{"title":"The dark side of algorithmic management: investigating how and when algorithmic management relates to employee knowledge hiding?","authors":"Ping Liu, Ling Yuan, Zhenwu Jiang","doi":"10.1108/jkm-04-2024-0507","DOIUrl":null,"url":null,"abstract":"<h3>Purpose</h3>\n<p>Over the past decade, artificial intelligence (AI) technologies have rapidly advanced organizational management, with many organizations adopting AI-based algorithms to enhance employee management efficiency. However, there remains a lack of sufficient empirical research on the specific impacts of these algorithmic management practices on employee behavior, particularly the potential negative effects. To address this gap, this study constructs a model based on the psychological ownership theory, aiming to investigate how algorithmic management affects employees’ knowledge hiding.</p><!--/ Abstract__block -->\n<h3>Design/methodology/approach</h3>\n<p>This study validates the model through a situational experiment and a multi-wave field study involving full-time employees in organizations implementing algorithmic management. Various analytical methods, including analysis of variance, regression analysis and path analysis, were used to systematically test the hypotheses.</p><!--/ Abstract__block -->\n<h3>Findings</h3>\n<p>The study reveals that algorithmic management exerts a positive indirect influence on knowledge hiding through the psychological ownership of personal knowledge. This effect is particularly pronounced when employees have lower organizational identification, highlighting the critical role of organizational culture in the effectiveness of technological applications.</p><!--/ Abstract__block -->\n<h3>Originality/value</h3>\n<p>This study is among the first empirical investigations to explore the relationship between algorithmic management and employee knowledge hiding from an individual perception perspective. By applying psychological ownership theory, it not only addresses the current theoretical gap regarding the negative effects of algorithmic management but also provides new theoretical and empirical support for the governance and prevention of knowledge hiding within organizations in the context of AI algorithm application. The study highlights the importance of considering employee psychology (i.e. psychological ownership of personal knowledge) and organizational culture (i.e. organizational identification) under algorithmic management. This understanding aids organizations in better managing knowledge risks while maximizing technological advantages and effectively designing organizational change strategies.</p><!--/ Abstract__block -->","PeriodicalId":48368,"journal":{"name":"Journal of Knowledge Management","volume":null,"pages":null},"PeriodicalIF":6.6000,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Knowledge Management","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1108/jkm-04-2024-0507","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose
Over the past decade, artificial intelligence (AI) technologies have rapidly advanced organizational management, with many organizations adopting AI-based algorithms to enhance employee management efficiency. However, there remains a lack of sufficient empirical research on the specific impacts of these algorithmic management practices on employee behavior, particularly the potential negative effects. To address this gap, this study constructs a model based on the psychological ownership theory, aiming to investigate how algorithmic management affects employees’ knowledge hiding.
Design/methodology/approach
This study validates the model through a situational experiment and a multi-wave field study involving full-time employees in organizations implementing algorithmic management. Various analytical methods, including analysis of variance, regression analysis and path analysis, were used to systematically test the hypotheses.
Findings
The study reveals that algorithmic management exerts a positive indirect influence on knowledge hiding through the psychological ownership of personal knowledge. This effect is particularly pronounced when employees have lower organizational identification, highlighting the critical role of organizational culture in the effectiveness of technological applications.
Originality/value
This study is among the first empirical investigations to explore the relationship between algorithmic management and employee knowledge hiding from an individual perception perspective. By applying psychological ownership theory, it not only addresses the current theoretical gap regarding the negative effects of algorithmic management but also provides new theoretical and empirical support for the governance and prevention of knowledge hiding within organizations in the context of AI algorithm application. The study highlights the importance of considering employee psychology (i.e. psychological ownership of personal knowledge) and organizational culture (i.e. organizational identification) under algorithmic management. This understanding aids organizations in better managing knowledge risks while maximizing technological advantages and effectively designing organizational change strategies.
期刊介绍:
Knowledge Management covers all the key issues in its field including:
■Developing an appropriate culture and communication strategy ■Integrating learning and knowledge infrastructure
■Knowledge management and the learning organization
■Information organization and retrieval technologies for improving the quality of knowledge
■Linking knowledge management to performance initiatives ■Retaining knowledge - human and intellectual capital
■Using information technology to develop knowledge management ■Knowledge management and innovation
■Measuring the value of knowledge already within an organization ■What lies beyond knowledge management?