Dataset Information

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.

SUBMITTER: Amelkin V

PROVIDER: S-EPMC6179230 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Dynamics of collective performance in collaboration networks.

Amelkin Victor V Askarisichani Omid O Kim Young Ji YJ Malone Thomas W TW Singh Ambuj K AK

PloS one 20181010 10

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 ...[more]

PMID: 30304044

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Project description:Increasingly, it is being suggested that translational gaps might be eradicated or narrowed by bringing research users and producers closer together, a theory that is largely untested. This paper reports a national study to fill a gap in the evidence about the conditions, processes and outcomes related to collaboration and implementation.A longitudinal realist evaluation using multiple qualitative methods case studies was conducted with three Collaborations for Leadership in Applied Health Research in Care (England). Data were collected over four rounds of theory development, refinement and testing. Over 200 participants were involved in semi-structured interviews, non-participant observations of events and meetings, and stakeholder engagement. A combined inductive and deductive data analysis process was focused on proposition refinement and testing iteratively over data collection rounds.The quality of existing relationships between higher education and local health service, and views about whether implementation was a collaborative act, created a path dependency. Where implementation was perceived to be removed from service and there was a lack of organisational connections, this resulted in a focus on knowledge production and transfer, rather than co-production. The collaborations' architectures were counterproductive because they did not facilitate connectivity and had emphasised professional and epistemic boundaries. More distributed leadership was associated with greater potential for engagement. The creation of boundary spanning roles was the most visible investment in implementation, and credible individuals in these roles resulted in cross-boundary work, in facilitation and in direct impacts. The academic-practice divide played out strongly as a context for motivation to engage, in that 'what's in it for me' resulted in variable levels of engagement along a co-operation-collaboration continuum. Learning within and across collaborations was patchy depending on attention to evaluation.These collaborations did not emerge from a vacuum, and they needed time to learn and develop. Their life cycle started with their position on collaboration, knowledge and implementation. More impactful attempts at collective action in implementation might be determined by the deliberate alignment of a number of features, including foundational relationships, vision, values, structures and processes and views about the nature of the collaboration and implementation.

Dataset Information

Dynamics of collective performance in collaboration networks.

Publications

Dynamics of collective performance in collaboration networks.

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