Richard Ivey Building 2325
- Artificial intelligence
- New media
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Mengxia Zhang is an Assistant Professor in Marketing at the Ivey Business School, Western University, Canada. Mengxia’s research focuses on artificial intelligence and new media. 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 is published 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.
Prior to joining Ivey, Mengxia completed her Ph.D. at the University of Southern California (USC). At USC Marshall, she has been granted a Ph.D. Student Outstanding Researcher Award for her research excellence and was nominated for the Ph.D. Student Teaching Award for her teaching excellence. In her spare time, Mengxia enjoys photography.
- Ph.D.: Marketing – University of Southern California
- Master: Management – Peking University; Finance – Hong Kong University
- Bachelor: Economics and Laws– Peking University
Recent Refereed Articles
Zhang, M. Z.; Luo, L., 2022, "Can Consumer-Posted Photos Serve as a Leading Indicator of Restaurant Survival? Evidence from Yelp", Management Science, January 69(1): 25 - 50. 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 macroconditions. We employ machine learning techniques to extract features from 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 restaurant characteristics (e.g., cuisine type, price level) and competitive landscape as well as entry and exit (if applicable) time from each restaurant’s Yelp/Facebook page, own website, or Google search engine. Using a predictive XGBoost algorithm, we find that consumer-posted photos are strong predictors of restaurant survival. Interestingly, the informativeness of photos (e.g., the proportion of food photos) relates more to restaurant survival than do photographic attributes (e.g., composition, brightness). Additionally, photos carry more predictive power for independent, young or mid-aged, and medium-priced restaurants. Assuming that restaurant owners possess no knowledge about future photos and reviews, photos can predict restaurant survival for up to three years, whereas reviews are only predictive for one year. We further employ causal forests to facilitate the interpretation of our predictive results. Among photo content variables, the proportion of food photos has the largest positive association with restaurant survival, followed by proportions of outside and interior photos. Among others, the proportion of photos with helpful votes also positively relates to restaurant survival.
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Honours & Awards
- 2020 AMA-Sheth Foundation Doctoral Consortium Fellow
- 2019 USC Marshall Ph.D. Student Outstanding Researcher Award
- 2018 ISMS Doctoral Proposal Competition Award Winner
- 2018 Shankar-Spiegel Award Runner-up