- Do Online Reviews Improve Product Quality? Evidence from hotel reviews on travel sites. -with Alexander Chaudhry and Amit Pazgal
Abstract: In this study, we use a game theoretic model to argue that the presence of online reviews can lead to product quality improvements for independent firms selling experience goods. Exploiting heterogeneous review platform penetration across markets, we test the predictions of our model using a dataset covering 40 thousand U.S. hotels and show that markets with greater TripAdvisor penetration exhibit greater gains in independent hotel quality. Independent hotels located in median peak penetration TripAdvisor markets improved their quality by an average of .129 stars as measured using composite online travel agent (OTA) star ratings, erasing 41% of the advantage held by chains in the absence of online reviews. We address measurement noise challenges for quality and platform penetration using state space models to reveal persistent quality and platform penetration trends. Additionally, we resolve endogeneity due to potential unobserved confounds correlated with penetration and quality across markets and time. We do so by exploiting review platforms’ imperfect market definitions that divide areas of hotel agglomeration into separate review platform markets, thus quasi-exogenously assigning hotels in the same area to varying levels of online review exposure. Our research suggests that online reviews play an important role in facilitating competition on quality.
- (Sub)optimality of managerial dynamic pricing (currently revising)
Abstract: This study contributes to the largely theoretical field of revenue management with an empirical investigation into the sub-optimality of managerial dynamic pricing policies as evidenced in the Las Vegas hotel market. We demonstrate that managers consistently choose prices that yield revenues approximately 25% below optimal levels. Specifically, managers appear to choose prices in a manner consistent with maximizing a mix of occupancy and revenue. We find support for the hypothesis that the unobservability of counterfactual revenues may drive managers’ suboptimal pricing policies when the hotel is expected to fill capacity. Additionally, we explore a novel managerial use of online reviews in pricing decisions and the effect of competitors’ pricing strategies on a focal hotel’s optimal prices. We discover that predicting mean reverting tendencies of online reviews can marginally improve the focal hotel’s bottom line during slow seasons. Similarly, we show that there is an economically significant impact of predicting competitors’ prices on the focal hotel’s pricing policies.
- Too little or too much seller assortment: the effects on buyers’ purchase probabilities in a food sharing platform – with Xueming Luo and Zhijie Lin
Abstract: Using a unique dataset from a large food sharing platform, we investigate the effect of supply side assortment size on users’ purchase probabilities. We find that users’ purchase probabilities are increasing in assortment size but at a decreasing rate. The initial increase in purchase probability is large. Users exposed to the mean 15 pages (150 options) of search results have an 8.4% higher purchase probability than those who only have access to one page of options. However, this effect plateaus rapidly with a peak lift in purchase probability compared to average session of 2.86% at 29 pages of options. Furthermore, choice overload exists in our empirical support, leading to a slight decrease in purchase probability versus the optimal assortment size of .1% at 32 pages. Surprisingly, the diminishing returns to assortment size are shown to be due to search costs rather than evaluation costs or decreasing marginal assortment diversification. Moreover, we show that users’ exposure to nearby offline options can offset the diminishing impact of assortment size on purchasing. To obtain these results, we resolve endogeneity challenges by introducing a variant of the border identification strategy that exploits discrete delivery distances featured in the data. We contribute to the literature by demonstrating the limits of a platform’s aggressive supply side growth, showing that search costs can dampen the purchase probability gains driven by assortment size leading to choice overload, and suggesting that users’ outside options can alleviate the concave effects of assortment size on purchase probabilities.
I see my projects as centered around interesting datasets. Currently, I am interested in the following unique data:
- Online reviews – millions of hotel, restaurant, and other travel reviews collected with Alex Chaudhry from across multiple review platforms.
- Movies – millions of reviews, movie revenues across the globe at daily/weekly levels, movie scripts, trailer videos
- Survey opinions – YouGov brand tracking surveys.
- Networks – Currently analyzing an expansive dataset of partners, suppliers, and competitors relationships in the economy.
- Advertising data – Ad$pender data augmented by USPTO trademarks data.
Ongoing projects in order of progress:
- The impact of movie trailers on box office performance. – with Alex Chaudhry
- Brand tracking: online reviews versus surveys, divergent insights on brand performance. -with Alexander Chaudhry and Rex Du
- Does online word of mouth displace offline word of mouth? Evidence from the U.S. movie industry. – with Alex Chaudhry and Seethu Seetharaman
- Advertising, mergers, and the network topology of economic supply chains.