Title: Charitable Giving, Corporate Image Building and Market Expansion with Entry Barriers: The Case of Walmart
Speaker: Jinghui Qian, University of Toronto
Time: Wednesday, September 20, 13:30-15:00
Place: Room K01, Guanghua Building 2
Firms such as big box retailers, especially those with unfavorable corporate images, constantly encounter local political entry oppositions against their market expansions (e.g., protests, lawsuits, rejections of zoning plans). Meanwhile, many firms invest a substantial amount of financial resources in charitable giving to improve corporate images. In this paper, we hypothesize that charitable giving can boost a firm’s local corporate images that further help a firm to reduce oppositions against its market expansion. Using detailed charitable giving data and social media data (Twitter) about Walmart in each city, we first provide evidence showing that the charitable giving of Walmart has a positive effect on Walmart’s city-level corporate images, as measured by tweet sentiments towards Walmart. In addition, we find that an improved corporate image is associated with diminished likelihood of the opposition against Walmart’s market expansion.
To quantify the economic value of charitable giving and to identify the main motivations underlying Walmart’s charitable giving, we build and estimate a dynamic structural model in which Walmart jointly makes market expansion decisions and charitable giving decisions. From the estimation, we recover Walmart’s profit function and cost of proposing a new store. The results of the counterfactual experiments suggest that (i) the average return per dollar of charitable giving ranges from $2.51 to $2.75; (ii) Walmart would reduce its charitable giving by about 25% if every store proposal would be approved regardless of its corporate images.
Mr. Jinghui Qian is a Ph .D. Candidate in Marketing at the Rotman School of Management, University of Toronto. His research interests are in the areas of brand management, digital marketing, and network effects. Methodologically, he combines econometric methods and machine learning methods to understand marketing related issues.
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