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Effects of human-machine interaction on employee's learning: A contingent perspective
Wang Sen1, Zhao Hong2, Zhu Xiaomei1
1Department of Business Administration, School of Management, Beijing Union University, Beijing, China.
Frontiers in Psychology
|September 26, 2022
Summary
Human-machine interaction shows a U-shaped effect on employee learning, mediated by vitality. Job characteristics and competence perception influence this relationship, offering insights for AI integration in workplaces.
Area of Science:
- Artificial Intelligence
- Organizational Behavior
- Human-Computer Interaction
Background:
- The increasing prevalence of intelligent machines necessitates effective human-machine interaction (HMI) for employee adaptation and learning.
- Limited research exists on how HMI specifically influences employee learning, particularly concerning job characteristics and competence perception.
Purpose of the Study:
- To investigate the relationship between human-machine interaction and employee learning.
- To examine the mediating role of employee vitality and the moderating effects of job characteristics (skill variety, job autonomy) and competence perception.
Main Methods:
- A questionnaire survey was administered to 500 employees in 100 artificial intelligence companies in China, yielding 319 valid responses.
- Hierarchical regression analysis was employed to test the proposed relationships.
Main Results:
- A U-shaped curvilinear relationship was found between human-machine interaction and employee learning, with employee vitality acting as a mediator.
- Job characteristics, specifically skill variety and job autonomy, moderated the curvilinear relationship between HMI and vitality, with effects varying based on employee competence perception.
Conclusions:
- The findings enrich the socially embedded model by elucidating the mechanisms through which HMI impacts employee learning.
- Provides practical managerial insights for enhancing employee adaptability during AI implementation.
- Highlights the need for future research on HMI's effects at different stages of AI development and across diverse industries and individual skill sets.
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