Gendered interaction patterns in small R&D teams. A microdynamic approach using dynamic actor network models (DyNAM).
Jörg Müller, Julio Meneses, Anne Laure HumbertInspired in part by advances in the study of the “microdynamics” and “emergent phenomena” in working groups, diversity research is moving away from “static collectivist” accounts that have focused on simple, linear effects between team-level diversity predictors and team-level outcomes. Rather, current developments pay closer attention to the structural features of interpersonal relations within teams and how the ensuing dyadic relationships, uneven member contributions, motivations, and behaviors shape team-level outcomes over time.
The following presentation contributes to an overall microdynamic account of team diversity by analyzing the choice of interaction partners as captured by wearable sensor proximity data. Integrating a status-based perspective on group interaction with the social categorization/similarity approach, our main aim is to discern the relative contribution and importance of gender, age, tenure, and team role for the emergence of global interaction patterns and information sharing.
Data and methods
The empirical data for this research has been collected in the framework of a H2020 project (gedii.eu). Eight R&D teams were recruited in autumn 2016 and spring 2017 in Spain and the UK. While three research teams are located at public universities, three teams work in research centers and two teams work in a private company. A total of 80 individuals wore during five consecutive working days wearable sensors (Sociometric Badges, Humanyze Boston, USA) collecting data on Bluetooth proximity, Infrared (face-to-face interaction), speaking time, and body movement. Participants also filled out a short questionnaire concerning socio-demographic data (age, gender, tenure, highest qualification, and team role) and indicated their friendship and advice-seeking ties with each of their team colleagues.
We use Dynamic Actor Network Models (DyNAM) to analyze the face-to-face interaction data among team members on a per-team basis. By analyzing interaction data with Dynamic Actor Network Models, we combine the structural features of interaction with their temporal dimensions and show how status hierarchies in teams and social affinity affect the choice of interaction partners, ultimately reproducing interpersonal status hierarchies within groups
Preliminary results indicate that gender and age are important structuring factors for team-internal interaction – after controlling for advice seeking and friendship relations. Critical issues are raised concerning the overall validity of data collected with wearable sensors as well as the potential of a time-based approach for team research. The paper also offers the opportunity to trace more precisely the limits of existing, static (team) diversity research. In addition, we underline the continued importance of gender for face-to-face group processes and provide new perspectives for exploring gender issues from a social network perspective.