學術論文

The primary challenge to assessing the legal origins view of comparative financial development is identifying exogenous changes in legal systems. We assemble new data on Shanghai's British and French concessions between 1845 and 1936. Two regime changes altered British and French legal jurisdiction over their respective concessions. By examining the changing application of different legal traditions to adjacent neighborhoods within the same city and controlling for military, economic, and political characteristics, we offer new evidence consistent with the legal origins view: the financial development advantage in the British concession widened after Western legal jurisdiction intensified and narrowed after it abated.

We study the financial implications of the 2018–2019 U.S.-China trade war for global supply chains. Around the dates when higher tariffs are announced, U.S. firms that depend more on exports to and imports from China experience larger declines in market value, with the negative effect spilling over to the affected firms' suppliers and customers through production networks. The trade war effect is mainly concentrated among U.S. firms that sell to Chinese customers with low R&D intensity or outsource to Chinese differentiated input suppliers. We also exploit the within-firm variation in tariff exposure according to the detailed product lists and conduct a reverse experiment based on the 2019 trade talks. To explain the findings, we propose a theoretical model that highlights how complex trade structures shape shareholder wealth.

Sharing economy platforms are pressed to rapidly grow user bases at the early stage by aggressively targeting potential users through competitive actions. Due to the volatile nature of the sharing economy and its disruption to industry norms, these platforms encounter legitimacy challenges that impede user base growth. This paper integrates competitive repertoire and institutional legitimacy theories to develop a research model that explains early-stage user base development in the sharing economy. We posit that the early-stage user base is associated with structural characteristics of the competitive repertoire, whose effects are moderated by a platform's socio-political legitimation efforts that address stakeholders’ regulatory and normative concerns. Using a comprehensive sample of 4644 monthly observations of 129 sharing economy platforms in China, we find that the volume of two context-specific competitive actions, offering economic incentives and staging high-visibility events, along with competitive repertoire complexity, are positively related to the platform's early-stage user base. We also identify a significant negative relationship between repertoire differentiation and user base. Direct relationships are moderated by socio-political legitimation, however, such that legitimation weakens the positive impact of context-specific action volume but enhances those of repertoire complexity and differentiation. Managerial and practical implications are discussed in light of the findings.

Many mobile applications use push notifications and reminders to explicitly educate, remind, and motivate users to perform healthy behaviors. However, users do not always act according to these explicit digital interventions. Our study investigates whether users’ self-regulation can be implicitly facilitated with a proper mobile interaction design. Specifically, we investigate the impacts of two touch modes that are supported by force-based interaction technology, that is, pressing and tapping. Drawing on the theory of embodied cognition, which suggests that people automatically infer meanings from their bodily actions, we conjecture that pressing, compared with tapping, enhances self-regulation because the action of pressing on the touchscreen embodies resolute approach motivation toward goals. We test our hypotheses in three experiments. The first experiment investigates beverage choices on a mobile app; the second experiment examines goal setting on a fitness app; and the third experiment focuses on personal hygiene learning on a mobile education app. The results from the three experiments show that pressing actions can improve users’ self-regulation in selecting a healthier but less tasty beverage (Study 1), setting higher exercise goals and performing more physical exercise (Study 2), and reducing lapses in maintaining personal hygiene (Study 3). In addition, such effects were more salient among users with a higher level of health knowledge and a promotion-focused health orientation. This study contributes to healthcare IT research by showing that mobile interaction can be leveraged to nudge users toward enhanced self-regulation.

Non-advertising-based mobile apps face several critical challenges when trying to monetize their free services; among them: the choice of pricing strategies (hard landing vs. soft landing, i.e., a “pay or churn” paywall or continue offering limited free services to existing users after monetization) and aspects of product design (whether to provide exclusive secondary offerings to paying users). We implemented a large-scale randomized field experiment with an app firm to test the causal effects of such pricing and product design strategies. Results show that both soft landing and exclusive secondary offerings decrease existing app users’ willingness to subscribe; but there is a positive interaction between these two strategies on subscriptions. We propose a theoretical framework, discuss potential mechanisms that might be at play, and conduct robustness checks to rule out several alternative explanations. A customer survey by the firm and an experiment on Prolific provide further support for the theoretical mechanism. To assess generalizability, we conducted a second field experiment and obtained consistent results. We also report the results from the actual implementation of the best performing strategy by the firm. Our research provides guidance on possible theoretical underpinnings of users’ responses and important managerial implications for app monetization.

Parenting motivation, the inspiration and drive to take care of one’s children, is regarded as a powerful instinct for facilitating human reproduction. In a set of hypotheses, the authors address how, why, and among whom parenting motivation affects a pervasive decision-making tendency, namely variety seeking. Six studies, including a large-scale panel data study and five online and lab studies, show that parenting motivation spurs feelings of time crunch that further result in less variety seeking among consumers. The effect is diminished when time-saving parenting support exists (which reduces feelings of time crunch in parenting), when consumers are led to believe that they have sufficient time available for shopping, or when they do not have much loyalty to any brand offered in the choice set and thus cannot save time by simply choosing the top-of-mind product option. The current research thus contributes to the growing literature on how parenting motivation affects consumer decision making. In addition, it augments the literature on variety seeking by identifying an important factor that can influence it.

Problem definition: We study the use of nonmonetary incentives based on reciprocity to facilitate capacity sharing between two service providers that have limited and substitutable service capacity. Academic/practical relevance: We propose a parsimonious game theory framework, in which two firms dynamically choose whether to accept each other’s customers without the capability to perfectly monitor each other’s capacity utilization state. Methodology: We solve the continuous-time imperfect-monitoring game by focusing on a class of public strategy, in which firms’ real-time capacity-sharing decision depends on an intuitive and easy-to-implement accounting device, namely the current net number of transferred customers. We refer to such an equilibrium as a trading-favors equilibrium. We characterize the condition in which capacity sharing takes place in such an equilibrium. Results: We find that some degree of efficiency loss (as compared with a central planner’s solution) is necessary to induce reciprocity. The efficiency loss is small when the two firms have similar traffic intensity even if they are different in service-capacity scale, whereas the efficiency loss can be considerably large when the two firms have significantly different traffic intensities. The trading-favors mechanism, surprisingly, can outperform the perfect-monitoring benchmark when the two firms exhibit high asymmetry in terms of service-capacity scale or traffic intensity because the smaller firm tends to deviate from collaboration. Managerial implications: Firms should consider engaging in nonmonetary reciprocal capacity sharing if regulations, transaction costs, or other market and operational frictions make it difficult to use a capacity-sharing contract based on monetary payments. The trading-favors collaboration can improve the firms’ payoff close to the centralized upper bound when the firms have similar traffic intensities. However, when their traffic intensities are highly different, firms are better off with a monetary-payment contract to induce more capacity sharing and are worse off investing in increasing their visibility to each other’s real-time available capacity, namely investing in perfect monitoring.

Problem definition: Shared micromobility vehicles provide an eco-friendly form of short-distance travel within an urban area. Because customers pick up and drop off vehicles in any service region at any time, such convenience often leads to a severe imbalance between vehicle supply and demand in different service regions. To overcome this, a micromobility operator can crowdsource individual riders with reward incentives in addition to engaging a third-party logistics provider (3PL) to relocate the vehicles. Methodology/results: We construct a time-space network with multiple service regions and formulate a two-stage stochastic mixed-integer program considering uncertain customer demands. In the first stage, the opera-tor decides the initial vehicle allocation for the regions, whereas in the second stage, the opera-tor determines subsequent vehicle relocation across the regions over an operational horizon. We develop an efficient solution approach that incorporates scenario-based and time-based decomposition techniques. Our approach outperforms a commercial solver in solution quality and computational time for solving large-scale problem instances based on real data. Manage-rial implications: The budgets for acquiring vehicles and for rider crowdsourcing significantly impact the vehicle initial allocation and subsequent relocation. Introducing rider crowdsour-cing in addition to the 3PL can significantly increase profit, reduce demand loss, and improve the vehicle utilization rate of the system without affecting any existing commitment with the 3PL. The 3PL is more efficient for mass relocation than rider crowdsourcing, whereas the latter is more efficient in handling sporadic relocation needs. To serve a region, the 3PL often relo-cates vehicles in batches from faraway, low-demand regions around peak hours of a day, whereas rider crowdsourcing relocates a few vehicles each time from neighboring regions throughout the day. Furthermore, rider crowdsourcing relocates more vehicles under a unimo-dal customer arrival pattern than a bimodal pattern, whereas the reverse holds for the 3PL.

A central problem in planning production capacity is how to effectively manage demand risk. We develop a model that integrates capacity planning and risk hedging decisions under a popular risk measure, conditional value at risk (CVaR). The CVaR objective generalizes the usual risk-neutral objective (such as the expected payoff) and allows for explicit modeling of the degree of aversion to downside risk (associated with low demand). The starting point of our model is to incorporate the impact on demand from a financial asset (including for instance, a tradable market index as a proxy for the general economy). This way, in addition to the capacity decision at the beginning of the planning horizon, there is also a dynamic hedging strategy throughout the horizon, and the latter plays the role of both mitigating demand risk and supplementing the payoff. The hedging strategy is restricted to partial information and constrained with a cap on loss (pathwise). To find the optimal hedging strategy, we construct and solve a dual problem to derive the optimal terminal wealth from hedging; the real-time hedging strategy is then mapped out via the martingale representation theorem. With the hedging strategy optimized, we show that optimizing the production quantity is a concave maximization problem. With both production and hedging (jointly) optimized, we provide a complete characterization of the efficient frontier and quantify the improvement over the production-only model. Furthermore, via sensitivity and asymptotic analyses, we spell out the impacts of the loss cap and the risk aversion level, along with other qualitative insights.