Strategic team design for sustainable effectiveness: A data-driven analytical perspective and its implications

Abstract

Teams are building blocks of organizations and essential inputs of organizational success. This article studies a data-driven analytical approach that exploits the rich data accumulated in organizations in the digital era to design teams, including prescribing team composition and formation decisions. We propose to evaluate a team regarding its performance and temporal stability, referred to as sustainable effectiveness (SE). Our approach estimates the team's performance and stability using machine learning models. It then optimizes an integrated objective of the team's performance and stability through mixed-integer programming models formulated according to predictive models. Consequently, this approach mines meaningful team compositions from historical data and guides strategic team formation accordingly. We conduct empirical studies using authentic data from our partner company in the real estate brokerage industry. The findings reveal that teams that adhere to our model's recommendations achieve an average percentage improvement of 153.1% to 156.5% higher than the benchmark teams, particularly when recruiting one or two members in their actual SE during the post-formation period. We further disclose the mechanism underlying this improvement from the perspective of changes in team compositions. Our study provides a decision support tool for team design and ensuing team dynamic management.