This study aims to examine how dissatisfaction among a few consumers can proliferate to cause disproportionate revenue losses across the broader customer network. The focus is on the adverse effects of negative word-of-mouth (NWoM) on firm revenue resulting from the interaction between consumer revenue heterogeneity and homophily, on the one hand, and consumer social network attributes, on the other hand.
An agent-based simulation modeling approach is used, comprising over 280,000 simulation runs that are executed on nine empirical consumer social network structures. Analyzing the resulting data captured the effects of comprehensive market, consumer and social network attributes that may influence the diffusion process.
Customer homophily and heterogeneity have a strong effect when the social network structure allows NWoM to reach revenue leaders rapidly while enabling it to spread across the social network. Specifically, the effect of consumer heterogeneity on revenue is observed when the average node distance is low or when the social network is less clustered. The effect of homophily shows a pattern similar to that of heterogeneity for the randomly designated group, but the opposite for the revenue leaders.
The variables examined in this study and their interactions have stronger or similar effects on reducing firm revenue than the percentage of dissatisfied consumers, which has been the primary focus of the majority of previous research.
Decision-makers can use the findings of this study to estimate the potential adverse impact of NWoM on market-level sales and develop appropriate remedies to prevent and control these effects. They can also incorporate this knowledge in systems that monitor online social interactions which may consequently suggest appropriate managerial responses to prevent and control the damages.
The study offers new insights into the complex processes that adversely affect firm profits by examining the interaction effects of two different sets of attributes. Heterogeneity and homophily relate to consumers’ revenue-generating potential, while social network attributes capture the complexity of consumers’ connections to one another.
