Information Value in Social Media: An Integrated Approach to Preventive Behaviors under COVID-19
Main Article Content
Abstract
Information content via social media has received attention from the community during the pandemic. This work aims to examine a preventive behavior model due to information value through social media under COVID-19 in Vietnam. This model is formulated based on the integration of Health Belief Theory (HBT) and the Elaboration Likelihood Model (ELM). A web-based survey is performed to collect data from 321 respondents who have utilized social media to seek and accumulate COVID-19-related information. The results indicate that perceived threat and self-efficacy are two vital predictors of readiness to uptake preventative behaviors. Additionally, information dimensions (i.e., information quality and information credibility) are crucial motivations underlying perceived threat and self-efficacy. Information quality well surmises information credibility. Finally, theoretical and practical implications are discussed.
Keywords:
Preventive behavior, social media, information credibility, perceived threat, Vietnam.*
References
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[7] Z.J. Yang, “Predicting Young Adults’ Intentions to Get the H1N1 Vaccine: An Integrated Model,” Journal of Health Communication, 20 (1) (2015) 69-79. https://doi.org/10.1080/10810730.2014.904023.
[8] R.E. Petty, J.T. Cacioppo, Communication & Persuasion: Central & Peripheral Routes to Attitude Change, Springer-Verlag, New York, 1986.
[9] M.J. Thomas, B.W. Wirtz, J.C. Weyerer, “Determinants of Online Review Credibility and Its Impact on Consumers’ Purchase Intention,” Journal of Electronic Commerce Research, 20 (1) (2019) 1-20.
[10] C.M.K. Cheung, D.R. Thadani, “The Impact of Electronic Word-of-Mouth Communication: A Literature Analysis and Integrative Model,” Decision Support Systems, 54 (1) (2012) 461-470. https://doi.org/10.1016/j.dss.2012.06.008.
[11] W. Yoo, D.H. Choi, K. Park, “The Effects of SNS Communication: How Expressing and Receiving Information Predict MERS-Preventive Behavioral Intentions in South Korea,” Computers in Human Behavior, 62 (2016) 34-43. https://doi.org/10.1016/j.chb.2016.03.058.
[12] Y.W. Fan, Y.F. Miao, “Establishing the Adoption of Electronic Word-of-Mouth Through Consumers’ Perceived Credibility,” International Business Research, 6 (3) (2013) 58-65. https://doi.org/10.5539/ibr.v6n3p58.
[13] R. Filieri, S. Alguezaui, F. McLeay, “Why Do Travelers Trust TripAdvisor? Antecedents of Trust Towards Consumer-Generated Media and Its Influence on Recommendation Adoption and Word of Mouth,” Tourism Management, 51 (2015) 174-185. https://doi.org/10.1016/j.tourman.2015.05.007.
[14] K. Witte, “Fear Control and Danger Control: A Test of the Extended Parallel Process Model (EPPM),” Communication Monographs, 61 (2) (1994) 113-134. https://doi.org/10.1080/03637759409376328.
[15] I. Ajzen, M. Fishbein, Understanding Attitudes and Predicting Social Behavior, Prentice Hall, Englewood-Cliffs, New Jersey, 1980.
[16] W.B. Dodds, K.B. Monroe, D. Grewal, “Effects of Price, Brand, and Store Information on Buyers’ Product Evaluations,” Journal of Marketing Research, 28 (3) (1991) 307-319. https://doi.org/10.2307/3172866.
[17] J.F. Hair, J.J. Risher, “When to Use and How to Report the Results of PLS-SEM,” European Business Review, 31 (1) (2019) 2-24. https://doi.org/10.1108/EBR-11-2018-0203.
[18] X.C. Le, “Charting Sustained Usage Toward Mobile Social Media Application: The Criticality of Expected Benefits and Emotional Motivations,” Asia Pacific Journal of Marketing and Logistics, Vol. ahead-of-print (No. ahead-of-print) (2021), https://doi.org/10.1108/APJML-11-2020-0779.