Crowdsourcing and Digital Workforce in the Gig Economy
Permanent URI for this collectionhttps://hdl.handle.net/10125/107507
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Item type: Item , Fair Dealings with Algorithms? Analyzing the Perceived Procedural Fairness of Managerial Algorithms and their Impacts on Gig-Workers(2024-01-03) Jabagi, Nura; Croteau, Anne-Marie; Audebrand, Luc; Marsan, JosianneThis study examines how gig-workers perceive the fairness of managerial algorithms on gig-work platforms using Organizational Justice Theory. Through a survey of 435 Uber drivers, we find that the perceived fairness of algorithmic decisions (both matching and performance evaluation decisions) is positively and significantly related to job satisfaction and perceived organizational support (POS). We also find that certain indicators of perceived algorithmic fairness are unique to the type of decision made and whether it is perceived to require mechanical or human skills. In answering calls to study the impacts of algorithmic fairness in real-world settings, we find that managerial algorithms play a key role in shaping gig-workers’ attitudes as technological artefacts and as organizational agents. Recommendations are provided to enhance perceived algorithmic fairness to address challenges in the gig-economy, like high turnover, by increasing satisfaction and POS.Item type: Item , Bad Client Feedback on Digital Labor Platforms: How Freelancers Navigate the Peril Posed by Negative Reviews on Upwork(2024-01-03) Gussek, Lisa; Thatcher, Jason Bennet; Wiesche, ManuelFreelancing on digital labor platforms is becoming an increasingly important source of income for workers. As self-employed professionals, freelancers rely on positive reviews to attract new jobs and new clients. Previous positive reviews help to reduce uncertainty about freelancers' ability to deliver their services. However, research to date has primarily examined positive reviews, but not how negative reviews affect freelancers' success nor what strategies freelancers use to navigate negative reviews. This is especially important because we know that negative reviews on other platforms undermine vendor sales. To identify strategies that freelancers use to overcome negative reviews, we conducted a qualitative fuzzy set analysis of 1,712 freelancer profiles. Thus, we show how sets of communication skills, collaboration skills, and sharing complete information influence freelancers' success. In the presence of negative ratings, we identify five pathways and six conditions that predict freelancer success on digital labor platforms.Item type: Item , A Different Kind of Sharing Economy: A Taxonomy of Platform Cooperatives(2024-01-03) Zhu, Jiang; Marjanovic, OliveraPlatform cooperatives are emerging as new digital organizing forms of traditional cooperatives and as alternatives to big tech platforms behind gig economy. Despite their rapid growth, current research on platform cooperatives is still scarce. This paper aims to develop a systematic taxonomy of platform cooperatives using a theoretically grounded and empirically validated taxonomy development method, which is extended with data visualization and cluster analysis. The resulting eight archetypes show that platform cooperatives not only contribute to a more ethical sharing economy, but also provide new opportunities for gig and other workers across industries. Platform cooperatives are also exploring new opportunities in the knowledge economy, such as cooperative business models centered on data sharing and the creation of platform cooperative ecosystems through mutual support and collaboration. These findings contribute to building the necessary foundations for further research on platform cooperatives as well as entrepreneurial practice focused on ethical sharing/gig economy.Item type: Item , Introduction to the Minitrack on Crowdsourcing and Digital Workforce in the Gig Economy(2024-01-03) Moussawi, Sara; Olsen, Tim; Taylor, Joseph