Chatbots and team-based working dynamics: management decision implications

Abstract

Purpose This study investigates the relationship between artificial intelligence (AI)-related system characteristics and two interpersonal states commonly associated with effective teamwork, namely employee well-being and mutual trust. While generative AI has shown potential to improve organizational performance, its specific effects on internal team-based working relationships remain underexplored. Design/methodology/approach A theoretical model is developed to explore the influence of three antecedent variables, quality of information, system quality and generative AI use, on collaboration within teams. Collaboration is operationalized using two key constructs: employee well-being and mutual trust. The model is empirically tested using data from a large-scale survey of 208 professionals working in team-based environments. Data analysis is conducted using partial least squares structural equation modeling (PLS-SEM). Findings The results confirm that all three antecedent variables positively influence team-based collaboration dynamics. Specifically, the use of generative AI chatbots, such as ChatGPT, is shown to enhance employee well-being and foster mutual trust within teams, both of which act as interpersonal enablers of team collaboration. These outcomes suggest that the integration of high-quality AI tools can meaningfully support collaborative processes in professional settings. Originality/value This study contributes to the emerging field of generative AI research by shifting the focus from performance outcomes to collaboration mechanisms within teams. It offers practical implications for managers seeking to optimize teamwork in AI-enabled environments, including investing in system quality, redesigning workflows to integrate AI effectively and promoting a culture of trust and transparency around AI adoption.

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