Purpose The purpose of this paper is to investigate the continued use behavior (CU) of link sharing tools based on uses and gratifications theory, the theory of planned behavior and expectation confirmation theory. It then builds a conceptual model that is empirically tested. Design/methodology/approach Data were collected from 343 students (undergraduates, masters, PhD students, and MBAs) from three Chinese universities via a two-phrase survey. The tools SPSS 18.0 and AMOS 18.0 were used to analyse the reliability, validity, model fits and SEM, respectively. Findings The results indicate that an individual’s CU of link sharing tools was determined by his or her continued use intention directly and subjective norm indirectly. Users’ satisfaction on link sharing tools was the main factor affecting the continuance intention. Individuals’ motivation needs such as cognitive needs, personal integrative needs, and social integrative needs were found to be the significant predictors of his or her satisfaction. Besides, people with high privacy concern tended to have less satisfaction with link sharing tools. Originality/value This study explores users’ CU of link sharing tools in social media for the first time. The theoretical model developed shows the predictors behind people’s CU.
With the development of social networks, the complexity of the factors affecting the users’ information dissemination is increasing and the complexity of online social networks and influencing factors of individual behaviors and attitudes make the development of online public opinion present a dynamic, complex, and multifactor evolution. Analyzing the influencing factors of public opinion dissemination is conducive to optimize company management and information diffusion management. However, there has been no comprehensive analysis of the complex factors that influence the dissemination of information; this study focused on synthesizing 20 empirical studies on the influencing factors of China public opinion dissemination from the perspective of the user, and a meta-analysis was conducted. We establish the influencing factors of users’ information adoption model from three aspects of information source reliability, perceived information quality, and the heat of public opinion events based on elaboration likelihood model. The results indicated that the main influencing factors of public opinion communication are authority, reliability, quality of information form, quality of information editing, quality of information utility, and event attendance preference. Among the factors, authority and quality of information editing have more significant impacts on users’ information adoption behavior in the dissemination of public opinion. In addition, whether the type of event was a public emergency had a moderating effect. The results are helpful to explore the universality of the influencing factors so as to help related regulators better build a multiangle supervision mechanism and conduct early warning of information diffusion.
Following the outbreak of a disease, panic often spreads on online forums, which seriously affects normal economic operations as well as epidemic prevention procedures. Online panic is often manifested earlier than in the real world, leading to an aggravated social response from citizens. This paper conducts sentiment analysis on more than 80,000 comments about COVID-19 obtained from the Chinese Internet and identifies patterns within them. Based on this analysis, we propose an agent-based model consisting of two parts—a revised SEIR model to simulate an offline epidemic and a scale-free network to simulate the Internet community. This model is then used to analyze the effects of the social distancing policy. Assuming the existence of such a policy, online panic is simulated corresponding to different informatization levels. The results indicate that increased social informatization levels lead to substantial online panic during disease outbreaks. To reduce the economic impact of epidemics, we discuss different strategies for releasing information on the epidemic. Our conclusions indicate that announcing the number of daily new cases or the number of asymptomatic people following the peak of symptomatic infections could help to reduce the intensity of online panic and delay the peak of panic. In turn, this can be expected to keep social production more orderly and reduce the impact of social responses on the economy.
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