文献类型：期刊文献

英文题名：The Dual Effects of Algorithmic Management on Platform Workers: An Attribution Perspective

作者：Zhou, Lian[1];Lei, Xue[2];Cooke, Fang Lee[3];Huang, Xinran[1];Zhang, Junwei[1]

机构：[1]Guangdong Univ Technol, Sch Management, Guangzhou, Peoples R China;[2]East China Univ Sci & Technol, Sch Business, Shanghai, Peoples R China;[3]Monash Univ, Monash Business Sch, Dept Management, Melbourne, Australia

年份：2025

卷号：64

期号：6

起止页码：1687

外文期刊名：HUMAN RESOURCE MANAGEMENT

收录：;WOS:【SSCI(收录号:WOS:001534683900001)】；

基金：This work was supported by the National Natural Science Foundation of China (grant 72002047, 72202064, and 72271065); Social Science Foundation of the Ministry of Education of China (grant 20YJC630237); Guangdong Basic and Applied Basic Research Foundation (grant 2024A1515011284), and Shanghai Pujiang Program (22PJC028). The support of the Economic and Social Research Council (ESRC) is gratefully acknowledged through the Digital Futures at Work Research Centre (grant no. ES/Z504713/1). We are appreciative of the associate editor and the anonymous reviewers for their constructive feedback. Thanks to Jiahui Li and Lanlan Pan for their research assistance. We extend our sincere gratitude to Yujie Zhan for her valuable comments. Open access publishing facilitated by Monash University, as part of the Wiley - Monash University agreement via the Council of Australian University Librarians.

语种：英文

外文关键词：algorithmic management; algorithmic transparency; attributions; customer-oriented service behavior; online labor platforms; work overload

摘要：Existing research often highlights the negative consequences of algorithmic management (AM) for platform workers. By contrast, less is known about what, how, and when AM may produce both positive and negative outcomes. Drawing on attribution theory, this study examines the dual effects of core AM dimensions (i.e., algorithmic recommending, restricting, evaluating, and rewarding) on platform workers' perceptions of work overload and customer-oriented service behavior. A two-wave survey of 213 online platform workers in China reveals that algorithmic recommending and rewarding improve customer-oriented service behavior and reduce work overload through AM commitment attributions. However, AM control attributions link algorithmic restricting, recommending, and evaluating (the latter two at low algorithmic transparency) to increased work overload. Algorithmic transparency moderates these effects, reducing the negative impacts of AM through AM control attributions. These findings contribute to a more nuanced understanding of the dual effects of core AM dimensions and provide practical insights for platforms seeking to enhance service quality while supporting worker well-being.