團隊

在香港這座人口密集的國際大都市，街頭流動的紅、綠、藍色計程車曾是城市活力的象徵。 然而，近年來，的士服務質素參差不齊、司機老齡化、拒載繞路頻發等問題日益凸顯，促使社會重新審視傳統出行模式的可持續性。 2025年10月，隨著《道路交通（修訂）（網約車服務）條例》正式刊憲，香港交通運輸生態迎來了歷史性轉折：網約車服務於2014年通過Uber等平臺進入香港市場，11年後終於獲得了合法地位，標誌著這座城市正式邁入的士與網約車共存互補的新時代。

Problem definition: We empirically study the market for ride-hailing services. In particular, we explore the following questions: (i) How do the two-sided market and prices jointly form in ride-hailing marketplaces? (ii) Does surge pricing create value, and for whom? How can its efficiency be improved? (iii) Can platforms’ strategy on revenue sharing with drivers be improved? (iv) What is the value generated by ride-hailing services, including hosting rival taxi services on ride-hailing apps? Methodology/results: We develop a discrete choice model for the formation of mutually dependent demand (customer side) and supply (driver side) that jointly determine pricing. Using this model and a comprehensive data set obtained from the largest mobile ride platform in China, we estimate customer and driver price elasticities and other factors that affect market participation for the company’s two main markets, namely, basic ride-hailing and taxi services. Based on these estimation results and counterfactual analysis, we demonstrate that surge pricing improves customer and driver welfare as well as platform revenues while counterintuitively reducing taxi revenues on the platform. However, surge pricing should be avoided during nonpeak hours because it can hurt both customer and platform surplus. We show that platform revenues can be improved by increasing drivers’ revenue share from the current levels. Finally, we estimate that the platform’s basic ride-hailing services generated customer value equivalent to $13.25 billion in China in 2024, and hosting rival taxi services on the platform boosted customer surplus by $3.6 billion. Managerial implications: Our empirical framework provides ride-hailing companies a way to estimate demand and supply functions, which can help with optimization of multiple aspects of their operations. Our findings suggest that ride-hailing platforms can improve profits by containing surge-pricing to peak hours only and boosting supply by increasing driver compensation. Finally, our results demonstrate that restricting ride-hailing services create significant welfare losses, whereas including taxi services on ride-hail platforms generates substantial economic value.

港大經管學院創新及資訊管理學副教授朱未名教授，在最近接受鳳凰衛視的訪問中指出，線上銷售的核心優勢之一在於其近乎無限的商品種類。他以淘寶為例，平台上可同時售賣數千萬件產品，而傳統實體零售店的存貨量（SKU）最多僅有數千至一萬件，兩者規模差異巨大。

Problem definition: This paper examines frictions in the shopping funnel using empirical clickstream data from an online travel platform. We analyze (a) customers’ heterogeneous search and purchase behaviors and (b) their reactions to changes in assortment size. We then develop a consider-then-choose model to generalize our findings. Methodology/results: We characterize the online customer journey as a two-stage consider-then-choose framework. In the consider stage, we analyze the consideration set formation and show that heterogeneity—familiarity with the assortment—amplifies the number of options; in the purchase stage, it drives preferences for niche versus popular choices. A real-world high-stakes field experiment reveals that shrinking the menu produces mixed results: highlighting the market for the long-tail for some customers and reflecting choice overload for others. Finally, we build a psychologically rich consider-then-choose model with (a) heterogeneous preferences for product features and (b) heterogeneous search costs moderated by search fatigue, theoretically characterizing the impact on consideration sets and conversion rates. Managerial implications: Identifying frictions in the shopping funnel is critical for online platforms, especially when pain points hurt click-through or conversion rates. Which options matter to which users? What is the right assortment size? Although online platforms can offer virtually unlimited assortments, managers may assume frictionless environments—which is not always the case. Our findings offer insights into improving the customer journey by considering heterogeneous preferences and boundedly rational heuristics.