Social Media Crisis Communication Model for Building Public Resilience: A Preliminary Study
Author(s) -
Umar Ali Bukar,
Marzanah A. Jabar,
Fatimah Sidi,
Rozi Nor Haizan Nor,
Salfarina Abdullah
Publication year - 2021
Publication title -
business information systems
Language(s) - English
Resource type - Journals
ISSN - 2747-9986
DOI - 10.52825/bis.v1i.55
Subject(s) - crisis communication , cronbach's alpha , social media , mediation , psychology , macro , psychological resilience , resilience (materials science) , regression analysis , reliability (semiconductor) , social psychology , variables , econometrics , computer science , statistics , political science , public relations , mathematics , sociology , psychometrics , clinical psychology , social science , physics , power (physics) , quantum mechanics , world wide web , thermodynamics , programming language
There is an ongoing discussion about the effectiveness of social media usage on the ability of people to recover from the crisis. However, the existing social media crisis communication models could not address the dynamic feature of social media users and the crisis, respectively. Therefore, the objective of this study is to conduct a preliminary investigation of the social media crisis communication model for building public resilience. Thus, 34 items were generated from the literature concerning the crisis, crisis response, social interaction, and resilience. The items were validated by three experts via content validity index and modified kappa statistics. After passing the validation test, the instruments were pre-tested by 32 participants. The reliability of the items was analyzed using Cronbach’s alpha. Also, the model fits and mediation were examined by the regression model, and the hypotheses were independently assessed in process macro models. Based on the result obtained, each of the constructs satisfied the internal consistency requirement; crisis (0.743), crisis response (0.724), social media interaction (0.716), and resilience (0.827). Furthermore, the result also indicates that the regression model is a good fit for the data. The independent variables statistically significantly predict the dependent variable, p < 0.05. Also, the result of the process macro models indicates that all the hypotheses are independently supported.
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