Driving mechanisms and impact effects of AI feedback-seeking behavior: A research proposal

doi:10.3724/SP.J.1042.2025.1647

Abstract

Abstract:

In the current VUCA (volatility, uncertainty, complexity, ambiguity) era, employees must proactively seek feedback to facilitate personal development and enhance their workplace competitiveness. Artificial intelligence (AI) offers new opportunities for proactive feedback-seeking, with a survey by Oracle Corporation indicating that over 50% of employees prefer seeking feedback from AI systems. However, traditional research on feedback-seeking behavior has yet to incorporate AI as a feedback source, leaving the mechanisms and consequences of employee feedback-seeking from AI largely underexplored. Moreover, emerging studies on AI feedback primarily position employees as passive feedback recipients, paying limited attention to their proactive feedback-seeking behaviors. Therefore, this research aims to bridge the gap by integrating insights from traditional feedback-seeking behavior literature with emerging studies on AI feedback, expanding the concept of feedback-seeking to include AI as a viable source, and contributing to the burgeoning field of emerging technologies and employee psychology and behavior.

Specifically，Study 1 adopts a human-AI interaction perspective to examine how AI system characteristics influence employees’ feedback-seeking from AI, as well as the underlying mechanisms. Drawing on mind perception theory and trust literature, this study proposes that employees’ perceptions of AI agency and experience, along with their cognition-based and affect-based trust in AI, serve as serial mediators in the relationships between two AI system features—transparency and anthropomorphism—and employees’ feedback-seeking from AI. Furthermore, acknowledging that task characteristics are essential factors in traditional feedback-seeking research, we suggest that problem solving moderates the serial mediated relationship. Building on the core outcome of interest in the feedback literature, Study 2 seeks to explore the consequences of employees’ feedback-seeking from AI on performance improvement and its underlying mechanisms. Based on feedback process theory and AI-related literature, this study proposes that feedback-seeking from AI positively predicts employees’ performance improvement. This effect is mediated by the accuracy and specificity of AI-generated feedback information. Moreover, the type of task—objective vs. subjective—is expected to moderate this mediation process.

In sum, the findings from the two studies offer several important theoretical contributions. First, this research innovatively positions artificial intelligence as a target of employees’ feedback-seeking behavior, thereby expanding the boundaries of the feedback-seeking literature. It lays a foundation for future research to explore the unique dynamics, antecedents, and consequences of seeking feedback from AI in the workplace. Second, by focusing on two critical system characteristics—transparency and anthropomorphism—this research identifies how system-level features, which have been emphasized in evaluations of AI by human users, influence employees’ feedback-seeking from AI. It reveals unique antecedents and mechanisms through which AI system features shape user behavior, deepens our understanding of the relationship between system characteristics and feedback-seeking behavior, and provides theoretical guidance for future research in AI-enabled work settings. Third, taking into account the distinctive nature of human-AI interaction, this research examines the consequences of feedback-seeking from AI on employee performance-related outcomes and uncovers the underlying mechanisms. Performance is a core outcome of interest in feedback-seeking research, yet prior studies have yielded inconsistent findings regarding the feedback-performance link. By exploring how feedback-seeking from AI influences performance improvement and through which mechanisms, this research helps clarify this relationship and broadens the theoretical boundaries of AI-related feedback-seeking research.

From a practical perspective, the findings offer actionable insights for managers seeking to encourage employees to proactively seek feedback from AI systems, helping them leverage potential benefits while mitigating associated risks. It also highlights the types of work tasks where such feedback-seeking is most appropriate and effective. Overall, this research positions AI feedback-seeking as a lens through which managers can better understand the evolving interplay between emerging technologies and employee behavior, and it invites practitioners to rethink how to foster synergy between humans and intelligent systems.

Key words: feedback-seeking from AI, transparency, anthropomorphism, performance improvement

CLC Number:

B849: C93

SUN Fang, LI Shaolong, LONG Lirong, LEI Xuan, ZENG Xianglin, HUANG Xiahong. Driving mechanisms and impact effects of AI feedback-seeking behavior: A research proposal[J]. Advances in Psychological Science, 2025, 33(10): 1647-1662.

Figures/Tables 3

References 94

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