學術論文

Problem definition: Ride-hailing platforms face a supply-demand imbalance. During peak periods, the demand from passengers far exceeds the supply of drivers, whereas during off-peak periods, there is an abundance of supply but weak demand. The well-studied surge pricing can be challenging to implement in markets where prices are subject to regulation and are inflexible to adjust. We study an alternative operational approach that shifts the supply of drivers from off-peak to peak periods to address the supply-demand imbalance. Methodology/results: We propose two novel incentive schemes: the qualification scheme and the prioritization scheme. Specifically, the platform sets a work target for the peak period. Under the qualification scheme, the platform assigns off-peak service requests only to drivers who meet the peak period target. Under the prioritization scheme, the platform prioritizes off-peak requests for drivers who meet the peak period target. We analyze the effectiveness of these schemes, considering the openness of the platform’s supply system. For platforms with a closed system that only allows full-time drivers to provide service, the qualification scheme improves the total matching volume to a greater extent but hurts full-time drivers more than the prioritization scheme. For platforms with an open system that also allows the abundant part-time drivers to serve off-peak requests, the prioritization scheme outperforms the qualification scheme in improving total matching volume. Furthermore, the implementation of an incentive scheme in an open system may benefit both the platform and full-time drivers. Managerial implications: Our study suggests an alternative to surge pricing for on-demand platforms to address the supply-demand imbalance when prices are inflexible. It provides insights for platforms with varying levels of supply system openness in choosing appropriate incentive schemes. The welfare results offer guidance for platforms and policymakers regarding both matching volume and worker welfare.

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.

One advantage of advertising on social media is leveraging users’ expression of “likes” to influence the perceptions and responses of others in their network. Through a largescale field experiment on WeChat, three online lab studies and a theoretical model, we explore whether and how displaying more “likes” in an ad can effectively lead to more ad “likes” and clicks. We find that displaying the first “like” can significantly increase users’ tendencies to both “like” and click on an ad. However, on average, showing additional “likes” does not further increase the clicking propensity, although it consistently attracts more “likes.” We further find that displaying more “likes” increases the clickthrough rate for lesser-known brands but not for well-known brands, and has a stronger impact on the “like” rate for more socially engaged users than for less socially engaged ones. These findings are consistent with the interplay between informational and normative social influences in social advertising. The public visibility of “likes” makes liking more susceptible to normative social influence than clicking. The coexistence of these two forces can lead to an enhanced conformity effect on liking and a crowding-out effect on clicking. Our findings offer novel implications for managing social advertising and designing social media platforms.

Using a novel measure that captures negative ESG incidents at both listed and private suppliers, we provide large-scale evidence on the value implications of supply chain ESG. We find that firms with fewer supply chain ESG incidents exhibit higher future accounting performance and that this effect is stronger in the presence of more conscious customers and vulnerable supply chains. We also find that firms with robust supply chain ESG exhibit higher future stock returns and that this effect is more pronounced when information frictions are higher, which suggests that it takes time for the market to understand the value implications of supply chain ESG. Overall, we highlight the benefits of managing supply chain ESG and the decision usefulness of the related information.

We investigate how U.S. firms adapt their supply chains in China in response to geopolitical risks from the U.S.–China rivalry. We offer a legitimacy-efficiency balancing perspective to understand firm decisions. We hypothesize that to maintain legitimacy with the home government, U.S. firms in strategic industries are more likely than those in non-strategic industries to align with the U.S. government’s derisking strategy by limiting suppliers in China. However, we expect this tendency to weaken for firms with high economic dependence on China. Analyzing firm-level supplier data from 2009 to 2022, we find that the gap in the number of Chinese suppliers between strategic and non-strategic U.S. firms has widened since 2017 (the first Trump administration). Firms in strategic industries maintain fewer Chinese suppliers, potentially reflecting a more cautious approach. This disparity was initially pronounced only among Republican-leaning firms but later extended to firms across the political spectrum under the Biden administration. The gap diminished among firms with greater reliance on China for revenue or supplies, suggesting that efficiency considerations might temper the inclination to align with national strategies. Thus, U.S. firms might seek to balance political legitimacy at home with the economic benefits derived from China when making supplier decisions.

We examine the relationship between competition and reward practices in the public education sector. We hypothesize and find that school principals who face more intense competition make greater use of performance-based financial rewards and apply greater differentiation in its distribution between higher versus lower performing teachers. However, we do not find similar evidence for nonfinancial rewards. Further analyses suggest that financial rewards help attract and retain teachers in the face of competition. We also hypothesize and find that, as competition intensifies, principals direct incentives toward student outcomes that are easier to measure and communicate (student achievement) as well as their key determinant (teacher competence), relative to outcomes that are harder to measure and communicate (student well-being and engagement). Our findings suggest that school principals view financial incentives as effective for gaining a competitive edge and that competition can influence the relative importance they place on different student outcomes.

This paper addresses the fundamental task of estimating covariance matrix functions for high-dimensional functional data/functional time series. We consider two functional factor structures encompassing either functional factors with scalar loadings or scalar factors with functional loadings, and postulate functional sparsity on the covariance of idiosyncratic errors after taking out the common unobserved factors. To facilitate estimation, we rely on the spiked matrix model and its functional generalization, and derive some novel asymptotic identifiability results, based on which we develop DIGIT and FPOET estimators under two functional factor models, respectively. Both estimators involve performing associated eigenanalysis to estimate the covariance of common components, followed by adaptive functional thresholding applied to the residual covariance. We also develop functional information criteria for model selection with theoretical guarantees. The convergence rates of involved estimated quantities are respectively established for DIGIT and FPOET estimators. Numerical studies including extensive simulations and a real data application on functional portfolio allocation are conducted to examine the finite-sample performance of the proposed methodology.

We exploit the implementation of a rural pension policy in China to estimate the average rural-to-urban migration cost for workers affected by the policy and the average underlying sectoral productivity difference. Our estimates, based on a large panel data set, reveal significant migration costs and substantial sectoral productivity differences, with sorting playing a minor role in accounting for sectoral labor income gaps. We construct and structurally estimate a general equilibrium household model with endogenous labor supply and migration. The results of this model align with the reduced-form findings and illustrate how the rural pension policy influences migration, GDP, and welfare through improving within-household labor allocation. Counterfactual analyses based on the model show that the positive effects of the policy remain even if migration costs were significantly lower, and that scaling up the rural pension policy would lead to even larger improvements in labor allocation, GDP, and welfare.

Previous studies have shown great interest in examining the performance impact of platform-based functions (PBFs) used by e-marketplace sellers and the contingent role of salient variables, such as seller reputation, in the e-marketplace. Their findings, however, are fragmented and inconsistent, as they generally focus on the net, separate effect of a single PBF with debatable findings. The theorization of how sellers should configure multiple types of PBFs as a whole to achieve high sales performance lags far behind the booming competition practice. To identify an effective PBF combination, this study takes a configurational perspective to identify appropriate PBF configurations that can achieve high sales performance for sellers with different product positions and reputations. A fuzzy-set qualitative comparative analysis of a longitudinal data set of over 3,300 apparel sellers in a large e-marketplace yields interesting findings. The configuration results reveal recipes for PBF combinations for achieving high sales performance that vary across different levels of seller reputation and product positioning strategies. Our configuration findings suggest that sellers should configure PBFs according to distinctive product strategies accompanied by seller reputation conditions, where the resulting PBF configurations play an essential and multifaceted role in achieving high sales performances. Interestingly, our configuration analysis uncovers that for reputable sellers offering high-priced products, the utilization of pricing and marketing functions is counterproductive. Additionally, we observe complex interplays between after-sales functions and online reputation, characterized by complementary, substitutive, and independent relationships. Furthermore, our results demonstrate an asymmetry relationship between high and low sales configurations. It contributes to the emergent investigation of causal complexity in competitive strategy studies of e-marketplace sellers and provides specific causal recipes and holistic guidelines for sellers and platform operators.