Frontiers in public health · 2026
When technology meets aging: the paradoxical association between cognitive appraisal of AI and older employees' successful aging at work.
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Hao Yujie, Wang Jiayi
Аннотация
OBJECTIVE: Against the dual backdrop of population aging and workplace digital transformation, promoting successful aging at work among older employees has become a critical issue in human resource management. Based on the Job Demands-Resources (JD-R) model, this study aims to examine the dual-pathway associations between older employees' cognitive appraisal of artificial intelligence (AI) and their successful aging at work, as well as the mediating role of work autonomy and the moderating role of selective optimization with compensation (SOC) strategies.
METHODS: This study surveyed 558 employees aged 45 and above from various enterprises in China via questionnaires. Hierarchical regression analysis and Bootstrap approach were employed using Stata 17.0 to test the relationships among variables.
RESULTS: The findings indicated that: (1) Older employees' cognitive appraisal of AI exhibits a double-edged correlational pattern regarding their successful aging at work. When older employees appraise AI as a challenge (theoretically corresponding to the resource-gain pathway), it is positively associated with their successful aging at work; when they appraise AI as a threat (theoretically corresponding to the demand-loss pathway), this association becomes negative. (2) Work autonomy was a significant mediator in both associational pathways. (3) SOC strategies positively moderated the relationship between challenge appraisal of AI (theoretically corresponding to the resource-gain pathway) and work autonomy, and buffered the negative relationship between threat appraisal of AI (theoretically corresponding to the demand-loss pathway) and work autonomy. (4) Moderated mediation analyses revealed that SOC strategies strengthened the indirect effect in the challenge appraisal (perceived resource-gain) pathway and weakened the indirect effect in the threat appraisal (perceived demand-loss) pathway.
CONCLUSION: This study sheds light on the differentiated mechanisms through which older employees' cognitive appraisal of AI relates to their successful aging at work, extending the application of the JD-R model to digital work contexts. The findings provide empirical evidence and practical insights for enterprises to promote older employees' sustainable development in the AI era by enhancing work autonomy and stimulating proactive adaptation behaviors.
METHODS: This study surveyed 558 employees aged 45 and above from various enterprises in China via questionnaires. Hierarchical regression analysis and Bootstrap approach were employed using Stata 17.0 to test the relationships among variables.
RESULTS: The findings indicated that: (1) Older employees' cognitive appraisal of AI exhibits a double-edged correlational pattern regarding their successful aging at work. When older employees appraise AI as a challenge (theoretically corresponding to the resource-gain pathway), it is positively associated with their successful aging at work; when they appraise AI as a threat (theoretically corresponding to the demand-loss pathway), this association becomes negative. (2) Work autonomy was a significant mediator in both associational pathways. (3) SOC strategies positively moderated the relationship between challenge appraisal of AI (theoretically corresponding to the resource-gain pathway) and work autonomy, and buffered the negative relationship between threat appraisal of AI (theoretically corresponding to the demand-loss pathway) and work autonomy. (4) Moderated mediation analyses revealed that SOC strategies strengthened the indirect effect in the challenge appraisal (perceived resource-gain) pathway and weakened the indirect effect in the threat appraisal (perceived demand-loss) pathway.
CONCLUSION: This study sheds light on the differentiated mechanisms through which older employees' cognitive appraisal of AI relates to their successful aging at work, extending the application of the JD-R model to digital work contexts. The findings provide empirical evidence and practical insights for enterprises to promote older employees' sustainable development in the AI era by enhancing work autonomy and stimulating proactive adaptation behaviors.