Studying Paths of Participation in Viral Diffusion Process

Authors propose a conceptual model of participation in viral diffusion process composed of four stages: awareness, infection, engagement and action. To verify the model it has been applied and studied in the virtual social chat environment settings. The study investigates the behavioral paths of actions that reflect the stages of participation in the diffusion and presents shortcuts, that lead to the final action, i.e. the attendance in a virtual event. The results show that the participation in each stage of the process increases the probability of reaching the final action. Nevertheless, the majority of users involved in the virtual event did not go through each stage of the process but followed the shortcuts. That suggests that the viral diffusion process is not necessarily a linear sequence of human actions but rather a dynamic system.Comment: In proceedings of the 4th International Conference on Social Informatics, SocInfo 201

Creating Social Contagion through Viral Product Design: A Randomized Trial of Peer Influence in Networks

We examine how firms can create word of mouth peer influence and social contagion by incorporating viral features into their products. Word of mouth is generally considered to more effectively promote peer influence and contagion when it is personalized and active. Unfortunately, econometric identification of peer influence is non-trivial. We therefore use a randomized field experiment to test the effectiveness of passive-broadcast and active-personalized viral messaging capabilities in creating peer influence and social contagion among the 1.4 million friends of 9,687 experimental users. Surprisingly, we find that passive-broadcast viral messaging generates a 246% increase in local peer influence and social contagion, while adding active-personalized viral messaging only generates an additional 98% increase in contagion. Although active-personalized messaging is more effective per message and is correlated with more user engagement and product use, it is used less often and therefore generates less total peer adoption in the network than passive-broadcast messaging

Social Interaction, Observational Learning, and Privacy: the "Do Not Call" Registry

Many empirical studies have inferred contagion in behavior from a correlation between individual behavior and the behavior of others in the same social group, rather than from any direct evidence. The correlation has been variously attributed to social interaction, word of mouth communication, and observational learning. As Manski (1993) famously observed, such correlation might be explained by peer group influence, but also, similar responses to common environmental changes. More generally, correlation in behavior raises two questions – how information is transmitted and why individuals follow the choices of others. We address these questions in the context of subscriptions to the U.S. "do not call" registry in June-August 2003. Using a rich set of data culled from multiple sources, including longitudinal observations of household choice, we are able to separately identify -- Methods by which information is transmitted – social interaction and news media; -- Reasons why households follow the choices of others – observational learning and telemarketing diversion, and the impact of household heterogeneity on such learning and diversion. Among methods of information transmission, social interaction was relatively more important than news media. Among reasons for contagion, telemarketing diversion was relatively more important than observational learning, while the extent of learning decreased with social heterogeneity.

Creating Social Contagion through Viral Product Design: A Randomized Trial of Peer Influence in Networks

We examine how firms can create word-of-mouth peer influence and social contagion by designing viral features into their products and marketing campaigns. To econometrically identify the effectiveness of different viral features in creating social contagion, we designed and conducted a randomized field experiment involving the 1.4 million friends of 9,687 experimental users on Facebook.com. We find that viral features generate econometrically identifiable peer influence and social contagion effects. More surprisingly, we find that passive-broadcast viral features generate a 246% increase in peer influence and social contagion, whereas adding active-personalized viral features generate only an additional 98% increase. Although active-personalized viral messages are more effective in encouraging adoption per message and are correlated with more user engagement and sustained product use, passive-broadcast messaging is used more often, generating more total peer adoption in the network. Our work provides a model for how randomized trials can identify peer influence in social networks

Determinants of pharmaceutical innovation diffusion: social contagion and prescribing characteristics

This article studies the determinants of pharmaceutical innovation diffusion among specialists. To this end, it investigates the influences of six categories of factors—social embeddedness, socio-demography, scientific orientation, prescribing patterns, practice characteristics, and patient panel composition—on the use of new drugs for the treatment of type 2 diabetes mellitus in Hungary. Here, in line with international trends, 11 brands were introduced between April 2008 and April 2010, outperforming all other therapeutic classes. The Cox proportional hazards model identifies three determinants—social contagion (in the social embeddedness category) and prescribing portfolio and insulin prescribing ratio (in the prescribing pattern category). First, social contagion has a positive effect among geographically close colleagues—the higher the adoption ratio, the higher the likelihood of early adoption—but no influence among former classmates and scientific collaborators. Second, the wider the prescribing portfolio, the earlier the new drug uptake. Third, the lower the insulin prescribing ratio, the earlier the new drug uptake—physicians’ therapeutic convictions and patients’ socioeconomic statuses act as underlying influencers. However, this finding does not extend to opinion-leading physicians such as scientific leaders and hospital department and outpatient center managers. This article concludes by arguing that healthcare policy strategists and pharmaceutical companies may rely exclusively on practice location and prescription data to perfect interventions and optimize budgets