Developing User Loyalty for Social Networking Sites: A Relational Perspective

2016 
1. IntroductionWe have witnessed the mushrooming of hundreds of social networking sites (SNSs) worldwide in recent years. Billions of people participate in these relationship-development-oriented Internet platforms every day. The total number of SNS users globally was 1.79 billion in 2014, and the number will increase to 2.44 billion in 2018 [Statista 2015]. Lured by the increasing popularity and the considerable business potential, SNS operators are eager to attract and retain users in the hopes of maintaining their foothold in such an intensely competitive market.Users' continued use and loyalty is apparently critical to the long -term success of SNS operators. Many researchers (e.g., [Al-Debei et al. 2013; Kang et al. 2009; Maier et al. 2012]) have examined this critical issue using various theoretical lenses, such as theory of planned behavior (TPB), expectation-confirmation model of IS continuance (ECM), and flow theory. Multiple factors such as perceived usefulness [Kang et al. 2009], sense of belonging [Lin et al. 2014], SNS-induced stress [Maier et al. 2012], and arousal [Wang et al. 2015] have been found to significantly affect SNS users' continuance intention and loyalty.The burgeoning literature on SNSs has enriched our understanding of the antecedents driving SNS continuance and loyalty. However, our knowledge of this phenomenon remains incomplete, and research gaps remain to be addressed. Specifically, extant studies have mostly employed traditional information systems (IS) theories such as ECM as a frame of reference (e.g., [Cao et al. 2013; Kang et al. 2009; Maier et al. 2012; Yin et al. 2013]). However, IS scholars have criticized this research paradigm for constraining our viewpoints to an extremely narrow range and thus confining our knowledge of the antecedent factors of individual adoption and continued use of IS in general [Bagozzi 1992; Benbasat & Barki 2007] and SNSs in particular [Cao et al. 2015]. In addition, the unique social network nature of SNSs has rendered traditional models inadequate to fully explain the various phenomena occurring in the SNS context [Vannoy & Palvia 2010]. In these regards, calls have been made to go beyond the traditional models and draw theories from different disciplines [Kane et al. 2014; Vannoy & Palvia 2010]. Furthermore, our current understanding on SNS continuance and loyalty is largely focused on the user-side factors such as users' motivations and beliefs. By contrast, little is known about the operator-side factors that may be able to offer more actionable recommendations to SNS practitioners.This study aims to address the limitations and gaps present in extant literature by taking a user-centric relational perspective to develop a research model that integrates operator-side and user-side factors to explain SNS user loyalty. Our choice of adopting a relational perspective is inspired by the relationship development nature of SNSs. The core of SNS service is serving the relationship development of users; thus, the influence derived from user-to-user relationships inevitably affects users' decision to continue their relationship with a particular SNS. Therefore, userto-user social influence is an important factor that SNS operators should consider in developing user loyalty. A question of interest thus arises: aside from the relational influence from users per se, what can operators do to exert their relational influence on users' continuance decision?To answer this question, we build upon relationship marketing theory (RMT) to investigate the effects of a set of operator-side factors (i.e., relational bonds) on the cultivation of user loyalty. The study of relational bonds is important because they represent mechanisms by which SNS operators can build and maintain relationships with users. In accordance with the rationale of the stimulus-organism-response (S-O-R) model, this study proposes that relational bonds indirectly affect user loyalty through the influence on satisfaction. …
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    88
    References
    17
    Citations
    NaN
    KQI
    []