Customer Engagement in Educational S-commerce Communities: An Empirical Study

Abstract

Many previous scholars have investigated user behavior on social media from multiple perspectives over the last decade. Studies have shown that people today regularly join online communities to find necessary information. Notably, we found that there are a large number of education-related online communities and community members in Hong Kong. In these communities, the majority of posts pertain to interest class enrolment, extracurricular tutorial class enrolment, organizing outdoor activity and parents-child campaign. So, what makes some posts more popular and appealing in this kind of communities? What motivates the interactive behaviour between posters and users? Nevertheless, there are few studies focusing on user behaviors in education-related communities. Therefore, This research aims to examine the impact of post content features in educational online community on customer engagement in online community related to education. Considering the trend of digital transformation in education after Covid-19, we also consider variables such as online vs. offline teaching and the availability of trial classes as potential influencing factors. Consequently, we have incorporated these variables into our research model. The research model employed in this research adopted the S-O-R (stimuli-organism-response) model, utilizing user trust as a mediating variable. This study adopts a quantitative approach and collects data in the form of a questionnaire. The questionnaires will be filled by active participators in education-related online communities on Facebook platform and some parents offline. The questionnaires gathered will be analyzed through PLS-SEM (structural equation modelling) utilizing smartpls. This research can bring value to the evolution of social media platforms. Developers can refine their algorithms for pushing posts based on the findings. In addition, the questionnaire can assist online community managers and users fast-track identification of trustworthy posts. Finally, posters are able to adjust the content of their posts based on the outcome derived from the analysis to improve the quality and attractiveness of their posts.