Combating COVID-19 Vaccine Hesitancy: A Synthetic Public Segmentation Approach for Predicting Vaccine Acceptance.

OBJECTIVE: Vaccine hesitancy impacts the ability to cope with coronavirus disease 2019 (COVID-19) effectively in the United States. It is important for health organizations to increase vaccine acceptance. Addressing this issue, this study aimed to predict citizens' acceptance of the COVID-19 vaccine through a synthetic approach of public segmentation including cross-situational and situational variables. Controlling for demographics, we examined institutional trust, negative attitudes toward, and low levels of knowledge about vaccines (ie, lacuna public characteristics), and fear of COVID-19 during the pandemic. Our study provides a useful framework for public segmentation and contributes to risk and health campaigns by identifying significant predictors of COVID-19 vaccine acceptance. METHOD: We conducted an online survey on October 10, 2020 (N = 499), and performed hierarchical regression analyses to predict citizens' COVID-19 vaccine acceptance. RESULTS: This study demonstrated that trust in the Centers for Disease Control and Prevention (CDC) and federal government, vaccine attitude, problem recognition, constraint recognition, involvement recognition, and fear positively predicted COVID-19 vaccine acceptance. CONCLUSIONS: This study outlines a useful synthetic public segmentation framework and extends the concept of lacuna public to the pandemic context, helping to predict vaccine acceptance. Importantly, the findings could be useful in designing health campaign messages.

Embodying Temporality in Response Efficacy: How Coping Appraisal Promotes Preventive Behavioral Engagement During the Ongoing Pandemic.

This study heightens the understanding of response efficacy as a determinant of engagement in preventive behavior during the ongoing COVID-19 pandemic. To achieve a more nuanced conceptualization of response efficacy, this study proposes and examines a time-based coping appraisal framework, drawing upon a part of the extended parallel process model (EPPM). The current study investigates how the temporality of coping response message features work in generating social distancing behavioral intentions and explicates the role of anticipated emotions in this motivational process. Results of the experimental study (N = 584) indicate that proximal future framed coping response message (vs. distant future message) led to greater response efficacy, which in turn led to greater anticipated guilt and pride, and by extension increased social distancing behavioral intentions. Mediation analyses also demonstrated the indirect effects of the temporality conditions (proximal future-oriented message vs. distant future-oriented message) on social distancing intentions through response efficacy, anticipated guilt, and anticipated pride. The theoretical and practical implications of this study are discussed.

Dealing with the COVID-19 crisis: Theoretical application of social media analytics in government crisis management.

Little theory-grounded research addresses how to use social media strategically in government public relations through machine learning. To fill this gap, we propose a way to optimize social media analytics to manage issues and crises by using the framework of attribution theory to analyze 360,861 tweets. In particular, we examined the attribution of crisis responsibility related to the spread of COVID-19 and its relations to the negative emotions of U.S. citizens on Twitter for six months (from January 20 to June 30, 2020). The results of this study showed that social media analytics is a valid tool to monitor how the spread of COVID-19 evolved from an issue to a crisis for the Trump administration. In addition, the federal government's lack of response and inability to handle the outbreak led to citizens' engagement and amplification of negative tweets that blamed the Trump White House. Theoretical and practical implications of the results are discussed.