Explaining and Predicting Mobile Government Microblogging Services Participation Behaviors: A SEM-Neural Network Method

• Main Authors: Yizhi Ding, Shuiqing Yang, Yuangao Chen, Qingqi Long, June Wei
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Mobile government microblogging; perceived value; neural network approach; perceived integration; participation behaviors
• Online Access: https://ieeexplore.ieee.org/document/8663362/

Citizens' visits and contributions are critical to the success of mobile government microblogging services (GMSs). Drawing on stimulus-organism-response (SOR) framework and the uses and gratifications (U&G) theory, a research model was developed to investigate the impacts of perceived integration and atmosphere on citizens' gratifications and subsequent impacts on mobile GMS participation behaviors. A two-staged structural equation modeling (SEM)-neural network approach was employed to test the proposed model by using data collected from 702 mobile GMS citizens in China. The empirical results showed that atmosphere and perceived integration positively influence the citizens' perceptions of social value, information value, and hedonic value, which further positively influence the citizens' intention to acquire and share information. Moreover, the neural network analysis showed that the impact of the atmosphere on information value and hedonic value is stronger than that of perceived integration. The information value is found to be the strongest antecedents of the intention to acquire intention, while the social value is the most influential factor in predicting intention to share intention. The theoretical and managerial implications for mobile GMS research are also discussed.