From boost to decline: How AI exposure shapes short-term absorptive-capacity dynamics during onboarding and knowledge...

Area of Science:

• Organizational Behavior
• Human Resource Management
• Artificial Intelligence in the Workplace

Background:

• Employee onboarding is critical for knowledge acquisition and integration.
• Understanding the impact of artificial intelligence (AI) on employee learning dynamics is crucial for modern organizations.
• Self-determination theory provides a framework for examining motivation in learning contexts.

Purpose of the Study:

• To investigate how AI exposure influences employees' short-term absorptive capacity dynamics during onboarding.
• To analyze the relationship between AI exposure and knowledge sharing intention and behavior.
• To compare the effects of voluntary versus mandatory AI exposure on employee learning outcomes.

Main Methods:

• Two field studies were conducted with onboarding employees in knowledge-intensive firms.
• Study 1 utilized a 10-day daily measurement design and latent growth modeling.
• Study 2 employed a three-condition design (voluntary AI, mandatory AI, control) and examined behavioral knowledge sharing.

Main Results:

• AI exposure positively affected the initial level of absorptive capacity but negatively influenced its trajectory.
• An inverted U-shaped relationship was observed between AI exposure and knowledge-sharing outcomes.
• Voluntary AI exposure led to more favorable outcomes, including stronger competence support and higher knowledge sharing, compared to mandatory exposure.

Conclusions:

• AI exposure acts as both a technological tool and a motivational context influencing employee learning and knowledge engagement.
• The structure of AI exposure during onboarding significantly impacts its effectiveness.
• Organizations should prioritize voluntary and discretion-preserving AI integration to foster sustained self-directed learning and knowledge sharing.