We introduce a new model of competition on growing networks. This extends the preferential attachment model, with the key property that node choices evolve simultaneously with the network. When a new node joins the network, it chooses neighbours by preferential attachment, and selects its type based on the number of initial neighbours of each type. The model is analysed in detail, and in particular, we determine the possible proportions of the various types in the limit of large networks. An important qualitative feature we find is that, in contrast to many current theoretical models, often several competitors will coexist. This matches empirical observations in many real-world networks.
The power of word-of-mouth has been a widely studied topic in the past half century, with research confirming the strong influence of word-of-mouth communication on consumer behavior @cite_5 @cite_53 @cite_50 @cite_33 @cite_47 . This research generally supports the assertion that word-of-mouth is more influential than external marketing efforts, such as advertising. In the current information age, online feedback mechanisms have changed the way customers share opinions about products and services @cite_1 , and online social networks are being exploited for viral marketing purposes @cite_18 . Nevertheless, traditional word-of-mouth recommendation networks still have a very important effect, and companies are advised to take advantage of this through their marketing efforts, e.g., via facilitating referrals @cite_45 @cite_24 . Due to the ever-changing ways individuals interact, it is important to analyze models---such as the one introduced in this paper---that study the interplay between how individuals interact and the effects of word-of-mouth communication in the given setting.
@cite_18 @cite_33 @cite_53 @cite_1 @cite_24 @cite_45 @cite_50 @cite_5 @cite_47
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Though word-of-mouth (w-o-m) communications is a pervasive and intriguing phenomenon, little is known on its underlying process of personal communications. Moreover as marketers are getting more interested in harnessing the power of w-o-m, for e-business and other net related activities, the effects of the different communications types on macro level marketing is becoming critical. In particular we are interested in the breakdown of the personal communication between closer and stronger communications that are within an individual's own personal group (strong ties) and weaker and less personal communications that an individual makes with a wide set of other acquaintances and colleagues (weak ties). 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. The effects of word-of-mouth (WOM) communications and specific attribute information on product evaluations were investigated. A face-to-face WOM communication was more persuasive than a printed format (experiment 1). Although a strong WOM effect was found, this effect was reduced or eliminated when a prior impression of the target brand was available from memory or when extremely negative attribute information was presented (experiment 2). The results suggest that diverse, seemingly unrelated judgmental phenomena--such as the vividness effect, the perseverance effect, and the negativity effect--can be explained through the accessibility-diagnosticity model. Copyright 1991 by the University of Chicago. Social media services, such as Twitter, enable commercial businesses to participate actively in online word-of-mouth communication. In this project, we examined the potential influences of business engagement in online word-of-mouth communication on the level of consumers’ engagement and investigated the trajectories of a business’ online word-of-mouth message diffusion in the Twitter community. We used path analysis to examine 164,478 tweets from 96,725 individual Twitter users with regards to nine brands during a 5-week study period. We operationalized business engagement as the amount of online word-of-mouth messages from brand and the number of consumers the brand follows. We operationalized consumers’ engagement as the number of online word-of-mouth messages from consumers both connecting to the brand and having no connection with the brand as well as the number of consumers following the brand. We concluded that the business engagement on Twitter relates directly to consumers’ engagement with online word-of-mouth communication. In addition, retweeting, as an explicit way to show consumers’ response to business engagement, indicates that the influence only reaches consumers with a second-degree relationship to the brand and that the life cycle of a tweet is generally 1.5 to 4 hours at most. Our research has critical implications in terms of advancing the understanding of the business’s role in the online word-of-mouth communication and bringing insight to the analytics of social networks and online word-of-mouth message diffusion patterns.