Marketing Seminar (2020-04)
Topic:Can User-Posted Photos Serve as a Leading Indicator of Restaurant Survival?
Evidence from Yelp
Speaker:Mengxia Zhang，University of Southern California
Time:Tuesday, 25 August, 9:00-10:30
Microsoft Teams:
https://teams.microsoft.com/l/meetup-join/19%3aade8e46052fa419798afcaf6aad0ad20%40thread.tacv2/1598000425338?context=%7b%22Tid%22%3a%2280b7b804-c47e-4119-8274-0f6835b8e89f%22%2c%22Oid%22%3a%22dae73385-f69d-4688-a153-ff18e3659b00%22%7d
Abstract:
Despite the substantial economic impact of the restaurant industry, large-scale empirical research on restaurant survival has been sparse. We investigate whether consumer-posted photos can serve as a leading indicator of restaurant survival above and beyond reviews, firm characteristics, competitive landscape, and macro conditions. We employ machine learning techniques to analyze 755,758 photos and 1,121,069 reviews posted on Yelp between 2004 and 2015 for 17,719 U.S. restaurants. We also collect data on these restaurants’characteristics (e.g., cuisine type; price level), competitive landscape, and their entry and exit (if applicable) time based on each restaurant’s Yelp/Facebook page, own website, or the Google search engine. Using a predictive XGBoost model, we find that photos are more predictive of restaurant survival than are reviews. Interestingly, the information content (e.g., number of photos with food items served) and helpful votes received by these photos relate more to restaurant survival than do photographic attributes (e.g., composition or brightness). Additionally, photos carry more predictive power for independent, mid-aged, and medium-priced restaurants. Assuming that restaurant owners do not possess any knowledge about future photos and reviews for both themselves and their competitors, photos can predict restaurant survival for up to three years, while reviews are only informative for one year. We further employ causal forests to facilitate interpretation of our predictive results. Our analysis suggests that, among others, the total volume of user-generated content (including photos and reviews)
Introduction:
Mengxia Zhang, a Ph.D. student at USC Marshall. She works with Lan Luo and Tianshu Sun (IS). Sha Yang, Dina Mayzlin, Yanhao (Max) Wei, and Michael Leung (Economics) are also on her committee. Before that, she received both her bachelor's and master's degrees from Peking University. Her research focuses on utilizing artificial intelligence for marketing research. In her job market paper, she uses machine learning methods to investigate whether consumer-posted photos can serve as a leading indicator of restaurant survival above and beyond reviews and other known factors. This paper received a minor revision at Management Science, and it won the 2018 ISMS Doctoral Proposal Competition Award and the 2018 Shankar-Spiegel Award Runner-up. In her other papers, she has studied the topics of consumer-AI co-creation and knowledge sharing.
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