Impact of Job Demands on Employee Learning: The Moderating Role of Human–Machine Cooperation Relationship. (6th December 2022)

• Record Type: Journal Article
• Authors: Sen, Wang; Xiaomei, Zhu; Lin, Deng
• Other Names: Khan Asif Irshad Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2022(2022)
• Publication Date: 2022-12-06
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2022/7406716
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : New artificial intelligence (AI) technologies are applied to work scenarios, which may change job demands and affect employees' learning. Based on the resource conservation theory, the impact of job demands on employee learning was evaluated in the context of AI. The study further explores the moderating effect of the human–machine cooperation relationship between them. By collecting 500 valid questionnaires, a hierarchical regression for the test was performed. Results indicate that, in the AI application scenario, a U -shaped relationship exists between job demands and employee learning. Second, the human–machine cooperation relationship moderates the U -shaped curvilinear relationship between job demands and employees' learning. In this study, AI is introduced into the field of employee psychology and behavior, enriching the research into the relationship between job demands and employee learning.