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

Area of Science:

• Organizational Behavior
• Knowledge Management
• Artificial Intelligence

Background:

• Knowledge sharing is vital for organizational learning and innovation.
• Artificial intelligence (AI) integration introduces new complexities to knowledge sharing behaviors.
• AI-related knowledge is a valuable tool but also a controversial technology, creating uncertainty for employees.

Purpose of the Study:

• To develop and test a moderated mediation model explaining AI-related knowledge sharing.
• To investigate the roles of personal cognitions (perceived AI usefulness) and social outcome expectations (anticipated positive response) in knowledge sharing.
• To examine the moderating effect of organizational competitive climate on the relationship between social outcome expectations and AI-related knowledge sharing.

Main Methods:

• A three-wave survey was conducted with 519 Chinese employees.
• Social Cognitive Theory (SCT) provided the theoretical framework.
• A moderated mediation model was employed to analyze the data.

Main Results:

• Perceived AI usefulness positively influences anticipated positive social response.
• Anticipated positive social response mediates the relationship between perceived AI usefulness and AI-related knowledge sharing.
• Organizational competitive climate weakens the positive effect of anticipated positive social response on knowledge sharing.

Conclusions:

• The study extends SCT by demonstrating how the dual nature of AI influences employee outcome expectations.
• A socially grounded cognitive pathway linking perceived AI usefulness to knowledge sharing is identified.
• Findings highlight the importance of perceived AI usefulness and social feedback in promoting AI knowledge sharing within organizations, moderated by competitive climate.