A Primer on Using Behavioral Data for Testing Theories in Advertising Research

Interactions with and between customers in digital, social, and mobile environments are commonly recorded, producing behavioral data that have the potential to advance advertising research. This article provides an accessible guide on how to leverage such data for advertising researchers who may have thus far relied mostly on lab experiment or survey data. Specifically, we suggest potential sources for behavioral data and present a process for analyzing and interpreting behavioral data. Each step of the process is discussed: exploring, understanding and preparing data; specifying and estimating models; and interpreting and presenting the results. Some fundamental issues with using multiple regression to analyze such data are covered, including standardization, outliers, transformations, multicollinearity, and the omitted variable bias. We also discuss issues that are especially problematic with using behavioral data in advertising research, including endogeneity, count data, data with many zeros, and grouped data. More advanced versions of regression that address these issues are surveyed, including instrumental variables, propensity scoring, generalized linear models, and mixed models. General advice for thinking about behavioral data is provided.     

Managing Brands in the Social Media Environment

The dynamic, ubiquitous, and often real-time interaction enabled by social media significantly changes the landscape for brand management. A deep understanding of this change is critical since it may affect a brand's performance substantially. Literature about social media's impact on brands is evolving, but lacks a systematic identification of key challenges related to managing brands in this new environment. This paper reviews existing research and introduces a framework of social media's impact on brand management. It argues that consumers are becoming pivotal authors of brand stories due to new dynamic networks of consumers and brands formed through social media and the easy sharing of brand experiences in such networks. Firms need to pay attention to such consumer-generated brand stories to ensure a brand's success in the marketplace. The authors identify key research questions related to the phenomenon and the challenges in coordinating consumer- and firm-generated brand stories.     

Rising to Stardom: An Empirical Investigation of the Diffusion of User-generated Content

With the explosive growth of online user-generated content and the desire by marketers to better utilize this space, it is beneficial to understand the viral diffusion of such content and to identify messages that are most likely to achieve popularity. In this paper, we combine network analysis and the diffusion literature to study the spreading of user-generated videos online. We identify three groups of factors that affect diffusion outcomes: network structure, content characteristics, and author characteristics. Using a proportional rates model, we analyze the diffusion of a sample of videos on YouTube. Our results show that it is preferable to have many subscribers who each has a few friends than to have a few subscribers with many connections. Furthermore, a curvilinear relationship exists between subscriber network connectivity and diffusion rate such that diffusion is at its highest under moderate connectivity. Examining content characteristics, we show that entertainment and educational values affect diffusion but production quality does not matter. Moreover, we find that quality as manifested by user ratings influences diffusion more than innate content quality. Not surprisingly, an author's past success carries over to the current content, and content from younger authors is more popular.     

Coordinating supply-related scarcity appeals with online reviews

Marketing Letters - Online reviews play an important role in consumer purchase decisions and have received much research attention. However, previous research has typically examined the effects of...