A knowledge structures exploration on social network sites

Purpose This paper aims to describe a method for combining perceived community support, relationship quality and the extended technology acceptance model in the same empirically derived associative network. The research also examines the moderating role of accumulation of knowledge (based on beliefs and opinions) derived from social interactions. Design/methodology/approach The Pathfinder algorithm is a valid approach for determining network structures from relatedness data. Such a graphical representation provides managers with a comprehensible picture of how social behaviours relate to loyalty-based dimensions. Findings As the benefits of community participation and integration might be differently evaluated by new and long-term users, the research examines the associative network by levels of user familiarity. This study indeed contributes to the analysis of enduring social bonds with respect to individuals’ decision-making processes, as it provides details representing specific relationships between diverse concepts based on true-loyalty. Practical implications The application of Pathfinder to the study of online social services and user behaviour appears to have potential for unveiling the structures of social network sites members and designing successful strategies for prospective community managers. Originality/value This is the first study to the author’s knowledge that empirically tests a theory-grounded framework for integrating individual characteristics and relational driver and focuses on associative structures evidenced as a representation of the most salient loyalty-based concepts by also studying the moderating effects of familiarity.