School of Computing, National University of Singapore, Singapore 117417, Republic of Singapore; School of Business, Nanjing University, Nanjing 210093, People s Republic of China
Challenging conventional wisdom, we unravel three paradoxes of word of mouth (WOM) in e-commerce. Specifically, the WOM valence paradox contends that higher WOM valence of a product results in a larger subsequent decrease in the WOM valence of the product, the WOM volume paradox propounds that higher WOM volume of a product results in a smaller subsequent increase in the WOM volume of the product, and the WOM spillover paradox proposes that an improvement in the WOM of a product also improves the WOM of connected products in a product network. These paradoxes caution online retailers that superior WOM may at times backfire and not boost further sales. Drawing theoretical support from expectation-confirmation theory and network theory, we collect data from China's largest business-to-consumer platform, Tmall.com, and use linear panel data models to examine WOM evolution in a product network, controlling for relevant factors at the individual product, product network, and time unit levels. Importantly, we base our identification strategies on the use of instrumental variables and the difference-in-differences estimation approach. Numerous statistical checks confirm the robustness and consistency of our findings. We contribute to a much richer theoretical understanding of WOM, by extending the applicability of expectation-confirmation theory and network theory to novel predictions and contexts, adding a dynamic perspective, unveiling three important WOM paradoxes, and offering practical insights. > >
Despite the popular use of social media by consumers and marketers, empirical research investigating their economic values still lags. In this study, we integrate qualitative user-marketer interaction content data from a fan page brand community on Facebook and consumer transactions data to assemble a unique data set at the individual consumer level. We then quantify the impact of community contents from consumers (user-generated content, i.e., UGC) and marketers (marketer-generated content, i.e., MGC) on consumers' apparel purchase expenditures. A content analysis method was used to construct measures to capture the informative and persuasive nature of UGC and MGC while distinguishing between directed and undirected communication modes in the brand community. In our empirical analysis, we exploit differences across consumers' fan page joining decision and across timing differences in fan page joining dates for our model estimation and identification strategies. Importantly, we also control for potential self-selection biases and relevant factors such as pricing, promotion, social network attributes, consumer demographics, and unobserved heterogeneity. Our findings show that engagement in social media brand communities leads to a positive increase in purchase expenditures. Additional examinations of UGC and MGC impacts show evidence of social media contents affecting consumer purchase behavior through embedded information and persuasion. We also uncover the different roles played by UGC and MGC, which vary by the type of directed or undirected communication modes by consumers and the marketer. Specifically, the elasticities of demand with respect to UGC information richness are 0.006 (directed communication) and 3.140 (undirected communication), whereas those for MGC information richness are insignificant. Moreover, the UGC valence elasticity of demand is 0.180 (undirected communication), whereas that for MGC valence is 0.004 (directed communication). Overall, UGC exhibits a stronger impact than MGC on consumer purchase behavior. Our findings provide various implications for academic research and practice.