{"title":"A critical review of algorithms in HRM: Definition, theory, and practice","authors":"Maggie M. Cheng, Rick D. Hackett","doi":"10.1016/j.hrmr.2019.100698","DOIUrl":null,"url":null,"abstract":"<div><p>The recent surge of interest concerning data analytics in both business and academia has been accompanied by significant advances in the commercialization of HRM (Human Resource Management)-related algorithmic applications. Our review of the literature uncovered 22 high quality academic papers and 122 practitioner-oriented items (e.g., popular press and trade journals). As part of our review, we draw several distinctions between the typical use of HRM algorithms and more traditional statistical applications. We find that while HRM algorithmic applications tend not to be especially theory-driven, the “black box” label often invoked by critics of these efforts is not entirely appropriate. Instead, HRM-related algorithms are best characterized as heuristics. In considering the implications of our findings, we note that there is already evidence of a research-practitioner divide; relative to scholarly efforts, practitioner interest in HRM algorithms has grown exponentially in recent years.</p></div>","PeriodicalId":48145,"journal":{"name":"Human Resource Management Review","volume":null,"pages":null},"PeriodicalIF":8.2000,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.hrmr.2019.100698","citationCount":"77","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Human Resource Management Review","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1053482219302682","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 77
Abstract
The recent surge of interest concerning data analytics in both business and academia has been accompanied by significant advances in the commercialization of HRM (Human Resource Management)-related algorithmic applications. Our review of the literature uncovered 22 high quality academic papers and 122 practitioner-oriented items (e.g., popular press and trade journals). As part of our review, we draw several distinctions between the typical use of HRM algorithms and more traditional statistical applications. We find that while HRM algorithmic applications tend not to be especially theory-driven, the “black box” label often invoked by critics of these efforts is not entirely appropriate. Instead, HRM-related algorithms are best characterized as heuristics. In considering the implications of our findings, we note that there is already evidence of a research-practitioner divide; relative to scholarly efforts, practitioner interest in HRM algorithms has grown exponentially in recent years.
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.