{"title":"人工智能与人力资源管理的跨学科回顾:挑战与未来方向","authors":"Yuan Pan , Fabian J. Froese","doi":"10.1016/j.hrmr.2022.100924","DOIUrl":null,"url":null,"abstract":"<div><p>Artificial intelligence (AI) has the potential to change the future of human resource management (HRM). Scholars from different disciplines have contributed to the field of AI in HRM but with rather insufficient cross-fertilization, thus leading to a fragmented body of knowledge. In response, we conducted a systematic, interdisciplinary review of 184 articles to provide a comprehensive overview. We grouped prior research into four categories based on discipline: management and economics, computer science, engineering and operations, and others. The findings reveal that studies in different disciplines had different research foci and utilized different methods. While studies in the technical disciplines tended to focus on the development of AI for specific HRM functions, studies from the other disciplines tended to focus on the consequences of AI on HRM, jobs, and labor markets. Most studies in all categories were relatively weak in theoretical development. We therefore offer recommendations for interdisciplinary collaborations, propose a unified definition of AI, and provide implications for research and practice.</p></div>","PeriodicalId":48145,"journal":{"name":"Human Resource Management Review","volume":"33 1","pages":"Article 100924"},"PeriodicalIF":8.2000,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"An interdisciplinary review of AI and HRM: Challenges and future directions\",\"authors\":\"Yuan Pan , Fabian J. Froese\",\"doi\":\"10.1016/j.hrmr.2022.100924\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Artificial intelligence (AI) has the potential to change the future of human resource management (HRM). Scholars from different disciplines have contributed to the field of AI in HRM but with rather insufficient cross-fertilization, thus leading to a fragmented body of knowledge. In response, we conducted a systematic, interdisciplinary review of 184 articles to provide a comprehensive overview. We grouped prior research into four categories based on discipline: management and economics, computer science, engineering and operations, and others. The findings reveal that studies in different disciplines had different research foci and utilized different methods. While studies in the technical disciplines tended to focus on the development of AI for specific HRM functions, studies from the other disciplines tended to focus on the consequences of AI on HRM, jobs, and labor markets. Most studies in all categories were relatively weak in theoretical development. We therefore offer recommendations for interdisciplinary collaborations, propose a unified definition of AI, and provide implications for research and practice.</p></div>\",\"PeriodicalId\":48145,\"journal\":{\"name\":\"Human Resource Management Review\",\"volume\":\"33 1\",\"pages\":\"Article 100924\"},\"PeriodicalIF\":8.2000,\"publicationDate\":\"2023-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Human Resource Management Review\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1053482222000420\",\"RegionNum\":1,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MANAGEMENT\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Human Resource Management Review","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1053482222000420","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MANAGEMENT","Score":null,"Total":0}
An interdisciplinary review of AI and HRM: Challenges and future directions
Artificial intelligence (AI) has the potential to change the future of human resource management (HRM). Scholars from different disciplines have contributed to the field of AI in HRM but with rather insufficient cross-fertilization, thus leading to a fragmented body of knowledge. In response, we conducted a systematic, interdisciplinary review of 184 articles to provide a comprehensive overview. We grouped prior research into four categories based on discipline: management and economics, computer science, engineering and operations, and others. The findings reveal that studies in different disciplines had different research foci and utilized different methods. While studies in the technical disciplines tended to focus on the development of AI for specific HRM functions, studies from the other disciplines tended to focus on the consequences of AI on HRM, jobs, and labor markets. Most studies in all categories were relatively weak in theoretical development. We therefore offer recommendations for interdisciplinary collaborations, propose a unified definition of AI, and provide implications for research and practice.
期刊介绍:
The Human Resource Management Review (HRMR) is a quarterly academic journal dedicated to publishing scholarly conceptual and theoretical articles in the field of human resource management and related disciplines such as industrial/organizational psychology, human capital, labor relations, and organizational behavior. HRMR encourages manuscripts that address micro-, macro-, or multi-level phenomena concerning the function and processes of human resource management. The journal publishes articles that offer fresh insights to inspire future theory development and empirical research. Critical evaluations of existing concepts, theories, models, and frameworks are also encouraged, as well as quantitative meta-analytical reviews that contribute to conceptual and theoretical understanding.
Subject areas appropriate for HRMR include (but are not limited to) Strategic Human Resource Management, International Human Resource Management, the nature and role of the human resource function in organizations, any specific Human Resource function or activity (e.g., Job Analysis, Job Design, Workforce Planning, Recruitment, Selection and Placement, Performance and Talent Management, Reward Systems, Training, Development, Careers, Safety and Health, Diversity, Fairness, Discrimination, Employment Law, Employee Relations, Labor Relations, Workforce Metrics, HR Analytics, HRM and Technology, Social issues and HRM, Separation and Retention), topics that influence or are influenced by human resource management activities (e.g., Climate, Culture, Change, Leadership and Power, Groups and Teams, Employee Attitudes and Behavior, Individual, team, and/or Organizational Performance), and HRM Research Methods.