{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T05:46:28Z","timestamp":1776404788273,"version":"3.51.2"},"reference-count":110,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T00:00:00Z","timestamp":1684454400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ITP"],"published-print":{"date-parts":[[2024,4,8]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>Algorithms are widely used to manage various activities in the gig economy. Online car-hailing platforms, such as Uber and Lyft, are exemplary embodiments of such algorithmic management, where drivers are managed by algorithms for task allocation, work monitoring and performance evaluation. Despite employing substantially, the platforms face the challenge of maintaining and fostering drivers' work engagement. Thus, this study aims to examine how the algorithmic management of online car-hailing platforms affects drivers' work engagement.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>Drawing on the transactional theory of stress, the authors examined the effects of algorithmic monitoring and fairness on online car-hailing drivers' work engagement and revealed the\u00a0mediation effects of challenge-hindrance appraisals. Based on survey data collected from 364 drivers, the authors' hypotheses were examined using partial least squares structural equation modeling (PLS-SEM). The authors also applied path comparison analyses to further compare the effects of algorithmic monitoring and fairness on the two types of appraisals.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>This study finds that online car-hailing drivers' challenge-hindrance appraisals mediate the relationship between algorithmic management characteristics and work engagement. Algorithmic monitoring positively affects both challenge and hindrance appraisals in online car-hailing drivers. However, algorithmic fairness promotes challenge appraisal and reduces hindrance appraisal. Consequently, challenge and hindrance appraisals lead to higher and lower work engagement, respectively. Further, the additional path comparison analysis showed that the hindering effect of algorithmic monitoring exceeds its challenging effect, and the challenge-promoting effect of algorithmic fairness is greater than the algorithm's hindrance-reducing effect.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>This paper reveals the underlying mechanisms concerning how algorithmic monitoring and fairness affect online car-hailing drivers' work engagement and fills the gap in the research on algorithmic management in the context of online car-hailing platforms. The authors' findings also provide practical guidance for online car-hailing platforms on how to improve the platforms' algorithmic management systems.<\/jats:p><\/jats:sec>","DOI":"10.1108\/itp-02-2022-0122","type":"journal-article","created":{"date-parts":[[2023,5,17]],"date-time":"2023-05-17T07:34:37Z","timestamp":1684308877000},"page":"1423-1448","source":"Crossref","is-referenced-by-count":32,"title":["Work engagement of online car-hailing drivers: the effects of platforms' algorithmic management"],"prefix":"10.1108","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8195-7733","authenticated-orcid":false,"given":"Weimo","family":"Li","sequence":"first","affiliation":[]},{"given":"Yaobin","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Hu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5943-5226","authenticated-orcid":false,"given":"Sumeet","family":"Gupta","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2023,5,19]]},"reference":[{"issue":"9","key":"key2024040602170727800_ref001","first-page":"2162","article-title":"Workplace spirituality, well-being at work and employee loyalty in a gig economy: multi-group analysis across temporary vs permanent employment status","volume":"51","year":"2021","journal-title":"Personnel Review"},{"issue":"4","key":"key2024040602170727800_ref002","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1111\/1748-8583.12250","article-title":"Electronic monitoring at work: the role of attitudes, functions, and perceived control for the acceptance of tracking technologies","volume":"29","year":"2019","journal-title":"Human Resource Management Journal"},{"issue":"1","key":"key2024040602170727800_ref003","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1007\/BF02723327","article-title":"On the evaluation of structural equation models","volume":"16","year":"1988","journal-title":"Journal of the Academy of Marketing Science"},{"issue":"1","key":"key2024040602170727800_ref004","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1080\/00236561003654776","article-title":"Workplace surveillance: an overview","volume":"51","year":"2010","journal-title":"Labor History"},{"key":"key2024040602170727800_ref005","article-title":"Productivity of gig workers on crowdsourcing platforms through artificial intelligence and gamification: a multi-theoretical approach","year":"2021","journal-title":"TQM Journal"},{"key":"key2024040602170727800_ref006","first-page":"1","article-title":"Gamifying the gig: transitioning the dark side to bright side of online engagement","volume":"25","year":"2021","journal-title":"Australasian Journal of Information Systems"},{"issue":"February 2022","key":"key2024040602170727800_ref007","first-page":"1378","article-title":"Gamification and gigification: a multidimensional theoretical approach","volume":"139","year":"2022","journal-title":"Journal of Business Research"},{"issue":"99","key":"key2024040602170727800_ref008","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1093\/epolic\/eiz007","article-title":"Uber happy? 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