Abstract: Generative artificial intelligence (GenAI) is deeply integrating into organizational decision-making. This trend means that leaders are now presented with advice originating not only from human employees but also from GenAI and even human-AI collaborative teams. This transformation raises a core research question: In the decision-making process, how do leaders compare and ultimately adopt advice from employees, GenAI, and collaborative human-GenAI teams? What are the underlying cognitive, affective, and behavioral reaction mechanisms? Existing research on this question remains insufficient. Grounded in social comparison theory, this study constructs a multi-level theoretical model. It aims to thoroughly unveil the mechanisms of leaders' differential perceptions regarding employee-GenAI advice, thereby filling a critical theoretical gap in this field and providing guidance for organizations to optimize human-AI collaborative decision-making.
While traditional theory has been confined to interpersonal comparisons, this study extends its application to the new dimension of “human-GenAI” comparison. We construct a three-dimensional framework encompassing social dynamic comparison, performance-reward comparison, and agency capability comparison. Based on the theoretical framework, we have designed five interlocking sub-studies that form a cross-level integrated model. This model spans from individual cognitive and affective responses to team-level risk attribution, and further extends to organizational intervention strategies.
Sub-Study 1: Advice quality comparison and the affective mediation pathway. This sub-study focuses on how leaders' comparison of advice quality between employees and GenAI influences their feeling of “appreciation,” which in turn drives their adoption intention. It introduces social comparison orientation as a critical boundary condition, revealing that leaders with high social comparison orientation may prefer high-quality GenAI advice to circumvent interpersonal threats.
Sub-Study 2: Dual pathways of advice source characteristics and decision effectiveness. Moving beyond generic quality, this sub-study delves into the objective characteristics of the advice source: explainability, accuracy, and affordance. It posits that these characteristics differentially trigger leaders' affective responses (appreciation vs. aversion), which subsequently mediate their decision to adopt or reject advice and ultimately impact decision effectiveness. The model also introduces the perceived relative superiority of the advice source as a moderator, acknowledging that leaders' subjective synthesis of these characteristics can amplify or weaken their reactions.
Sub-Study 3: Contextual interaction effects of advice content and strategy. This sub-study investigates the interaction between the content of the advice (task-work vs. interpersonal work) and the strategy of its delivery (direct vs. indirect voice). The model proposes that the effectiveness of advice from different sources varies significantly by task type, while revealing the critical mediating roles played by leader identity threat and identity recognition. This framework systematically clarifies the specific contexts in which employee or GenAI advice proves more effective, and how communication strategies can moderate this dynamic process.
Sub-Study 4: Team advice risk and responsibility attribution mechanism. Scaling the investigation to the team level, this sub-study analyzes the risks associated with three team configurations: employee teams, GenAI teams, and employee-GenAI collaborative teams. Based on attribution theory, we propose three core psychological mechanisms—employee team responsibility attribution, GenAI team responsibility attribution, and shared responsibility attribution—that connect team risks with leaders' decisions to adopt or reject advice. Particularly, the proposition that “shared responsibility attribution” leads to leaders rejecting all team advice profoundly reveals the “responsibility ambiguity trap” in human-AI collaboration.
Sub-Study 5: Identification of advice barriers and validation of intervention strategies. The final sub-study identifies the psychological and functional barriers that hinder human-AI collaborative advice at the micro-individual level, constructing an influential pathway model of “barriers - motivation - leader response.” Furthermore, by empirically testing the effectiveness of intervention strategies such as training and incentives, this research provides both a theoretical foundation and practical pathways for organizations to overcome these barriers and enhance the quality of human-AI collaborative advice.
In summary, the primary theoretical contributions of this research are fourfold: First, it extends the application boundaries of social comparison theory by constructing a multidimensional framework—encompassing social dynamic comparison, performance-reward comparison, and agency capability comparison—tailored to human-AI interaction scenarios. Second, it develops a cross-level integrated model spanning from individual to team levels, systematically revealing the multi-layered mechanisms underlying leaders' advice response. Third, it systematically identifies the dual-effect of GenAI advice adoption and empirically tests intervention strategies, offering a critical transition from theoretical exposition to practical guidance. Fourth, it fosters deep interdisciplinary integration across management, psychology, and artificial intelligence studies, providing a referential variable system and modeling paradigm for subsequent cross-disciplinary research.

Key words: leadership, GenAI advice, advice response, social comparison theory, perceptual differences