{"title":"The double-edged sword in the digitalization of human resource management: Person-environment fit perspective","authors":"Chunping Deng, Huimin Li, Yuye Wang, Rong Zhu","doi":"10.1016/j.jbusres.2024.114738","DOIUrl":null,"url":null,"abstract":"<div><p>Digital technology brings opportunities and challenges for human resource management (HRM). However, little is understood about how the compatibility between employees’ needed and organizations’ supplied digitalization of HRM (DHRM) is associated with employee outcomes. In this study, we drew on the person-environment (P-E) fit theory and utilized a manager-employee paired sample from 205 firms to explore the relationship between fit in employees’ needed and organizations’ supplied DHRM (i.e., algorithmic recording and automatic analysis) and employees’ cognitive responses. Results indicate that fit in DHRM is a double-edged sword. While fit in the algorithmic recording is positively related to perceived insider status, fit in the automatic analysis is negatively related to competence mobilization. Furthermore, the relationship between misfit in DHRM and employees’ cognitive responses is moderated by leaders’ influence tactics in terms of leader empathy and coalition influence tactics. This study enriches research on DHRM by examining fit from a dyadic perspective.</p></div>","PeriodicalId":15123,"journal":{"name":"Journal of Business Research","volume":null,"pages":null},"PeriodicalIF":10.5000,"publicationDate":"2024-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Business Research","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S014829632400242X","RegionNum":1,"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
Digital technology brings opportunities and challenges for human resource management (HRM). However, little is understood about how the compatibility between employees’ needed and organizations’ supplied digitalization of HRM (DHRM) is associated with employee outcomes. In this study, we drew on the person-environment (P-E) fit theory and utilized a manager-employee paired sample from 205 firms to explore the relationship between fit in employees’ needed and organizations’ supplied DHRM (i.e., algorithmic recording and automatic analysis) and employees’ cognitive responses. Results indicate that fit in DHRM is a double-edged sword. While fit in the algorithmic recording is positively related to perceived insider status, fit in the automatic analysis is negatively related to competence mobilization. Furthermore, the relationship between misfit in DHRM and employees’ cognitive responses is moderated by leaders’ influence tactics in terms of leader empathy and coalition influence tactics. This study enriches research on DHRM by examining fit from a dyadic perspective.
期刊介绍:
The Journal of Business Research aims to publish research that is rigorous, relevant, and potentially impactful. It examines a wide variety of business decision contexts, processes, and activities, developing insights that are meaningful for theory, practice, and/or society at large. The research is intended to generate meaningful debates in academia and practice, that are thought provoking and have the potential to make a difference to conceptual thinking and/or practice. The Journal is published for a broad range of stakeholders, including scholars, researchers, executives, and policy makers. It aids the application of its research to practical situations and theoretical findings to the reality of the business world as well as to society. The Journal is abstracted and indexed in several databases, including Social Sciences Citation Index, ANBAR, Current Contents, Management Contents, Management Literature in Brief, PsycINFO, Information Service, RePEc, Academic Journal Guide, ABI/Inform, INSPEC, etc.