Dennis J. Zhang

Pros vs Joes: Agent Pricing Behavior in the Sharing Economy

One of the major differences between markets that follow a “sharing economy” paradigm and traditional two-sided markets is that the supply side in the sharing economy often includes individual nonprofessional decision makers, in addition to firms and professional agents. Using a data set of prices and availability of listings on Airbnb, we find that there exist substantial differences in the operational and financial performance of professional and nonprofessional hosts. In particular, properties managed by professional hosts earn 16.9% more in daily revenue, have 15.5% higher occupancy rates, and are 13.6% less likely to exit the market compared with properties owned by nonprofessional hosts, while controlling for property and market characteristics. We demonstrate that these performance differences between professionals and nonprofessionals can be partly explained by pricing inefficiencies. Specifically, we provide empirical evidence that nonprofessional hosts are less likely to offer different rates across stay dates based on the underlying demand patterns, such as those created by major holidays and conventions. We develop a parsimonious model to analyze the implications of having two such different host groups for a profit-maximizing platform operator and for a social planner. While a profit-maximizing platform operator should charge lower prices to nonprofessional hosts, a social planner would charge the same prices to professionals and nonprofessionals.

Does Social Interaction Improve Learning Outcomes? Evidence from Field Experiments on Massive Open Online Courses

This paper studies how service providers can design social interaction among participants and quantify the causal impact of that interaction on service quality. We focus on education and analyze whether encouraging social interaction among students improves learning outcomes in massive open online courses (MOOCs), which are a new service delivery channel with universal access at reduced, if not zero, cost. We analyze three randomized experiments in a MOOC with more than 30,317 students from 183 countries. Two experiments study large-group interaction by encouraging a random subset of students to visit the course discussion board. The majority of students treated in these experiments had higher social engagement, higher quiz completion rates, and higher course grades. Using these treatments as instrumental variables, we estimate that one additional board visit causally increases the probability that a student finishes the quiz in the subsequent week by up to 4.3%. The third experiment studies small-group i...

Does Social Interaction Improve Service Quality? Field Evidence from Massive Open Online Education

This paper studies how service providers can design social interaction among participants and quantify the causal impact of that interaction on service quality. We focus on education and analyze whether encouraging social interaction among students improves learning outcomes in Massive Open Online Courses (MOOCs), which are a new service delivery channel with universal access at reduced, if not zero, cost. We analyze three randomized experiments in a MOOC with more than 30, 317 students from 183 countries. Two experiments study large-group interaction by encouraging a random subset of students to visit the course discussion board. The majority of students treated in these experiments had higher social engagement, higher quiz completion rates, and higher course grades. Using these treatments as instrumental variables, we estimate that one additional board visit causally increases the probability that a student finishes the quiz in the subsequent week by up to 4.3%. The third experiment studies small-group interaction by encouraging a random subset of students to conduct one-on-one synchronous discussions. Students who followed through and actually conducted pairwise discussions increased their quiz completion rates and quiz scores by 10% in the subsequent week. Combining results from these three experiments, we provide recommendations for designing social interaction mechanisms to improve service quality

Learning from Inventory Availability Information: Field Evidence from Amazon

Many online retailers provide real-time inventory availability information. Customers can learn from the inventory level and update their beliefs about product quality. Thus, consumer purchasing behavior may be impacted by the availability information. Based on a unique setting from Amazon lightning deals, which displays the percentage of inventory consumed in real time, we explore whether and how consumers learn from inventory availability information. Identifying the effect of learning on consumer decisions has been a notoriously difficult empirical question due to endogeneity concerns. We address this issue by running two randomized fields experiment on Amazon, in which we create exogenous shocks on the inventory availability information to a random subset of Amazon lightning deals. In addition, we track the dynamic purchasing behavior and inventory information for 23,665 lightning deals offered by Amazon in August 2016 and use their panel structure to further explore the relative effect of learning. We find evidence of consumer learning from inventory information: a decrease in product availability causally attracts more sales in the future; in particular, a 10 percent increase in past sales leads to a 2.08 percent increase in cart add-ins in the next hour. Moreover, we show that buyers use observable product characteristics to moderate their inferences when learning from others; a deep discount weakens the learning momentum whereas a good product rating amplifies the learning momentum.

The Value of Pop-Up Stores in Driving Online Engagement in Platform Retailing: Evidence From a Large-Scale Field Experiment With Alibaba

We study the value of short-lived and experientially oriented pop-up stores, a popular type of omnichannel retail strategy, on both retailers that participate in pop-up store events and retailing p...

Effects of Bonus on the Demand for Auto Loans and the Long-Term Consequences

Abstract We study how receiving a bonus changes the demand for auto loans and the risk of future delinquency. Unlike traditional consumer products, auto loans have a long-term impact on consumers’ financial state because of the monthly payment obligation. Using a large consumer panel data set of credit and employment information, we find that, receiving a bonus increases auto loan demand by 21 percent. These loans, however, are associated with a higher risk, as the delinquency rate increases by 18.5-31.4 percent depending on different measures. In contrast, an increase in the base salary will increase the demand for auto loans but not the delinquency. Our empirical findings help shed light on how consumers make financial decisions and have important implications for financial institutions on when demand for auto loans and the associated risk arise.

Learning from Inventory Availability Information: Evidence from Field Experiments on Amazon

Many online retailers provide real-time inventory availability information. Customers can learn from the inventory level and update their beliefs about the product. Thus, consumer purchasing behavior may be impacted by the availability information. Based on a unique setting from Amazon lightning deals, which displays the percentage of inventory consumed in real time, we explore whether and how consumers learn from inventory availability information. Identifying the effect of learning on consumer decisions has been a notoriously difficult empirical question because of endogeneity concerns. We address this issue by running two randomized field experiments on Amazon in which we create exogenous shocks on the inventory availability information for a random subset of Amazon lightning deals. In addition, we track the dynamic purchasing behavior and inventory information for 23,665 lightning deals offered by Amazon and use their panel structure to further explore the relative effect of learning. We find evidence...

Vicarious Goal Pursuit Outweighs Herding in Crowdfunding: Evidence from Kickstarter.com

In reward-based crowdfunding, creators of entrepreneurial projects solicit capital from potential consumers to reach a funding goal and offer future products/services in return. We examine consumers’ contribution patterns using a novel dataset of 28,591 projects collected at 30-minute resolution from Kickstarter.com. Extending past research assuming that economic considerations (such as project quality and campaign success likelihood) drive backers’ decisions, we provide the cleanest field evidence so far that consumers also have prosocial motives to help creators reach their funding goals. We find that projects collect funding faster right before (vs. right after) meeting their funding goals because consumers not only are more likely to fund projects, but also contribute greater amounts of money prior to goal attainment. This effect is amplified when the nature of a project tends to evoke consumers’ prosocial motivation and when a project’s creator is a single person. These results suggest that consumers’ prosocial motives not only play a role in reward-based crowdfunding, but also can outweigh the opposing effects of economic factors including rational herding and certainty about campaign success.

How Do Price Promotions Affect Customer Behavior on Retailing Platforms? Evidence from a Large Randomized Experiment on Alibaba

Dynamic pricing through price promotions has been widely used by online retailers. We study how a promotion strategy, one that offers customers a discount for products in their shopping cart, affec...