firm networks and firm development-2

Firm networks and firm development: The role ofthe relational mixB

Christian Lechnera,*, Michael Dowlingb,1, Isabell Welpec,2

aESC Toulouse, 20, Bd. Lacrosses, 31068 Toulouse Cedex 7, FrancebUniversity of Regensburg, Management of Innovation and Technology, Regensburg 93040, Germany

cExist-HighTEPP Program, University of Regensburg, Regensburg 93040, Germany

Abstract

This study examines the role of different networks, called the relational mix, on the development

of the entrepreneurial firm. Our regression analysis of survey data from 60 venture capital-financedfirms questions the importance of network size on firm development. Rather, our results suggest thatdifferent types of networks are more important for firm development. In particular, we found a

significant positive relationship for reputational networks and a weak significant negativerelationship for cooperative technology networks at founding with time-to-break-even. Socialnetworks at founding have no direct effect on time-to-break-even and a significant negative

relationship with sales in the years after foundation. Furthermore, our findings show the importantrole of marketing information and co-opetition networks (relationships with direct competitors) onfirm development in the years after foundation. These results suggest that the relational mix is a more

appropriate construct for explaining network development than network size alone.D 2005 Elsevier Inc. All rights reserved.

Keywords: Entrepreneurship; Networks; Firm growth; Performance

0883-9026/$ - see front matter D 2005 Elsevier Inc. All rights reserved.

doi:10.1016/j.jbusvent.2005.02.004

B An earlier version of this paper was presented at the 2003 Babson Kauffman Entrepreneurship Research

Conference, June 5–7, at Babson College, Boston, MA.

* Corresponding author. Tel.: +33 561 29 49 23; fax: +33 561 29 49 94.

E-mail addresses: [email protected] (C. Lechner)8 [email protected]

(M. Dowling)8 [email protected] (I. Welpe).1 Tel.: +49 941 9433226; fax: +49 941 943 3230.2 Tel.: +49 941 943 1704; fax: +49 941 943 3230.

Journal of Business Venturing 21 (2006) 514–540

1. Executive summary

The use of external relationships is considered an important development factor for theentrepreneurial firm. Previous research on inter-firm networks has focused on the role ofthe entrepreneur in network building, on the initial size of an entrepreneurial firm’snetwork in regard to firm performance, or on structural characteristics of networks. Thisstudy addressed a different question: During the process of firm development, what is thevalue of different kinds of network relationships?

We adapted a model of the brelational mixQ (i.e., that firms use different types ofnetworks in different development phases). The relational mix consists of value-addednetworks that go beyond exclusively economic relationships and includes:

– social networks: relationships with other firms based on strong personal relationshipswith individuals such as friends, relatives, long-standing colleagues that became friendsbefore foundation, and so forth;

– reputational networks: made up of partner firms that are market leaders, or highlyregarded firms or individuals, and where one of the main objectives in entering into thisrelationship is to increase the entrepreneurial firm’s credibility;

– marketing information networks: relationships that allow for the flow of marketinformation through distinct relationships with other individuals / firms;

– co-opetition networks: relationships with direct competitors;– co-operative technology networks: technology alliances involving joint technologydevelopment or innovation projects.

The adapted model assumes that the relational mix changes with a firm’s developmentand that inability to change leads to a growth barrier.

To test hypotheses based on this model, we used survey data from 60 venture capital-financed start-up firms of less than 10 years of age in Austria, Germany, and Switzerland.The evidence from the regression analysis supports our model: the relational mix is a moreimportant factor for explaining firm development than sheer network size. Reputationnetworks have a positive influence while cooperative technology networks at foundationhave a negative effect on time to break-even. Entrepreneurs should be aware of theimportant signaling effects that reputational partners can have in overcoming liability ofnewness. Early technology partnering might be an indicator that firms are not yet ready toexploit business opportunities or not attractive enough to enter into more value-addingpartnerships. Moreover, it seems that firms use their initial technology base to exploit abusiness opportunity and that technology partnering is a means to enhance the technologyplatform later on to prepare the company for the future. We also found that marketinginformation networks and co-opetition networks in the years after foundation arepositively associated with sales. Relationships with competitors play an important rolefor firm development since competitors can be a source for firm development when usedas sub-contractors, or for the joint realization of larger projects that might otherwise beunattainable for the start-up. In this sense, co-opetition networks seem to increase theentrepreneurial firm’s flexibility and to ensure sales growth in times of uncertainty. We didnot find a significant positive influence of social networks at foundation on time-to-break-

C. Lechner et al. / Journal of Business Venturing 21 (2006) 514–540 515

even. It seems that while constituting the start-up base of the entrepreneurial firm andbeing the most common type of relationship at the beginning of the firm, social networksare not a good discriminator for the success or speed of firm development. Social networksseem to have an indirect effect. Over-reliance on social networks over time may in factconstitute a growth problem, since it may indicate that firms are not capable of developingother important ties. In conclusion, our study suggests that networking should be aproactive task of entrepreneurs and that strategic network building over time is animportant factor for the development of the entrepreneurial firm. Further research on thedevelopment of certain network types, the role of management style on developingnetworks, and the contingent value of network types is needed to enhance ourunderstanding of the development processes of entrepreneurial firms.

2. Introduction and literature

Survival, performance, and development of the entrepreneurial firm are at the heart ofentrepreneurship research (Gartner, 1985; Bygrave and Hofer, 1991; Venkataraman, 1997;Virtanen, 1997; Shane and Venkataraman, 2000). Entrepreneurial firms are characterizedby a lack of internal resources and other start-up handicaps as expressed in the theoreticalconstructs of liability of newness (Stinchcombe, 1965) and liability of smallness (Baum,1996). The strategic use of external resources through inter-firm networks in manydifferent industries (Powell, 1987; Lorenzoni and Ornati, 1988; Jarillo, 1989) that are oftenembedded in regional clusters (Boari and Lipparini, 2000; Lechner and Dowling, 2000) isregarded as one effective means to overcome these liabilities. In this context, inter-firmnetworks are considered an important model of organization development (Richardson,1972; Powell, 1987, 1990) to enable an entrepreneurial firm to grow and survive (Jarillo,1988; Lorenzoni and Ornati, 1988; Nohria, 1992; Johannisson, 1998; Venkataraman andVan de Ven, 1998; Freel, 2000).

There is a long history of research on networks in the management and organizationtheory literature. Networks can be defined as a specific set of linkages between a definedset of actors with the characteristic that the linkages as a whole may be used to interpret thesocial behavior of the actors involved (Mitchell, 1969; Alba, 1982; Lincoln, 1982).Different types of relations define different types of networks even if the same units areconnected (Knoke and Kuklinsky, 1983).

Network research distinguishes three components: network content, network structure,and network governance in order to explain the role of networks in firm performance.According to the theory of structural embeddedness, network structure and a firm’s or aperson’s network position are considered to be both opportunities and constraints (Aldrichand Zimmer, 1986). Favorable positions are regarded as network resources (Granovetter,1974, 1985; Burt, 1992; Easton, 1992; Hakansson and Snehota, 1995; Gulati, 1999;McEvily and Zaheer, 1999); over-embeddedness, however, (btrapped-in-its-own-netQ) canlead to inability to act (Uzzi, 1997; Gargiulo and Benassi, 2000). A rich literature suggeststhat networks are a particular governance form in which the development of trust plays amajor role in influencing resource exchange and costs compared to market coordination orintegration of activities (Richardson, 1972; Thorelli, 1986; Powell, 1987, 1990; Larson,

C. Lechner et al. / Journal of Business Venturing 21 (2006) 514–540516

1992; Lorenzoni and Lipparini, 1999). In this sense, inter-firm networks constitute a thirdway of organizing the business, which is neither by markets nor by hierarchies (DiMaggio, 1986; Powell, 1990; Lorenzoni, 1992; Jarillo, 1993).

Empirical research has shown an association between the development andtransformation of relationships, size, and growth of a firm’s inter-firm networks and firmgrowth (Jarillo, 1989; Zhao and Aram, 1995; Ardichvili and Cardozo, 2000; Chell andBaines, 2000; Huggins, 2000; Delmar et al., 2003; Hoang and Antoncic, 2003; Sawyer etal., 2003). Besides research on personal networking and networks, network contentresearch has concentrated strongly on the differences between strong and weak ties (Hoangand Antoncic, 2003), otherwise known as the structural approach (McEvily and Zaheer,1999). Recently, it was shown that specific kinds of relations (network content) are moreimportant in a different economic context (Gulati and Higgins, 2003).

Research on firm networks as a mode of transaction governance, the role of strong andweak ties, and the analysis of structural properties of networks has produced importantinsights (Hoang and Antoncic, 2003). However, research on different network types andon network evolution remains relatively underdeveloped. bThe aggregate network can beviewed as an overlapping set of networks of different transactional content. The onlyconceptually meaningful strategy of analysis is to distinguish each network by its content,[and] analyze it separatelyQ (Fombrun, 1982, p. 280). And as Burt (1997, p. 357) observed,bnetwork content is rarely a variable in the studies—analysts agree that informalcoordination through interpersonal networks is important as a form of social capital, buttheir eyes go shifty like a cornered ferret if you push past the network metaphor for detailsabout how specific kind of relations matter.Q

Networking has also been found to be important for entrepreneurial firms. Mostresearch in this setting has analyzed egocentric networks (i.e., the relationships of onefocal actor with other actors) (Wassermann and Faust, 1994; Johannisson, 1998). Becauseof the new ventures’ liabilities, such firms must mobilize social relationships (Starr andMacMillan, 1990) to access external resources. An entrepreneur’s personal networks areall relationships between an entrepreneur and other individuals (Dubini and Aldrich,1991). An entrepreneur’s social networks are strong personal ties such as family andfriends. Research has shown that the entrepreneurs’ personal and social networks maybeare the most important strategic resources of entrepreneurs for the start-up firm (Dubiniand Aldrich, 1991; Ostgaard and Birley, 1994; Johannisson, 1995, 1998, 2000; Lippariniand Sobrero, 1997; Aldrich, 1999; Ardichvili et al., 2003). Organizational networks orinter-firm networks are relations between organizations that can have various functions,also called sub-networks according to their relational content (Dubini and Aldrich, 1991;Lomi, 1997; Podolny and Page, 1998; Lechner and Dowling, 2000). The merging ofpersonal and organizational networks seems to be a common feature of young firms (Zhaoand Aram, 1995; Johannisson, 1998, 2000). Founders and the firm are inseparable at start-up (Dollinger, 1985; Begley and Boyd, 1986). As the firm grows, the founders’ personalnetworks and firm networks merge (Zhao and Aram, 1995; Hite and Hesterly, 2001;Cooper, 2002). These merged networks can be considered an organizational form(Johannisson, 2000).

Initially, it seems that networking for entrepreneurial firms is based on pre-existingrelationships, which become more complex over time by having different functions and


being more socially embedded: social relations are transformed into socio-economic and,finally, into more complex relations (Larson and Starr, 1993). Green and Brown (1997)have suggested that over time, newly developed organizational relationships become moreimportant than the a priori personal networks. Previous research has shown that the overallnetwork structure of egocentric networks changes from an unplanned to a planned and,finally, a structured network (Lorenzoni and Ornati, 1988; Lorenzoni and Baden-Fuller,1995). Once structured, however, it seems that both over-embeddedness (Uzzi, 1997;Gargiulo and Benassi, 2000) and a firm’s limited relational capability (i.e., the capability toestablish, maintain, and develop relationships) (Dyer and Singh, 1998; Lorenzoni andLipparini, 1999; Pihkala et al., 1999; Lechner and Dowling, 2003) poses a potential barrierto growth. However, this research does not explain which network types (content) are mostimportant to manage or how firms should overcome these growth barriers.

Research on network types and performance has focused on pre-start-up activities inorder to explain nascent entrepreneurship (i.e., personal networks have been analyzed at orprior to the creation of the company in terms of size and networking activity) (Aldrich,1999). Context-specific research investigated the positive or negative role of socialnetworks at start-up, but which types (content) of network matter most for firmperformance have not been studied extensively (Ostgaard and Birley, 1994). Someresearch was conducted on the role of single network types such as reputational (e.g.,Stuart et al., 1999) and cooperative technology networks (e.g., Deeds and Hill, 1996;Kelley and Rice, 2002), but little is known for example about the role of co-opetitionnetworks. Studies analyzing multiple network types are rare (Baum et al., 2000).Moreover, entrepreneurship research on networks usually lacks a development perspective(Hoang and Antoncic, 2003) and an analysis of the value of different ties (Gulati andWestphal, 1999). It has been argued that research bon the process of network developmentin the entrepreneurial contextQ and the link between process and firm performance is themost promising path (Hoang and Antoncic, 2003, p. 12).

Lechner and Dowling (2003) proposed a network development model based on varyingnetwork types. Based on case study research in a German IT cluster, they identified thatfirms use relationships for a variety of purposes and that every firm has an individualrelational mix. They argued that the relational mix (i.e., the different types of networks)changes over time in order to enable firm growth. Finally, they proposed a four-phasedevelopment model of entrepreneurial firms. In phase 1, firms seek to overcome liabilityof newness by basing the development of the network mainly on social (understood asstrong ties such as family and friends) and reputational networks (relationships withprominent firms that can lend the young firm reputation). While the relative importance ofsocial and reputational networks decreases with the firms’ development, co-opetitionnetworks (i.e., cooperation with competitors) increase over time. In phase 2, firms usemarketing information and co-opetition networks to overcome the usual period of unstablesales growth; in phase 3, co-opetition remains a relevant issue but cooperative technologynetworks are most important. Finally, in phase 4, firm growth is limited by path-dependentrelational capability that eventually reaches its limits and leads to the reconfiguration of amore stable network by introducing hierarchic levels within the network or to theintegration of activities previously performed outside the firm. This qualitative studyseems to be one of the few where different network types were used to understand the role


of networks at and beyond foundation, but the importance of different network types atand beyond foundation and their impact on the performance of the entrepreneurial firmhave not been tested empirically with larger samples.

For the study reported here, we adapted the model of the relational mix developed byLechner and Dowling (2003). We tested the influence of their proposed network types atand beyond foundation by using their main arguments concerning the importance ofparticular networks. Our first and fundamental research question is: Is the relational mix abetter predictor of firm performance, both at and after foundation, than the size of theoverall network?

3. Development of hypotheses

3.1. Relational mix versus network size

Different studies have proposed a simple relationship between network size and firmperformance (Johannisson, 2000). Case study research has suggested that network size isrelated to the growth of firms (Zhao and Aram, 1995). Inter-firm networks can provideaccess to complementary resources to develop, produce, and market products (Deeds andHill, 1996). Entering into inter-firm relationships has costs, risks, and benefits. Costsinclude both financial resources and time. The marginal benefits of alliances can declinewhile costs increase (Deeds and Hill, 1996). Some researchers that have specificallystudied technology partnering have demonstrated a curve–linear relationship betweennetwork size and performance (Deeds and Hill, 1996). It has been argued that therelational capability (i.e., the capability to enter and maintain relationships) is limited(Pihkala et al., 1999) but path-dependent. In other words, firms learn to better managemore relationships over time (Deeds and Hill, 1996; Lechner and Dowling, 2003). Inaddition, certain types of inter-firm relationships can be managed in greater number(Rothaermel and Deeds, 2001). It seems, however, that for young firms, there is still apositive relationship between size and performance (Rothaermel, 2001). A start-up’s initialperformance has been shown to increase with the size of the alliance network of firms atfoundation (Baum et al., 2000). Overall, previous research has led to the generalhypothesis that network size is a good indicator for explaining the performance of youngfirms.

Hypothesis 1. Network size (i.e., the total number of network ties) increases firmperformance.

However, it has also been argued that the size of the network hides more importantnetwork properties (Fombrun, 1982). These properties include the relational mix (i.e.,different network types; social networks, co-opetition networks, marketing informationnetworks, reputation networks, and cooperative technology networks) that enabled growthin different stages of firm development (Lechner and Dowling, 2003). These types ofnetworks are not pure exchange networks but are socially embedded complex relationships(Larson, 1992). A more fine-grained picture of network development should therefore be abetter indicator for firm development than sheer network size (measured as total numbers


of relations). In Section 3.2, we develop a set of more detailed hypotheses linking specifictypes of networks and therefore the relational mix to start-up and later performance inentrepreneurial firms.

3.2. Social networks

We define social networks as strong and active relationships with other individuals thatexisted before the creation of the firm. These include family (non-business), friends, andformer colleagues. It seems to us important to distinguish between active relationships(i.e., those effectively used for business purposes) and existing but unused relationships,called latent networks (Ramachandran and Ramnarayan, 1993), in order to understand theactual role of networks for firm performance.3 Family and friends (i.e., non-businessnetworks) are considered to be part of the start-up resources of the firm (Johannisson,2000). The entrepreneurs’ social networks are regarded as an important resource for thestart-up firm (Ostgaard and Birley, 1994; Johannisson, 1995; Lipparini and Sobrero,1997). These strong ties have various benefits for the entrepreneur at the start of the firmby providing access to resources. Social networks help entrepreneurs avoid opportunismand uncertainty through trust, predictability, and voice. Because the entrepreneur can trustthe other party, it is easier to predict his/her behavior, avoid problem in the relationship,but better deal with them when they do occur (Aldrich, 1999). Therefore, resource accessis immediate and the working relationship does not need a bwarm-up periodQ in which thetwo partners get to know each other. As a consequence, such networks should allowentrepreneurs to achieve performance targets faster, such as realizing the first sale andreaching profitability (Uzzi, 1997; Davidsson and Honig, 2003). The more socialrelationships the start-up possesses, the faster the entrepreneurs should be able to accessnecessary resources. Social networks act as fast entrance ticket to an industry (Lechner andDowling, 2003). Large family and friends networks should therefore affect firmperformance positively (Johannisson, 2000). Founders’ personal networks at the start ofthe firm have also been associated with growth (measured as number of employees) in thesubsequent year (Hansen, 1995) as well as social capital in the pre-start-up phase withinitial success such as the first sale (Lechner and Dowling, 2003). Therefore, their initialsize should influence growth (Zhao and Aram, 1995; Baum et al., 2000).

Hypothesis 2. The number of social network ties at foundation will reduce the timeneeded to reach performance targets.

However, some researchers have argued that the quality of relationships, oftendepending on the founders’ context, will influence firm performance (Ostgaard and Birley,1994). Over-reliance on social networks, for example, could cause problems after the start-up phase. First, while social networks give certain, low-cost, rapid access to resources, thenumber of appropriate social ties that are beneficial for the requirement of the businessmight be limited. There may be diminishing marginal returns for adding new relationships(Deeds and Hill, 1996). A software entrepreneur reported in a study by Lechner and

3 We would like to thank Howard Aldrich for this distinction during a personal communication.


Dowling (2003): bAt the beginning, we only teamed up with friends but how many friendscan you have? Sometimes friends were not the right partners for the job requirementQ (p.11). Second, networks can give access to different resources such as financial, information,or help in finding first customers or suppliers. Social network analysis has shown thatstrong ties are often characterized by high similarity and that strong relationships betweenone focal actor with two other members of a network tend to lead to at least one weak tiebetween the two other actors (Granovetter, 1974; Burt, 1992). Structural hole theory (Burt,1992, 1997) has stressed the benefits of brokerage opportunities that are created by thelack of connection between third parties.4 Since social networks are strong ties, theinformation gathered from one member of the network might be rich, but the informationgathered from all members of this network might be redundant. Third, besides quality andredundancy issues, large social networks can have an additional negative effect onsubsequent firm performance. Structural hole theory also suggests that a lack of structuralholes in the network of a focal actor not only reduces the diversity of resources accessiblebut also the actor’s autonomy to engage in new relations (Burt, 1992, 1997). Thisphenomenon of social over-embeddedness could eventually lead to a growth barrier (Uzzi,1997; Gargiulo and Benassi, 2000). Social networks, while important for the establishmentof the firm and for reaching first milestones (Johannisson, 2000), may not be sufficient forsubsequent firm development (Lechner and Dowling, 2003). As a consequence, othertypes of networks should become more important after start-up (Dollinger, 1985; Greenand Brown, 1997). These existing networks could subsequently be transformed into richerand more complex networks (Larson, 1991, 1992; Dubini and Aldrich, 1991) to enablefirm development. It has been, however, argued that the decreasing importance of familyand friends networks is more implicit that explicit in most research (Cooper, 2002).Therefore, if we compare firms and their networking behavior in subsequent years, othernetworks should become more important for firm performance.

Hypothesis 3. The number of social network ties will be negatively associated with firmperformance in the years after foundation.

3.3. Reputation networks

Larson (1992) has suggested that social networks and reputation were pre-conditionsfor economic exchange. Therefore, relationships with other firms can have importantreputational or signaling effects (Stuart et al., 1999; Gulati and Higgins, 2003; Deeds et al.,2004). We define reputational networks as ties with firms where entrepreneurs estimatedthat the main reason for entering into the relationship was to gain reputation. Reputationalnetworks consist of ties with prominent firms and individuals such as well-known venturecapital firms, leading firms in a given market, or highly reputed customers. However, toenter into reputational relationships, entrepreneurial firms must be able to offer attractiveresources (Eisenhardt and Schoonhoven, 1996). But even excellent resources may not

4 The lack of connection between two actors B and C, who are both connected to an actor A, is called a

structural hole and is the basis of structural hole theory (Burt, 1992).


suffice to overcome the reluctance of firms to engage into exchange relationships withvery young firms since economic exchange with these firms is perceived to be risky(Bhide, 2000). Gaining reputation is an effective means of overcoming liability of newnessand increasing firm performance (Larson, 1992; Gulati and Higgins, 2003; Roberts andDowling, 2002).

Industries can be understood as a community with credible and reputable firms.Affiliation with one of these firms increases the reputation of the start-up company andshould therefore be considered a corporate goal. Reputation leads to social and economictrust in capabilities (Larson, 1992), and reputational networks can replace experience andthe lack of a track record (Stuart et al., 1999): b. . .the impact of inter-organizationalrelations is driven more by who a company is associated with than by the volume of itsrelations (Stuart et al., 1999, p. 345). Increased reputation should facilitate the access toother stakeholders such as suppliers and clients who are willing to engage with the youngfirm since the perceived risk of an exchange relationship is lower. As a consequence, theexistence of reputational partners should facilitate selling products. Therefore, entrepre-neurial firms that build reputational networks early on should reach performance targetsfaster. In later phases, the firm will need fewer reputational networks as it develops its ownreputation. In addition, with growing size, firms tend to integrate activities and reduce thenumber of inter-firm relationships (Rothaermel and Deeds, 2001). Overall, reputationalnetworks should accelerate the development of start-ups through the creation of optionsfor developing more and richer relationships in the future (Larson, 1992; Lechner andDowling, 2003). Reputational networks were proposed to influence firm growth (Lee etal., 2001) and to reduce the time to reach performance targets such as time-to-IPO (Stuartet al., 1999). The existence of reputational networks at the start of a firm’s life cycle shouldtherefore reduce the time needed to reach performance targets such as realizing the firstsale, realizing a certain sales volume, or reaching break-even. A lack of reputationalnetworks should slow down firm development (i.e., lengthen the time needed to reachperformance targets). In this sense, the opportunity to gain reputation partners is, first, theoutcome of firm capabilities and resources. Second, the active cooperation withreputational partners is an important form of networking. The resulting network influencesas a consequence firm performance.

Hypothesis 4. The number of reputational network ties at foundation will reduce the timeneeded to reach performance targets.

3.4. Co-opetition

Co-opetition networks involve relationships with direct competitors. The managementliterature generally considers industries to be collections of firms bound together byrivalry, therefore questioning the value of relationships with competitors (Dollinger, 1985).Particularly technology alliances with competitors are supposed to harm firm development(Baum et al., 2000). However, it has been argued that relationships with competitors canhelp entrepreneurs towards a better understanding of their firm context and opportunities,thus influencing firm performance (Dollinger, 1985). Co-opetition (i.e., cooperation


between competitors) seems to be a widespread phenomenon of entrepreneurial firms(Dowling et al., 1998). Firms can use competitors as subcontractors in times where thefirm has temporarily reached full capacity. This cooperative behavior, especially withregional competitors, will increase the likelihood of the favor being returned. Moreover,firms can form alliances with competitors in order to handle large projects. Overall,relationships with competitors can give access to temporarily needed resources or lead tothe temporary pooling of resources, which should positively influence firm performanceespecially in the years after foundation, when sales tend to grow discontinuously (Lechnerand Dowling, 2003). While it has been argued that co-opetition networks at foundationmight be harmful because such relationships could lead to the disclosure of competitiveinformation (Baum et al., 2000), lack of co-opetition networks can also constrain firmdevelopment in the years following foundation. Entrepreneurial firms that viewcompetitors not only as pure rivals but also as a potential resource should therefore bemore successful (Lechner and Dowling, 2003).

Hypothesis 5. The number of co-opetition networks will positively influence performancein the years after foundation.

3.5. Marketing information networks

Marketing information networks are relationships that allow for the flow of marketinformation through distinct relationships with other individuals/firms. Marketingnetworks are useful for informal environmental scanning while marketing research isbased on formal environmental scanning. Research on marketing planning in large firmshas, in general, shown a positive relationship between formal marketing planning and firmperformance, but its usefulness was questioned for small firms that favor personal andinformal environmental scanning (Brush, 1992). Customers have also been shown to be animportant input for the development of innovations (Von Hippel, 1978; Malecki andPoehling, 1999). In addition, suppliers, competitors, and distribution channels are valuableinformation sources (Dollinger, 1985) that can influence strategy-making in entrepreneur-ial firms (Ostgaard and Birley, 1994). Family and friends networks are perceived as oflittle value for marketing information (Brush, 1992). It has been argued that marketinginformation networks are associated with an open management style in which theresponsibility for networking is an important function for all employees (Lechner andDowling, 2003) since marketing networks can be considered an outcome of networkcomplexity (Larson and Starr, 1993; Lorenzoni and Lipparini, 1999; Hite, 2000). Thismeans also that relationships with other firms develop only over time into marketingnetworks. First clients with whom a firm had developed a simple exchange relationshipbecome real partners over time. Clients or suppliers of new firms can increase theircommitment over time with the new firm since they have a stake in its survival (Bhide,2000). Customers become dlead usersT only through learning-by-using, which impliessome path dependency (Von Hippel, 1978). In addition, it seems that start-ups have only afew important clients at the beginning, which serve later on as referrals (Lechner andDowling, 2003). Marketing information networks should therefore positively influencefirm performance in the years after foundation, suggesting that external marketing


information networks are more important than an internal marketing planning function(Brush, 1992; Malecki and Poehling, 1999).

Hypothesis 6. The number of marketing information network ties will positively influencefirm performance in the years after foundation.

3.6. Cooperative technology networks

Cooperative technology networks are relationships with other firms in order to jointlydevelop technology for the creation of innovation. Initially, start-up companies often lackthe reputation and resources to enter into cooperative technology networks with otherfirms (Eisenhardt and Schoonhoven, 1996; Ahuja, 2000). It seems also that start-upsinitially exploit their own technology base before entering into cooperative technologynetworks (Lechner and Dowling, 2003). Because they are time- and resource-intensive,cooperative technology networks should be developed after the start-up phase but before afirm’s maturity. Building technology portfolios has been shown to be a way to fosteralliance formation, which in turn influences the firm’s innovation rate and therefore leadsto the continuous development of the entrepreneurial firm beyond its original technologyportfolio (Kelley and Rice, 2002).

However, technology partnering is a costly and time-intensive form of inter-firmcollaboration. The nature of this type of relationships limits the extensive use of this typeof relationship. In fact, a curve–linear relationship between the size of cooperativetechnology networks and new product development was demonstrated in the study byDeeds and Hill (1996). Nevertheless, we believe the advantages of technology cooperationwill still positively affect firm performance.

Hypothesis 7. The number of cooperative technology network ties will positivelyinfluence performance in the years after foundation.

4. Methods

4.1. Sample and survey design

The hypotheses were tested using data from a pre-tested survey sent to CEOs andfounders of VC-financed firms in German-speaking countries in 2003. Since there was nocomprehensive database on VC-financed companies in Germany, Switzerland, Austria,Liechtenstein, and Luxembourg, over a period of 6 months, we developed a databaseidentifying 1453 VC-backed companies in these countries. In a first step, data of theGerman Venture Capital Association and the European Venture Capital Association wereaccessed, leading to 182 VC firms, which should cover about 90% of venture capital firmsin German-speaking countries. We discovered other 47 venture capital firms via lists ofother German researchers, business plan competitions, and personal networks. In a secondstep, available material on these VC firms concerning their portfolio firms was analyzed,and the VC firms were contacted directly for information on portfolio companies. Based


on this information, we compiled a list of firms that had received venture capital in thesecountries. We only considered VC-financed companies that were still actively managed(i.e., they had to have venture capital money under management). We also excluded firmsthat had received growth-stage and late-stage financing (controlled additionally by aquestion in the survey) but concentrated on seed and early-stage financing. We estimatethat the firms cover about 80% of the entire population of VC-backed companies withthese characteristics5 and that the database is representative since it does not contain anysystematic omission errors.

A survey was the most appropriate means of collecting data because secondaryresources did not contain detailed information regarding network types, network size,attitudes towards networking, etc. (Davidsson and Wiklund, 2000). Therefore, allinformation, network, and performance data were self-reported, but previous researchgives support to the reliability and validity of self-reported measures (Brush andVanderwerf, 1992; Orpen, 1993), especially if other sources are unavailable, as in thestudy of young and privately held small firms (Dess and Robinson, 1984). To ensure that ahigh proportion of the answers was valid, the questionnaires were sent to the CEOs and/orfounders of the start-ups, using a key informant approach (Huber and Power, 1985; Brushand Vanderwerf, 1992; Chandler and Hanks, 1993): CEOs and founders are considered thesingle most knowledgeable and valid information sources (Hambrick, 1981; Vanderwerfand Brush, 1989; Glick et al., 1990). We are, of course, aware of the trade-off betweenobjective data collected from secondary sources at several different times and data richnessderived from primary sources; given the unavailability of sufficient data, we therefore hadto opt for a survey approach of self-report data (Davidsson and Wiklund, 2000; Lyon et al.,2000).

Following Vanderwerf and Brush’s (1989) recommendations, we restricted the samplefirms in the following way. The firms were less than 10 years of age, which is consistentwith research on entrepreneurial firms (Covin et al., 1990). The average age of the firms inour sample of 5 1/2 years comes close to the stricter recommendations of other researchers(Robinson, 1999; Zahra et al., 2000). Founding management had to still be with the firm(Robinson, 1999). To be able to measure performance effects, we also excluded firms thatreported zero sales for all years of data collection. The choice of VC-backed firms wasdriven by different considerations. In our opinion, the restriction might dampen industryeffects that are typical in strategic management research (Dess et al., 1990), since VCs inEurope particularly invest in attractive industries and therefore we assume minor industryeffects across industries. Moreover, the selection of these firms acts as a form of control offounding conditions that renders the start-up base of the firms more homogeneous exceptfor network composition (Baum et al., 2000). Additionally, VC-backed firms are notsubject to corporate sponsorship and can therefore be considered independent entrepre-neurial ventures (Robinson, 1999).

The questionnaire was sent to a random sample of 570 firms in the developed database.A total of 142 questionnaires (i.e., about a quarter of the questionnaires returned) came

5 This estimate is based on the analysis of Finance magazine (Finance Magazin, February 2002) and an

approximation given by the Bundesverband Deutscher Kapitalgesellschaften (BVK).


back because the firms had gone out of business, reducing the effective sample to 428firms. Of the 75 questionnaires received, 60 met all sample selection criteria and weresufficiently completed. This translates to an effective response rate of 14%, which can beconsidered acceptable given that the average age of the firms was only about 5 1/2 years.6

The final sample therefore consisted of 60 VC-financed start-ups, which had to be lessthan 10 years old, had a sales track record, and were located in one of the countriesmentioned above. Sample firms come mainly from the IT, services, media, bio-tech, andenvironmental industries.

5. Measures

5.1. Dependent variables

A difficult decision in entrepreneurship research is the choice of performance measures.There are no commonly accepted performance measures for new ventures (McGee andDowling, 1994). We used both time-to-break-even and sales as performance measures.Reaching break-even can be considered one of the firm’s basic goals and therefore anadequate performance measure for start-ups (Davidsson and Honig, 2003). Respondentswere asked when they had reached break-even (months after foundation), or when theythought they would reach break-even (in months). For those who had not yet reachedbreak-even, time-to-break-even was calculated by adding their estimates to the age of thefirm (recorded in months). Davidsson and Honig (2003) studied pre-venture endowmentsin the form of social and human capital and their effect on business outcomes by applyinga time measure of performance (manifestation of first sale/profitability 18 months afterfoundation). While they use a nominal measure (yes/no) at time intervals, we determined areal time measure for moving into the profit zone (time-to-break-even in months).Gatewood et al. (1995) also applied a time measure for the influence of start-up behaviorand success. Teach et al. (1989) also used the variable time-to-break-even as aperformance measurement in order to evaluate the discovery of new venture ideas onperformance. Overall, it seems that time measures are particularly appropriate formeasuring effects of pre-start-up and start-up endowments or activities and firmperformance. The use of a time measure seems to us both reasonable and importantespecially when measuring the effect of initial network endowments, since other researchon inter-organizational relations has underlined the importance of speed to reachperformance targets (Stuart et al., 1999).

To measure network effects on firm performance in the years after foundation, we useda 1-year time lag between networks and sales. We chose sales and not sales growthbecause of possible distortions due to minimal or non-existent sales at the beginning of thefirm’s existence. Sales volume and sales growth are a common performance measureespecially for small and young firms (Begley and Boyd, 1986; Cardozo et al., 1996; Rue

6 McDougall et al. (1994), for example, reported a 11% response rate and Chandler and Hanks (1993) reported

a 19% response rate.


and Ibrahim, 1998; Weinzimmer et al., 1998; Robinson, 1999; Lebrasseur et al., 2003;Delmar et al., 2003) and are arguably a sufficient single indicator for firm performance(Venkatraman and Ramanujam, 1987; Delmar et al., 2003). Sales are relatively insensitiveto capital intensity and degree of integration, and therefore an appropriate measure forstudying networks even if they are sensitive to inflation (Weinzimmer et al., 1998; Delmaret al., 2003). In addition, the relatively low inflation rate during the period of interest in thecountries concerned does not call for a particular control for inflation.

Since many researchers expect a cause–effect relation between networking activity,network composition, and firm performance (Birley, 1985; Aldrich and Zimmer, 1986;Dubini and Aldrich, 1991), we thought that some form of time lag was conceptuallyimportant for our study: entrepreneurs develop networks not only as a response toimmediate needs but for future use (Johannisson, 2000). In line with other research, weused an 1-year time lag between network size or relational mix and sales (i.e., wecompared the independent variables in t0 with sales in t1). In their study, Lebrasseur et al.(2003) used a 1-year time lag between start-up activities and sales in the subsequent year.Lee et al. (2001) compared sponsorship-based linkages and performance in the subsequent2 years. Hansen (1995) and Chrisman and McMullan (2000) compared the entrepreneur’ssupport networks with organizational growth measured as employees and amount ofpayroll in the subsequent year, therefore also applying a 1-year time lag.

In summary, we used time-to-break-even as a time measure of performance atfoundation and sales as performance measure in the years after foundation.

5.1.1. Model specificationsWe tested two main models according to our hypotheses using OLS regression. In

both models, we compared the explanatory power of a control model using networksize only with a sub-model using the relational mix. We are aware that some research(e.g., Deeds and Hill, 1996) has demonstrated a curve–linear relationship betweenspecific network types (technology partnering) and specific forms of intermediaryperformance (new product development). The arguments against a simple linearrelationship between networks and overall firm performance are costs, benefits, risks,and relational capability over time (Deeds and Hill, 1996; Pihkala et al., 1999;Lechner and Dowling, 2003). Limited relational capability might eventually lead tointegration of some activities or restructuring of the existing network (Lorenzoni andOrnati, 1988; Delmar and Davidsson, 1998; Lechner and Dowling, 2003; Rothaermeland Deeds, 2001). We made the assumption that the firms in our sample were notyet affected by diminishing returns of inter-firm relationships because of their youngage (Rothaermel, 2001).

In the first model, we tested the influence of networks at foundation on time-to-break-even. The independent variables were network size and the relational mix in the first fullyear of the firms’ existence. We had actual registration dates of the company foundationand therefore decided to take the first full year of existence as the start-up point (i.e., thefirst year in which the company existed effectively for 12 months), since the fact, thatsome companies have existed only for 2 or 3 months in the first year registered, wouldhave strongly biased the results.


In the second model, we tested the influence of networks on firm performance afterfoundation. We calculated networking years for this purpose in order to gain a betterunderstanding of the role of networks in the years after foundation. This method gave aricher and more consistent data set (Baum et al., 2000) instead of taking firms of differentages and their last year’s networks, which would actually mean using networking yearswithout being able to control for some form of evolution. We had asked the firms toindicate different network types at the start of the firm’s existence and for the 5 yearsfollowing. For this model, we did not use firms as observations but the networking years2–4, leading to 159 cases. We added the age of the firm–measured in months–as a controlvariable.

5.2. Independent variables

The important independent variables of interest were the cumulative number of differenttypes of inter-firm relationships.We defined partners, in general, as those active relationshipswith other firms that go beyond the simple exchange function. We measured number ofnetwork ties for the variables below by counting the number of direct relationships.

5.2.1. Overall network sizeThe respondents were asked specifically to indicate the total number of relationships

that they regarded as important for their business, independent of the value of economicexchange. Network size was therefore the cumulative number of all active inter-firmrelationships that were instrumental for the business.

5.2.2. Social networksWe defined social networks as strong personal relationships with members of other

firms or stakeholder before the founding of the firm based on social relationships withindividuals, such as friends, relatives, long-standing colleagues who have become friends,and so forth. Respondents were asked to indicate how many of these active and strongrelationships they have had before starting the company.

5.2.3. Reputation networksReputation partners are firms that are market leaders or highly regarded firms that can

give reputation or legitimacy to the young firm. Respondents were asked to give thecumulative number of relationships where the main objectives was to enter into suchrelationships to increase the entrepreneurial firm’s credibility.

5.2.4. Co-opetition networksCo-opetition networks were defined as relationships with direct competitors. Different

types of relationships were possible: competitors used as sub-contractors, being a sub-contractor of the competitor, competitor as partner to respond to call for tenders, orcompetitor as an important source of information. The variable measured the cumulativenumber of all these types of relationships with competitors.7

7 We did not use the different co-opetition relationships in this study but aggregated the data.


5.2.5. Marketing information networksMarketing information networks are those relationships with other firms that lead to

information about market opportunities. They fall into the following categories: (1) theother firm is an important source of information concerning products, markets, or clients;(2) the other firm serves as referral in order to establish contact with a new client; and (3)the other firm helps to better tailor a product or service to market needs. Marketinformation networks is a count variable.

5.2.6. Cooperative technology networksCooperative technology networks were defined as the number of technology alliances

such as joint research and/or development projects, licensing, and cross-licensingagreements.

5.2.7. Measurement of the relational mixThe relational mix is made up of social, reputation, co-opetition, marketing

information, and cooperative technology networks. The measurement of the relationalmix poses a methodological and practical problem, since there may be double counting oroverlap of different network types in one relationship. A firm might serve as a member ofboth a firm’s reputational network and its co-opetition network, and even be part of thefirm’s cooperative technology network later. Marketing information might come from asocial network member for a week, a technology collaborator the next week, andreputational network company the week after. We acknowledge this problem, but priorcase study research suggests that entrepreneurs can distinguish between value-addingrelationships and all other relationships with other firms (Dubini and Aldrich, 1991).Second, entrepreneurs can identify the relationships they consider to be most important(Larson, 1992). Third, in a given time period, entrepreneurs tend to label their economicexchange partners and classify them according to the main benefit that the partner provides(Lechner and Dowling, 2003). Despite possible overlaps, networks can be analysed with arational-choice framework (Johannisson, 2000). In the pre-test of the questionnaire, wereceived various comments in this sense (e.g., bFirm A, one of our suppliers, adds value toour business by giving us timely information about market trends; we regard A mainly as amarket informant; Firm B, one of suppliers, co-develops product solutions with us, theyare a real innovation partner,Q etc.). Obviously, the importance or the main benefit of oneactor can change over time. While we cannot control in our study whether entrepreneursadded new ties to develop different kinds of relationships or if they were able to transformrelationships according to new priorities, we can provide a snapshot about the types ofrelationships used at a point in time. The lack of auto-correlation of the independentvariables seems to support our assumption. Additionally, one would expect the number oftotal relationships reported by the firms to be smaller than the sum of the different networktypes if double counting were an important methodological problem. The results, however,showed that the total number of important relationships reported was slightly higher thanthe sum of the relationships of the different network types. This can indicate that therespondents were able to classify most of their relationships according to a specificfunction and therefore as specific network type while only a small fraction of relationshipswas difficult to classify.


5.3. Descriptive statistics

Table 1 shows the descriptive statistics of the sample in general, for networks in the firstfull year of existence, and for the subsequent years. The 60 firms have, on average, a time-to-break-even of 53 months. As expected, social networks are the most numerousnetworks. The number of social networks and the overall size of networks are consistentwith other research (Aldrich, 1999; Dodd and Patra, 2002). Sales ranges and theirdevelopment show that this is a sample of growth firms.

6. Results

6.1. The importance of the relational mix at foundation

As discussed above, with partial model 1b (Table 2), we tested the influence of therelational mix at foundation on time-to-break-even. The model was significant, withmoderate explanatory power. Durbin–Watson statistics and collinearity statistics were alsoacceptable. In this model, social and reputation networks had negative coefficients asexpected (i.e., they reduce the time-to-break-even but only reputation networks had asignificant influence on time-to-break-even; p =0.03). Hypothesis (2) is therefore notsupported but Hypothesis (4) is supported. Surprisingly, technology networks showed apositive coefficient and the relationship was weakly significant ( p=0.09), indicating thatearly technology partnering delays reaching firm performance targets.

6.2. The importance of the relational mix on firm performance in the years afterfoundation

Table 3 presents the OLS regression for the partial model 2b. As already discussed, thismodel is significant ( p b0.001) with a relatively high explanatory power (adjusted

Table 1

Descriptive statistics

Average time-to-break-even (months) 53.18

Number of firms 60

Networks in year 1 (mean) Networks in years 2–4 (mean)

Social networks 6.57 8.51

Reputation networks 2.80 4.47

Co-operation networks 0.84 2.40

Marketing information networks 2.64 5.25

Technology networks 1.34 2.19

Sales data in year 1 Sales data in years 2–5

Average sales (in o) 1,582,642 4,608,141

Median (in o) 1,000,000 1,600,000

Range (in o) 0–15,000,000 30,000–80,000,000


R2=0.39); the Durbin–Watson coefficient of 1.61 was especially positive considering thatwe tested network years against sales and not individual firms. Collinearity statistics werequite strong. In this model, the control variable age did not have a significant influence on

Table 3

OLS regression—networks after founding

Model 2

Dependent variable: sales (in o) lagged by 1 year Model 2a: network size Model 2b: relational mix

Standardized regression coefficients (Significance)

Intercept (0.095) (0.368)

Controls

Age 0.167 (0.035) 0.073 (0.217)

Research variables

Network size 0.213 (0.007)

Social networks !0.147 (0.043)*

Reputational networks 0.168 (0.020)*

Co-opetition networks 0.508 (0.000)*

Marketing networks 0.208 (0.006)*

Technology networks 0.058 (0.370)

Adjusted R2 0.074 ( p b0.001) 0.389 ( p b0.001)F 7.149 17.459

df 2/153 6/149

Durbin–Watson 1.67 1.61

n 159 159

OLS regression—network size in the years after founding (years 2–4) and sales in the following year versus

relational mix and sales in the following year.

Table 2

OLS regression—networks at founding

Model 1

Dependent variable: time-to-break-even (in months) Model 1a: network size Model 1b: relational mix

Standardized regression coefficients (significance)

Intercept (0.000) (0.000)

Research variables

Network size !0.223 (0.087)

Social networks !0.218 (0.139)

Reputational networks !0.298 (0.029)*

Co-opetition networks 0.011 (.936)

Marketing networks !0.069 (0.627)

Technology networks 0.214 (0.093)

Adjusted R2 0.003 ( p b0.087) 0.123 ( p b0.032)F 3.0346 2.658

df 1/58 5/54

Durbin–Watson 2.06 1.87

n 60 60

OLS regression—network size at founding (first full year of existence) and time-to-break-even versus relational

mix at founding (first full year of existence) and time-to-break-even.


sales. Co-opetition and marketing information networks had a significant and the strongestinfluence on sales in the years after foundation. Hypotheses (5) and (6) are thereforestrongly supported. As expected, social networks had a negative impact on sales.Hypothesis (3) is therefore supported. Reputation networks had a moderate effect onsales. Cooperative technology networks had no relevant and significant influence onsales. Hypothesis (7) is therefore not supported.

6.3. Relational mix versus network size

One of the goals of this study was also to compare performance effects of the networksize (i.e., the total number of relationships with other firms) versus a more fine-grainedmeasurement (i.e., different network types and their mix). Therefore, two main OLSregression models were tested, with total network size and the relational mix asexplanatory variables for firm performance measured in model 1 by time-to-break-evenand in model 2 by sales. In model 1 (Table 2), we used the individual firms as cases(n=60) and tested the size of networks in the first full year of existence against time-to-break-even. Descriptive statistics showed that overall time until break-even was ratherlong (mean=53 months, or almost 4 1/2 years). Model 1a with network size alone hadweak explanatory power and was only significant at the 0.10 level. Therefore, we concludethat network size at founding, at best, weakly influences time-to-break-even. The modelusing the relational mix (model 1b), on the other hand, was significant, with moderateexplanatory power (adjusted R2=0.12, p =0.032). Therefore, we can conclude from theresults of models 1a and 1b that the relational mix is more effective in explainingperformance at foundation than network size only.

In model 2 (Table 3), we tested the role of network size and relational mix in the yearsafter foundation (years 2–4) on the following year’s sales with firm age as the controlvariable. Model 2a was significant but with relatively low explanatory power (adjustedR2=0.074, p b0.001). Age as a control variable and network size had a positive andsignificant influence on sales. The model therefore showed a firm’s network size to have avery moderate influence on a firm’s development (measured in sales). However, thesecond partial model, model 2b, which tested the influence of the relational mix on sales,was a better explanatory model. The overall model was significant and demonstrated amuch higher explanatory power than network size model (adjusted R2=0.39).

Overall, we conclude that network size is moderately linked to firm performance, andthe relational mix is a better explanatory concept than network size for firm performance atand beyond foundation.

7. Discussion

The objective of this study was to empirically test the importance of the relational mixon entrepreneurial firm development. The tested models demonstrated that the relationalmix has a higher explanatory power than network size alone. There was a positive andsignificant relationship between reputation networks at start and a shorter time-to-break-even. On the other hand, there was a negative relationship between early technology


networks and time-to-break-even, meaning that technology networks may actually delayachieving break-even. These findings are consistent with the model of Lechner andDowling (2003). Gaining reputation can be considered an important means of gaininglegitimacy with positive signaling effects to the markets of resource providers (Stuart etal., 1999; Roberts and Dowling, 2002; Deeds et al., 2004). Our analysis suggests thatreputation networks also play a moderate role after the start-up phase, meaning thatreputational networks remain positively associated with firm development. Concerningtechnology networks, it seems that firms use their initial technology base to exploit abusiness opportunity and that technology partnering is a means to enhance the technologyplatform later on to prepare the company for the future (Kelley and Rice, 2002). Earlytechnology partnering might therefore be an indicator that firms are not yet ready toexploit business opportunities or not attractive enough to enter into more value-addingpartnerships (Eisenhardt and Schoonhoven, 1996). Somewhat surprising was the non-significant relationship between social networks and time-to-break-even. Social networksare considered an important start-up resource of the entrepreneurial firm (Johannisson,1995; Zhao and Aram, 1995; Baum et al., 2000). Our study did show that social networksare the firm’s largest networks at the start of its existence (mean=6.57 in the first full yearof existence, whereas every other network type contained no more than three relation-ships). It may be that since social networks are the start-up base for almost all firms, thatthey are not a good discriminator of successful firm development. It is also possible thatsocial networks are more important at the critical stage of pre-foundation whenentrepreneurs assemble their very first resources and have some initial level of financialbacking and a small number of customers and suppliers, but not when firms are alreadyfocusing on sales growth and time-to-break-even. The literature on nascent entrepreneur-ship seems to support this position (Aldrich, 1999; Davidsson and Honig, 2003). Socialnetworks seem to have an indirect effect. The quality of social networks can be verydifferent and the use of social networks might also make a difference. The negative andsignificant influence of social networks on sales after foundation, however, was expectedand confirmed, supporting the view that social networks move into the background asother types of networks emerge to support the growth of the firm. A high dependence onsocial networks over time could be considered an indicator that firms are not capable ofdeveloping other important ties. It might also indicate a tendency towards over-embeddedness (Gargiulo and Benassi, 2000). Our analysis demonstrated the significantand positive influence of co-opetition and marketing information networks on sales afterthe start-up phase. Our findings therefore support other research that underlines theimportance of external and informal marketing information scanning instead of a moreformal marketing approach for young firms (Brush, 1992). Little is known, however, abouthow firms develop marketing networks. It has been suggested that marketing networks area function of management style and open firm culture, and therefore a task of allemployees (Lechner and Dowling, 2003). In contrast, it has been argued that networks aredeveloped mainly by the entrepreneurial team at the beginning of the firm’s life cycle(Ostgaard and Birley, 1994; Johannisson, 1995; Lipparini and Sobrero, 1997). Concerningthe importance of co-opetition networks, the analysis of the survey data suggests that ofthose firms that had relationships with other competitors, about one third was usingcompetitors as subcontractors and receiving subcontractor jobs from competitors as well as


carrying out large projects together with competitors. Co-opetition networks seem toincrease the entrepreneurial firm’s flexibility and to promote sales growth after the start-upphase. Our analysis was not able to demonstrate a relationship between technologynetworks and sales development after foundation. As expected, technology networks arerather small in number (mean=1.87 over all networking years), which might explain thelow relevance in the overall model. Other research also suggests that the building oftechnology networks has more of an indirect effect on firm performance (Kelley and Rice,2002) and results might only be revealed in the long run. The time frame of our studymight not have been sufficiently broad to test the influence of technology networks on firmdevelopment.

In general, our study emphasizes the importance of the relational mix and the change ofthe relational mix on firm development. This result supports the perspective inentrepreneurship research that many factors interacting in a complex way in a dynamiccontext determine firm development (Shane and Venkataraman, 2000). The studyquestions further the use of overall network size as a simple measure.

We acknowledge several limitations to our research. We have already addressed thequestion of self-reported data. We cross-checked the financial data randomly forconsistency through press releases issued on the websites of 12 companies. This researchwas limited to venture capital-financed firms and therefore to high-tech firms—non-venture capital-financed firms may experience different constraints (especially of afinancial nature). We see this limitation more as a strength since networking has beenassociated with lack of resources and we were able to demonstrate the link betweennetworking and firm development of less constrained firms. The selection of these firmsacts as a form of control on foundation conditions that renders the start-up base of thefirms more homogeneous except for network composition (Baum et al., 2000). Thisapproach finally led to an initial homogenous group of firms, reducing the bias of cross-industry analysis. However, given the selection process of VC firms, our sample mightnaturally have a growth bias. Moreover, we did not include regional effects in our model.It is suggested that the development of egocentric networks depends on larger socio-centric or regional networks and vice versa, as can be found in regional clusters(Johannisson, 1998; Lechner and Dowling, 2000). Therefore, we did not control for sparseregions or industries (i.e., regions or industries where networking was difficult because ofthe lack of partner firms) (Dubini, 1989; Davidsson, 1995). However, other researchsuggests that entrepreneurs in sparse regions try to establish relationships with other firmsoutside their original location (Birley and Westhead, 1990). In addition, our sample did nothave any systematic regional bias due to the construction of the overall population.Another argument concerns possible signalling effects due to the fact that the firms areVC-backed. Venture capital firms may also facilitate access to other reputational partners.However, this does not explain intra-sample differences. Concerning reputation networks,we mentioned above that reputation networks can be the outcome of excellent resourcesand that these excellent resources explain firm performance. While we cannot control forthis reverse causality in the model, we argue based on case study research (Lechner andDowling, 2003) that excellent resources without reputation networks are not sufficient toovercome liability of newness. In this study, we did not measure network structure (i.e.,neither indirect ties nor non-redundant ties). The influence of measures such as direct


network size is particularly heavily constrained by the efficiency of the overall network.Replication with an ever larger sample would, of course, be desirable.

8. Conclusions

Networking is very important for entrepreneurial firms. This study supports the viewthat entrepreneurial networking is as much about adding new and different relationships asabout transforming existing relationships, therefore empirically supporting case-basedresearch in the field (Larson, 1992; Dubini and Aldrich, 1991; Lechner and Dowling,2003). This study has both theoretical and practical implications. Complex developmentmodels are still rare in entrepreneurship research (Hoang and Antoncic, 2003). This studyaddresses the area with a specific aspect of networking (i.e., the role of the relational mixon firm development). We think that our findings advance research because theydemonstrate that the complex measure of the relational mix provides more explanatorypower than previously used measures such as network size. Second, it underlines theimportance of different networks in different situations (Gulati and Higgins, 2003). Webelieve that more development-oriented and detailed network studies are needed toenhance our understanding of the complex development processes of entrepreneurialfirms. Both quantitative and qualitative studies investigating network structure efficiencythrough network mapping over time would lead to new insights and propositions to furtherresearch on networks in entrepreneurial firms. Additionally, we lack knowledge about howto develop certain network types and think that studies linking management style andnetworking could produce promising insights.

From a practical perspective, understanding network dynamics helps answer thequestion of which ties matter when. Young firms that are constrained by liability ofnewness should use their social networks to develop early reputation networks to fosterfirm development. Our study also showed that marketing information networks play animportant role in firm development. External and informal marketing information scanningseems not only to be a necessity for young firms because of resource constraints but alsoan effective means of detecting and exploiting market opportunities instead of a moreformal approach to marketing. Furthermore, the study revealed the importance ofrelationships with competitors for firm development. While it is not an easy decision for anentrepreneur, our research suggests that there are true benefits to entering into arelationship with a competitor. Finally, technology partnering seems to be a means ofenhancing the technology platform of the firm and to be more important at a later stage ofthe firm’s development. Overall, our study suggests that networking should be a proactivetask of entrepreneurs and that strategic network building over time is an important factorfor the development of the entrepreneurial firm.

References

Ahuja, G., 2000. The duality of collaboration: inducements and opportunities in the formation of interfirm

linkages. Strategic Management Journal 21/3, 317–344.


Alba, R.D., 1982. Taking stock of network analysis: a decade of results. In: Bachran, S.B. (Ed.), Research in the

Sociology of Organizations, vol. 1. JAI, Greenwich, CT.

Aldrich, H., 1999. Organizations Evolving. Sage, London.

Aldrich, H., Zimmer, C., 1986. Entrepreneurship trough social networks. In: Sexton, D., Smilor, R. (Eds.), The

Art and Science of Entrepreneurship. Balliner, New York, pp. 3–23.

Ardichvili, A., Cardozo, R., 2000. A model of the opportunity recognition process. Journal of Enterprising

Culture 8 (2), 103–119.

Ardichvili, A., Cardozo, R., Ray, S., 2003. A theory of entrepreneurial opportunity identification and

development. Journal of Business Venturing 18 (1), 105–123.

Baum, J., 1996. Organizational ecology. In: Clegg, S., Hardy, C., Nord, S. (Eds.), Handbook of Organization

Studies. Sage, London.

Baum, J., Calabrese, T., Silverman, B., 2000. Don’t go it alone: alliance network composition and startups’

performance in Canadian biotechnology. Strategic Management Journal 21 (3), 267–294.

Begley, T., Boyd, D., 1986. Executive and corporate correlates of financial performance in smaller firms. Journal

of Small Business Management, 8–15 (April).

Bhide, A., 2000. The Origin and Evolution of New Businesses. Oxford University Press, Oxford.

Birley, S., 1985. The role of networks in the entrepreneurial process. Journal of Business Venturing 1,

107–117.

Birley, S., Westhead, P., 1990. dNorth–southT contrasts in the characteristics and performance of small firms.

Entrepreneurship and Regional Development 2, 27–48.

Boari, C., Lipparini, A., 2000. Networks within industrial districts: organising knowledge creation and transfer by

means of moderate hierarchies. Journal of Management and Governance 99 (3), 339–360.

Brush, C., 1992. Marketplace information scanning activities of new manufacturing ventures. Journal of Small

Business Management 30 (4), 41–51.

Brush, C., Vanderwerf, P., 1992. A comparison of methods and sources for obtaining estimates of new venture

performance. Journal of Business Venturing 7 (2), 157–170.

Burt, R., 1992. Structural Holes. Harvard University Press, Cambridge, MA.

Burt, R., 1997. A note on social capital and network content. Social Networks 19, 355–373.

Bygrave, W., Hofer, C., 1991. Theorizing about entrepreneurship. Entrepreneurship Theory and Practice 16 (2),

13–22.

Cardozo, R., Elder, T., Harmon, B., 1996. When does growth pay? In: Reynolds, P., Birley, S., Butler, J., Bygrave,

W., Davidsson, P., Gartner, W., McDougall, P. (Eds.), Frontiers of Entrepreneurship Research. Babson

College, Wellesley, MA, pp. 480–490.

Chandler, G., Hanks, S., 1993. Measuring the performance of emerging businesses: a validation study. Journal of

Business Venturing 8, 391–408.

Chell, E., Baines, S., 2000. Networking, entrepreneurship and microbusiness behaviour. Entrepreneurship and

Regional Development 12, 195–215.

Chrisman, J., McMullan, E., 2000. A preliminary assessment of outside assistance as a knowledge resource: the

longer-term impact of new venture counseling. Entrepreneurship Theory and Practice, 37–53 (Spring).

Cooper, A., 2002. Networks, alliances and entrepreneurship. In: Hitt, M., Ireland, D., Camp, M., Sexton, D.

(Eds.), Strategic Entrepreneurship. Blackwell, Oxford, pp. 203–222.

Covin, J., Slevin, D., Covin, T., 1990. Content and performance of growth-seeking strategies: a comparison of

small firms in high- and low-technology industries. Journal of Business Venturing 5, 391–412.

Davidsson, P., 1995. Culture, structure and regional level of entrepreneurship. Entrepreneurship and Regional

Development 7, 41–62.

Davidsson, P., Honig, B., 2003. The role of social and human capital among nascent entrepreneurs. Journal of


Davidsson, P., Wiklund, J., 2000. Conceptual and empirical challenges in the study of firm growth. In: Sexton, D.,

Landstrom, H. (Eds.), Handbook of Entrepreneurship. Blackwell, Oxford, pp. 26–44.

Deeds, D., Hill, C., 1996. Strategic alliances and the rate of new product development: an empirical study of

entrepreneurial biotechnology firms. Journal of Business Venturing 11, 41–55.

Deeds, D., Mang, P., Frandsen, M., 2004. The influence of firms’ and industries’ legitimacy on the flow of capital

into high-tech ventures. Strategic Organization 2/1, 9–34.


Delmar, F., Davidsson, 1998. A taxonomy of high-growth firms. In: Reynolds, P.D., Bygrave, W.D., Carter,

N.M., Manigart, S., Mason, C.L.M., Meyer, G., Shaver, K.G. (Eds.), Frontiers of Entrepreneurship

Research 1998. Arthur M. Blank Center for Entrepreneurship, Babson College, MA, pp. 399–413.

Delmar, F., Davidsson, P., Gartner, W., 2003. Arriving at the high-growth firm. Journal of Business Venturing 18,

189–216.

Dess, G., Robinson, R., 1984. Measuring organizational performance in the absence of objective measures.

Strategic Management Journal 5 (3), 265–273.

Dess, G., Ireland, D., Hitt, M., 1990. Industry effects and strategic management research. Journal of Management

16 (1), 7–27.

Di Maggio, P., 1986. Structural analysis of organizational fields. Research in Organizational Behavior, vol. 8, pp.

335–370.

Dodd, S., Patra, E., 2002. National differences in entrepreneurial networking. Entrepreneurship and Regional

Development 14, 117–134.

Dollinger, M., 1985. Environmental contacts and financial performance of the small firm. Journal of Small

Business Management 23 (1), 24–30.

Dowling, M., Lechner, C., Bau, F., 1998. Kooperative Wettbewerbsbeziehungen-eine empirische Untersuchung.

In: Franke, N., von Braun, C.F. (Eds.), Innovationsforschung und Technologiemanagement. Springer,

Berlin.

Dubini, P., 1989. The influence of motivations and environment on business start-ups: some hints for public

policies. Journal of Business Venturing 4, 11–26.

Dubini, P., Aldrich, H., 1991. Personal and extended networks are central to the entrepreneurial process. Journal

of Business Venturing 6, 305–313.

Dyer, J., Singh, H., 1998. The relational view: cooperative strategy and sources of interorganizational competitive

advantage. Academy of Management Review 23 (4), 660–679.

Easton, G., 1992. Industrial networks: a review. In: Axelsson, B., Easton, G. (Eds.), Industrial Networks: A New

View of Reality. Routledge, London.

Eisenhardt, K., Schoonhoven, C., 1996. Resource-based view of strategic alliance formation: strategic and social

effects in entrepreneurial firms. Organization Science 7 (2), 136–150.

Fombrun, C., 1982. Strategies for network research in organizations. Academy of Management Review 7,

280–291.

Freel, M., 2000. External linkages and product innovation in small manufacturing firms. Entrepreneurship and

Regional Development 12, 245–266.

Gargiulo, M., Benassi, M., 2000. Trapped in your own net? Network cohesion, structural holes, and the

adaptation of social capital. Organization Science 11 (2), 183–196.

Gartner, W., 1985. A conceptual framework for describing the phenomenon of new venture creation. Academy of

Management Review 10, 696–706.

Gatewood, E., Shaver, K., Gartner, W., 1995. A longitudinal study of cognitive factors influencing start-up

behaviors and success at venture creation. Journal of Business Venturing 10, 371–391.

Glick, W., Huber, G., Miller, C., Doty, D., Sutclif, K., 1990. Studying changes in organizational design

and effectiveness: retrospective event histories and periodic assessments. Organization Science 1,

293–312.

Granovetter, M., 1974. Getting a Job: A Study of Contacts and Careers. Harvard University Press, Boston, MA.

Granovetter, M., 1985. Economic action and social structure: the problem of embeddedness. American Journal of

Sociology 91 (3), 481–510.

Green, P., Brown, T., 1997. Resource needs and the dynamic capitalism typology. Journal of Business Venturing

12, 161–173.

Gulati, R., 1999. Network location and learning: the influence of network resources and firm capabilities on

alliance formation. Strategic Management Journal 20 (5), 397–419.

Gulati, R., Higgins, M., 2003. Which ties matter when? The contingent effects of interorganizational partnerships

on IPO success. Strategic Management Journal 24 (2), 127–144.

Gulati, R., Westphal, J., 1999. Cooperative or controlling? The effects of CEO–board relations and the content of

interlocks on the formation of joint ventures. Administrative Science Quarterly 44, 82–111.

Hakansson, H., Snehota, I., 1995. Developing Relationships in Business Networks. Routledge, London.


Hambrick, D., 1981. Environment, strategy and power within top management teams. Administrative Science

Quarterly 26, 253–276.

Hansen, E., 1995. Entrepreneurial networks and new organization growth. Entrepreneurship Theory and Practice

19 (4), 7–19.

Hite, J., 2000. Patterns of multidimensionality in embedded network ties of emerging entrepreneurial firms. Paper

Presented at the Annual meeting of the Academy of Management, Toronto, Canada.

Hite, J., Hesterly, W., 2001. The evolution of firm networks: from emergence to early growth. Strategic

Management Journal 22, 271–286.

Hoang, H., Antoncic, B., 2003. Network-based research in entrepreneurship: a critical review. Journal of Business

Venturing 18, 165–187.

Huber, G.P., Power, D.J., 1985. Retrospective reports of strategic managers: guidelines for increasing their

accuracy. Strategic Management Journal 6, 171–180.

Huggins, R., 2000. The success and failure of policy-implanted inter-firm network initiatives: Motivations,

processes and structure. Entrepreneurship and Regional Development 12, 111–135.

Jarillo, C., 1988. On strategic networks. Strategic Management Journal 9, 31–41.

Jarillo, C., 1989. Entrepreneurship and growth: the strategic use of external sources. Journal of Business


Jarillo, C., 1993. Strategic Networks. Butterworth, Heinemann.

Johannisson, B., 1995. Paradigms and entrepreneurial networks: some methodological challenges. Entrepreneur-

ship and Regional Development 7, 215–232.

Johannisson, B., 1998. Personal networks in emerging knowledge-based firms: spatial and functional patterns.


Johannisson, B., 2000. Networking and entrepreneurial growth. In: Sexton, D., Landstrom, H. (Eds.), Handbook

of Entrepreneurship. Blackwell, Oxford, pp. 368–386.

Kelley, D., Rice, M., 2002. Advantage beyond founding: the strategic use of technologies. Journal of Business


Knoke, D., Kuklinsky, I., 1983. Network Analysis. Sage, Beverly Hills, CA.

Larson, A., 1991. Partner networks: leveraging external ties to improve entrepreneurial performance. Journal of


Larson, A., 1992. Network dyads in entrepreneurial settings: a study of the governance of exchange relationships.

Administrative Science Quarterly 37, 76–104.

Larson, A., Starr, J., 1993. A network model of organization formation. Entrepreneurship Theory and Practice 17

(2), 5–15.

Lebrasseur, R., Zanibbi, L., Zinger, T., 2003. Growth momentum in early stages of small business start-ups.

International Small Business Journal 21 (3), 315–330.

Lechner, C., Dowling, M., 2000. The evolution of industrial districts and regional networks: the case

of the biotechnology region Munich/Martinsried. Journal of Management and Governance 99 (3),

309–338.

Lechner, C., Dowling, M., 2003. Firm networks: external relationships as sources for the growth and

competitiveness of entrepreneurial firms. Entrepreneurship and Regional Development 15, 1–26.

Lee, C., Lee, K., Pennings, J., 2001. Internal capabilities, external networks and performance: a study on

technology-based ventures. Strategic Management Journal 22, 615–640.

Lincoln, J.R., 1982. Intra- (and inter-) organizational networks. In: Bachararan, S. (Ed.), Research in the

Sociology of Organizations, vol. 1. JAI, Greenwich, CN, pp. 1–38.

Lipparini, A., Sobrero, M., 1997. Co-ordinating multi-firm innovative process: entrepreneur and catalyst in small

firm network. In: Ebers, M. (Ed.), The Formation of Inter-Organisational Networks. Oxford University Press,

New York.

Lomi, A., 1997. The Relational Analysis of Organizations. Il Mulino, Bologna.

Lorenzoni, G., 1992. Accordi, reti e vantaggio competitivo. Etas libri, Milano.

Lorenzoni, G., Baden-Fuller, C., 1995. Creating a strategic centre to manage a web of partners. California

Management Review 37 (3), 146–163.

Lorenzoni, G., Lipparini, A., 1999. The leveraging of interfirm relationships as a distinctive organizational

capability: a longitudinal study. Strategic Management Journal 20 (4), 317–337.


Lorenzoni, G., Ornati, O., 1988. Constellations of firms and new ventures. Journal of Business Venturing 3,

41–57.

Lyon, D., Lumpkin, G., Dess, G., 2000. Enhancing entrepreneurial orientation research: operationalizing and

measuring a key strategic decision making process. Journal of Management 26 (5), 1055–1085.

Malecki, E., Poehling, R., 1999. Extroverts and introverts: small manufacturers and their information sources.


McDougall, P., Phillips, P., Covin, J., Robinson, R., Herron, L., 1994. The effects of industry growth and strategic

breadth on new venture performance and strategy content. Strategic Management Journal 15 (7), 537–554.

McEvily, B., Zaheer, A., 1999. Bridging ties: a source of firm heterogeneity in competitive capabilities. Strategic

Management Journal 20 (12), 1133–1156.

McGee, J., Dowling, M., 1994. Using R&D cooperative arrangements to leverage managerial experience: a study

of technology-intensive ventures. Journal of Business Venturing 9, 33–48.

Mitchell, J.C., 1969. The concept and the use of social network. In: Mitchell, J.C. (Ed.), Social Networks in

Urban Situations. Manchester University Press, Manchester, pp. 1–32.

Nohria, N., 1992. Is a network perspective a useful way of studying organisation? In: Eccles, R. (Ed.), Networks

and Organisations. Harvard Business School Press, Boston, MA.

Orpen, C., 1993. Strategic planning, scanning activities and the financial performance of small firms. Journal of

Small Business and Entrepreneurship 11 (1), 62–72.

Ostgaard, T., Birley, S., 1994. Personal networks and firm competitive strategy—a strategic or coincidental

match? Journal of Business Venturing 9, 281–305.

Pihkala, T., Varamaki, E., Vesalainen, J., 1999. Virtual organization and the SMEs: a review and model

development. Entrepreneurship and Regional Development 11, 335–349.

Podolny, J., Page, K., 1998. Network forms of organization. Annual Review of Sociology 24, 57–76.

Powell, W., 1987. Hybrid organizational arrangements: new form or transitional development? California

Management Review, 67–87 (Fall).

Powell, W., 1990. Neither market nor hierarchy: network forms of organization. Research in Organizational

Behavior 12, 295–336.

Ramachandran, K., Ramnarayan, 1993. Entrepreneurial orientation and networking, some Indian evidence.

Journal of Business Venturing 8 (6), 513–524.

Richardson, G., 1972. The organisation of industry. Economy Journal, 883–896.

Roberts, P., Dowling, G., 2002. Corporate reputation and sustained superior financial performance. Strategic

Management Journal 23 (12), 1077–1092.

Robinson, K., 1999. An examination of the influence of industry structure on eight alternative measures of

new venture performance for high potential independent new ventures. Journal of Business Venturing 14,

165–187.

Rothaermel, F., 2001. Complementary assets, strategic alliances, and the incumbent’s advantage: an

empirical study of industry and firm effects in the biopharmaceutical industry. Research Policy 30,

1235–1251.

Rothaermel, F., Deeds, D., 2001. More good things are not necessarily better: an empirical study of strategic

alliances, experience effects, and innovative output in high-technology start-ups. Academy of Management

Proceedings. TIM.

Rue, L., Ibrahim, N., 1998. The relationship between planning sophistication and performance in small

businesses. Journal of Small Business Management 36 (4), 24–32.

Sawyer, O., McGee, J., Peterson, M., 2003. Perceived uncertainty and firm performance in SMEs—the role of

personal networking. International Small Business Journal 21 (3), 269–290.

Shane, S., Venkataraman, S., 2000. The promise of entrepreneurship as a field of research. Academy of

Management Review 25, 217–226.

Starr, J., MacMillan, I., 1990. Resource cooptation via social contracting: resource acquisition strategies for new

ventures. Strategic Management Journal 11, 79–92 (Special issue).

Stinchcombe, A., 1965. Social structure and organizations. In: March, J. (Ed.), Handbook of Organization. Rand

McNally, Chicago, IL.

Stuart, T., Hoang, H., Hybels, R., 1999. Interorganizational endorsements and the performance of entrepreneurial

ventures. Administrative Science Quarterly 44 (2), 315–349.


Teach, R., Schwartz, R., Tarpley, F., 1989. The recognition and exploitation of opportunity in the software

industry: a study of surviving firms. In: Brockhaus, R. (Ed.), Frontiers of Entrepreneurship Research. Babson

College, Wellesley, MA, pp. 383–397.

Thorelli, H., 1986. Networks: between markets and hierarchies. Strategic Management Journal 7 (1), 37–51.

Uzzi, B., 1997. Social structure and competition in interfirm networks: the paradox of embeddedness.

Administrative Science Quarterly 42, 35–67.

Vanderwerf, P., Brush, C., 1989. Achieving empirical progress in an undefined field. Entrepreneurship Theory

and Practice, 45–58 (Winter).

Venkataraman, S., 1997. The distinctive domain of entrepreneurship research: an editor’s perspective. In: Katz,

J., Brockhaus, J. (Eds.), Advances in Entrepreneurship, Firm Emergence, and Growth. JAI Press,

Greenwich, CT.

Venkatraman, N., Ramanujam, V., 1987. Measurement of business economic performance: an examination of

method convergence. Journal of Management 13 (1), 109–122.

Venkataraman, S., Van de Ven, A., 1998. Hostile environmental jolts, transaction set, and new business. Journal of


Virtanen, M., 1997. The role of different theories in explaining entrepreneurship. Journal of Best Papers, ICSB

42nd World Conference.

Von Hippel, E., 1978. A customer-active paradigm for industrial product ideas. Research Policy 7, 240–266.

Wassermann, S., Faust, K., 1994. Social Network Analysis. Cambridge University Press, New York.

Weinzimmer, L., Nystrom, P., Freeman, S., 1998. Measuring organizational growth: issues, consequences and

guidelines. Journal of Management 24 (2), 235–262.

Zahra, S., Ireland, R., Hitt, M., 2000. International expansion by new venture firms: international diversity, mode

of market entry, technological learning and performance. Academy of Management Journal 43, 925–950.

Zhao, L., Aram, J., 1995. Networking and growth of young technology-intensive ventures in China. Journal of



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