Investigating the online social network development through the Five Cs Model of Similarity
研究了相似性的五个维度（条件、背景、催化剂、结果和连接相似性）对Facebook社交网络发展的影响，基于245名罗马尼亚大学生的问卷调查，发现模型解释了超过52%的变异。
Purpose The purpose of this paper is to explore the influence of five dimensions of similarity (i.e. condition similarity, context similarity, catalyst similarity, consequence similarity and connection similarity) on Facebook social networks development. Design/methodology/approach A questionnaire-based survey was conducted with 245 Romanian college students. SmartPLS 3 statistical software for partial least squares structural equation modeling was chosen as the most adequate technique for the assessment of models with both composites and reflective constructs. Findings More than 52 percent of the variance in social network development was explained by the advanced similarity model. Each dimension had a positive effect on Facebook social networks development, the highest influences being exerted by condition similarity, context similarity and consequence similarity. Research limitations/implications The current approach is substantively based on the homophily paradigm in explaining social network development. Future research would benefit from comparing and contrasting complementary theories (e.g. the rational self-interest paradigm, the social exchange or dependency theories) with the current findings. Also, the research is tributary to a convenience-based sample of Romanian college students which limits the generalization of the results to other cultural contexts and, thus, invites further research initiatives to test the model in different settings. Social implications Similarity attributes and mechanisms consistently determine the dynamics of online social networks, a fact which should be investigated in depth in terms of the impact of new technologies among young people. Originality/value This study is among the first research initiatives to approach similarity structures and processes within an integrative framework and to conduct the empirical analysis beyond US-centric samples.