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Judging in the Dark: How Delivery Riders Form Fairness Perceptions Under Algorithmic Management.

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Titel: Judging in the Dark: How Delivery Riders Form Fairness Perceptions Under Algorithmic Management.
Autoren: Xiang, Yuan1 (AUTHOR) yxiang@whu.edu.cn, Du, Jing1 (AUTHOR) jdu@whu.edu.cn, Zheng, Xue Ni1 (AUTHOR) xnzheng2@whu.edu.cn, Long, Li Rong2 (AUTHOR) lrlong@mail.hust.edu.cn, Xie, Huan Yan1 (AUTHOR) hy_xie@whu.edu.cn
Quelle: Journal of Business Ethics. Jul2025, Vol. 199 Issue 3, p653-670. 18p.
Schlagwörter: *GIG economy, *ALGORITHMS, FAIRNESS, DISTRIBUTIVE justice, DECISION making & psychology
Abstract: The application of algorithms in organizations is becoming more widespread. Previous research has aimed to enhance employees' perceptions of algorithmic fairness by focusing on technical features. However, individuals often struggle to observe and comprehend these features, hindering their ability to form rational fairness judgments. Drawing upon fairness heuristic theory, this study explores how individuals perceive algorithmic fairness when technical features are invisible because of algorithmic opacity. Research conducted with food delivery riders in China suggests that, in the absence of transparent algorithmic information, riders heuristically form perceptions of algorithmic fairness based on more salient and accessible distributive fairness information. These heuristic perceptions of algorithmic fairness further predict outcomes such as task performance and helping behavior. We also found that different distributive fairness information holds varying importance in the process of shaping heuristic perceptions of algorithmic fairness and that algorithmic transparency perceptions and tenure moderate this process. The findings extend fairness heuristic theory and have practical implications. [ABSTRACT FROM AUTHOR]
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Datenbank: Business Source Index
Beschreibung
Abstract:The application of algorithms in organizations is becoming more widespread. Previous research has aimed to enhance employees' perceptions of algorithmic fairness by focusing on technical features. However, individuals often struggle to observe and comprehend these features, hindering their ability to form rational fairness judgments. Drawing upon fairness heuristic theory, this study explores how individuals perceive algorithmic fairness when technical features are invisible because of algorithmic opacity. Research conducted with food delivery riders in China suggests that, in the absence of transparent algorithmic information, riders heuristically form perceptions of algorithmic fairness based on more salient and accessible distributive fairness information. These heuristic perceptions of algorithmic fairness further predict outcomes such as task performance and helping behavior. We also found that different distributive fairness information holds varying importance in the process of shaping heuristic perceptions of algorithmic fairness and that algorithmic transparency perceptions and tenure moderate this process. The findings extend fairness heuristic theory and have practical implications. [ABSTRACT FROM AUTHOR]
ISSN:01674544
DOI:10.1007/s10551-024-05883-w