Research Area

Working Papers

(With Mingfeng Lin, Mo Xiao ,and Lu Fang)           Revising for third-round review at Management Science

[Abstract] E-commerce platforms match online buyers and sellers using their search technologies. Although a more precise search algorithm may improve search targetability, it may also reduce cross-selling opportunities as consumers spend less time exploring different products.  We empirically quantify these tradeoffs through a collaboration with Alibaba Group. Specifically, we take advantage of a 2019 quasi-experiment on, in which the platform refined some product categories into finer subgroups to return more targeted search results to online shoppers. Using granular data on consumer search and purchase behaviors across multiple search sessions and product categories, we find that the improvement in search precision leads to a 37.3% increase in consumers’ click-through rates and a 64.4% increase in gross merchandise volume in the product category we study. The improvement in matching outcomes in the short run, however, comes at the cost of a substantial decrease in consumer engagement and unplanned purchases in the long run for consumers prone to spending more time searching. On average, these consumers conduct 5.5% fewer searches, spend 4.1% less time on the platform, and decrease their spending on related categories by 2.2% in the following week after the search precision increases. Overall, our findings illustrate the tradeoff between exploitation and exploration in e-commerce search design that has not yet been previously documented in the literature.


(With Zidong Wang)                                                                  NET Institute Working Paper 20-12

[Abstract]E-commerce platforms guide consumers’ search traffic toward online retailers that are classified into different product categories. An online retailer can either list itself under a broad category to reap larger search traffic, or choose a narrow category, often a subcategory of a broad category, to target a niche audience. In collaboration with, China’s largest e-commerce platform, we exploit a change in the platform’s search algorithm to study online retailors’ location decisions in the digital world.  In our framework, each market is defined by a search query, which matches an online retailer’s product either closely or distantly. The platform allocates search traffic into different categories, and online retailers compete for the search traffic in each product category with heterogeneous abilities to convert search traffic into revenue. Using detailed data on search queries, search exposure, and seller revenue, we find that an online retailer faces a tradeoff between market size and competition intensity, and a retailer is better at converting closely matched search traffic into revenues. By refining a broader category into narrow subcategories, the e-commerce platform gives retailers the flexibility to forgo higher volumes of search traffic in order to gain a better conversion rate. Eliminating category refinement would lead to about 17% revenue losses for sellers in product categories we study, with incidence mostly on sellers that specialize in niche products. Our results suggest that e-commerce platforms as entrepreneurial incubators can help small business owners thrive on the platform through targeted search traffic allocation. 

     (With Tanjim Hossain, Mo Xiao, and Zhe Yuan)     

[Abstract] In the United States, the Federal Communications Commission auctions off spectrum licenses, which typically cover geographically distinctive areas. A bidder may be uncertain about a license’s value, given stochastic future demand and unclear competitive landscape. We investigate the possibility of bidder herding on competitors’ bidding decisions in Spectrum Auction 73. We exploit rich bidder-license fixed effects and leverage on competing bidders’ private information about a license’s contribution to their potential winnings to causally identify the herding effect. We find strong evidence of bidder herding in the initial chaotic rounds of the auction. Compared with small and medium-sized bidders, large national carriers (including AT&T and Verizon) are more likely to herd on competitors’ bidding decisions.

[Abstract] This paper studies entry and expansion decisions of chains in a new and growing industry with high market uncertainty. Using a unique firm-level data set on the dramatic expansion of the movie theater industry in China from 2012-2016, I analyze how a chain’s expansion decision is affected by the presence of heterogeneous rivals. Based on the insight that chains are more likely to enter the market closed to their existing networks due to economies of density, I devise an instrumental variable strategy to address the endogeneity of rivals’ entry behaviors. I find significant evidence of asymmetric competition effects in the sense that chains are more likely to enter markets where independent theaters have a larger scale of presence, but less likely to enter markets occupied by other chains. Further empirical analysis suggests that learning is more likely to explain the positive competition effect of a rival on the firm’s entry decisions. My study lends support to another explanation for firm clustering: information spillovers of entry behaviors.