Extemporaneous Coordination in Specialist Teams: The Familiarity Complementarity

Abstract

Team production is ubiquitous in the economy, but managing teams effectively remains a challenge for many organizations. This paper studies how familiarity among teammates influences the performance of specialist teams, relative to nonspecialist teams. Applying theories of team production to contexts where team members coordinate interdependent activities extemporaneously, we develop predictions about factors that shift the marginal returns to specialization along two dimensions of familiarity: social familiarity and functional familiarity. We test our hypotheses in the context of Defence of the Ancients 2 (DOTA2), a major e-sports game where, in some formats, players are exogenously assigned to five-person teams. After analyzing nearly 6.5 million matches, we find that specialist teams are relatively more successful when members are more socially and functionally familiar with one another. The results suggest that the plug-and-play perspective on specialist teams is incomplete; rather, specialization and familiarity are complements in dynamic environments where team members coordinate extemporaneously. Funded: Financial support from the UCL School of Management and Worcester Polytechnic Institute is gratefully acknowledged.

Using Sports Data to Advance Management Research: A Review and a Guide for Future Studies

“…Thus, if we can find a clean operationalization of loss framing in other contexts, the same findings should hold” (2021, p. 1048). In their study on the importance of status inequality, Christie and Barling (2010) point out that NBA's public visibility might enhance the salience of the status markers they use, thus making their findings less generalizable to organizational settings where status cues are more difficult to pick up. Finally, Ertug and Castellucci’s (2013) work on the role of NBA players’ reputation and status for team-level outcomes notes how findings might not generalize to settings where it is difficult to separate status from reputation (or from performance).…”

“…These technologies have led to the diffusion of professional virtual simulators, which are used for training in sports such as sailing, car racing, rowing, golf, cycling, and winter sports, and provide additional sources of data. Similar technologies have also been adopted for the development of official tournaments, thus supporting the rise of e-sports (Hallmann & Giel, 2018;Heere, 2018), a new sports context that has recently started to be explored in management journals (e.g., Ching, Forti, & Rawley, 2021).…”

“…Sports also present positive examples of inclusivity, for example, by providing equal opportunities to people with disabilities (as in sailing, where in the International 2.4mR boat class, both able-bodied and disabled athletes participate on equal terms, and where a disabled athlete became a world champion), by representing a way to help migrants integrate (e.g., teams of migrants playing in local soccer leagues), and by affording children in less-developed countries access to education (e.g., the UNICEF-supported program in Nigeria helping children and adolescents—especially girls—to get an education via specific academies that combine academics and soccer, thus increasing their chances of professional success). Recent work leveraging sports data has started to deal with and identify biases, differences in treatment, and more in general exclusion—as well as inclusion—based on a variety of dimensions, such as race (Ertug & Maoret, 2020; Pope et al, 2018; Timmerman, 2007; Zhang, 2017, 2019), gender (Adriaanse, 2016; Micelotta, Washington, & Docekalova, 2018; Ortlieb & Sieben, 2019), nationality (Chatman, Greer, Sherman, & Doerr, 2019), and core-periphery position (Christie & Barling, 2010; Fonti & Maoret, 2016; Humphrey, Morgeson, & Mannor, 2009; Stuart, 2017). Some work has also highlighted how sports can provide a more inclusive environment (e.g., better career prospects across genders; cf.…”

“…Sports settings offer fine-grained statistics that have been leveraged to contribute to conversations on status and reputation. In their contributions to these conversations, scholars have used different kinds of data, such as players’ competitive actions (Bothner, Kang, & Stuart, 2007), salaries (Christie & Barling, 2010; Marr & Thau, 2014), indications of deference (Anderson, Willer, Kilduff, & Brown, 2012; Huang & Washington, 2015), rankings (Bothner, Kim, & Smith, 2012; Kim & King, 2014), and performance-related outcomes (Bothner et al, 2012; Christie & Barling, 2010; Flynn & Amanatullah, 2012; Maoret, Marchesini, & Ertug, in press; Waguespack & Salomon, 2016). Scholars have also examined status- and reputation-related effects and mechanisms that apply to sports executives (Radaelli, Dell’Era, Frattini, & Messeni Petruzzelli, 2018), coaches (Kilduff, Galinsky, Gallo, & Reade, 2016; Treadway et al, 2014), and institutions (Sahib, 2015).…”

“…Although these data have been used in some studies, future research could leverage them to investigate the implications of status and reputation in more direct and proximal ways. In addition to data collected during practices or competitions, athletes and teams also feature prominently in a variety of media outlets, which provide data to assess their popularity (Christie & Barling, 2010). Additionally, sports settings would also allow to study career mobility more directly than some other settings (e.g., as compared to the mobility of inventors as assessed via patent data), as the dates on which players join or leave a team or play their first or last game—and even possibly their first or last day in training—are often publicly available for most major sports, which is often not the case in management contexts.…”