We present an analysis of a person-to-person recommendation network, consisting of 4 million people who made 16 million recommendations on half a million products. We observe the propagation of recommendations and the cascade sizes, which we explain by a simple stochastic model. We analyze how user behavior varies within user communities defined by a recommendation network. Product purchases follow a ‘long tail’ where a significant share of purchases belongs to rarely sold items. We establish how the recommendation network grows over time and how effective it is from the viewpoint of the sender and receiver of the recommendations. While on average recommendations are not very effective at inducing purchases and do not spread very far, we present a model that successfully identifies communities, product, and pricing categories for which viral marketing seems to be very effective.
@cite_0: This article explores the potential impact of individual consumer decisions to transmit or withhold word-of-mouth information on the flow of information in a market. Information flow is examined in networks composed of graphs and nodes, where graphs represent channels that foster information flow and nodes represent sentient decision makers who can potentially impede or foster information flow. We assert that actors in embedded markets first judge the moral hazards imposed by the information they consider for transmission and the social context of transmission, and then moderate their decisions to transmit the information in light of these judgements. We analyze this process using a modified version of Marshall Sahlins's social exchange theory, examine the plausibility of this analysis using two laboratory experiments, and explore the consequences of this behavior at the aggregate level using a computer simulation. Our view highlights the importance of the interplay of individuals and their social context for understanding word-of-mouth processes.
@cite_7: Abstract The authors examine behavioral outcomes following a customer-initiated contact (CIC) with a manufacturer and develop a framework to explain the impact of vendor performance during a CIC on a customer’s share of category requirements with a focal brand and word-of-mouth incidence following contact. The authors propose customer characteristics and context–specific factors that may relate to differences in the key characteristics of the underlying source model of share of category requirements and word of mouth. The authors then assess the overall importance of the explanatory variables in the source model and simultaneously test for systematic differences related to CIC-specific factors using survey data from more than 1700 CICs that involve more than 60 brands. A key assumption in much prior research that has examined customer-firm interactions is that CIC-specific factors, if they are included at all, create an automatic regularity that must be controlled for. The authors propose and find an addi...
Factors that influence customers' willingness to actively share the information with others via word of mouth have also been studied. Frenzen and Nakamoto @cite_0 surveyed a group of people and found that the stronger the moral hazard presented by the information, the stronger the ties must be to foster information propagation. Also, the network structure and information characteristics interact when individuals form decisions about transmitting information. Bowman and Narayandas @cite_7 found that self-reported loyal customers were more likely to talk to others about the products when they were dissatisfied, but interestingly not more likely when they were satisfied.