{"title":"Exploring the Impact of AI on Human Resource Management: A Case Study of Organizational Adaptation and Employee Dynamics","authors":"Hong Zhang","doi":"10.1109/TEM.2024.3457520","DOIUrl":null,"url":null,"abstract":"This study investigates the transformative impact of artificial intelligence (AI) on human resource management (HRM) practices through a quantitative descriptive approach. Data were collected from 285 employees and 144 HR professionals across seven organizations using purposive sampling to explore AI's influence on recruitment, performance assessment, job satisfaction, and workforce planning. A key novelty of this research lies in its comprehensive analysis of AI's holistic influence on HRM dynamics, going beyond isolated aspects of AI implementation. Findings reveal that organizations leveraging AI in HR processes experience significantly higher recruitment efficiency and employee productivity compared to those without AI integration. Moreover, successful adaptation to AI in HR correlates with increased levels of employee job satisfaction and reduced turnover rates, highlighting AI's potential to enhance organizational performance and employee well-being. Additionally, positive perceptions of AI in HR positively correlate with elevated levels of organizational trust and employee engagement. These insights contribute to a nuanced understanding of AI's role in reshaping HRM strategies and fostering a supportive workplace environment conducive to sustainable organizational success. Practical implications are discussed to assist HR professionals and organizational leaders in effectively harnessing AI to optimize HR practices and adapt to evolving workforce dynamics.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Engineering Management","FirstCategoryId":"91","ListUrlMain":"https://ieeexplore.ieee.org/document/10723108/","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 0
Abstract
This study investigates the transformative impact of artificial intelligence (AI) on human resource management (HRM) practices through a quantitative descriptive approach. Data were collected from 285 employees and 144 HR professionals across seven organizations using purposive sampling to explore AI's influence on recruitment, performance assessment, job satisfaction, and workforce planning. A key novelty of this research lies in its comprehensive analysis of AI's holistic influence on HRM dynamics, going beyond isolated aspects of AI implementation. Findings reveal that organizations leveraging AI in HR processes experience significantly higher recruitment efficiency and employee productivity compared to those without AI integration. Moreover, successful adaptation to AI in HR correlates with increased levels of employee job satisfaction and reduced turnover rates, highlighting AI's potential to enhance organizational performance and employee well-being. Additionally, positive perceptions of AI in HR positively correlate with elevated levels of organizational trust and employee engagement. These insights contribute to a nuanced understanding of AI's role in reshaping HRM strategies and fostering a supportive workplace environment conducive to sustainable organizational success. Practical implications are discussed to assist HR professionals and organizational leaders in effectively harnessing AI to optimize HR practices and adapt to evolving workforce dynamics.
期刊介绍:
Management of technical functions such as research, development, and engineering in industry, government, university, and other settings. Emphasis is on studies carried on within an organization to help in decision making or policy formation for RD&E.