Structural Role Complementarity in Entrepreneurial Teams

Abstract

Abstract.To refine the understanding of the social network characteristics of entrepreneurial teams, we present a new construct: structural role complementarity. In particular, we examine the variation between team members' respective abilities to act as network brokers. Based on the cofounding networks of 9,461 entrepreneurs and 2,446 large-scale industrial enterprises over 45 years in Russia's emerging economy , our findings show that variation among team members' brokering ability significantly predicts the starting capital raised by their firm. The effect is moderated by the team's average brokering potential. When both the team's average and variation in brokering potential is high, firms raise greater starting capital. By using multiple membership models, we demonstrate that greater starting capital is largely attributable to team factors rather than the attributes of the individual team members. We also take advantage of discriminatory laws that were passed in 1887 in an instrumental variable analysis to address potential endogeneity issues.

The effects of individual status and group performance on network ties among teammates in the National Basketball Association

“…Much like email networks and other behavioral data, an additional advantage of Twitter data is that they are unaffected by the biases and demand effects that characterize interview-based methods [ 66 , 67 ]. Further extensions of this approach could examine the effects of status and group performance on inter-group relationships among members of different teams or organizations [ 68 , 69 ]. Longitudinal data would permit research on the dynamics of team cohesion and performance, potentially permitting inferences about the causal relationship between those variables.…”

Connecting content and structure: A review of mechanisms in entrepreneurs’ social networks

“…This co‐evolutionary process (see Figure 2, 3C) involves going back and forth between the individual and the firm level as the conceptual pacts that are created for the firm subsequently lay a strong claim on mutual understanding and subsequent interactions between individuals within and across firm boundaries—and thus on any network interactions that emerge at the micro level (see Vedres & Stark, 2010). It is therefore an advantage if network diversity can be complemented by diversity at the venture level, such as in the venture team (Aven & Hillmann, 2018; Vissa & Chacar, 2009).…”

“…Individual behavioural limits and cognitive inaccuracies (e.g. Ertan et al., 2019) can be compensated at the team or organization level if a team has sufficient specialization and the maintenance of relationships is divided over the team members (Aven & Hillmann, 2018). Yet, in contrast, with limited specialization, all team members have to deal with the same actors and are prone to experience relational and cognitive lock‐in (Maurer & Ebers, 2006).…”

Statistical Methods and Software for the Multilevel Social Relations Model

“…6 Finally, the SRM is appropriate for only dyadic outcome variables, not variables in which network ties are used to calculate node-level measures, such as centrality or constraint (Freeman, 1978;Burt, 1992). This generalization applies whether the node-level network measures are the dependent (e.g., Aven, 2015) or the independent variable (Ahuja, 2000;Shipilov andLi, 2008, Aven andHillmann, 2018). 5 Users may wish to access datasets and templates across multiple Stat-JR sessions, in which case it is possible to store datasets in their personal data store (C:\Users\<username>\.statjr\datasets) and templates in their personal storage of templates (C:\Users\<username>\.statjr\templates).…”

“… 3. Note that the SRM is appropriate only for dyadic outcome variables, not analyses in which network connections are used to calculate node-level measures, such as centrality or constraint (Burt 1992; Freeman 1978). This applies whether the node-level network measures are the outcome variable (e.g., Aven 2015) or a predictor variable (Ahuja 2000; Aven and Hillmann 2018; Shipilov and Li 2008). …”