Does team diversity really matter? The connection between networks, access to financial resources, and performance in the context of university spin-offs

A growing body of literature focusing on the effects of team diversity on the performance for USOs (e.g., Hahn et al. (2019)) has three main directions—the founder or inventor, the team, as well as the skills and networks (Mathisen and Rasmussen (2019), Diánez-González and Camelo-Ordaz (2019)). In this paper, we focus on the effects of team composition, because this literature is still a bit scarce and “one-dimensional.” Earlier studies have focused more strongly on this one or two dimensions of diversity or measured only by team size and the effects on USO performance. Team diversity is commonly measured by human capital in terms of team members’ previous industrial or management experience, with industrial experience serving as a key predictor for firm performance (Diánez-González and Camelo-Ordaz (2016), Delmar and Shane (2006)). Several studies have confirmed that the composition of a team significantly improves the USO performance when complementary human and social capital, such as business management expertise or market and technological knowledge, is important in the founding team, explaining that team heterogeneity is important for success without controlling for other factors, such as team size or deepness of diversity (Toole and Czarnitzki (2009), Gimmon and Levie (2010), Wennberg et al. (2011), D’Este et al. (2012), Borges and Jacques Filion 2013), Criaco et al. (2014), Fernández-Pérez et al. (2014), Nielsen (2015), Ciuchta et al. (2016), Helm et al. (2018)). For instance, Diánez-González and Camelo-Ordaz (2016) validated that the recruiting of non-academic individuals in the management team of USOs could outweigh the missing experience of the academics; hence, they are in favor of the heterogeneous combination of these skills and manager types to enhance USO performance. The same holds true for the study of Huynh et al. (2017) showing that the capabilities of the founding team have a positive influence on USO performance. Kilduff et al. (2000) similarly find that, in the case of demographic diversity, heterogeneity in the age of founder team members has a positive influence on performance (units sold, market share, other performance indicators) as well as differences in tenure (Jehn and Bezrukova (2004)). Furthermore, Eisenhardt and Schoonhoven (1990) affirmed that a growing team size leads to higher sales growth or productivity (Campion et al. (1993), Magjuka and Baldwin 1991). McGee et al. (1995) and Eisenhardt and Schoonhoven (1990) show that functional diversity (industry and work experience) affects performance positively, as well as heterogeneity in the proportion of different job categories (Magjuka and Baldwin (1991)). Regarding personality traits, Mohammad and Angell (2003) and Neuman et al. (1999) showed that diversity in team extraversion and emotional stability affects performance positively.

Conversely, studies like Shane and Stuart (2002) have delivered the insight that the industry experience of a USO founding team has no effect on the survival of the USO as a success measure. Instead, they show when some team members have different and higher levels of industry experience the time to market is shortened if the USOs survive the seed stage. Furthermore, entrepreneurial experience has no additional impact on the new venture success of USOs (e.g., Nerkar and Shane (2003)). For other measures, too, such as team diversity of age, religion, and family background, the results are not robust in delivering positive or negative performance effects (Roberts (1991)), but are rather inconsistent. Therefore, when considering even more the specific USO context, it has to be emphasized that the business idea is mostly created around a technological idea or very specific knowledge, often embedded or tacit in the head of one scientist or a team doing research on this issue (Markman et al. (2008), Clarysse et al. (2007b)). Thus, when starting a business, these team members are commonly needed to transfer the invention into an innovation and a market-ready prototype or product (e.g., Knockaert et al. 2011, Di Gregorio and Shane (2003), Zucker and Darby (1998)). Therefore, this literature typically finds that a homogeneous team might be more promising due to the overlap of knowledge and (technological) understanding (Knockaert et al. (2011)). Amason et al. (2006) found that overall the increase of team heterogeneity leads to a decrease in new venture performance, processes, and effectiveness, as well as functional diversity (Carpenter (2002), Jehn and Bezrukova (2004), Pitcher and Smith (2001), (Knight et al. (1999), (Pelled et al. (1999), Ancona and Caldwell (1992), (Hambrick et al. (1996)). Diversity in extraversion as a personality trait (Mohammad and Angell (2004)) inhibits processes and diversity in neuroticism to performance (Halfhill et al. (2005)). Specifically, with regard to demographic aspects, such as race/ethnicity, gender, age, tenure, and even education, Jackson et al. 2003, Kirkman et al. (2001), Leonard et al. (2004), Li and Hambrick (2005), Mohammad and Angell (2004), Simons et al. (1999), Timmerman (2000), Townsend and Scott (2001), and Watson et al. (1998) verified that these diversity measures diminish performance as well and make processes more complicated. Webber and Donahue (2001) as well as Campion et al. (1993) found no effect of demographic diversity or skill heterogeneity on performance Thus, we can observe fairly inconsistent results (see again Mathisen and Rasmussen 2019).

Other research has verified that, in this case, the practical expertise is missing to commercialize the invention successfully. Therefore, a mix of both types of expertise would be better in a more heterogeneous team (Hahn et al. (2019), Knockaert et al. (2011), Visintin and Pittino 2014). Some of these studies show that the balance or rate of diversity has to be controlled for to minimize the problems in heterogeneous teams (Hahn et al. (2019), Knockaert et al. (2011), Visintin and Pittino (2014)). A further solution can be a specific mix of characteristics (Ben-Hafaïedh et al. (2018)). If not, the already discussed negative effects of team heterogeneity will exceed and lead to problems and negative performance in the case of USOs (Ferretti et al. (2018)).

Hence, Knockaert et al. (2011) found in their in-depth qualitative analysis of nine cases of USOs that these spin-offs were mostly founded around the research team and thus very homogeneous from the technological background and somehow the science fields but at the same time heterogeneous in terms of the age of the members along the hierarchies in the team (experienced researchers and PhDs or PostDocs). This is crucial for the initial success to develop the idea to a marketable level. At the same time, they can show that, for further development, bringing in other individuals with more commercial experiences is crucial; that is, a heterogeneous team is necessary but in a balanced way so as not to cause misunderstandings. They emphasized that tacit knowledge among the team members is the most important aspect; thus, the homogeneity and understanding of these team members appear to be more crucial. Visintin and Pittino (2014), who analyzed 103 Italian USOs, similarly observed that a team with both academic and non-academic backgrounds fosters the performance of USOs positively (sales and employment growth) but only when the duality of experiences is tempered by other characteristics such as sharing a common background (same university or field) and a smaller team size (Visintin and Pittino (2014)).

Ferretti et al. (2018) also showed quite distinctive and differentiating results in their panel study on 138 Italian USOs from 1999 until 2009. They found some positive effects of the academic and non-academic heterogeneity in teams on performance (sales). The ratio of academic to non-academic team members in a USO is an important success factor. Furthermore, Ben-Hafaïedh et al. (2018) showed with their analysis of 165 USOs in Italy from 2000 to 2007 that, for the subgroups of the academic and non-academic founders of USOs, the mix or the homogeneity is substantial for the different performance measures (innovation or sales growth). Thus, for innovation, having a pure academic team appears to make more sense, while for sales growth, the balanced mix and size of the subgroups have a positive impact. The authors moderate this effect by implementing the stakeholder effects of either university or commercial stakeholders. Summing up, prior research results indicate that the relation of team composition on outcomes is unclear. The effects of team composition depend on the input variables, the embedding context (Jackson et al. (2003)), time (how long team members stay together) (Harrison et al. (1998)), and organizational culture (Brickson (2000), Ely and Thomas (2001)). The diverse research results lead to the conclusion that the effect of team diversity on performance is difficult to grasp, and the question rises whether uncovering the relationship of team diversity and performance on a direct level is possible at all either for general start-ups or for the context of USOs (for an in-depth overview, see Mathisen and Rasmussen (2019)). Moreover, most of these studies take place in an Italian context, are focusing USOs from all types of disciplines, and rarely combine team composition and other potential success factors as recommended for further research (see again Mathisen and Rasmussen (2019) or Nikiforou et al. (2018)). On the basis of this controversial debate about the effectiveness of homogeneous or heterogeneous teams in USOs and their inconsistent results on performance, we hypothesize the following, measuring performance as growth in sales and employees following standard measurement processes in USO research (McKelvie and Wiklund (2010), Mathisen and Rasmussen (2019):

We observe a research gap for the analysis of team diversity and the interrelated effects with critical success factors for USOs (Mathisen and Rasmussen (2019)). Some studies on USO performance control for the university or commercial stakeholders, or they control for the size of the team or check for technology transfer offices or other institutional settings (e.g., Ben-Hafaïedh et al. (2018)). On the basis of the mentioned literature and the following data, we address that the access to financial resources and specifically team members’ network are the most critical success factors. As we will explain in the following, we assume that these success factors are interrelated with the team composition, specifically the diversity of USO teams. Due to the network success hypothesis and social capital theory (Granovetter (1973), Brüderl and Preisendörfer (1998)) and the fact that informal and formal networks serve to embrace entrepreneurial opportunities (Baron and Tang (2009), Baron (2006), Ozgen and Baron (2007), Florin et al. (2003)), we posit that firm and specifically team member networks are among the most important success factors for new venture firms and USOs. Shane and Stuart (2002) postulated that the direct and indirect contacts of the founding team with venture capitalists in their social network reduce the likelihood of failure. Furthermore, Grandi and Grimaldi (2003) confirmed that the frequency of interaction with externals before founding the firm has an impact on the new venture’s network and interaction frequency that boosts firm performance. Another reason why firm networks serve as a major success factor is that the effect of social capital could be more important than teamwork capabilities (Brinckmann and Hoegl (2011)) and could enhance performance (Vissa and Chacar (2009), Balkundi and Harrison (2006), Walter et al. (2006)). Mosey and Wright 2007 addressed the notion that differences in the existing social capital and networks of academic entrepreneurs help overcome barriers to new venture development. Academics who have business ownership experience are more adept at building relationships with experienced managers and potential equity investors (Mosey and Wright (2007)), which might be helpful and supportive to gain better performance. Other studies have confirmed that USO innovativeness and performance are also positively associated with the networks of academic founders in a team with different backgrounds (Rasmussen et al. (2015), Rasmussen et al. (2011), Walter et al. (2011), Scholten et al. (2015)). Thus, team diversity leads to a more diversified and greater network (e.g., Hossinger et al. (2019), Reagan et al. (2004), Burt (1992), Granovetter (1973)). Additionally, networks to other individuals may also enhance the entrepreneurial orientation and performance of USOs (Knockaert et al. (2011), Hayter (2013), Diánez-González and Camelo-Ordaz (2016), Prencipe (2016)). A higher degree of different external networks of team members who are less overlapping should provide more unique information inflows (e.g., Granovetter (1973), Reagan et al. (2004)) and lead to a larger pool of external advisers and more innovation (e.g., Hambrick (1994), Hansen (1999), Alexiev et al. (2010)), which in turn is conducive to a stronger performance of USOs. Vissa and Chacar (2009), Balkundi and Harrison (2006), and Walter et al. (2006) argued that a greater and more diversified network should permit more business activities and therefore enhance USO performance. Moreover, Huynh et al. (2017) and Huynh (2016)found only the indirect (positive) effects of USOs’ network on performance. Regarding the relevance of network and social capital, we posit that team diversity has a strong impact on firm networks, and in turn the firm’s network has an impact on the access to resources and firm performance (see Hypotheses 2 and 3):

A positive impact of network on the access to financial resources was found by Jarillo (1989), Birley (1986), and Starr and MacMillan (1990). In a more recent critical review of networks in the entrepreneurship literature, Hoang and Antoncic (2003) showed that a developed network could be an advantage for spin-offs or new venture firms to obtain access to financial resources. Furthermore, Brüderl and Preisendörfer (1998) and Zhao and Aram (1995) confirmed that network ties could enhance the access to financial resources. Lindstrom and Olofsson 2001 also validated that USOs are suffering due to greater difficulties in obtaining finance than start-ups from other origins. Therefore, several researchers have, especially for USOs, searched for supportive factors for obtaining finance and found that the team founders’ human capital (commercial experience, technical knowledge, and academic status) (e.g., Huynh (2016) focusing on the impact of industrial, managerial, and entrepreneurial experiences of founding teams to enhance the financing of USOs) and social capital, such as number and density or broadness of networks (Nahapiet and Ghoshal (1998), Huynh (2016)), could increase the chances of getting externally financed (e.g., Gimmon and Levie (2010)). Similarly, Shane (2004) and Vohora et al. (2004) explained the quality of a USO team’s network as an external resource, having a strong impact on the financing process (seed, starting, and growth) (Lindstrom and Olofsson (2001)). Effective financing supports USO founders in bringing the idea to market, and thus, this financing has a positive effect on performance (Powers and McDougall (2005)) and growth (Rosman and O’Neill (1993)), such as Wright et al. 2006 and Shane (2004) also show for USOs. With regard to the resource-based view (Wernerfelt (1984)), the financial resources of new venture firms or USOs constitute a critical success factor. This leads to the next two hypotheses that the access to financial resources can be enhanced by the diverse networks of USO teams and that having an effective financial resource enhances the performance of USOs:

According to pecking-order theory (Myers and Majluf (1984)), venture capitalists tend to invest in USOs after the seed stage whereas business angels or universities during the seed stage of USOs. Hence, the financing of USOs with venture capital is considered to be the most important funding source (Wright et al. (2006)). Thus, several studies have analyzed how USOs are evaluated by investors and financing institutions and whose evaluation criteria must be fulfilled to obtain financing. One of the most important evaluation criteria concerns the entrepreneurial team (e.g., Silva (2004)). The most frequently mentioned team characteristics are industry experience, leadership experience, managerial skills, and engineering/technological skills that attract venture capital (Franke et al. (2008)). Human capital can serve as a signaling effect; therefore, heterogeneous teams are preferred because of their functional diversity (Franke et al. (2008)). This again is found in other studies such as Huynh (2016), where the capabilities of the USO founding team lead to financing in different stages. The same holds true for the studies of Clark (2008) or Muzyka et al. (1996) where investors require sufficient business skills as the main aspect to finance a USO. Thus, the stage of a USO’s diverse team skills is highly valued by financing institutions (Shane (2004)). These findings lead to the following hypothesis:

The previous hypotheses have focused on the direct effects of team diversity, networks, and access to finance as important success factors for the performance of USOs. The indirect paths of our research approach must also be analyzed to understand how USO teams can exploit networks and access to finance to enhance the impact of team diversity on performance. Hence, the mediating effects of networks and access to finance on team diversity are included in the analysis. This has been rarely undertaken in the USO research. Exceptions include, e.g., Huynh (2016) and Huynh et al. (2017) for networks and team capability. It is important not to ignore possible mediating mechanisms that could explain the impact of team diversity on performance more in-depth and shed light as to why the direct diversity impact generates inconsistent results. We build a mediation model that is able to investigate direct and indirect effects. We suppose that the direct effect of team composition on firm performance is mediated by the firms’ network and financial resources. Why should there exist indirect effects of networks and access to finance that serves to clarify the nature of relationship between team diversity and performance? We posit that a combined analysis of success factors and their interrelated (indirect) effects can clarify the relationship between team diversity and performance better than the analysis of single direct effects. This can be explained through the fact that USO performance depends on a huge amount of factors so that entrepreneurial resources and personal characteristics in a diverse team could be of secondary importance (Stringfellow and Shaw (2009)) if we measure them as direct effects (Huynh (2016), Huynh et al. (2017)).

We therefore focus on the indirect effects of the success factors as well, but the selection of these variables must be carefully considered. Shane (2004)and Knockaert et al. (2011) verified that USOs are very specific in terms of their business idea, background, and development, because they are created around research inventions, new solutions, and ideas solving a problem. Thus, at the beginning, having a team of founders with scientific and technological knowledge is crucial to drive this idea into a marketable innovation. At this point of development of a USO, the common understanding of the involved team members as a kind of homogeneity is important (Knockaert et al. (2011)). At the same time, researchers in this field have found that it is important to identify how founding teams in USOs co-evolve with the stages of firm development and that this kind of change might have an impact on USO performance or survival (Clarysse and Moray (2004)), often driven by context or other success factors like financing (e.g., Huynh (2016), Huynh et al. (2017)), Vohora et al. (2004), Wright et al. (2006)).

The same holds true regarding the network of USOs as Ferretti et al. (2019), Ben-Hafaïedh et al. (2018), Huynh (2016), and Huynh et al. (2017) found. Their studies highlighted how social capital and networks of a USO team develop and change over time due to more market contacts and different kinds of the involvement of university institutions (e.g., TTOs) and governmental support programs. The composition and diversity of a USO team and thus the direct effect might influence these contacts and network relations, bringing in non-academics or academics with commercial and market experiences (Clarysse and Moray (2004), Vohora et al. (2004), Vanaelst et al. (2006)). This leads to our Hypotheses 7 (a*b) and 8 (c*d).

Summarizing the discussion and bringing together the hypotheses, the mediation model is developed (Fig. 1). Following the former results, discussion, and analysis, we create a model including four latent variables: Team Diversity, Network, Finance, and Performance, which are measured with 31 items (for a detailed explanation, see the next section). The paths between the four latent variables represent the hypotheses in our model. We assume that the variables Network and (access to) Finance are mediators for the effect of Team Diversity on Performance. These hypothesized causal chains, in which team diversity affects financial resources and networks that, in turn, affect performance, are derived from theoretical considerations explained above.

Figure 1 shows the model. The paths in the figure are labeled to distinguish easily between direct and indirect effects.

Prior literature findings are ambiguous concerning the direct effect of team diversity on the firm performance of USOs (e.g., Knockaert et al. 2011, Visintin and Pittino (2014), Huynh et al. (2017), as well as Ensley and Hmieleski (2005), Amason et al. (2006), Webber and Donahue (2001)). Our study emphasizes that the investigation of a direct effect is insufficient to analyze the effect of team diversity on USO performance. Thus, more sophisticated models in regard to measurements or methods are necessary to open the black box of team diversity effects (Hahn et al. (2019), Carpenter et al. (2004), Mathieu et al. (2008), Klotz et al. (2014)), and we provide the requisite empirical evidence to this debate.

The current study finds statistically direct positive effect of team diversity on the access to financial resources for USOs. This is in line with other studies, where the team composition is considered to be an important signal to potential investors to trust the team to bring an invention to the market and run a USO successfully. When team diversity is built on a balanced mix of academics and non-academics, investors (either VCs or business angel of universities) are willing to finance the USO or to invest higher and sufficient amounts of money (Beckman et al. (2007), Zimmerman (2008), Franke et al. (2008)), showing that functional diversity attracts investors either in early stage financing or in the latter stages (Huynh (2016), Clarysse and Moray 2004, Wright et al. (2006)).

We further find a direct positive impact of team diversity on the network. This relationship is not surprising regarding network theory (e.g., Burt (1992), Granovetter (1973)) and is in line with USO research as well. Rasmussen et al. (2011), Rasmussen et al. (2015), and Scholten et al. (2015) revealed in their studies that a USO team obtaining a mix of skills, including entrepreneurial experience and innovative capabilities, will positively affect the networks of the USO team. Similarly, Mosey and Wright (2007) showed that the diversity of human capital skills in a team enhances the ability of a team to build social capital and networks, especially when there exists prior experience in owning a firm and having commercial experiences in the team. This could lead to easier network building with business managers and financial investors.

The positive and highly statistical significant impact of the firm’s network on the access to financial resources shows that network ties are highly relevant in obtaining finance. This result confirms previous findings concerning the interaction between network and financial resources (Hoang and Antoncic (2003), Zhao and Aram (1995)). Thus, our results are in line with the outcomes of prior researchers, which as well underscores the important effect of the founding team’s networks on financing using the reputation (capabilities) of the team and the team member networks (Heuven and Groen (2012), Rasmussen and Sørheim (2012)) and Shane and Cable (2002), Shane and Stuart (2002)). Our results partially contradict the results of Huynh (2016) who did not find a direct but rather an indirect effect of networks on financing. This might be because the USOs we analyze are in a later stage than those in the study of Huynh (2016). Thus, our USOs might have a chance to obtain different forms of financing. Finally, the indexing of financing forms might explain this difference as well.

Another direct effect is reflected by the relationship between network and performance. We posited a positive relationship because a stronger network should increase productivity and thus performance (Reagans and Zuckerman (2001)). Instead, our results reveal no significant impact of networks on performance. The effects of the network variable show that USOs in the high-tech sector should have a great diversified network, having other positive effects on essential resources like financing. However, network diversity does not influence performance directly in our study. This is in line with the results of Huynh et al. (2017) who do not find a direct effect of the network of USO teams on performance but rather an indirect effect. This result limits the network success hypothesis for USOs in the biotechnology sector in some way. Unsurprisingly, the impact of the financial resources of USOs on performance is significantly positive and confirms the importance of financial resources of firms (Wernerfelt (1984)). Thus, our study confirms former results and research that the more diverse financial resources a USO can gain, or the more effective the fundraising is, the better will be the performance of the USO (Powers and McDougall (2005)). If this is not the case, USOs will tend to exit from the market at any stage (Rosman and O’Neill (1993)).

The upper echelon approach (Hambrick and Mason (1984)) states that team effectiveness leads directly to firm outcomes. Our results are in line with the organizational behavior and entrepreneurship literature suggesting a more complex relationship between team diversity and outcomes (e.g., Knockaert et al. (2011)). Hence, it seems reasonable to test team diversity effects in mediation models (e.g., Huynh (2016), Huynh et al. (2017), or as recommended by Mathisen and Rasmussen 2019). We do not find direct team diversity effects on USO performance. Nonetheless, does this really mean that team diversity has no impact at all on USO performance? To answer this question, we test and identify two mediation effects in our model creating two sources of the distinctiveness and originality of our paper. One of these is the mediation of the relationship between team diversity and performance by the access to financial resources.

USOs are specific due to their business idea and development because this is related to a unique technological or research invention (Shane (2004), Knockaert et al. (2011)). In general, the teams of USOs start with academic founders incorporating this specific knowledge to develop USOs to enter the prototyping or market stage. Thus, involved team members show a kind of homogeneity (Knockaert et al. (2011)). Prior research has confirmed that, often, founding teams in USOs do evolve in parallel with the stages of USO development because of financing issues, or other context and success factors, i.e., universities providing access to staff triggering this (Clarysse and Moray (2004), Huynh et al. (2017), Huynh (2016), Vohora et al. (2004), Wright et al. (2006)). As a consequence, often the structure of the USO team changes and is influenced to procure fundraising or financial support, implying that the direct effect of team diversity might be affected by the access to financing. Thus, the fine tuning or balancing of the USO team has a strong relationship with financial issues (Vanaelst et al. (2006)).

The second mediation concerns the relationship between network and USO performance—mediated by access to finance. The analysis of the indirect effects shows that team diversity has a positive indirect impact on USO performance and that the relevance of firm networks and social capital must be emphasized. Our results can be linked to the work of Brinckmann and Hoegl (2011), Vissa and Chacar (2009), Balkundi and Harrison (2006), and Walter et al. (2006) who consider firm networks and social capital as two of the most important success factors. In our model, networks are highly relevant for the access to financial resources and for firm performance. The results show that team diversity is positively related to firm performance, and we therefore conclude that a heterogeneous team composition is favorable for USOs, because of the link between network ties and the access to financial resources.

The same holds true regarding the network of USOs, as Ferretti et al. 2019, Ben-Hafaïedh et al. (2018), and Huynh (2016) found. They show that ways that are different from social capital and networks of a USO team develop and change over time due to more market contact or different kinds of the involvement of the university institutions (e.g., TTOs) and governmental support programs. These network relations might influence the team composition and diversity of a USO team and thus the direct effect by changing or enlarging the team composition, bringing in non-academics or academics with commercial or market experience (Clarysse and Moray (2004), Vohora et al. (2004), Vanaelst et al. (2006)). Huynh et al. (2017) found that, in the USO development stage, the networks of a founding team indirectly affect performance. Universities commonly provide the chance to “staff” a USO team with new members with industry background (Clarysse and Moray (2004)), fine tuning and balancing the USO team composition (Vanaelst et al. (2006)), which enhances later performance.

A main contribution of this paper is the multifaceted measuring of diversity working with the PLS method: this makes it possible analyzing the importance of diversity items simultaneously. The diversity of teams measured by item study programs and degrees, doctorates, industrial experience, age, nationality, and the quantity of team members has a significant impact in the model where the items other titles, soft skills, contacts and network character have no significant impact. This delivers new insights to better understand the effects of diversity in teams.

To discuss this more in-depth, does the negative significant values of the items age and doctorates mean that an increase of heterogeneity in these items leads to lower team diversity? At first glance, this appears to be a paradox, but a more detailed analysis provides a compelling resolution. Team members with different ages are more similar to one another than team members with equal ages. In other words, the probability for diverse human and social capital is higher if team members have a homogeneous age. This can be explained by the specific USO context and the formation of a team: As Knockaert et al. (2011) and Visintin and Pittino (2014) and other studies (Mathisen and Rasmussen (2019), Hossinger et al. (2019)) have found, the starting team of a USO is often built around the technology or invention and typically created in a research group or project (Clarysse et al. (2007b), Markman et al. (2008), Heirman and Clarysse (2004)). In these groups, the scientific and technological background is more or less the same, due to the research aspect. At the same time, these teams generally consist of different hierarchy levels in the scientific context, e.g., research group leaders and professors/chair holders, PostDocs, PhDs, or technical assistants; thus, the technological background of team members might be close, but age levels might be different. The explanation for the diversification of doctorates is almost the same: in these research teams, all people are interested in the same technology or research focus, especially in biotechnology; often professors or PhDs from different departments or fields from academia are involved, e.g., biology, medicine, or physics. Thus, the more team positions are blocked from the beginning with these different PhD academics, the less diversity might exist regarding non-academic experience or hierarchy levels. Another explanation for these negative path weights in formatively measured constructs could be suppressor effects (Cohen and Cohen (1983)). In this case, one or more of the predictor variables explains the variance in other predictor variables and thus reduces or reverses the path weight of these predictors with the construct variable even if there are no great problems of multicollinearity (Cenfetelli and Bassellier (2009)). We see no indications for a suppressor effect; hence, we analyze the absolute importance of an indicator to its construct with the help of the zero-order correlation of the item with the construct (loadings in Table 2). The absolute importance of an indicator helps us identify how the item correlates with the construct value. The correlation for doctorates is nearly zero; this item is not of such an absolute importance for the construct. The relative importance measured by the negative path weight occurs if doctorates are estimated in the multiple regression controlling for all other predictors in the measurement model.

We contribute as well to the discussion on entrepreneurial teams with our results for the formatively measured team diversity construct. We find that study programs and degrees, industrial experience, and nationality have a statistically positive impact on team diversity. The industrial experience result underlines the result of other studies that a balanced combination and mix of the former homogeneous team of academics needs a non-academic component to develop an USO and enhance the performance (e.g., Knockaert et al. (2011), D’Este et al. (2012), Hayter (2013), Visintin and Pittino (2014), Ciuchta et al. (2016), Huynh et al. (2017), Ferretti et al. (2018)). The result of an increase in nationality diversity delivers helpful insights, too, to generate effective mixed USO teams because this diversification might open new kinds of social capital and enhance the chances to develop an internationalization path for USOs (Burer et al. (2013), Civera et al. (2019a), Ferretti et al. (2018), Ferretti et al. (2019)). Moreover, bringing together international researchers might enlarge diversity regarding research or management culture, methods how to deal with questions, and approaching a problem (Civera et al. (2019a), Ben-Hafaïedh et al. (2018)). Thus, this delivers a real effect on diversity. The study programs and degrees as positively enhancing diversity deliver a new insight, too, that when team members still have a PhD in the same field, their study field and degree could be different ones. Thus, the spread in study fields like in the case of nationality can increase the diversity in the way that methods to approach a problem or the point of views and research tools are different (Ferretti et al. (2019)). Therefore, this also contributes to increase the heterogeneity of teams.

Finally, the negative path weight of the quantity of team members also appears to be surprising at first glance. Does this mean that a higher number of team members leads to a lower team diversity? We explain this effect in two ways. (1) Imagine that a team with two team members has completely different soft skills. One of them is a professional in LaTeX, and the other knows Microsoft Word well. The two founders intend to expand their team with two more colleagues. Suppose that the two new team members are both professionals in Microsoft Word as one of the originally team members. In this case, the relative team diversity concerning soft skills decreases if the number of team members increases. (2) Moreover, imagine how teams are built: in general, individuals prefer other individuals when they have something in common (Zacharakis (2010), Jungwirth and Moog (2004)). This means that, even increasing the team size, it does not at all mean increasing the team diversity in the same ratio and effect. Thus, we can imagine that there might exist a number of team members that is most effective or diminishing effects of team size regarding the advantages of big versus small teams (e.g., Backes-Gellner et al. (2006)).

Keeping in mind that we observe the relation of team diversity, network, finance, and the performance of USO in the biotechsector, the team diversity construct results confirm that the diversity of “soft characteristics” does not play a significant role. Those characteristics are difficult to observe for investors and could be secondary for network building and maintaining ties. It seems that, for USOs diversity, the “hard characteristics” of the team have a significant stronger influence on performance as well as the indirect effects. These insights are new.

We observe the same for the quantity of team members. Teams with a high number of team members are more homogeneous than work groups with fewer team members. These results indicate that age heterogeneity and the quantity of team members should be used carefully as a proxy for team diversity in single item or simple index measures. We must distinguish here between age and team size as single proxies for success (e.g., Kilduff et al. (2000), Eisenhardt and Schoonhoven (1990)) and age and team size as items that are a component of a team diversity construct. The question that occurs here is if investors or network partners evaluate this situation equally. In other words, do investors or network partners realize team diversity as a whole construct, or do they concentrate on selected diversity items to evaluate diversity of the team? We cannot answer this question at this point, but we find that team size and age diversity could stand for a homogenous team composition and therefore emphasize the use of more complex statistical methods to analyze diversity effects. The use of the PLS method and the analysis of the team diversity construct make it possible to detect relationships of different diversity items in an entire model and minimize the probability of biased results.