Social Media and Brand Purchase: Quantifying the Effects of Exposures to Earned and Owned Social Media Activities in a Two-Stage Decision Making Model

Abstract

This study investigates the effects of exposures to earned and owned social media activities and their interaction on brand purchase in a two-stage decision model (i.e., likelihood to purchase and the amount purchased offline). Our study is instantiated on a unique single-source dataset of 12-month home-scanned brand purchase records of a group of fast-moving consumer good brands and Facebook brand Fan Page messages related to the brands. We first find that exposures to earned and owned social media activities for brands have significant and positive impacts on consumers’ likelihood to purchase the brands. Their effects are, surprisingly, suppressive on each other. Second, exposures to earned and owned social media activities have almost no impact on the amount purchased offline with presence of in-store promotions. Our study contributes to our knowledge body of social media marketing by demonstrating that social media activities for a brand can foster the consumer base of the brand, but that effort is not necessarily sales-oriented. In addition, our study is conducive to guiding marketers onto the strategic allocation of advertising dollars to online social channels featuring a mixture of earned and owned social media.

Key words and phrases:

• brand community
• brand purchase
• Facebook Fan Page
• multilevel modeling
• social media
• social media marketing

Notes

1. Fast-moving consumer goods (FMCG) are products that are sold quickly and at relatively low cost. Examples include nondurable goods such as soft drinks, chocolate bars, candies, yogurt, processed foods and many other mundane consumables that are repeatedly purchased by households.

2. As requested by the data sponsor, information about households, manufacturers, brands, and categories have been masked.

3. The distribution of weekly amount purchased offline is highly skewed (the skewness= 4.47) and we also assumed that ε_ijt in Equation (8) is normally distributed. To make the highly skewed amount purchased offline data less skewed, we log-transformed the quantity and its skewness considerably reduced to 1.33. In addition, the log-transformed quantity makes our results more interpretable and helps us to better explain the counterfactual inferences in our analysis.

Additional information

Notes on contributors

Karen Xie

Karen Xie is an assistant professor at the Daniels College of Business, University of Denver. She holds a Ph.D. from Temple University’s Fox School of Business and Management. Her research interests include hospitality technology and marketing, and business information and analytics. She is a recipient of Wharton Customer Analytics Initiative Research Opportunities (2012 and 2014), Hospitality Technology Research Award (2013), and Best Paper Award of Caesars Hospitality Research Summit (2011). She was previously an information analyst at Accenture Management Consulting and an event marketer at UBS. Her work has appeared in journals such as International Journal of Hospitality Management, International Journal of Contemporary Hospitality Management, and Journal of Hospitality Marketing and Management.

Young-Jin Lee

Young-Jin Lee is an assistant professor in the Department of Business Information and Analytics at Daniels College of Business, University of Denver. He received a master’s degree in information systems management from Carnegie Mellon University, and a Ph.D. in information systems from the University of Washington. His research interests include economic and marketing aspects of online social media, mobile IT markets, and adoption and diffusion of IT innovations. His work has appeared in journals such as Management Science, Journal of Management Information Systems, and in conference proceedings of the International Conference of Information Systems, Workshop on Information Technology and Systems, Workshop on Information Systems and Economics, and others.