Background: It is crucial for companies to understanding users’ choices and learning behaviors, and the corresponding influencing factors and cognitive patterns regarding social network services to communicate with potential customers.
Methods: In this study, a casual structural model was constructed and developed to model and characterize the relationships between problems to be resolved as antecedent variables and success factors as consequent variables with the intermediary variables based on human learning behaviors, whereas the concept of social network service was introduced to summarize the current issues of social network services and empirically factors affecting effectiveness of social network services.
Discussion: This study highlighted the corporate need to examine the intelligent role and learning effectiveness of social network services when studying social creativity and intelligence in a social networking environment. Firstly, the framework and hypotheses of social network services were introduced to summarize the current issues of social network services and the main influencing factors affecting the working patterns of social network services. Subsequently, the empirically established model was further tested to explore the possible meaningful relationships among those variables used.
Results: The study revealed that the social network services provider and the customer should improve their social creativity and community collaboration; these could be expanded and enhanced by increasing the social intelligence to raise the social network services’ effect and the customer’s externalization. Furthermore, social intelligence, community collaboration, and customer externalization were factors significantly influencing customers’ social creativity, while the customer externalization and community collaboration were the two important factors affecting social intelligence.
Conclusion: The study implied that social network services providers should provide more and more intelligent and inspiring services for their customers.
Keywords: empirical analysis, computational modeling, intelligent influence factor, social network services, human learning behaviour