Little Teams, Big Data: Big Data Provides New Opportunities for TeamsTheory

Abstract

Over the past 25 years, industrial and organizational (I-O) psychologists have made great strides forward in the area of teams research. They have developed and tested meso-level theories that explain and predict the behavior of individuals in teams and teams operating within and across organizations. The continued contributions of I-O psychologists to theory and research on teams require us to address the challenges—several of which were well described in the focal article (Guzzo, Fink, King, Tonidandel, & Landis, 2015)—and embrace the opportunities that are being ushered in by big and broad data streams (Hendler, 2013). We suggest that a principal unique value add of the I-O psychologist to the basic scientific endeavor of understanding small teams comes in the form of theory—theories that explain why, when, how, and to what end individuals form relationships needed for teams to function in unison toward the accomplishment of collective goals. Some have argued that the big data revolution means “the end of theory,” suggesting petabyte data render theoretical models obsolete (Anderson, 2008). On the contrary, we submit that big-data enabled social science holds the promise of rapid progress in social science theory, particularly in the area of teams.

Use of Technology in the Study of Team-Interaction and Performance

“…Several of the articles criticize the I-P-O model (e.g., Carter et al, 2015; Kozlowski, 2015) because the P (process) is often treated as a black box, as the I (input) and O (output) are used as independent and dependent variables. In general, algorithms for analyzing the data collected from sensors, will be a new type of black box, demanding taking into account new types of validity (Tonidandel et al, 2018).…”

“…Such a tool shows a snapshot of a group and makes it difficult to map change and the development of dynamic conditions in the group over time (Kozlowski, 2015). New types of sensors, handling of large amounts of data, data processing, and developments in user interface have, during the last decade, been predicted to revolutionize research on small groups, especially when it comes to dynamic conditions (Carter et al, 2015). But it turns out to be challenging to find fruitful forms of collaboration that connect established group research and engineering disciplines.…”

“…There is an increasing use of electronic sensors or wearables in empirical studies of team-interactions (Alshamsi et al, 2015(Alshamsi et al, , 2016Chaffin et al, 2017;Gundogdu et al, 2017;Kim et al, 2012;Orbach et al, 2015;Wageman et al, 2012;Watanabe et al, 2014). Although there is also an increasing number of different technologies to support team research (Carter et al, 2015;Chaffin et al, 2017;Kozlowski, 2015;Kozlowski et al, 2016;Luciano et al, 2018;Santoro et al, 2015;Tonidandel et al, 2018), a vast number of these are still purely technology driven and do not sufficiently connect to existing theories and knowledge in the small group field (Chaffin et al, 2017).…”

“…There is also a real danger that measurements are only able to catch snapshots of what is going on, which is what most of the articles argue. When measurements (e.g., retrospective questionnaires), methods (e.g., cross sectional design) and models (e.g., I-P-O) support this, using sensors to collect continuous streams of data, will provide a more complete understanding of the constructs investigated (Carter et al, 2015).…”

Modeling Teams Performance Using Deep Representational Learning on Graphs

“…Several factors such as communication, coordination, distinctive roles, interdependent tasks, shared norms, personality traits, and diversity have been found to be relevant aspects shaping team performance [26,16,59,10,60,27]. Yet, our understanding of teams as evolving systems of interacting individuals as well as the relation between team composition and performance is still partial [25,54,19].…”