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Research on the impact of online labor platforms’ technology feature set on gig workers’ service performance
Sun Yuan, Lyu Xiaodan, Wu Lixia, Wen Xiaorong
Science Research Management ›› 2026, Vol. 47 ›› Issue (6) : 142-151.
PDF(1241 KB)
PDF(1241 KB)
Research on the impact of online labor platforms’ technology feature set on gig workers’ service performance
Against the backdrop of the booming gig economy, how to improve gig workers’ service performance has become a key concern for both the industry and academia. This paper employed a mixed-methods approach, combining qualitative and quantitative approaches, to systematically examine the influence mechanism of online labor platform technology feature set on gig workers’ service performance. Through a procedural grounded theory analysis of 97 articles about Upwork and interviews with 21 gig workers on it, this study revealed that the social interaction feature set and the job control feature set are two core types of online labor platforms’ technology feature sets that influence gig workers’ service performance. Grounded in this finding and in self-determination theory, this study developed a mechanism model for how online labor platforms’ technology feature set influence gig workers’ service performance. We collected data from a large-sample survey of 298 gig workers on Upwork and performed analysis using the PLS method. The results showed that: (1) Both social interaction feature set and job control feature set positively influence the satisfaction of gig workers’ basic psychological needs. (2) The satisfaction of gig workers’ competence needs mediates the influence of social interaction feature set and job control feature set on their service performance. (3) Career centrality positively moderates the mediating effect of job control feature set on gig workers’ service performance through the satisfaction of both autonomy and competence needs. This study innovatively identified two types of technology feature sets of online labor platforms and validated their influence mechanisms and boundary conditions on gig workers’ service performance, enriching the body of research on how online labor platforms shape the service performance of gig workers within the broader gig economy literature, and will provide effective guidance for online labor platforms to rationally design their technology features to enhance gig workers’ service performance.
online labor platform / technology feature set / self-determination theory / career centrality / service performance
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Proactive personality, a special trait, refers to a disposition toward taking personal initiatives to influence one’s environment. Employees with high proactive personality are more likely to identify and seek opportunities, and stick to their goals. By systematically reviewing and integrating the existing literature on proactive personality, this study summarizes proactive personality’s origin and concept, measurements, consequences, mediating mechanisms and moderating mechanisms, and the moderating effects of Chinese indigenous culture. We identify the trends of future research and point out that future research should focus on improving concept, developing new measurement that fits to cultural and economic background, and expanding research levels and consequences.
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On-demand delivery through sharing platforms represents a rapidly expanding segment of the global workforce. The emergence of sharing platforms enables gig workers to choose when and where to work, allowing them to do so in a flexible manner. However, such flexibility brings notorious challenges to platforms in managing the gig workforce. Thus, understanding the incentive and behavioral issues of gig workers in this new business model is inherently meaningful. This paper investigates how the incentive mechanisms of sharing platforms—earnings, ratings, and penalties—affect the working decisions of gig workers and their nuanced relationships. To achieve this goal, we use data from one leading on-demand delivery platform with more than 50 million active consumers in China and implement a two-stage Heckman model with instrumental variables to estimate the impact of earnings, ratings, and penalties. We first show that better ratings motivate gig workers to work more. However, interestingly, when ratings are employed together with earnings, the two positive effects of ratings and earnings can be substitutes for each other. Second, we reveal that higher past penalties discourage workers from working more, whereas, interestingly, workers with higher past penalties tend to be more sensitive toward an increase in earnings. Finally, we conduct follow-up surveys to understand the underlying mechanisms of the observed moderating effects from both psychological and economic perspectives. The ultimate goal of this work is to provide managerial implications to help platform managers understand how earnings, ratings, and penalties work together to affect gig workers’ working decisions and how to manage high- and low-quality workers.
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