Why free does not mean fair: Investigating users’ distributive equity perceptions of data-driven services

Wagner, Amina; Wessels, Nora; Brakemeier, Hendrik; Buxmann, Peter

doi:10.1016/j.ijinfomgt.2021.102333

Cited by 4 publications

(3 citation statements)

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“…Increasingly, e-commerce platforms are looking to technology-based tools to help merchants engage consumers and enhance the user experience [ 27 ]. Therefore, the academic community is beginning to focus on DDS [ 28 – 30 ], data-driven decision-making [ 31 ], data-driven performance [ 32 ], and the big data discriminatory pricing on Internet platforms [ 33 ]. Reviewed the research on data-driven retail operations in the field of operations research/management science in recent years.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Kaiser et al [ 30 ] conducted an empirical analysis study on DDS through the data generated during the use of vehicles and interviews with 11 well-known experts in the automotive industry of China and Europe. Wagner et al [ 31 ] found that the distribution of free DDS providers may not be fair and believed that users should analyze privacy risks and benefits when deciding to trade with them. Gawankar et al [ 32 ] concluded through empirical analysis of sales in India that using data technology to process consumer data can help companies improve service levels and enhance the consumers shopping experience.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Sharing this big data with merchants can help them make better sales decisions, and the platform will also make profits so that big data can drive marketing. The conditions for implementing DDS strategies have been studied but unclear (e.g., [ 29 , 31 ]), and the impacts of the DDS strategy on all parties of the platform are not quantified. Thus, whether DDS will affect the competition between self-operated and third-party platforms is worth studying.…”

Section: Literature Reviewmentioning

confidence: 99%

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The role of data-driven services strategy in platform competition: A system performance perspective

Xie²,

Zhang³

2023

PLoS ONE

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In the era of big data, data-driven services (DDS) have become critical competitive strategies for digital platform-based enterprises. This paper considers two operational modes of e-commerce platforms, which are self-operated and third-party modes, respectively, and they each lead a platform system. The Hotelling model is adopted to describe the competitive market of both platforms. We characterize their system performance functions. The optimization models are built using game theory to discuss the DDS and price decisions. We obtain the implementation conditions of DDS strategies for both platforms and the dominant situations of their respective DDS levels. We find that a platform adopting the price reduction strategy can improve the performance of its platform system while reducing the competitor’s system performance. From the system performance perspective, continuous improvement of the DDS level may appear “harming others may not benefit oneself”; that is, continuously improving the DDS level leads to a decrease in the competitor’s system performance but not necessarily an increase in its system performance. Further, consumer welfare within both platform systems shows the law of “as one falls then another rises”. As the big data industry matures, self-operated platforms would demonstrate the advantages of service level, profit, and system performance. In contrast, third-party platforms would have an advantage in consumer welfare. These conclusions have important implications for e-commerce platforms developing data-driven operations-based strategies.

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