In recent years, we have witnessed the rapid proliferation and widespread adoption of a new class of information technologies, commonly known as social media. Researchers often rely on social network analysis (SNA) when attempting to understand these technologies, often without considering how the novel capabilities of social media platforms might affect the underlying theories of SNA, which were developed primarily through studies of offline social networks. This article outlines several key differences between traditional offline social networks and online social media networks by juxtaposing an established typology of social network research with a well-regarded definition of social media platforms that articulates four key features. The results show that at four major points of intersection, social media has considerable theoretical implications for SNA. In exploring these points of intersection, this study outlines a series of theoretically distinct research questions for SNA in social media contexts. These points of intersection offer considerable opportunities for researchers to investigate the theoretical implications introduced by social media and lay the groundwork for a robust social media agenda potentially spanning multiple disciplines.

What's different about social media networks? a framework and research agenda

Missing link

Seeding strategies have strong influences on the success of viral marketing campaigns, but previous studies using computer simulations and analytical models have produced conflicting recommendations about the optimal seeding strategy. This study compares four seeding strategies in two complementary small-scale field experiments, as well as in one real-life viral marketing campaign involving more than 200,000 customers of a mobile phone service provider. The empirical results show that the best seeding strategies can be up to eight times more successful than other seeding strategies. Seeding to well-connected people is the most successful approach because these attractive seeding points are more likely to participate in viral marketing campaigns. This finding contradicts a common assumption in other studies. Well-connected people also actively use their greater reach but do not have more influence on their peers than do less well-connected people.

Seeding Strategies for Viral Marketing: An Empirical Comparison

User acceptance of mobile e-government services: An empirical study

Viral marketing is a form of peer-to-peer communication in which individuals are encouraged to pass on promotional messages within their social networks. Conventional wisdom holds that the viral marketing process is both random and unmanageable. In this paper, we deconstruct the process and investigate the formation of the activated digital network as distinct from the underlying social network. We then consider the impact of the social structure of digital networks (random, scale free, and small world) and of the transmission behavior of individuals on campaign performance. Specifically, we identify alternative social network models to understand the mediating effects of the social structures of these models on viral marketing campaigns. Next, we analyse an actual viral marketing campaign and use the empirical data to develop and validate a computer simulation model for viral marketing. Finally, we conduct a number of simulation experiments to predict the spread of a viral message within different types of social network structures under different assumptions and scenarios. Our findings confirm that the social structure of digital networks play a critical role in the spread of a viral message. Managers seeking to optimize campaign performance should give consideration to these findings before designing and implementing viral marketing campaigns. We also demonstrate how a simulation model is used to quantify the impact of campaign management inputs and how these learnings can support managerial decision making.

The Effects of the Social Structure of Digital Networks on Viral Marketing Performance

This research seeks to temper the enthusiasm surrounding viral marketing by claiming that there is no such thing as a pure viral marketing campaign and that the successes of many campaigns labeled ‘viral’ are due to many factors including traditional mass communication. The article presents a conceptual framework for viral marketing, inspired by epidemic modeling and the theory of branching processes, that enables a campaign to be separated into its viral and non-viral components. We then introduce a mathematical model for the viral process and develop three campaign performance metrics: overall campaign performance (reach), the dynamic performance of the viral component (dynamic criticality) and performance of the viral component relative to the non-viral component of the campaign (relative viral reach). Page 1 of 7 ANZMAC 2009

A conceptual framework for viral marketing

Few marketing campaigns purely are viral. Most comprise both viral (peer-to-peer transmission) and non-viral (firm-initiated transmission) components. Unfortunately, current metrics for assessing viral-campaign performance seldom differentiate between these components, potentially overstating the true contribution made by the viral component. Moreover, reach (number of users who received the content) is a time-based metric that typically is favored at the expense of campaign sharing, which fundamentally is a generation-based metric (transmission of content to others). This paper presents and tests a mathematical model that addresses these issues, thereby providing a more accurate and valid means for assessing campaign performance.

Dineli R. Mather

Papers

The Effects of the Social Structure of Digital Networks on Viral Marketing Performance

A conceptual framework for viral marketing

How contagious is your viral marketing campaign? A mathematical model for assessing campaign performance