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.
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.
@cite_0 @cite_7
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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. This article presents a network analysis of word-of-mouth referral behavior in a natural environment. The relational properties of tie strength and homophily were employed to examine referral behavior at micro and macro levels of inquiry. The study demonstrates different roles played by weak and strong social ties. At the macro level, weak ties displayed an important bridging function, allowing information to travel from one distinct subgroup of referral actors to another subgroup in the broader social system. At the micro level, strong and homophilous ties were more likely to be activated for the flow of referral information. Strong ties were also perceived as more influential than weak ties, and they were more likely to be utilized as sources of information for related goods. The Internet makes it possible for consumers to obtain electronic word of mouth from other consumers. Customer comments articulated via the Internet are available to a vast number of other customers, and therefore can be expected to have a significant impact on the success of goods and services. This paper derives several motives that explain why customers retrieve other customers' on-line articulations from Web-based consumer opinion platforms. The relevance of these motives and their impact on consumer buying and communication behavior are tested in a large-scale empirical study. The results illustrate that consumers read on-line articulations mainly to save decision-making time and make better buying decisions. Structural equation modeling shows that their motives for retrieving on-line articulations strongly influence their behavior. The advent of the Internet has made the transmission of personally identifiable information more common and often unintended by the user. As personal information becomes more accessible, individuals worry that businesses misuse the information that is collected while they are online. Organizations have tried to mitigate this concern in two ways: (1) by offering privacy policies regarding the handling and use of personal information and (2) by offering benefits such as financial gains or convenience. In this paper, we interpret these actions in the context of the information-processing theory of motivation. Information-processing theories, also known as expectancy theories in the context of motivated behavior, are built on the premise that people process information about behavior-outcome relationships. By doing so, they are forming expectations and making decisions about what behavior to choose. Using an experimental setting, we empirically validate predictions that the means to mitigate privacy concerns are associated with positive valences resulting in an increase in motivational score. In a conjoint analysis exercise, 268 participants from the United States and Singapore face trade-off situations, where an organization may only offer incomplete privacy protection or some benefits. While privacy protections (against secondary use, improper access, and error) are associated with positive valences, we also find that financial gains and convenience can significantly increase individuals' motivational score of registering with a Web site. We find that benefits-monetary reward and future convenience-significantly affect individuals' preferences over Web sites with differing privacy policies. We also quantify the value of Web site privacy protection. Among U.S. subjects, protection against errors, improper access, and secondary use of personal information is worth @math 44.62. Finally, our approach also allows us to identify three distinct segments of Internet users-privacy guardians, information sellers, and convenience seekers.