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Knowledge Sharing in AI-Enabled Workplaces: A Social Cognitive Perspective on Usefulness Perceptions and Competition

Year: 2025

Type: article

Abstract: The integration of artificial intelligence (AI) into organizational processes brings both opportunities and challenges for knowledge sharing. Knowledge sharing remains a cornerstone of organizational learning and innovation, yet the emergence of AI introduces new complexities to this behavior. In particular, AI-related knowledge is both a valuable tool and a controversial technology, whose dual nature generates uncertainty in employees' evaluations and decisions about sharing it. Drawing on social cognitive theory (SCT), this study develops a moderated mediation model to explain how personal cognitions and environmental cues jointly shape AI-related knowledge sharing. Within the SCT framework, perceived AI usefulness represents a personal cognitive evaluation, whereas anticipated positive response reflects a social outcome expectation. When employees perceive AI as highly useful, they are more likely to expect positive social feedback, which in turn motivates AI-related knowledge sharing behavior. Moreover, organizational competitive climate weakens this effect by acting as a social discount factor that reduces the perceived significance of social rewards. A three-wave survey of 519 Chinese employees supports these hypotheses, extending SCT by revealing how the dual nature of AI shapes employee outcome expectations and by identifying a socially grounded cognitive pathway that links perceived AI usefulness to knowledge sharing behaviors in organizational contexts.

Source: Behavioral Sciences

DOI: 10.3390/bs15121635

Authors: Chuling Zhong, Yao Wang

Institution: China University of Political Science and Law

Language: English

Topic: Knowledge Management and Sharing

Subfield: Communication

Field: Social Sciences

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $2,654

Funder: China University of Political Science and Law