DISENTANGLING THE EFFECT OF NEW VENTURE TEAM HETEROGENEITY ON VENTURE SUCCESS

Transcription

1 DISENTANGLING THE EFFECT OF NEW VENTURE TEAM HETEROGENEITY ON VENTURE SUCCESS Uwe Cantner, Maximilian Goethner *, Michael Stuetzer * 1 Friedrich Schiller University Jena, Department of Ecnomics, Chair of Microeconomics, Carl-Zeiss-Str. 3, Jena, Germany addresses: uwe.cantner@uni-jena.de (Uwe Cantner), maximilian.goethner@uni-jena.de (Maximilian Goethner, michael.stuetzer@uni-jena.de (Michael Stuetzer) * Corresponding authors: M. Goethner (Tel ) and M. Stuetzer (Tel ): fax: Draft Version November 2009 Do not cite or quote Abstract: Are heterogeneous new venture teams more successful than homogenously compounded teams, or is it the other way around? Some scholars regard heterogeneous teams as more effective in solving non-routine problems because of the broader knowledge scope. However, heterogeneous teams may suffer from disparity, which leads to communication problems and poor strategic decision making. Therefore, heterogeneity is often regarded as a double-edged sword (Milliken & Martins 1996, p. 403), illustrating the lack of consensus on how team composition influences team outcomes. In this paper we develop new measures of team heterogeneity to model both edges of the sword by using one source of information the functional background of the team members. Analyzing data from 337 start-up teams, we find both edges to be sharp and affecting the economic and innovative performance of startups. Key words: Entrepreneurship, New venture teams, Team heterogeneity, Team diversity JEL classification:

2 1. Introduction Entrepreneurship is the process through which in the absence of a market for future goods and services, these goods and services come to be in existence (Shane & Venkataraman, 2000). The alert individual recognizes an opportunity and packages a business idea that forms the basis for a new business venture. In spite of the popular image of the lone entrepreneur, our paper is concerned with the team that packages the idea. The choice of entrepreneurial teams as the object of the present study is appropriate, given that high-tech start-ups are more often created by groups of people than by individuals (Roberts, 1991; Francis & Sandberg, 2000). In fact, entrepreneurial teams are identified as an omnipresent [economic] phenomenon describing the superior entrepreneurial start-up concept (Lechler, 2001). The apparent success of start-up teams can be attributed to the logic, that high technology industries might require more skills than an individual would be likely to have, necessitating that individuals combine their abilities in teams in order to start an organization successfully (Gartner 1985, p. 703). Despite its obvious importance, a sizeable gap exists in the normative and empirical literature on the subject of start-up teams (Cooney, 2005). Only recently, entrepreneurship scholars have begun to address this research area, focusing on venture team composition in terms of heterogeneity and its effect on new venture performance (e.g., Cooper & Dailly, 1997; Chowdhury, 2005). As yet, this literature has come to inconclusive results (Williams & O Reilly, 2

3 1998). In keeping with the cognitive resource diversity perspective (Cox & Blake, 1991), team heterogeneity might be beneficial by stimulating creativity, and increasing the cognitive ability of a team to solve problems and make decisions. On the other hand, the similarity-attraction paradigm (Byrne, 1971) would propose that heterogeneity has an adverse impact on team performance. Heterogeneous teams might lack a common language and suffer from inherent tensions and emotional conflicts arising from differences among its members. Rather than subscribing to either perspective on the heterogeneityperformance relation, the current paper considers the propositions of both approaches to obtain a more comprehensive explanation of new venture team performance. Therefore, we adapt measures from the field of ecology and economics to separately model both dimensions of team heterogeneity using information on the prior functional background of the team members. More precisely, we investigate the relationship between the start-up team s composition of functional roles and the start-up s economic performance (firm survival and employment growth in the first three years of operation) as well as innovative performance (number of filed patents in the first four years of operation). Testing our hypotheses, we rely on a unique dataset of 337 new venture teams established between 1994 and 2006 in innovative industries in the eastern German state of Thuringia. The remainder of the paper is organized as follows. Next we review the theory and previous findings on heterogeneity. We, then, describe the methods 3

4 used for data collection and analysis. After that, we present our results and draw some conclusions. 2. Theory and Hypotheses 2.1 Two Sides of the Coin Theoretical Perspectives on the Heterogeneity- Performance Link The literature reveals two competing schools of thought that have been advanced in order to explain the effects of team heterogeneity, namely the similarity-attraction paradigm (Cox & Blake, 1991) and the cognitive resource diversity theory (Byrne, 1971). Proponents of the similarity-attraction perspective conclude that team heterogeneity has an adverse impact on team outcomes (Williams & O Reilly, 1998). Grounded in social psychology, the similarity-attraction paradigm argues that the more similar group members are to one another, the more attracted they will be to the group. Particularly, based on perceptions of similarities and differences people subconsciously tend to place themselves and others in social categories (Tajfel, 1982). This might lead to in-group and out-group memberships within teams, where similar in-group members are regarded as more trustworthy, honest and cooperative compared to dissimilar out-group members (Joshi & Jackson, 2003). Heterogeneous teams are, therefore, hypothesized to be less productive because of inherent tensions and emotional conflicts arising from member differences (Ancona & Caldwell, 1992; Eisenhardt et al., 1997). Conversely, homogeneous groups are more 4

5 likely to work well together because of their shared characteristics, thereby increasing team cohesion and performance (Milliken & Martins, 1996; Horwitz, 2005). Adopting the cognitive resource diversity perspective, scholars from management and organizational research emphasize the positive effects of team heterogeneity on performance. Contrary to similarity-attraction theory, this theory proposes that diverse teams are more effective in solving complex, non-routine problems, which are common to entrepreneurial start-ups. The underlying assumption is that the unique skills, opinions and perspectives of dissimilar members may improve the breadth of a team s cognitive ability (Hambrick et al., 1996; Milliken & Martins, 1996; Williams & O Reilly, 1998). Increased cognitive breadth, in turn, should foster synergistic processes which are characterized by flexibility and open communication, encouraging team members to frequently exchange and interpret subjective and even divergent information (Stewart & Barrick, 2000). Ultimately, heterogeneity among team members may stimulate creativity, innovation, problem-solving and decision-making processes and thus results in superior performance relative to homogenous teams (Bantel & Jackson, 1989; Horwitz & Horwitz, 2007). To sum up, theoretical arguments suggest that team heterogeneity offers both an opportunity as well as a challenge for the development of new ventures. On the one hand, it is claimed that heterogeneous teams are more capable because of the cognitive heterogeneity of team members. On the other 5

6 hand, heterogeneity while increasing the amount of information available to the team can also be counterproductive as it may produce detrimental emotional conflicts (Pelled et al., 1999). These competing assessments of team heterogeneity have also been manifested with mixed empirical findings (Williams & O Reilly, 1998). Accordingly, heterogeneity is often regarded as a double-edged sword (Milliken & Martins 1996, p. 403), illustrating the lack of consensus on how team composition influences team outcomes. In order to simultaneously explore both effects we develop two arguments, namely scope and disparity. The scope argument relates to the cognitive resource diversity perspective. It, therefore, captures the productive effect of team heterogeneity stemming from the breadth of a team s knowledge base. The disparity argument relates to the similarity-attraction paradigm and captures the destructive effect of heterogeneity, resulting from divergences in a team s knowledge base. Hypotheses with respect to both arguments are outlined below. 2.2 Theory and Hypotheses Research in organizational demography and small groups has shown that heterogeneity in important issues, such as team members functional experience, negatively affects new venture performance (Jackson et al., 1991). Due to different backgrounds of training and professional expertise, members of heterogeneous groups may possess different professional vocabularies (Lovelace et al., 2001). Hence, in heterogeneous, rather than in homogeneous, 6

7 groups the team interaction process is more likely to be hampered by difficulties people have in understanding each other (Ancona & Caldwell, 1992). Moreover, divergent perceptions on how the team should ideally operate in order to realize its goals increase the chances that misunderstanding and misinterpretation will trigger affective disputes (Ensley et al., 2002). Affective conflict tends to reduce a team s strategic decision quality as it often focuses on personal dislikes or animosities between team members (Amason & Sapienza, 1997). In accord with the similarity-attraction-paradigm, such conflict might lead to hostile atmosphere that could undermine consensus and produce ineffective communication results (Amason & Sapienza, 1997; Eisenhardt et al., 1997). Hence, we expect: H1a: A high degree of a new venture team s knowledge disparity is related to lower levels of the new venture s economic performance. Other research shows that the level of a team s heterogeneity in functional experience is likely to have a positive impact on new venture performance (Ensley et al., 1998). Roure and Maidique (1986) reported that a team s completeness as the degree to which key positions (e.g., marketing, engineering, finance) were staffed by members of the founding team, was positively associated with firm success. The general argument made for the positive relationship between functional heterogeneity and firm performance can be traced back to the cognitive resource diversity perspective. 7

8 Accordingly, higher levels of functional heterogeneity within a team are expected to be associated with less duplication of specific skills (Bantel & Jackson, 1989). Thus, when a number of entrepreneurs with heterogeneous functional backgrounds choose to work together, a variety of expertise, information, and resources are applicable that might not have been available if all members were from the same functional area (Stam & Schutjens, 2006). We previously stated that divergences may also engender differences in opinions. However, when manifesting themselves in the form of constructive cognitive disputes, different opinions of team members may produce positive effects. Instead of emotional conflicts turning down venture team effectiveness, cognitive conflicts may lead to frequent informal communication and discussions of alternative options of operation, thereby reducing groupthink and obtaining solutions for complex problems more rapidly (Amason & Sapienza, 1997; Pelled et al., 1999). Taken together, we expect higher numbers of functional representations in the venture team to positively affect new venture performance. The corresponding hypothesis is formulated as follows: H1b: A high degree of a new venture team s knowledge scope is related to higher levels of the new venture s economic performance. Cross-functional groups bringing together individuals with pertinent expertise are considered effective means to foster a firm s innovative 8

9 performance (Ancona & Caldwell, 1992). It is generally acknowledged that the greater the heterogeneity in the knowledge stocks held by individuals in the firm, the greater the potential for new knowledge to be generated (Cohen & Levinthal, 1990). Accordingly, founding teams that encompass heterogeneous functional backgrounds should have a higher level of absorptive capacity to tap a broad array of relevant information and, thus, have greater innovative capabilities. Similarly, arguments from the cognitive resource diversity paradigm would suggest that teams with diverse backgrounds and perspectives are more likely to challenge conventional ways of doing things and to successfully generate new products and services than more homogenous teams (Jackson, 1992). Empirical evidence supporting this view is provided by the management and new product development literatures (e.g. Bantel & Jackson, 1989; Hambrick et al., 1996; Keller, 2001). For example, Bantel and Jackson (1989) demonstrate that organizational innovations in the banking industry were positively associated with a multifunctional composition of the top management teams. Furthermore, highly heterogeneous teams were found to decrease the time-to-market of new products by bringing more creative potential to problem solving and ensuring availability of critical input (Sethi et al., 2001). Zirger and Hartley (1996) show that each additional functional role represented in the development team reduced the time needed to take a new product to the market by 0.4 months. Yet, team functional heterogeneity might not generally unfold positive effects on innovative performance. While different perspectives may enrich 9

10 the decision-making process, at some point, heterogeneity of inputs can create information overload diminishing a team s ability to handle various, often contradictory solutions (Milliken & Martins, 1996; Sethi et al., 2001). It is, then, not uncommon to find teams resorting to algorithmic or routine problemsolving, which is assumed to reduce the likelihood to discover innovative ideas (Sethi et al., 2001). Moreover, functionally heterogeneous groups made up of individuals from entirely different thought worlds may find it difficult to develop a shared purpose since they may have different perceptions of the product development process and even different definitions of basic terms such as product and market (Ancona & Caldwell, 1992). In accord with the similarity-attraction paradigm, it should be rather difficult to achieve an effective teamwork aiming to generate innovative output under these circumstances (Lovelace et al., 2001; Sethi et al., 2001). Following from these arguments, we suggest an inverse U-shaped relationship between founding team members functional experiences and innovative output of the new venture. As functional heterogeneity increases from a low to a moderate level, the team s innovative performance increases. Beyond a moderate level, however, heterogeneity of represented functional areas in the team has a negative effect on innovative performance. We, thus, propose the testable hypotheses: H2a: A high degree of a new venture team s knowledge disparity is related to lower levels of the new venture s innovative performance. 10

11 H2b: Innovative performance of the new venture is highest at a moderate level of venture team s knowledge scope. According to a large number of entrepreneurship studies, human capital positively influences organizational performance in new venture start-ups (e.g., Gimeno et al., 1997; Bosma et al., 2004; Delmar & Shane, 2006). One might therefore argue that, due to the larger amount of human capital resource at their disposal, firms founded by a larger group of entrepreneurs should be more successful than firms of smaller venture teams (Ucbasaran et al., 2003). On the other hand, authors have pointed out diminishing returns of team size. A larger number of team members may cause coordination problems and communication errors due to increases in team member interactions (Horwitz, 2005). Larger teams may also be faced with severe free-riding problems. Kandel and Lazear (1992) recognize that in large groups the individual group member has an incentive to work less than at the efficient level. Furthermore, previous research shows that the larger the number of team members the more likely teams encompass divergent perspectives and personal goals (Ancona & Caldwell, 1992; Eisenhardt et al., 1997). Consistent with the similarityattraction paradigm, larger teams, more than smaller teams, might therefore suffer from dysfunctional emotional conflicts (Amason & Sapienza 1997). As indicated by these findings, we expect a diminishing effect of team size: 11

12 H2c: The relationship between a venture team s knowledge disparity and the new venture s economic performance is stronger for larger venture teams. H2d: The relationship between a venture team s knowledge disparity and the new venture s innovative performance is stronger for larger venture teams. 3. Dataset and Methods 3.1 Dataset The Thuringian Founder Survey (Thüringer Gründer Studie) an interdisciplinary research project on success and failure of innovative start-ups in the eastern German state of Thuringia provided the data for the current study. This dataset draws from the entries of private and commercial companies in the commercial register (Handelsregister) between the years 1994 to 2006 in Thuringia. We restricted this base to entries in innovative industries (Grupp & Legler, 2000) 1. The survey population consisted of the first registered owner-managers (4,215 founders) for each new Handelsregister entry (2,971 companies). From this survey population a random sub-sample of 3,671 founders was drawn and contacted. From January to August 2008, we conducted 639 structured faceto-face interviews with solo-entrepreneurs or with the leading entrepreneurs of 1 Grupp and Legler (2000) define innovativeness at the level of the industry. On average companies in innovative industries spend more than 3.5% of their turnover for research and development. 12

13 start-up teams (response rate of about 25%). For this analysis we omitted soloentrepreneurs - focussing solely on the 410 team-started companies. After removing not genuinely start-ups and observations with missing data, our final sample consisted of 337 companies. The structured interviews were conducted by members of the research project and student research assistants who were trained in various sessions in December We use a retrospective design to collect the data. To overcome the bias of hindsight as well as memory decay (Davidsson 2006) we adapt the tool life-history-calendar from psychology in order to receive the information for the venture creation process. The life-history-calendar is a useful tool to reconstruct individual processes and developments (Caspi et al. 1996; Freedman et al. 1988). One gains more reliable and valid retrospective information compared to traditional questionnaires (Belli et al., 2001). When the interview commenced, the participants together with the interviewer filled in major life events and sequences in the life-history-calendar (family life, working sequences, historical events, and important dates of the business history). During the interview the life-history-calendar was visible to the participants. Before each retrospective item (e.g. team composition) was started, we asked the interviewee to look at the specific time point in the lifehistory-calendar and recalled verbally special events taking place during that time in order to better remember that time. The interview strategy and the lifehistory-calendar are in line with the recommendation by Belli et al. (2004). 13

14 The procedure used for this study enables us to make first arguments about causality. 3.2 Dependent Variables Our dependent variables attempt to measure the innovative and economic performance of team start-ups (italics indicate the respective variable name). We measure innovative performance of the team start-ups by capturing their patenting activities. Therefore, data from the German Patent Office was accessed. We counted the number of patents which either members of the founding team (as inventors) or the company (as applicant) filed during the first four years of business operation. Patents have often been used as an output indicator of the innovation process (e.g. Ernst, 2001; Bloom & van Reenen, 2002). We are fully aware of the weaknesses of that approach, but they are a direct, objective and detailed measure of innovation (Griliches, 1990). The first indicator of economic performance is the survival status of the firms after the first two years of business operation. Venture growth, our second economic performance indicator, is approximated by the firms absolute number of employees in the third business year. Since our sample consists of genuinely new firms, we assume zero employment at the time of start-up. If a new venture does not exceed its third business year we recoded the number of employees as zero. 14

15 Traditional outcome variables like firm value, profitability or turnover are not applied in this study for two reasons. First, the self-reported measure of sales turned out to be unreliable information. While respondents could assess the amount of sales generated in the first three business years, monetary reform in Germany replacing the D-Mark with the Euro in several steps between 1999 and 2001 made it difficult for the entrepreneurs to correctly attribute the sales to either currency. Second, secondary data from business information providers could not be used, because such databases tend to focus on larger and surviving firms, reducing substantially the overlap with our dataset. Nevertheless we undertook an effort to check the validity of our dependent variable. Two business information providers (Creditreform and Bureau van Dijk) provided us with secondary data regarding employment growth in the first three business years. These data, however, referred only to 66 start-ups in our data set. We found that our measure of number of employees and the corresponding information provided by Creditreform and Bureau van Dijk (2009) are highly correlated (r =.78, p <.001). 3.3 Independent Variables Preliminary Notes and Traditional Indicators Functional heterogeneity in teams is typically conceptualized as referring to the distribution of team members prior work experience in different categories (e.g. Carpenter & Fredrickson, 2001; Zimmerman, 2008). The 15

16 majority of the empirical literature uses one overall indicator for functional heterogeneity (e.g. Amason & Sapienza, 1997; Bantel & Jackson, 1989; Zimmerman, 2008; Polzer et al., 2002). The most prominent example of these indicators is Blau s (1977) index = 1 ( n i = 1 2 p ), (1) i where p denotes the proportion of the team members whose main functional background is in the ith functional category. This, in the words of Bunderson and Sutcliffe (2002), dominant function diversity does not take into account, that from a theoretical point of view (as discussed in section 2), there might be two separate heterogeneity dimensions: knowledge scope and knowledge disparity. Consequently Blaus s index measures an overall net effect of the productive and destructive impact of heterogeneity and might explain the inconclusive results on the heterogeneity-performance relationship found in prior studies (see for an overview, Mathieu et al., 2008; Williams & O Reilly, 1998) To the best of our knowledge Bunderson & Sutcliffe (2002) were the first to show that different conceptualizations of functional heterogeneity differentially affect team processes and performance. They introduced the concept of intrapersonal functional diversity to address the extent to which the team consists of functional generalists or specialists. From a theoretical point of view this is an indicator of our knowledge scope dimension, since it pays attention to the scope of the knowledge base of the team. However, Bunderson and Sutcliffe (2002) do not use a proper indicator to cover the 16

17 knowledge disparity dimension, although they are aware of this dimension. Instead, they rely on the above describe dominant function diversity measure. We aim to improve measurement of heterogeneity by clearly disentangling the both dimensions of functional heterogeneity knowledge scope and knowledge disparity. Our goal is to separately model both dimensions of the new venture team s knowledge base. Knowledge scope shall capture the productive effects of functional heterogeneity resulting from the collective expertise and information available to the team. Knowledge disparity shall capture the destructive effect of functional heterogeneity, stemming from differences in the inner structure of the functional background of the team members. We argue that such differences lead to communication problems, inherent tensions and emotional conflicts. Owing to the broad nature of both dimensions, we use more than one indicator for each dimension. More precisely, variety and diversity indices form the measure of knowledge scope, while dissimilarity and non-redundancy indices form the knowledge disparity measure. In order to generate these indices, we rely on information on the functional background of the new venture team members prior to the first steps into the venture creation process. We asked the interviewee whether or not the team members possessed prior work experience in 1) management, 2) marketing / sales / promotion, 3) accounting / controlling / financing, 4) engineering / R&D, 5) production, and 6) personnel. As team members we defined individuals who were actively involved in the venture creation process and were intended to become or had 17

18 already been an owner of the company. For reasons of time restriction, we collected data on a maximum of five team members. Next, we illustrate how the indices of variety, diversity, dissimilarity and non-redundancy were calculated in order to finally obtain our measures of knowledge scope and knowledge disparity. For the sake of clarity and to give other researchers the opportunity to replicate our results in different settings and datasets, we apply a strict formal approach for the derivation of our indices, but provide an illustrative example for a better understanding. [Table 1 about here] [Table 2 about here] Indices Measuring Knowledge Scope: Variety and Diversity The starting point of all indices is the grid of the prior work experience of the different team members in different functional categories. Let n be the total number of functional categories in which one entrepreneur can acquire practical experience prior to the start-up. Let m denote the number of the team members. If an entrepreneur has experience in one or more categories, this can be written as 1 if team member j has experiencein the functional category i x ij = (2) 0 otherwise. In order to calculate the two knowledge scope indices, variety and diversity, we weight the number of team members experience in a given 18

19 functional category against the absolute number of experiences the team as a whole enjoys in all categories. The weighting procedure can then be formalized as s i = m n m x = 1 ij = = j / x, (3) i 1 j 1 ij where s i denotes the weighted probability of team members experience in a given category. An example of a team with three members having different work histories is provided in table 1. The basis for these two indices of a venture team s knowledge scope is a general measure of entropy, as formulated by Hill (1973) and Baumgaertner (2004). They adopted concepts of information theory (Shannon, 1948; Rényi, 1961) to develop measurement indices for ecological and product heterogeneity. Following the notation of Baumgaertner (2004) this general measure of entropy is then given by n a 1/(1 a ) ( s ) ; a 0, a 1 i= 1 i va ( s) = (4) n a 1/(1 a ) lima 1( s ) ; a = 1. i= 1 i Subspecies of this formula family can be derived by including different values of a. For example, one obtains the Shannon (1948) entropy index 2 for a = 1, or the Herfindahl-Hirschman index for a = 2 (Herfindahl, 1950; Hirschman, 1964). In general, a determines the weight imposed on the absolute variety of the functional background (low values of a) of the whole team or on the evenness of the distribution of the functional backgrounds of the team 2 To entrepreneurship and management scholars, this index might be better known as Teachman (1980) index. 19

20 members (high values of a). We calculate the indices of variety and diversity with the extreme cases of a = 0 and a + to cover the broad nature of the knowledge scope dimension. For variety, equation (4) reduces to n 0 Variety v ( s = =1 s = z n (5) 0 ) i i where z is the number of categories in which at least one of the team members had prior experience. 3 In our illustrative example, the team has a rather broad functional background with team member A being experienced in five categories, leaving uncovered accounting / controlling / financing. Team members B and C are both experienced in three functional categories which, however, completely overlap with the experience categories of A. As indicated by equation (4), our measure of variety then takes the value of five (see table 2). For further analyses and to achieve comparability, we transform all heterogeneity measures to a unit interval. In the present case, a standardized value of.8 is obtained. As a approaches infinity, equation (4) collapses to Diversity v + ( s) = 1/ max( si ). (6) Diversity only hinges on the weighted probability of the categories in which the team is most experienced. It therefore captures the (dis)concentration of a 3 Equation (5) slightly differs from the original equation of Baumgaertner (2004). This is due to the fact that Baumgaertner only runs the calculations with the categories, which are effectively occupied. In our case the entrepreneurial team may have no experience in one or two of the six predefined categories, leaving them empty and s i =0. For values of zero the equation is not defined. We resolve the problem by calculating the equation only for those categories which are not empty or in other words in which at least one of team members gained experience. Nonetheless, this detour keeps the spirit of Hill s and Baumgaertner s idea completely intact. 20

21 team s work experience in the different functional categories. The difference to the first measure, variety, lies in the fact that diversity is a relative measure paying attention to the distribution of work experience among team members. In our illustrative example (table 1), management and production are the categories in which all team members reported to have had experience prior to first steps into the venture creation process. With the track of the team members work experiences adding up to ten covered cells in the grid, both of these categories achieve a weighted probability s i of.3. This team possesses a moderately diverse working history which manifests in a standardized diversity value of.47 (table 2) Indices Measuring Knowledge Disparity: Dissimilarity and Nonredundancy The measure of dissimilarity is based on pair wise comparison of the functional background patterns of the entrepreneurs. Exemplary, we write for two entrepreneurs A and B of a team [ 1,..., ] A B A, B 1 if xi = 1 xi = 1 for all i N i n f = (7) i 0 otherwise. A, B Thereafter, we sum up f i over all categories and obtain F = i = n 1 f A,B i, the number of categories with overlapping experiences between entrepreneurs A and B. After that we compare this real overlap between those two entrepreneurs with the potential overlap they could have given their functional background. Thus, dissimilarity is calculated by 21

22 Dissimilarity A,B A, B = F 1. (8) n B ( x ) / 2 n A x + i= 1 i i= 1 i This variable ranges from zero indicating complete overlap of functional backgrounds to unity indicating complete heterogeneity of the functional backgrounds of entrepreneurs A and B. 4 We, finally, take the mean of all pair wise dissimilarity measures to obtain the dissimilarity index at the team level. The illustrative example in table 1 shows rather similar patterns for team members B and C, sharing knowledge in two of their three functional categories. In contrast, the work experience pattern of team member A slightly differs from that of B and even more compared to C. Accordingly, pair wise measurement of dissimilarity provides the highest value for the combination A-C (.43), and lower values for A-B (.25), and for B-C (.20), respectively. At the mean the dissimilarity values of the whole team is at.29 (see table 2). Generally, the higher the value of dissimilarity the more disperse is the team s knowledge base. A suitable starting point to derive our non-rendundancy index is the above described measure of the variety of a team s functional background. Here, we weight the number of categories z in which at least one of the team members reported to have prior working experience with the absolute number of experiences the team as a whole has in the z categories. Non-rendundancy is therefore calculated by the following equation n m Non-redund ancy = z / x. (9) i = 1 j = 1 ij 4 Note that we rely on prior experiences of the team members instead of missing experiences to calculate this measure. 22

23 Non-redundancy measures the extent to which the teams prior work experience pattern exceeds the level necessary to maintain a certain amount of functional variety within the team. The higher non-redundancy the thinner is on average the team members shared experience in the different functional categories. For example, table 1 suggests that the team could abandon the experiences of team members B and C without reducing the number of overall covered functional categories. Given that B and C contribute five out of the team s ten functional track record, this amounts to a value of non-redundancy of.5 (standardized.38) (see table 2). 3.4 Verification of the Respondents Information To verify the information of the interviewee, for a random sample of 55 cases we conducted an additional face-to-face interview with another member of the start-up team and received 48 matchable and usable responses. We performed dependent t-tests for paired samples on the equality of means concerning our four indicators variety (t = -1.25; p =.22), diversity (t = -.70; p =.49), dissimilarity (t = -.48; p =.11) and non-redundancy (t = -.94; p =.36). The tests reveal no statistical differences in all cases giving evidence for the reliability of our independent variables. 3.5 Factor Analysis 23

24 Table 3 provides descriptive statistics and correlations of the variables used in this study. The start-up teams are characterized by high levels of variety. Concerning the diversity, dissimilarity and non-redundancy indices, we find the teams showing moderate levels of heterogeneity. As expected, our measures of variety and diversity are highly correlated (r >.8). The same holds true for the measures of dissimilarity and non-redundancy (r >.8). Note also that both pairs of heterogeneity indices show low between correlations (in all cases r <.12). A principal component analysis further revealed that both pairs of team heterogeneity indices load on two separate factors (see table 4). This lends further evidence to our measurement of two different dimensions of teams functional heterogeneity, namely knowledge scope capturing the variety and diversity of a team s knowledge base, and knowledge disparity capturing the team members differences in their work experience patterns. [Table 3 about here] [Table 4 about here] Knowledge scope is finally computed by taking the mean of the variety and diversity indices. Higher values indicate a broader and less concentrated knowledge base of the team. Knowledge disparity measures are derived by the mean of dissimilarity and non-redundancy indices. High values of knowledge disparity indicate more pronounced differences within the knowledge base of the team. Interestingly, although knowledge scope and knowledge disparity 24

25 draw from the same source of information (team members prior work experience in six predefined functional categories) they do not show strong correlation (r =.1, p =.07). Since our approach of disentangling functional heterogeneity is new to the literature, we also compute functional heterogeneity by applying a modified version of the Blau (1977) index. This traditional functional heterogeneity is computed by = 1 ( n i = 1 i 2 s ), (10) where s i as defined in equation 3 denotes the weighted probability of team members experience in a given category. This traditional functional heterogeneity indicator serves as a standard of comparison to our two measures. 5 Like all other functional heterogeneity indices, traditional functional heterogeneity was normed to a unit interval. 3.6 Controls This study includes variables controlling for other indicators of team heterogeneity discussed in the literature (see e.g., Chowdhurry, 2005; Ruef et al., 2003; Bantel & Jackson, 1989). Applying Blau s (1977) index (and normed to a unit interval), we consider age heterogeneity and industry experience heterogeneity. We also control for the relationship among team members prior to the creation of the new venture team (1 = spouse or partner, 5 We could not compute traditional functional heterogeneity according to equation 1, because we do not possess the information in which functional area the team members were most experienced. 25

26 2 = relative, 3 = friend or colleague from previous employment, 4 = acquaintance, 5 = stranger). By taking the mean of all pair wise combinations among the team members we compute venture teams relational composition. Furthermore, control variables are included referring to team size, start-up capital (amount of financial capital available in the first year of business operation), innovativeness of the start-up (1 = conducting R&D in the venture creation phase and the first three years of business was a major activity for the start-up; otherwise=0). Growth aspirations of the start-up team were measured on a five point scale concerning the question if the goal of the interviewee at time of the start-up was 1 = to be a small player in the market vs. 5 = to become the market s leader. We conducted a median split to distinguish startups with above and below the average growth aspirations. Final controls refer to industry sector, and start-up year. 4. Results 4.1 Regression Results for Knowledge Scope and Knowledge Disparity In this section we explore the effects of knowledge scope and knowledge disparity on new venture performance. Our fist indicator of the economic performance of a new venture is venture survival. Thereby we employ logistic regressions on a binary variable with the value of one if the new venture was still active after the first two business years. Our second indicator for firms economic performance (the number of employees in the third business year), as well as our indicator for innovative performance (the number of filed 26

27 patents till the fourth business year) are count data. The descriptive statistics in table 3 reveal that both variables exhibit a high degree of overdisperion suggesting the use of a negative binomial regression model. Furthermore, especially for the number of filed patents we observe a high fraction of 0 (85%) in the data. Therefore, estimating a zero-inflated negative binomial model could also be appropriate. Consequently, we performed the Vuong (1983) test for nested models to test the approriateness of either approach. The test statistic for model 1 in table 5 (z = 1.16; p =.12) favors the standard negative binomial regression. The results of the regressions on venture survival, number of employees and number of filed patents are displayed in table 5, 6, and 7. [Table 5 about here] [Table 6 about here] [Table 7 about here] We turn first to the regressions on venture surival (table 5). In model 1 the variables knowledge disparity and knowledge scope as well as control variables relating to the new venture project are entered in the regression. Teams with higher levels of knowledge disparity show a significantly lower probability of business continuation after the first two business years (B = -.447; p <.05). Unexpectedly, the knowledge scope of a team s knowledge base does not affect venture survival. These results hold true when we enter in 27

28 model 3 additional control variables relating to the new venture team: team size, industry experience heterogeneity, age heterogeneity and the relational composition. None of this variables show up significant. Turning to the same regression models on the number of employees after three business years (table 6), we find that new venture teams profit from their knowledge scope (B =.153; p <.01), whereas knowledge disparity other than expected does not affect employment. Although knowledge scope and knowledge disparity only have a significant effect on one of the two dependent variables of economic performance, we conclude the hypotheses 1a and 1b to be supported. High degrees of knowledge disparity negatively and high degrees of knowledge scope positively affect new ventures economic performance. Now we turn to the innovative activity of a new venture team (table 7). Consistent with our hypothesis 2a knowledge disparity (model 1) had a negative effect (B = ; p <.01) on innovative activity of the company. In order to test Hypothesis 2b we include the squared term of knowledge scope in model 1. Both terms turn out to be significant (p <.05) in this model, with the maximum of the innovative activity being reached at a value of.47 for knowledge scope. Entering the additional control variables team size, industry experience heterogeneity, age heterogeneity and the relational composition in model 3 into the regression somewhat influences the impact of knowledge scope, but not knowledge disparity. Both knowledge scope coefficients decrease and suffer from reduced significance levels. This is mainly due to the impact of team size on innovative performance (B =.502; p <.05). 28

29 Accordingly, larger teams report significantly more filed patents. However, we conclude our hypothesis 2a and 2b to be supported. Hypothesis 3a stated that the team size has a moderating effect on the linkage between knowledge disparity and new venture performance. The logistic regression on survival (model 4 in table 5) and of the negative binomial regression on the number of employees (model 4 in table 6) show that the interaction of team size x knowledge disparity is non-significant. Thus, hypothesis 3a is not supported by the data. Hypothesis 3b proposed a moderating effect of team size on the linkage between knowledge scope and the innovative performance. Inspecting model 4 in table 7 we find the respective interaction term to be a significant (B = -.68; p <.05) predictor of the number of filed patents. Running an OLS regression instead of a negative binomial regression confirms these results even at a higher level of significance (p <.01). On average, high degrees of knowledge disparity already reduced the number of filed patents of a company. We examine the impact of the team size in more detail in figure 1. As illustrated, larger teams filed fewer patents when they suffered from high degrees of knowledge disparity supporting hypothesis 3b. 4.2 Additional Analysis: The Traditional Functional Heterogeneity In order to compare our proposed two dimensional heterogeneity model with the uni-dimensional model we rerun the regressions on economic and innovative performance using the traditional functional heterogeneity 29

30 variable. The results of these regressions are displayed in model 4 in the tables 5-7. We find that traditional functional heterogeneity soloely has a positive signficant impact (B =.124, p <.05) on the number of employees, whilst not contributing to explain survival (B = -.511, n.s.) and patenting activities (linear term: B = 1.536, n.s.; quadratic term: B = , n.s.) of new ventures. Additionally we compare of the goodness of fit of our proposed two heterogeneity dimensions solution (model 1 in tables 5-7) on the one hand and the uni-dimensional traditional functional heterogeneity (model 2 in tables 5-7) on the other hand. In every case the Akaike s (1973) information criterion slightly favors the two dimension solution above the uni-dimensional solution. Additionally, the explanatory power as indicated by pseudo R-squared is always higher for the proposed two heterogeneity dimension solution than for the uni-dimensional heterogeneity approach. 5. Discussion and Conclusion 5.1 Interpretation and Discussion of the Results The goal of our study was to help reconcile the inconclusive results of prior research on the link between functional heterogeneity of new venture teams and subsequent firm performance on theoretical as well as empirical grounds. Our research pursues the work of Bunderson & Sutcliffe (2002) who were first to show that different conceptualizations of heterogeneity differentially affect team processes and performance. However, we argue that the problem is more fundamental in nature. We believe that the cause of the inconclusive results in 30

31 prior studies is that functional heterogeneity is there treated as a onedimensional concept. The first important contribution of our study is to show that there exist two dimensions of functional heterogeneity (knowledge scope and knowledge disparity). Although we conceded much space to methodological part of the paper, our ideas have a sound theoretical base. Knowledge scope stands in the tradition of the cognitive resource diversity perspective (Cox & Blake, 1991). It represents the productive impact of heterogeneity coming from broader expertise and information in the critical start-up phase, enhancing capacity for being creative, higher learning capabilities and superior decision-making processes. Knowledge disparity is related to the similarity attraction paradigm (Byrne, 1971) and an indicator for the destructive impact of heterogeneity, leading to group think, communication problems and affective conflicts. We show that both dimensions of heterogeneity can be empirically separated from each other. Our second important contribution deals with the differential impact of knowledge scope and knowledge disparity on new venture performance. Looking first at the economic performance, we find that higher levels of knowledge disparity significantly reduced the survival chances of the newly founded business. However, knowledge scope had no impact on surviving. We interpret this result, that the new venture team especially needs cohesion, trust and a shared language to accomplish a basic and short-term goal like survival. A broad knowledge base or a high potential for team members to learn from 31

32 each other neither supports nor jeopardizes the accomplishment of the basic goal to survive. To accomplish subsequent venture growth the advantages of low knowledge disparity levels diminish. Regressions on the number of employees in the third business year as dependent variable clearly show that knowledge disparity does not affect this performance indicator. The same analysis, however, reveals that knowledge scope has a strong and positive impact on the number of employees. Concerning innovative success both dimensions have a substantial impact. Thereby, consistent with theory we show that the relationship between knowledge scope and filed patents is inverse U-shaped. Innovative success reaches its maximum in teams with an intermediate level of knowledge scope. High values of knowledge disparity are associated with lower innovative success. We could also show that team size moderated the relationship between knowledge disparity and patent applications. The destructive impact of knowledge disparity is more pronounced for larger teams. The results suggest that in larger teams communication problems and cacophonie stemming from background disparities endanger team work. Taken together our results suggest that the impact of heterogeneity is task dependent. Focussing on the ecnomomic performance of the new ventures, the two heterogeneity dimensions knowledge scope and knowledge disparity differ in their impact on the two indicators survival status and the number of employees. This result complement the the findings of Hambrick et al. (1996), 32

33 Pelled et al. (1999) and Bowers et al. (2000) who were among to first to find the impact of team heterogeneity differed by tasks. For example studying the U.S. airline industry Hambrick et al. (1996) find that heterogeneity in top management teams had different impacts on the propensity and speed of certain strategies and actions. Heterogenous teams were bolder in competitive actions than homogenous teams. Homogenous top management teams are faster in their actions and responses to competitor moves, but less likely to respond to competitors initiatives. Pelled et al. (1999) report that the routiness of a task moderates the relationship between heterogeneity of a team and emotional as well as affective conflict. Our results are robust to array of variations of the models. 6 First and most important, running the regressions separtely for different team sizes does not change the sign of the coefficients of knowledge scope and knowledge dispartiy in the models on the number of employees and filed patents. However, due to reduced sample sizes the coefficients suffer from reduced significance levels. Concerning the regression on new ventures survival status the coefficients of knowledge scope show up with a positive sign, but remain insignificant. Second, the consideration of entry in and exit from the team during the venture creation process also does not seriously alter our results. Respective coefficients for entry as well as exit dummies are not significant in regressions on the survival status and the number of employees. Concerning the regressions on the innovative performance we find entry having a 6 These results are available upon request from the authors. 33