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Retail foot traffic 20229/21/2023 ![]() ![]() Our article addresses this research gap while providing a means for modeling hierarchical data, such as products in stores or stores in countries. The limited literature that does measure social media’s impact on physical retail only evaluates a single product, company, or product category without estimating global elasticities. Current research on retail and social media is predominantly focused on e-commerce data, and those insights do not necessarily apply to brick-and-mortar stores. Additionally, the average internet user spends 147 min on social media a day (Statistica 2022). Census Bureau 2021), it is crucial to understand the ways online activity and communication affect physical retailers. With sales at brick-and-mortar stores representing 85.2% of U.S. This story illustrates how online word-of-mouth (WoM) communication can drive traffic to offline stores. The heightened social media activity provided information about a product launch and increased customer engagement around its brand. ![]() ![]() The so-called “Chicken Sandwich War” led to an unexpected surge in demand that caused Popeyes across the United States to run out of their newly released chicken sandwich (Suddath 2019). In the summer 2019, a Twitter spat between the fast-food giants Popeyes and Chick-fil-A went viral. Our approach is novel due to (1) the large scale of data, (2) the breadth of analysis, (3) the multi-level specification, and (4) in estimating global elasticities between changes in electronic word-of-mouth (WoM) communication about brands and changes in store visits of those brands. The weak cross-brand effects show that social media has distinct and larger influence on brands individually rather than universally. This modest but meaningful effect, however, fully dissipates within 1 week. Despite wide variation, when brand mentions increase by one standard deviation-either in likes or disagreement-then next-day foot traffic to stores of that brand will increase by 0.04 standard deviations (3–4%). We use hierarchical linear regression to account for the random effects of brand and store heterogeneity, which is superior to ordinary linear regression. We consider seven measures of social media activity within a Social Impact Theory framework and test under what context does online chatter about a brand lead to higher foot traffic to those brand stores. This paper answers how changes in social media activity influence customers to visit nationally known, brick-and-mortar retail stores. ![]()
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