The rise of electronic social networks and implications for advertisers

Zakaria Babutsidze
2018 Technological forecasting & social change  
The rise of modern digital communication technologies, most notably electronic social networks, transforms structures through which consumers interact with one another. In this paper we distinguish between two channels through which product promotion affects sales. The direct channel always positively affects consumers' pre-purchase valuation. The indirect channel goes through word-of-mouth (WoM) and can be either positive or negative. The sentiment contained in WoM is generated by the complex
more » ... nteraction process and depends on the aggressiveness of the advertising campaign. We investigate the implications of the current changes in social network architectures for the effectiveness of the indirect channel. We show that changes in social structures have increased the efficiency of WoM across a host of industries. Our results call for "smart" advertising policies. T 14 Also notice that, despite the fact that for a given average shortest path length the clustering coefficient is different for the two networks, this feature does not seem to be driving the results (hence the relative cleanliness of this exercise). Given the difference between static and dynamic WoM network implementations, clustering would likely explain the differences across the two setups as dynamic WoM would imply a rapid fall in clustering as we progress into the dynamics of interaction. However, we see no visible difference in two panels of Fig. 6 in the Appendix B. 15 Notice that this results highlights the importance of the integrity of highly connected individuals in social networks to counter-act the diffusion of "fake news". These "stars" seem to have the power to counteract the generic effect of decreasing shortest path length that electronic social media have. However, this hinges on their motivation to contribute to finding the truth which (implicitly) exists in our model, but might not exist in the field. 16 This is confirmed by the regression analysis too: if we run the any of the regressions presented in the paper on data combining static and dynamic WoM network setups and include a dummy variable for one of the scenarios, say static WoM, the coefficient of this dummy is always significant. 17 I thank an anonymous referee for pointing out this insight.
doi:10.1016/j.techfore.2018.06.010 fatcat:sjfnfu5vcrditg2d3ioanyuuim