Improvement of Customer Satisfaction and Operational Efficiency by Data-Driven Management — a Case Study of Uber
DOI:
https://doi.org/10.54097/2x63se88Keywords:
Data-driven management; Case study method; Customer satisfaction; Operational efficiency.Abstract
With the development of the digital economy, sharing economy platforms are facing challenges of supply and demand matching and the improvement of efficiency. As the representative of sharing economy platforms, Uber’s management model possesses vital research value. This research analyzes the improvement of customer satisfaction and operational efficiency by using data-driven management at Uber. The research uses the case study method, focusing on critical technologies of Uber such as dynamic pricing, intelligent scheduling and a two-way rating system. The analysis shows the data-driven management mode helps corporate operations through algorithms and data support. However, the research also finds that there is still controversy remaining in algorithm management, for instance, the order dispatching algorithm lacks transparency, and drivers are forced to accept low-profit orders to maintain their performance levels. The conclusion points that data-driven increases its operational efficiency conspicuously, but still lacks balancing efficiency and equity. In the future, efforts should be made to explore the transparency of algorithms and the establishment of a driver rights protection mechanism to achieve sustainable development.
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[1] Cao Yue. Innovative Strategies and Value Creation in Enterprise Management and Operation under the Background of Digital Transformation. Modern Business Research, 2024, (20): 155-157.
[2] Li Yuxin, Jin Xiaoying. The Impact of Digital-Physical Integration on the Resilience of the Manufacturing Industry Chain: An Analysis Based on the Dual Drive of "Technology-Data" for Enterprises. China Circulation Economy,2025, 1-16.
[3] Anmuth D. Uber Technologies: Strong Topline Performance & Mobility Investments Right for the Long-Term; Remain Overweight w/$90 PT. J.P.Morgan Equities Research Reports, 2025 Feb 05:19.
[4] Mei Yan, Wang Siyang. Comparative Analysis of Business Models of Uber and Didi Chuxing from the Perspective of Sharing Economy. Journal of Hangzhou Dianzi University, 2023, 19(4): 9-16.
[5] Chu Hejia. Research on Online Scheduling Method of New Energy Ride-hailing Vehicles Based on Real-time Demand and Its Application. Chongqing Jiaotong University, 2024.
[6] García-Melero G, Sainz-González R, Coto-Millán P, Valencia-Vásquez A. Ridesourcing mode choice: A latent class choice model for UberX in Chile. Transportation Research Interdisciplinary Perspectives, 2022, 16: 100722.
[7] Xiong F, Xu S, Zheng D. An investigation of the Uber driver reward system in China – an application of a dynamic pricing model. Technology Analysis & Strategic Management, 2020, 33(1): 44–57.
[8] Varghese H. The role of dynamic pricing strategies in e-commerce: a case study of Uber and its impact on consumer behavior. International Journal for Multidisciplinary Research (IJFMR), 2025, 7(2): 1–2.
[9] Thais R, Gonzalez J N, Gomez J, Romero F, Vassallo J M. Exploring ride-hailing fares: An empirical analysis of the case of Madrid. Transportation, 2022, 49(2): 373-393.
[10] Xu Z, Yin Y, Ye J. On the supply curve of ride-hailing systems. Transportation Research Part B: Methodological, 2020, 132: 29–43.
[11] Lokhandwala M, Cai H. Dynamic ride sharing using traditional taxis and shared autonomous taxis: A case study of NYC. Transportation Research Part C: Emerging Technologies, 2018, 97: 45-60.
[12] Fu Y, Soman C. Real-time Data Infrastructure at Uber. In: Proceedings of the 2021 International Conference on Management of Data (SIGMOD '21). Virtual Event, China: ACM; 2021: 2503-2515.
[13] Bennett R, Sacks G, Zorbas C, Huse O, Wood B, Backholer K. Exploring the commercial determinants of health in the online food delivery sector: a case study of Uber Eats in Australia. Critical Public Health, 2025, 35(1): 2469776.
[14] Chan N K. The rating game: The discipline of Uber's user-generated ratings. Surveillance & Society, 2019, 17(1): 183-190.
[15] Cram W A, Wiener M, Tarafdar M, Benlian A. Examining the Impact of Algorithmic Control on Uber Drivers’ Technostress. Journal of Management Information Systems, 2022, 39(2): 426-453.
[16] Rosenblat A, Stark L. Algorithmic ethics: Interrogating incentives in the age of Uber. Social Research, 2016, 82(2): 559–579.
[17] Molino P, Zheng H, Wang Y. COTA: Improving the Speed and Accuracy of Customer Support through Ranking and Deep Networks. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '18). New York: ACM; 2018: 586–595.
[18] Wenzel T P. Net change in energy use from ridehail services in five California regions. Future Transp, 2024, 4: 891–918.
[19] Rosenblat A, Stark L. Gaining transparency in Uber’s algorithmic management. Commun ACM, 2016, 59(11): 62–71.
[20] Liu Shanshi, Pei Jialiang, Ge Chunmian, et al. Algorithmic Management on Online Labor Platforms: Theoretical Exploration and Research Prospects. Management World, 2022, 38(02): 225-239 + 14-16.
[21] Gino F. Uber shows how not to apply behavioral economics. Harvard Business Review, April 14, 2017.
[22] Greer C, Harney B. Uber Technologies Inc.: dilemmas with its performance management system. Case Centre, 2022.
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