The Science Behind the Right People
Much of work is now organized around teams. I am passionate about researching how team composition, or the configuration of team member attributes, influences team effectiveness. My research systematically examines the extent that team member attributes (e.g. personality, values, general mental ability, demographics), and unit-level (e.g., dyadic, team-level) configurations (e.g., uniformly high, diversity) of these attributes, influence team member interaction, the development of individual, relational, and team states (e.g., affective ties between team members, shared mental models), and other valued outcomes (e.g., team performance). Team composition research has practical application. The information can be used in team design (e.g., the selection and placement of team members). When operational constraints (e.g., restricted availability of potential team members, nature of the attribute) do not allow for team composition to be used in team design, team composition information can be used to better manage teams, for example, by tailoring training interventions or prioritizing leadership behaviors.
In my early work, I focused on establishing team composition as an important predictor of team performance, and identifying the specific attributes and configurations that are most predictive of team performance. I published two meta-analysis related to this (Bell, 2007; Bell, Villado, Lukasik, Belau, & Briggs, 2011). I also conducted empirical research that explored novel composition variables (e.g., subfacets of personality, facets of psychological collectivism) and the mechanisms (e.g., shared mental models, implicit coordination, team members coordination) through which they relate to team performance over time (e.g., Dierdorff, Bell, & Belohlav, 2011; Fisher Bell, Dierdroff, & Belohlav, 2012).
Our lab has a number of ongoing team composition research projects. Primarily these: (a) seek to bridge the science-practice gap related to team composition, (b) use a micro dynamic approach to understanding team composition, (c) and seek to strategically integrate context. An example is our NASA-funded research in which we are creating a predictive team composition model for long-duration space exploration such as Mission to Mars, and a Crew Recommender System and interface. You can read about the project under CREWS.