Explaining and Predicting Mobile Government Microblogging Services Participation Behaviors: A SEM-Neural Network Method
| Autor: | Yuangao Chen, Yizhi Ding, Shuiqing Yang, June Wei, Qingqi Long |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
neural network approach
General Computer Science Microblogging media_common.quotation_subject 02 engineering and technology Mobile government microblogging Structural equation modeling Perception 0502 economics and business perceived integration 0202 electrical engineering electronic engineering information engineering General Materials Science Social media perceived value media_common Government Artificial neural network Information value 05 social sciences General Engineering Advertising Test (assessment) Value (economics) participation behaviors 020201 artificial intelligence & image processing lcsh:Electrical engineering. Electronics. Nuclear engineering Psychology lcsh:TK1-9971 050203 business & management |
| Zdroj: | IEEE Access, Vol 7, Pp 39600-39611 (2019) |
| ISSN: | 2169-3536 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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