Cognitive factors on the performance of group decision-making: a behavioral and eye-tracking study

To foster innovation and optimization in engineering product design, it is crucial for engineering professionals to effectively integrate knowledge and make informed decisions within interdisciplinary collaborative environments. Understanding the factors that influence group decision-making performance can enhance communication and knowledge integration among experts from diverse disciplinary backgrounds. By analyzing decision-makers' attention allocation and information processing at the cognitive level, the innovation and practicality of solutions can be significantly improved. However, the complexity and multitude of factors affecting decision-making performance pose challenges, particularly due to the lack of quantitative research and unified metrics at both group and cognitive levels. This gap hinders the quality and efficiency of engineering group decisions.

This study introduces an eye-tracking method to investigate interdisciplinary group decision-making in engineering design, leveraging group decision-making performance theory and eye-tracking technology. Experiments were conducted in the context of Chinese cruise ship cabin design. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), a quantitative model was developed to assess the impact of visual attention on group decision performance.

The results demonstrate that group average gaze duration and group average number of gazes directly influence group decision-maker satisfaction and decision acceptability. Furthermore, these factors indirectly affect interdisciplinary group decision-making performance by impacting group decision quality.

The findings provide a foundation for developing effective interdisciplinary group decision support systems, enhancing cognitive performance, and offering new methodological insights for future engineering design decisions. This research contributes to bridging the gap in quantitative assessment of group decision-making performance, paving the way for improved decision quality and efficiency in engineering contexts.