Online Social Networks: Recommendation Diffusion and Co-Consumption Influence

Introduction

Social networks are an interesting phenomenon, which are gaining business and researchers’ attention for their potential for market information and attraction. Customer´s virtual collaboration is, therefore, emerging as organizations embrace new e-business processes to take advantage of electronic communication technologies. In such turbulent times, with marketing pressure for more efficient targeting of resources, marketers are rediscovering the importance of social contagion, facilitating brand communities and social network sites for customer “voice.” Many factors underlie this interest including the ability of brand communities or social networks to influence members’ perceptions and actions, often through frequent social interactions, to rapidly disseminate information, to learn consumer evaluations about new offerings, to speed up market product novelty and so forth; and to maximize opportunities to engage and collaborate with highly loyal customers. One way to create linkages to external resources, as a proxy to market (e.g. customer referrals), is through personal electronic communication networks. In the present-day competitive and often hostile marketing environment, many researchers believe social multiplication of marketing efforts gained from social networks is both cost effective and powerful (Balasubramanian & Mahajan, 2001; Cole, 2007; Varian, 2002). Electronic networks create enormous potential for interaction that would be impossible or too costly through traditional media. In such interconnected times, individuals and consumers have an increasing power of information diffusion and influential behavior in social networks. Despite the attraction of online social networks for “viral marketing” however, such practices are more associated with spreading online content anonymously (Ho & Dempsey, 2010). What we do not know is which consumer is “influential” and plays an important role in engaging in collaborative recommendation diffusion through online network communication: customer participation, ties and intensity of use of online networks. Thus, identifying the best customers to give network referrals, not only involves customer potential diffusion and influence, but also dispositional behavior.

Whereas e-businesses look for appropriate responses for virtual collaborative recommendation diffusion and how to target those people with the highest network value, we should also ask what drives consumers to engage in collaborative action, sharing market information, offering personal opinions and giving free and voluntary market recommendation through their network of connections, and who is influential in engaging others in joint consumer action. Despite the power of online social networks for information diffusion and social contagion (Cole, 2007; Rezabakhsh, Bornemann, Hansen, & Schrader, 2006), however, less research has focused on the traits and abilities of consumers who are best-suited to the process: How and when do network patterns matter? How does network structure affect individuals’ behaviors? What kind of social networks are relevant for which types of marketing decisions?

Which consumer has the highest potential for recommendation diffusion and contagion? Which motivations drive consumer to share and spread market information?

This chapter discusses these questions by reviewing theoretical and empirical studies on social network analysis and e-consumer behavior. The primary purpose of this chapter is to bring together apparently disparate and yet interconnected strands of research and present an integrated theoretical framework of virtual recommendation diffusion and influence on co-consumption. Building on the central prediction of the “better match” theory in economics (Castilla, 2005; Simon & Warner, 1992); the question here is whether individual similarities and close ties in small networks, i.e. match dyads, help to determine better-matched recommendations and also to determine whether better recommendation diffusion helps to explain co-shopping influence. The secondary objective of this chapter is to stimulate more research in these under-explored areas.