From leadership research to district action: Perceived fairness of algorithmic decision-making and university teachers’ job burnout: a moderated chain-mediation model

AI-assisted editorial implementation guide. Proposed district actions are separate from the original authors’ findings and recommendations. Independent peer review of this guide is not asserted.

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Evidence and transfer

Education-related source: verify which participants, institutions and outcomes were actually studied before district adoption. Source publication type: research-article. License and identity checks do not establish study quality or causality.

Original authors’ abstract, reproduced under the source license:

Abstract 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; β = −0.002, 95% CI [−0.005, −0.000]); algorithm anxiety was the single strongest mediator, accounting for 23.60% of the total effect. Third, organizational support moderated the algorithm anxiety–burnout association: Johnson–Neyman analysis indicated a significant positive association at low support (below 4.16) that became non-significant in the mid-range and, above 5.36, reversed in sign. Because the design is cross-sectional, this reversal should be read as a boundary pattern that requires replication rather than as evidence of a causal switch. By situating FAD, psychological safety, algorithm anxiety, and job burnout within one framework, the study clarifies the technology–individual–organization interplay and offers evidence for fairer algorithmic governance and for protecting teachers’ occupational health. Keywords: algorithm anxiety, job burnout, organizational support, perceived fairness of algorithmic decision-making, psychological safety, university teachers

Study methods

This study used random sampling to administer electronic questionnaires to university teachers in Shanghai and Gansu, China. A total of 923 questionnaires were returned, and after excluding invalid responses, 895 valid questionnaires were obtained, yielding an effective response rate of 96.97%. The sample was dominated by women (57.88%), teachers aged 36 and above (73.29%), and those holding associate-senior titles (50.95%); the disciplinary distribution covered the humanities and social sciences (34.86%), science/engineering/agriculture/medicine (31.84%), and education (27.82%), among others; teaching experience was mostly concentrated within 10 years (76.65%), and work intensity was generally high, with 59.00% working more than 41 h per week. The sample structure broadly corresponds to the overall distribution of university teachers in China; details are presented in Table 1.

Limitations

Several limitations qualify these conclusions and point to next steps. The design is cross-sectional, so the relationships reported here are associative, and the appraisal sequence implied by the model should be confirmed with longitudinal or experimental data. The measures were entirely self-reported, which leaves room for common method variance despite the diagnostic checks reported above; multi-source or multi-wave designs would strengthen inference. The sample was drawn from only two provinces, Shanghai and Gansu; although this contrast was chosen to widen variation, institutional type, disciplinary culture, and regional context may each shape how teachers perceive algorithmic fairness and experience burnout, so the findings should be generalized with care and re-examined across a broader range of institutions and regions. Finally, the sign reversal of the anxiety–burnout association at high organizational support, though intriguing, rests on cross-sectional evidence and awaits replication before it can bear strong theoretical weight. With these cautions in mind, the study constructed and tested a moderated chain-mediation model showing that perceived fairness of algorithmic decision-making is associated with lower job burnout among university teachers, largely through greater psychological safety and lower algorithm anxiety, and that organizational support conditions the final step from anxiety to burnout. It thereby offers an integrated, appraisal-based account of teachers’ occupational health in the algorithmic era, together with practical guidance for fairer and more supportive algorithmic governance in higher education.

A 90-day district inquiry

Days 1–15 · Appraise. Read the source methods, recruitment, comparator, outcomes and limitations. Record how the study setting differs from your district and which claim the design can support.
Days 16–30 · Design. Map one recurring source of staff workload with voluntary staff input. Agree one reversible schedule, support or task-allocation change and name its owner. Agree participation, privacy protections, baseline and stopping rules before starting.
Days 31–60 · Observe. Collect the pre-agreed measures, keep a log of implementation changes and invite confidential voluntary feedback. Preserve existing support for staff and learners.
Days 61–90 · Decide. Compare workload hours, a consistent voluntary wellbeing measure, absence and retention intentions with the pre-pilot baseline. Report response rates and avoid causal claims from an uncontrolled comparison. Make a dated decision to stop, revise or extend the inquiry.

Board decision brief

Present the local problem, source setting, evidence limits, intervention, responsible owner, total cost, measured changes and uncertainty. Separate staff perceptions, implementation measures and student learning outcomes.

Attribution and reuse

Jiwei Wang; Decheng Li; Xin Zhao. Perceived fairness of algorithmic decision-making and university teachers’ job burnout: a moderated chain-mediation model. Frontiers in Psychology (2026-08-26). https://doi.org/10.3389/fpsyg.2026.1901039

Prepared 2026-11-07. Source authorship and DOI are preserved. The abstract and source passages are attributed extracts; this editorial guide is offered under the same CC BY 4.0 license.