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  • A Systematic Framework for Measuring Team Performance and Group Decision-Making: Pitfalls, Solutions, and Transdisciplinary Insights
  • https://doi.org/10.3233/atde251093Copy DOI Icon

A Systematic Framework for Measuring Team Performance and Group Decision-Making: Pitfalls, Solutions, and Transdisciplinary Insights

  • Oct 14, 2025
  • Ignacio Vazquez +3 more
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Abstract

Evaluating and enhancing team performance is a cornerstone of transdisciplinary engineering, yet the absence of a formal method to categorize and assess the efficacy of group performance experiments remains a significant challenge. In particular, what kinds of results can an experiment on team performance produce, and how can researchers ensure these results remain epistemologically sound? This study addresses these foundational questions by proposing a structured framework to categorize and assess team performance experiments. The framework integrates cognitive measurements, statistical rigor, and meta-analytical evaluations of experimental maturity to enhance the credibility and reliability of study outcomes. The approach is illustrated through the analysis of data from two team performance experiments, serving two primary objectives. First, the analysis demonstrates the actionable insights into team dynamics enabled by the framework, showcasing its practical applicability. Second, it systematically addresses critical factors influencing experimental reliability, including the identification of methodological pitfalls, thereby offering tools and strategies to enhance the design and evaluation of team performance studies. This work bridges the gap between theoretical concepts and practical implementation, contributing to the advancement of methodologies for transdisciplinary teamwork and performance measurement. The findings are particularly relevant to researchers and practitioners seeking robust, reproducible methodologies for assessing team dynamics across diverse disciplines, offering a guideline for future studies aimed at refining team performance evaluation methods.

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