Ride-hailing platforms (RHP) are sharing economy platforms that connect passengers who need to order a private ride or to share a vehicle with drivers who want to share a ride. However, after rapid growth, major RHPs (e.g., Uber, Lyft, and Didi) have begun to face severe driver shortages. Attracting and maintaining a large driver base is critical to the survival and success of any RHP no matter its size. While practitioners are urging to seek suggestions from academia to prevent driver loss, limited research attention has been paid to RHP drivers’ discontinuance. To fill this gap, this dissertation aims to explore factors that motivate drivers to discontinue using RHPs from the perspective of algorithm unfairness.
The algorithm is the boss of ride-hailing drivers as they are matched, paid, and evaluated by various algorithms. While algorithms have the potential to make the ride-hailing process more efficient, they also yield socially biased outcomes which create inequalities and uncomfortable experiences for both drivers and riders which may further influence their decisions to use to not use RHPs. Following the logic, the research question of the current dissertation is “how does algorithm unfairness of RHPs affect drivers’ discontinuance?” Stressor-strain-outcome model and organizational justice theory are adapted to the ride-hailing context based on the contextualization approach to serve as theoretical frameworks of the current study.
An online survey is conducted to empirically test drivers’ discontinuance of ride-hailing platforms. Research participants of the studies are recruited by employing the service provided by Prolific. co. Data analysis is conducted by employing the covariance-based structural equation modeling approach by following previously defined approaches. The results support most of the hypotheses.
The study is expected to contribute to the current literature on information systems discontinuance, ride-hailing, IT stress, AI-empowered algorithm management, algorithm unfairness, dark side of AI, stressor-strain-outcome model, and organizational justice theory. My dissertation is also expected to offer rich insights into how to retain the user base effectively for practitioners in emerging sharing economy platforms. Moreover, the results of the current dissertation also offer rich implications on how to manage dispersed workforces using AI-empowered algorithms.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-6529 |
| Date | 12 May 2022 |
| Creators | Tang, Zhenya |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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