Influence of personality traits on employee’ adoption of generative AI: a qualitative comparative analysis

Abstract

The rapid advancement of Generative AI (Gen-AI) has made promoting employees’ adoption a critical pathway for boosting enterprise productivity and innovation. Although existing literature has examined some predictors of Gen-AI adoption, there remains a paucity of research investigating underlying factors within specific situational contexts. To bridge this gap, this study develops a situation–individual framework to examine employees’ adoption intention. First, based on prospect theory, this study investigates adoption intention within the context of specific task-level risk and gain scenarios. Second, it focuses on the impact of personality traits on employees’ adoption intention, exploring the personalised differences in adoption intention from an individual micro perspective. Third, given that Gen-AI adoption is a complex systemic issue, this study employs fuzzy-set QCA (fsQCA) to analyse the synergistic effect of personality traits on adoption intention in specific situations. Based on a survey of 1,066 full-time employees, this study reveals that the configurations leading to high and low level adoption intention differ across risk and gain scenarios, with distinct personality traits playing central roles in each. These findings provide a comprehensive theoretical framework for understanding the drivers of Gen-AI adoption, while offering practical implications for targeted managerial training strategies.

The authors' abstract, as published at the source. Behaviour and Information Technology, 2026 · DOI ↗