Dynamics of collective performance in collaboration networks

Abstract

Today, many complex tasks are assigned to teams, rather than individuals. One reason for teaming up is expansion of the skill coverage of each individual to the joint team skill set. However, numerous empirical studies of human groups suggest that the performance of equally skilled teams can widely differ. Two natural question arise: What are the factors defining team performance? and How can we best predict the performance of a given team on a specific task? While the team members’ task-related capabilities constrain the potential for the team’s success, the key to understanding team performance is in the analysis of the team process, encompassing the behaviors of the team members during task completion. In this study, we extend the existing body of research on team process and prediction models of team performance. Specifically, we analyze the dynamics of historical team performance over a series of tasks as well as the fine-grained patterns of collaboration between team members, and formally connect these dynamics to the team performance in the predictive models. Our major qualitative finding is that higher performing teams have well-connected collaboration networks—as indicated by the topological and spectral properties of the latter—which are more robust to perturbations, and where network processes spread more efficiently. Our major quantitative finding is that our predictive models deliver accurate team performance predictions—with a prediction error of 15-25%—on a variety of simple tasks, outperforming baseline models that do not capture the micro-level dynamics of team member behaviors. We also show how to use our models in an application, for optimal online planning of workload distribution in an organization. Our findings emphasize the importance of studying the dynamics of team collaboration as the major driver of high performance in teams.

Competencias de gestión de los directivos que inciden en el cumplimiento de metas de las PYMES del sector textil en Cuenca, Ecuador

“…A los planteamientos anteriores se puede sumar la definición de Hellriegel ( 2009) en cualquier cantidad de gente que comparte metas. En forma similar el rendimiento del trabajo en equipo se considera dentro de las competencias de los miembros del equipo para lograr un desempeño del grupo, porque permiten intercambiar conocimiento mientras realizan las actividades asignadas (Amelkin, Askarisichani, Kim, Malone, & Singh, 2018). Porras, et al (2018) determinan que es necesario analizar el desempeño del equipo que refleja las interacciones del equipo de trabajo en el desarrollo de sus actividades laborales.…”

Predictive models for human–AI nexus in group decision making

“…For example, collective intelligence refers to a group's ability to produce intelligence and behaviors beyond the individual; 23 in this body of work, human groups can display magnified cognitive capacity and unique cognitive abilities that emerge from the interaction between the group members. 24 Theory of mind [25][26][27][28][29] Broadly speaking, the theory of group mental models focuses on a broader content domain than the theory of TMS.…”

“…While we focus only on a few selected sociocognitive constructs, it is important to recognize that other modeling results have also been put forth. For example, collective intelligence refers to a group's ability to produce intelligence and behaviors beyond the individual; 23 in this body of work, human groups can display magnified cognitive capacity and unique cognitive abilities that emerge from the interaction between the group members 24 . Theory of mind 25–29 broadly refers to humans' ability to represent the mental states of others, including their desires, beliefs, and intentions.…”

Four Lenses on People Management in the Public Sector

“…Communication has an essential role in developing transaction memory systems overtime because discussions serve to continuously improve the mapping of team members about who is an expert in certain areas (Liang et al, 1995;Rulke and Rau, 2000;Hollingshead and Brandon, 2003;Lewis, 2004). 6 Several empirical studies, mostly targeting regular individuals in labs or educational environments, confirm that better performing teams have well-communicated functional networks (Brewer and Holmes, 2016;Amelkin et al, 2018;Marks et al, 2002). An alternative approach to attaining shared cognition is reinforcing knowledge and information sharing, because groups can make the most of the initial know-how and informational resources of their members, and therefore improve team effectiveness, creativity, and innovation (Hülsheger et al, 2009;Mesmer-Magnus and DeChurch, 2009;Kessel et al, 2012).…”