Abusive supervision and AI adoption in Chinese higher education workplaces: Psychological mechanisms of resource disillusionment and organizational dynamics.

This study employs a sequential mixed-methods design examining how abusive supervision influences Chinese university faculty's intention to adopt AI teaching tools, considering emotional exhaustion as well as trust and ethical perceptions as mediators and resource gains as a moderator. Qualitative interviews (N = 34) informed the cultural adaptation of the quantitative instrument and later contextualized the interpretation of results, while PLS-SEM analysis of theoretically derived hypotheses was conducted on survey data (N = 219). Results showed no significant direct or mediated effects of abusive supervision on AI adoption intention. In contrast, resource gains positively predicted adoption but negatively moderated the abusive supervision-adoption relationship, amplifying adverse effects under high gains. This pattern is consistent with a resource disillusionment interpretation, whereby elevated expectations intensify perceived inequities in hierarchical contexts. Qualitative findings revealed efficiency benefits alongside privacy concerns and supervisory strain, supporting the dual role of AI-derived resources. Extending the Technology Acceptance Model and Conservation of Resources theory in a non-Western context, this study highlights how cultural hierarchies shape technology adoption dynamics. Practical implications include leadership training, transparent AI governance, and expectation management strategies to foster sustainable AI integration in higher education.