A comparative study of user intention to recommend content on mobile social networks

Abstract

This study aims to explore user intention to recommend multimedia content on mobile social networks. To better understand user behavioral differences in content recommendations, this study utilizes user behavioral responses on social network services to determine heavy and light users. By analyzing data collected from 258 respondents, the findings reveal that the factors that influence intention to recommend vary among heavy and light users. First, trust, subjective norm, perceived ease of use, and perceived usefulness are considered as predictors for heavy users. Second, subjective norm, trust, perceived ease of use, and perceived usefulness are not influencing factors relative to recommendation intention for light users. Third, trust facilitates heavy users to share their content recommendations on mobile social networks. From theoretical perspectives, the results confirm that dynamic trust transfer could be integrated using the theory of planned behavior with a technology acceptance model. Considering practical implications, our findings regarding the prediction of heavy users provide business insights to content recommendation service. Our study highlights trust strategies related to migrating light users to heavy users. Overall, mobile social network providers must consider user technology perception enhancements and reduce trust concerns. Our findings contribute to theoretical applications and provide practical implications for social service providers in relation to social applications on mobile devices.