial Financing.

Niches are defined as distinct profiles {xij , j =1, . . . , m}. Let the number of venture capitalfirms in each niche k be nk

The total number of venture capital firms, n, is par-titioned as n = ∑l

k=1 nk, where l is the numberof distinct niches

The overlap density for the ith niche is computedas di = ni + ∑

j �=i

njwij

The niche overlap weight wij for niches i and j

are computed as follows:

wij = #{xik = 1 and xjk = 1, k = 1, .., m}#{xik = 1, k = 1, .., m}

(Notice that wij �= wji)


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NICHE AND PERFORMANCE: THE MODERATING ROLE OF NETWORK EMBEDDEDNESS

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