人工智能协同环境下的员工学习行为:基于工作需求-控制模型的视角

Aihui Chen, Tuo Yang, Jinfeng Ma, Yao-bin Lu
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引用次数: 1

摘要

大多数研究都集中在人工智能应用对管理属性、管理决策和管理伦理的影响上。然而,人工智能协作背景下的工作需求和工作控制如何决定员工的学习过程和学习行为,以及人工智能协作如何调节员工的学习过程和学习行为,仍然是未知的。为了回答这些问题,作者采用工作需求-控制(Job Demand-Control, JDC)模型来探讨员工个人学习行为的影响因素。设计/方法/方法本研究在使用人工智能的组织中使用问卷调查来收集数据。采用偏最小二乘(PLS)预测算法和SPSS对假设进行检验。发现工作需求和工作控制正向影响自我效能感,自我效能感正向影响学习目标取向,学习目标取向正向影响学习行为。学习目标取向在自我效能感与学习行为之间起中介作用。同时,与人工智能的协作正向调节员工工作需求对自我效能感的影响和自我效能感对学习行为的影响。独创性/价值本研究引入自我效能感作为JDC模型的结果,论证了学习目标取向的中介作用,并引入了与人工智能相关的协同因素。本研究进一步丰富了人机交互的理论体系,拓展了组织学习理论的内容。
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Employees' learning behavior in the context of AI collaboration: a perspective on the job demand-control model
PurposeMost 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/approachThis 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.FindingsJob 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/valueThis 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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