Employees' learning behavior in the context of AI collaboration: a perspective on the job demand-control model

知识管理 心理学 背景(考古学) 体验式学习 控制(管理) 组织学习 应用心理学 社会心理学 计算机科学 人工智能 数学教育 古生物学 生物
作者
Aihui Chen,T. Andrew Yang,Jinfeng Ma,Yaobin Lu
出处
期刊:Industrial Management and Data Systems [Emerald (MCB UP)]
卷期号:123 (8): 2169-2193 被引量:7
标识
DOI:10.1108/imds-04-2022-0221
摘要

Purpose Most studies have focused on the impact of the application of AI on management attributes, management decisions and management ethics. However, how job demand and job control in the context of AI collaboration determine employees' learning process and learning behaviors, as well as how AI collaboration moderates employees' learning process and learning behaviors, remains unknown. To answer these questions, the authors adopted a Job Demand-Control (JDC) model to explore the influencing factors of employee's individual learning behavior. Design/methodology/approach This study used questionnaire survey in organizations using AI to collect data. Partial least squares (PLS) predict algorithm and SPSS were used to test the hypotheses. Findings Job demand and job control positively influence self-efficacy, self-efficacy positively influences learning goal orientation and learning goal orientation positively influences learning behavior. Learning goal orientation plays a mediating role between self-efficacy and learning behavior. Meanwhile, collaboration with AI positively moderates the impact of employees' job demand on self-efficacy and the impact of self-efficacy on learning behavior. Originality/value This study introduces self-efficacy as the outcome of JDC model, demonstrates the mediating role of learning goal orientation and introduces collaborative factors related to artificial intelligence. This study further enriches the theoretical system of human–AI interaction and expands the content of organizational learning theory.
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