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In Hong Kong, a densely populated international metropolis, the red, green, and blue taxis flowing through the streets once symbolized the city's vitality. However, in recent years, issues such as inconsistent taxi service quality, an aging driver demographic, and frequent instances of refusal to pick up passengers or taking circuitous routes have become increasingly prominent, prompting society to re-examine the sustainability of traditional travel modes.

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.

US President Donald Trump’s tariffs on Chinese imports are biting into the income of some American retail-focused companies, with at least two already in bankruptcy court and others forecasting significant losses.

In a recent interview with Phoenix TV, Prof. Weiming Zhu, Associate Professor in Innovation and Information Management at the HKU Business School, shared his insights on the transformative power of online retail.

Host-generated property images as a visual channel reveal substantial information about properties. Selecting proper images to display can lead to higher demand and increased rental revenue. In this paper, we define, estimate, and optimize the impacts of Airbnb photos on customers’ renting decisions. We apply ResNet-50, a convolutional neural network model, to build two separate, supervised learning models to evaluate the image quality and room types posted by Airbnb hosts. Then, we characterize the overall impacts of photo layout by the room type featured in the photo, photo quality, and order of display on the listings’ web pages. To address two estimation challenges in the Airbnb setting, namely, censored demand and changing consideration sets, we propose a novel pairwise comparison model that utilizes customers’ booking sequence data to consistently estimate the impact of photo layout on customers’ renting decisions. Our estimation results suggest that the cover image has a significantly larger impact than noncover photos and a high-quality bedroom cover image leads to the largest increase in demand. Furthermore, we build a nonlinear integer programming optimization problem and develop an algorithm to determine the optimal photo layout. Our counterfactual analysis suggests that a listing’s unilateral adoption of optimal photo layout leads to 11.0% more bookings on average. Moreover, depending on the neighborhood and market size, when listings simultaneously switch to the optimal photo layout, they get booked for two to five additional days in a year on average, which boosts revenue by $500 to $1,100.