Voice to algorithmic leader instead of human leaders——the mediating role of fairness perception and psychological safety

Background The development of AI technology has enabled algorithms to gradually take on some of the management tasks previously completed by human managers in organizations, such as task allocation, performance review, employee promotion, and so on. When the algorithm becomes the manager, the new human-machine interaction of algorithmic leadership-human subordinates emerges as the times require. Therefore, it is worth exploring how people respond to algorithmic leaders and how they respond differently to human leaders. This study investigates voice behavior, a key form of upward communication and pro-organizational behavior in employee-leader interactions. It explores differences in employees' voice behavior when led by algorithmic versus human leaders, examining the underlying mechanisms that drive these differences. Methods We conducted three experimental studies, all using scenario-based materials. Study 1 examined the differences in voice behavior of human employees toward different types of leaders (algorithmic leader vs. human leader). Study 2 explored the moderating role of task type, investigating how employees' voice behavior differed across cognitive and emotional tasks when interacting with different types of leaders. Study 3 focused on the chain mediation mechanism by measuring participants' fairness perception and psychological safety in the experiment. Results The study found that employees are more likely to voice to algorithmic leaders than human leaders. Task type (cognitive vs. emotional) influenced the differences between the two leader types, with employees more likely to voice to algorithmic leaders than human leaders on cognitive tasks. This effect was absent in the emotional tasks. This study also found that individuals have higher fairness perception towards algorithmic leader than human leader, leading to a higher psychological safety, which in turn increases their voice behavior. Conclusions This study reveals people's preferences for voice behavior to algorithmic leader, which is in cognitive tasks not in emotional tasks, and reveals the sequential mediation model of fairness perception and psychological safety. This study explores the impact of algorithmic leadership on human subordinates, highlighting its positive effects and contributing to the literature on human-machine interaction and collaboration. The findings also offer practical insights for deploying and designing algorithmic systems in organizational settings.