Research

Publication

When Food Gets Political: How Does News Drive Green Food Offerings?

with Tong Guo and Daniel Yi Xu, forthcoming in Marketing Science

• NET Institute Research Grant, 2023

• Media attention: Fuqua Insights, Pamplin Spotlights

Abstract. We study how social media publicity affects the early-stage adoption of sustainable food technologies by local businesses.  Understanding this connection is challenging because of the lack of empirical measurement of local business decisions at scale and, more importantly, the endogeneity of social media publicity to unobserved local demand shocks. Focusing on the case of impossible meat products, we devise a unique location-specific adoption metric based on social media announcements between 2015 and 2019. We propose a novel identification strategy that leverages the quasi-random variations of county-quarter-level news production for different topics to causally identify the linkage between social media publicity and adoption. We find that local news coverage of sustainable food technology increases the adoption of impossible meat products by local restaurants and stores. Interestingly, the elasticity of adoption with respect to news is higher among more liberal regions, and news content about producer financials was the main contributor to identifying the causal effect of news on local businesses' adoption of impossible meat products.

Working Papers & Selected Work in Progress

1. A Scalable Recommendation Engine for New Users and Items, with Yiting Deng and Carl Mela

• ASA Statistics in Marketing Doctoral Dissertation Research Award Winner, 2024

• MSI Working Paper Series

2. Editorial Style as the Instrument: Identifying Causal Effects of Media Coverage on Demand, with Tong Guo and Anqi Zhao

3. Influencer Controversy Marketing, with Zijun Tian and Tong Guo

4. Finfluencer Communication on Inflation, with Tao He* and Hao Pang

5. Moving Showrooms: How Commercial EV Adoption Drives Consumer Demand, with Ruolin Yao*, Yuan Guo, and Shuai Ling

6. Creating for the Feed: How Recommender Systems Shape New Content on Social Media?

* Denotes a student coauthor at the time the project was initiated.