文献类型：期刊文献

英文题名：Effects of human-machine interaction on employee's learning: A contingent perspective

作者：Wang Sen[1];Zhao Hong[2];Zhu Xiaomei[1]

通讯作者：Zhao, H[1]

机构：[1]Beijing Union Univ, Sch Management, Dept Business Adm, Beijing, Peoples R China;[2]Nanjing Univ Finance & Econ, Sch Business Adm, Dept Business Adm, Nanjing, Peoples R China

第一机构：北京联合大学管理学院

通讯机构：[1]corresponding author), Nanjing Univ Finance & Econ, Sch Business Adm, Dept Business Adm, Nanjing, Peoples R China.

年份：2022

卷号：13

外文期刊名：FRONTIERS IN PSYCHOLOGY

收录：;Scopus(收录号：2-s2.0-85138399379);WOS:【SSCI(收录号:WOS:000856036600001)】；

基金：Funding This study was supported by the National Natural Science Foundation of China (No. 72002013), the Project of Beijing Municipal Education Commission (No. SM202111417005), the Key Social Science Project of Beijing Municipal Commission of Education (No. SZ202011417026), the National Social Science Foundation of China (No. 20BGL135), the Social Science Foundation of Jiangsu Province (22GLB005), and the Academic Research Projects of Beijing Union University (No. SK40202101).

语种：英文

外文关键词：human-machine interaction; employee vitality; employee learning; job characteristics; competence perception

摘要：The popularization of intelligent machines such as service robot and industrial robot will make human-machine interaction, an essential work mode. This requires employees to adapt to the new work content through learning. However, the research involved human-machine interaction that how influences the employee's learning is still rarely. This paper was to reveal the relationship between human-machine interaction and employee's learning from the perspective of job characteristics and competence perception of employees. We sent questionnaire to 500 employees from 100 artificial intelligence companies in China and received 319 valid and complete responses. Then, we adopted a hierarchical regression for the test. Empirical results show that human-machine interaction has a U-shaped curvilinear relationship with employee learning, and employee's vitality mediates the curvilinear relationship. In addition, job characteristics (skill variety and job autonomy) moderate the U-shaped curvilinear relationship between human-machine interaction and employee's vitality, especially the results of moderating effects varying with employee's competence perception. Exploring the mechanism of the effect of human-machine interaction on employee's learning enriches the socially embedded model. Moreover, it provides managerial implications how to enhance individual adaptability with the introduction of AI into firms. However, our research focuses more on the impact of human-machine interaction on employees at the initial stage of AI development, and the level of machine intelligence in various industries will reach a high degree of autonomy in the future. The future research can explore the impact of human-machine interaction on individual's behavior at different stages, and the results may vary depending on the technologies mastered by different individuals. The study has theoretical and practical significance to human-machine interaction literature by underscoring the important of individual's behavior among individuals with different skills.