With the advent of the algorithmic era,artificial intelligence(AI)and big data have become the technological foundation for the latest form of monitoring.Particularly,following the outbreak of COVID-19,the adoption of home offices has increased significantly,leading companies to seek more AI-based technologies to monitor employees.However,existing research indicates that the impact of AI algorithm monitoring on employees remains controversial.On one hand,studies sug-gest that these devices turn workplaces into"electronic sweatshops"of the modern era,causing increased work pressure,pri-vacy infringements,trust crises,counterproductive behaviors,and lower performance among employees.On the other hand,AI algorithm monitoring,with its powerful data processing capabilities,can automate human factors in work to enhance personal efficiency and improve performance.However,there is still a lack of sufficient explanation in existing research re-garding the reasons for these differential outcomes,and the underlying mechanisms remain a"black box"yet to be opened.Drawing upon the goal orientation theory,this study collected questionnaire data from 494 employees in Chinese enter-prises monitored by AI algorithms to test the core hypothesis that"differential feedback characteristics displayed by AI algo-rithm monitoring may lead to differential outcomes in employee innovation performance to some extent."The results re-vealed the following findings:(1)AI developmental feedback had a positive impact on employee innovation performance,while AI controlling feedback had a negative impact.(2)Learning goal orientation mediated the relationship between AI de-velopmental feedback and employee innovation performance,while avoidance goal orientation mediated the relationship be-tween AI controlling feedback and employee innovation performance.(3)AI algorithm monitoring transparency positively moderated the relationship between AI developmental feedback,learning goal orientation,and employee innovation perfor-mance.With the improvement of AI algorithm monitoring transparency,the promoting effect of AI developmental feedback on learning goal orientation and employee innovation performance was enhanced.In this case,the moderating effect of AI al-gorithm monitoring transparency on the relationship between AI developmental feedback and employee innovation perfor-mance was fully mediated by learning goal orientation.Additionally,(4)AI algorithm monitoring transparency negatively moderated the relationship between AI controlling feedback and its negative impact on employee innovation performance,and this moderating effect was partially mediated by avoidance goal orientation.These research findings provide new theo-retical perspectives and management suggestions for the field of workplace monitoring at the level of individual achieve-ment motivation.This study makes several contributions.Firstly,it enriches the emerging topic of how AI technology assists organiza-tions in implementing performance management.Existing research on how companies can better adopt AI technology to en-hance internal functions is relatively limited,and this study offers a beneficial exploration from an internal perspective.Sec-ondly,this study complements the research in the field of workplace monitoring.Big data and algorithmic analysis represent the latest stage of monitoring applications,yet current research in this area is scarce.This study extends the application of AI algorithms in monitoring research,particularly by providing empirical evidence on the positive impact of AI algorithm moni-toring on employee innovation performance.Thirdly,based on the goal orientation theory and from the perspective of indi-vidual motivation,this study explores the underlying mechanisms and boundary conditions of the impact of AI developmen-tal feedback and AI controlling feedback on employee innovation performance,filling the gap in previous research that lacks discussions on the mechanisms of monitoring's influence on employee innovation performance.Lastly,this study offers valuable insights for businesses and policymakers.It demonstrates that AI algorithm monitoring can enhance employee inno-vation performance more effectively than traditional monitoring,especially at higher levels of AI algorithm monitoring trans-parency.This suggests that investing in AI applications in corporate management can yield significant business returns.For policymakers concerned about the well-being of employees,regulations aimed at increasing transparency have been imple-mented.The findings of this study to some extent support this approach.
AI algorithmic monitoringfeedback characteristicsgoal orientationemployee innovation performanceAI algorithmic monitoring transparency
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