Perceived fairness of algorithmic decision-making and university teachers' job burnout: a moderated chain-mediation model.

As big data and artificial intelligence (AI) technologies become embedded in the governance of higher education, algorithmic decision-making has improved managerial efficiency but has also led teachers to question the fairness of evaluation and to face difficulties in psychological adaptation, making job burnout an increasingly salient concern. How perceived fairness of algorithmic decision-making (FAD) relates to university teachers' job burnout, and through what psychological mechanism, remains unclear. Drawing on the transactional theory of stress and organizational support theory, we built a moderated chain-mediation model in which psychological safety and algorithm anxiety operate as sequential mediators and organizational support acts as a moderator. Questionnaire data were collected from 895 university teachers in Shanghai and Gansu, China, and the model was tested with the SPSS PROCESS macro using standardized coefficients and bias-corrected bootstrapping. Three findings emerged. First, FAD was negatively associated with job burnout, and this relationship was fully accounted for by the proposed mediators. Second, psychological safety and algorithm anxiety each mediated the link and together formed a significant serial path (FAD → psychological safety → algorithm anxiety → job burnout;