The development of artificial intelligence (AI) technology has led to a significant number of AI-based robots entering the workplace. This technological advancement is transforming the landscape of work, where traditional human-human collaboration is progressively giving way to human-robot collaboration. In these emerging team structures, robots and human employees are finding common ground, sharing numerous similarities in areas such as competence, characteristics, and behaviors. This convergence is leading to a paradigm shift where robots are increasingly being integrated as members of conventional human teams. As a result of this integration, a new dynamic is surfacing in the realm of organizational management, particularly in the behavior of leaders. The concept of ‘leaders’ human-robot comparison behavior’ is emerging as a topic of interest and discussion. However, empirical research on the impact of this behavior is scant. The few existing studies primarily focus on the differences between humans and robots in areas such as decision fairness, emotional expression, competence, and performance. Research remains limited on this new form of leadership behavior, known as ‘leaders’ human-robot comparison,’ and its effects. Furthermore, traditional leadership research primarily focuses on behaviors targeting human subordinates, often overlooking those directed towards emerging technologies like robots. Hence, there is limited insight into the implications of such nascent leadership behaviors, particularly regarding leaders’ human-robot comparison behavior. To address these gaps, this study introduces the novel concept of ‘leaders’ human-robot comparison behavior.’ Drawing on objectification theory, the study aims to explore whether, how, and when this behavior impacts subordinate job performance.This research, comprising two studies, considers both internal and external validity in its design. Study 1 used an experimental research method to collect 167 samples from a Western cultural context (i.e., the UK). This phase of the study focused on examining the causal relationship between leaders’ human-robot comparison behavior and subordinates’ perceived objectification. Study 2 expanded on these findings through a multi-wave, multi-source field survey method. It involved a larger and culturally diverse sample, gathering data from 65 leaders and 196 subordinate dyads in an Eastern cultural context, China. This approach allowed for a broader understanding of the phenomenon across different cultural settings. Hierarchical linear modeling (HLM) was employed to replicate Study 1’s findings and to further test the full model. The results reveal a positive relationship between leaders’ human-robot comparison behavior and subordinates’ perceived objectification, which in turn negatively impacts their job performance. Moreover, subordinates’ challenge appraisal of robots moderates this process. Specifically, when subordinates possess a heightened challenge appraisal of robots, the negative indirect effect of leaders’ human-robot comparison behavior on subordinate job performance, through perceived objectification, intensifies. Furthermore, in the supplementary analysis section, this study controlled for the subordinates’ perceived intelligence of robots and their familiarity with robots. The data were re-analyzed without adding any control variables, and the results indicated that all hypotheses were still supported.This research offers several theoretical contributions to the literature on human-robot collaboration and objectification theory. First, this study investigates a frequently observed yet under-researched leadership behavior in human-robot collaborative teams, namely leaders’ human-robot comparison behavior. It examines its detrimental effect on subordinate job performance, deepening our understanding of leadership behaviors in such teams. Furthermore, it responds to the academic call to focus on ‘AI-related organizational phenomena and their impacts on subordinate psychology or behavior.’ It also endorses a recent perspective on leadership in human-robot collaborative teams: effective leadership in these teams encompasses managing human subordinates and overseeing the relationship between humans and robots. Second, by integrating objectification theory, this research provides fresh insights into how the introduction of robots into workplaces affects subordinate job performance, emphasizing the mediating role of subordinates’ perceived objectification and the moderating role of the subordinate’s challenge appraisal of robots. Lastly, this research significantly contributes to the field by enriching studies on the antecedents of perceived objectification, particularly from the unique perspective of the behavior recipient. This approach provides an important supplement to prior studies, which have predominantly focused on objectification from an actor’s perspective. By shifting the focus to the recipients of behavior, this study offers a new dimension in understanding how objectification is experienced and processed in workplace settings, especially in the context of human-robot interactions. Practically, this research makes a substantial contribution to our understanding of the influence of leaders’ human-robot comparison behavior. By delving into this dynamic, it sheds light on how leaders’ human-robot comparison impacts team dynamics and performance in settings where both humans and robots are team members. This insight is crucial for the effective management of human-robot collaborative teams, particularly in the rapidly evolving AI era. It also has significant practical implications for leadership transformation in the age of AI.