Network Analysis of Traditional Word of Mouth

Abstract

Network analysis of Word of Mouth (WOM) examines how customers exchange opinions within their social networks. Compared to standard survey questions, which typically measure the likelihood to recommend, the network approach provides more metrics (e.g., average path length, clustering coefficient, density, average degree) that can be used to diagnose customer chatter. Unfortunately, traditional WOM has not benefitted from network analysis, which usually is applied to online WOM due to the availability of stored data. Despite the pervasiveness of online WOM, however, recent commercial reports reveal that traditional WOM still surpasses online WOM by a large margin. Traditional WOM also is perceived as more trustworthy and persuasive than online WOM. Considering the strong standing of traditional WOM and the advances in network analysis due to online WOM, this study fills a gap by demonstrating how a network analysis can be applied to traditional WOM. Network analysis is more demanding on the researcher and the respondents, but as the study illustrates, it also is more diagnostic than a standard survey. A preliminary study confirmed that people, indeed, are more likely to share traditional WOM then online WOM. The main study utilized network analysis by using an alter-alter survey method, which was used to map the network structures of a variety of WOM networks. Specifically, we examined the WOM networks structure as a function of product type (search, experience, and credence products) and opinion valence (positive vs. negative). The results reveal that WOM is affected primarily by product type. People are most likely to share opinions about experience products, followed by opinions about search products, and least likely to talk about credence products. The effect of opinion valence is limited. Practitioners can use these findings to manage WOM primarily based on product type by including search, experience, or credence qualities in promotional messages. This is the first study to compare WOM networks to the existing social network, which can serve as a benchmark for evaluating WOM campaigns. The results reveal that for most products, people do not utilize all of their social connections for WOM, but there are exceptions, such as sharing a positive opinion about a movie, where WOM chatter can exceed the social network. The study discusses the WOM network metrics from a practical perspective and how they can be used to optimize WOM campaigns. Overall, the conclusion is that network analysis is a viable technique for studying traditional WOM, which brings new research directions.