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. 2016 Sep:62:34-43.
doi: 10.1016/j.chb.2016.03.058. Epub 2016 Mar 31.

The effects of SNS communication: How expressing and receiving information predict MERS-preventive behavioral intentions in South Korea

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The effects of SNS communication: How expressing and receiving information predict MERS-preventive behavioral intentions in South Korea

Woohyun Yoo et al. Comput Human Behav. 2016 Sep.

Abstract

Individuals use social network sites (SNSs) as an effective tool for communicating relevant information with others during the outbreak of infectious diseases. However, little is known about the underlying mechanism through which communicative behaviors influence preventive behaviors. Thus, in the context of Middle East respiratory syndrome (MERS) in South Korea, this study investigated how two communicative behaviors (message expression and reception) in SNSs affected the communicators' intentions to engage in MERS-preventive behaviors. Using data collected from a nationally representative panel survey of 1000 Korean adults aged 19 or older, we examined a theoretical expression and reception effects model. Results support the presence of effects from expressing and receiving MERS-related information via SNSs and their underlying mechanism during South Korea's MERS outbreak. Public health officials and communication professionals should actively use SNS communication in coping with public health crisis caused by emerging infectious diseases.

Keywords: MERS; Perceived threat; Preventive behavioral intentions; SNS; SNS communication; Self-efficacy.

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Figures

Fig. 1
Fig. 1
Hypothesized model for examining the effects of expressing and receiving MERS- related information on handwashing intention and cough etiquette intention.
Fig. 2
Fig. 2
The effects of expressing and receiving MERS-related information on handwashing intention and cough etiquette intention. Estimates are standardized coefficients. Age, gender, education, income, and health status are included as exogenous variables, but not shown here. χ2 = 2.60, df = 1, p = 0.11, RMSEA = 0.04 (90% CI = 0.00 to 0.10), CFI = 0.99, TLI = 0.94, SRMR = 0.01. ∗∗p < 0.01, ∗∗ p < 0.001.

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