Basukie, J., Wang, Y. orcid.org/0000-0003-1575-0245 and Li, S. orcid.org/0000-0002-0147-8888 (2020) Big data governance and algorithmic management in sharing economy platforms : a case of ridesharing in emerging markets. Technological Forecasting and Social Change, 161. 120310. ISSN 0040-1625
Abstract
Although the growth of a sharing economy platform relies heavily on the governance of data and algorithms to optimise service process and operation, relatively little attention has been given to deepening the understanding of the data governance of sharing economy platforms. Accordingly, we performed an in-depth case study at Go-Jek, a locally-owned ride-sharing platform in Indonesia to explore the dark side of big data and analytical algorithms regarding work assignment, performance and rating system, and legal and ethical concerns. Nineteen semi-structured interviews were conducted with four primary Go-Jek stakeholders: (1) drivers, (2) consumers, (3) data scientists, (4) regulatory bodies. This study contributes to the practice of big data governance and algorithmic management in the emerging market context in three ways. First, this study finds the negative consequences of algorithmic management in the ridesharing platform. Second, this study presents the legal and ethical concerns as the main regulatory challenges in the emerging market. Finally, we open up the “black box” of big data governance for the stakeholders of the sharing economy platform.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Elsevier Inc. |
Keywords: | Sharing economy; Ridesharing; Big data management; Algorithmic management; Emerging markets |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Management School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 19 Feb 2021 08:29 |
Last Modified: | 19 Feb 2021 08:29 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.techfore.2020.120310 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:171220 |