An extended health belief model for COVID-19: understanding the media-based processes leading to social distancing and panic buying.
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ABSTRACT: Supplementary information
The online version contains supplementary material available at 10.1007/s11747-022-00865-8.
SUBMITTER: Hita MLR
PROVIDER: S-EPMC9109429 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:There is inadequate understanding of how social media can shape fear and consumer responses in the manner of consumer panic buying while Covid-19 is spreading across the world. Through taking a social constructionism position this research focused on what people think and feel at an individual and collective level on the role of social media to create panic behaviour. Data have been collected from thirty-four consumers who have minimum one active social media account. These participants are contacted through telephonic interview due to maintaining the social distancing. Findings reveal that uncertainties and insecurities proof, buying as persuasion, product unavailability proof, authorities' communication, global logic, and expert opinion are some of the causes on social media platforms that developed the situation of consumer panic buying during Covid-19 crises. Furthermore, the unprecedented level of real-time information on Covid-19 at users' fingertips can give them the tools they need to make smart decisions, but also make them more anxious about what is to come; experts say that may lead to panic buying or stockpiling of products. The study has tried to develop understanding about how social media generates social proof and offers a window into people's collective response to the coronavirus outbreak and shapes panic buying reaction. The study has provided a consumer panic buying theory based on the results of this study and on existing theories, such as global capitalism/information society risk society, social influence, and social proof. These theories help to understand how global logic is built due to the use of social media and how different social proofs are generated which developed the behaviour of consumer panic buying due to Covid-19 pandemic.
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Project description:BackgroundPanic buying is an emerging phenomenon observed during, but not restricted to, pandemic.AimWe aimed to evaluate the nature, extent, and impact of panic buying as reported in the media.MethodsThis study was conducted by collecting the information from the English media reports published till 22nd May 2020. A structured format was developed to collect data. Searching was done by using the keyword "panic buying". We have excluded the social media posts discussing the panic buying.ResultsThe majority of media reporting was from the USA (40.7 %), and about 46 % of reports highlighted the scarce item. Approximately 82 % of the reports presented the causes of panic buying whereas almost 80 % report covered the impact of it. About 25.7 % of reports highlighted the rumor about panic buying and only 9.3 % of reports blamed the government. Only 27.1 % reports described the remedial measures, 30.8 % reports conferred the news on the psychology behind panic buying and 67.3 % news displayed the images of empty shelves.ConclusionA high proportion of reports on panic buying have been found from the developed countries discussing the causes & impact of panic buying on the basis of expert opinion.
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Project description:In view of 2020 outbreak of the pandemic COVID-19, the paper examines the relationship between government measures for combating the pandemic and their side effects. Panic buying is identified as one such side effect. Among various models and measures undertaken by government to manage the pandemic, timed-intervention policy is commonly practiced by most countries. This paper examines the timing effect between government measures and panic buying. Three studies were undertaken to understand the timing effect and identify a connection between timed measures and consumer behaviours. Semantic analysis, secondary data search, and big data analytics were deployed to address the research aim. Although claiming a causal relationship is cautioned, the findings reveal a connection between timing of government measures and panic buying. These findings are discussed with the support of real-life evidence. Implications for researchers and practitioners conclude this paper.
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Project description:Higher education has been shifted toward blended learning during the COVID-19 pandemic. An increase in social media usage intensity and reduced face-to-face interaction due to the COVID-19 pandemic urged instructional communication researchers to revisit the dynamics of learners' group development in terms of their socialization and academic performance during the COVID-19 crisis. This research aimed to determine the mediating role of social media sociability between face-to-face socialization and academic performance of higher education students in blended learning environments during the COVID-19 pandemic. It was also the aim of the study to determine the moderating effect of social media usage intensity on social media sociability. A cross-sectional survey was conducted with the students (n = 340) enrolled in science teacher education departments of universities in Pakistan. Partial least squares structural equation modeling (PLS-SEM) was used for multivariate analysis. Results revealed that face-to-face socialization gave an essential start to develop a learning group. However, when face-to-face socialization was reduced due to the COVID-19 pandemic, it was mediated by social media usage in blended learning environments to increase their socialization and academic performance during the crisis. The findings of the study are useful for higher education institutions to adopt social media strategies for students' socialization during the crisis.
| S-EPMC8583489 | biostudies-literature
Project description:The present research investigated a backfiring effect of social interaction on well-being and general confidence in Western populations during the COVID-19 pandemic. Across two studies, we observed that stronger self-other connectedness and frequent social communication with others during the first few weeks into the quarantine period were associated with worsened well-being and decreased general confidence. In Study 1 (n = 331), we showed that people who reported higher social connectedness and more frequent social interaction experienced declined well-being. In Study 2 (n = 327), we replicated the backfiring effect and showed that those who engaged in frequent social interaction, especially in COVID-19 related conversations, reported decreased general confidence, which mediated the accelerating effect of social interaction on panic buying. Overall, our findings indicated that frequent social interaction under a highly novel and uncertain crisis can relate to negative consequences on mental health and behavior.
| S-EPMC8357996 | biostudies-literature
Project description:This study promotes the news repertoire framework as an analytical approach best suited for studying news engagement on social media (SM), considering its multifaceted nature. To demonstrate the theoretical benefits of this proposal, the study seeks to (1) identify user profiles based on SM news viewing and sharing, and news consumption on other platforms; (2) determine profile predictors; and (3) evaluate their possible outcomes. A panel study (N = 1786) demonstrated the emergence of identifiable profiles, attributed to differences in SM use and political interest. In addition, profiles embodied different effects on political participation over time. A second study (N = 86) was conducted thereafter, in which users' Facebook news feed use was analyzed to determine differences in news supply according to profiles. Findings that could not have been achieved using the more common unidimensional news consumption methods are discussed in light of new theoretical gains provided by the repertoire approach.
| S-EPMC9315192 | biostudies-literature
Project description:BACKGROUND:Public health authorities have been recommending interventions such as physical distancing and face masks, to curtail the transmission of coronavirus disease (COVID-19) within the community. Public perceptions toward such interventions should be identified to enable public health authorities to effectively address valid concerns. The Health Belief Model (HBM) has been used to characterize user-generated content from social media during previous outbreaks, with the aim of understanding the health behaviors of the public. OBJECTIVE:This study is aimed at developing and evaluating deep learning-based text classification models for classifying social media content posted during the COVID-19 outbreak, using the four key constructs of the HBM. We will specifically focus on content related to the physical distancing interventions put forth by public health authorities. We intend to test the model with a real-world case study. METHODS:The data set for this study was prepared by analyzing Facebook comments that were posted by the public in response to the COVID-19-related posts of three public health authorities: the Ministry of Health of Singapore (MOH), the Centers for Disease Control and Prevention, and Public Health England. The comments made in the context of physical distancing were manually classified with a Yes/No flag for each of the four HBM constructs: perceived severity, perceived susceptibility, perceived barriers, and perceived benefits. Using a curated data set of 16,752 comments, gated recurrent unit-based recurrent neural network models were trained and validated for text classification. Accuracy and binary cross-entropy loss were used to evaluate the model. Specificity, sensitivity, and balanced accuracy were used to evaluate the classification results in the MOH case study. RESULTS:The HBM text classification models achieved mean accuracy rates of 0.92, 0.95, 0.91, and 0.94 for the constructs of perceived susceptibility, perceived severity, perceived benefits, and perceived barriers, respectively. In the case study with MOH Facebook comments, specificity was above 96% for all HBM constructs. Sensitivity was 94.3% and 90.9% for perceived severity and perceived benefits, respectively. In addition, sensitivity was 79.6% and 81.5% for perceived susceptibility and perceived barriers, respectively. The classification models were able to accurately predict trends in the prevalence of the constructs for the time period examined in the case study. CONCLUSIONS:The deep learning-based text classifiers developed in this study help to determine public perceptions toward physical distancing, using the four key constructs of HBM. Health officials can make use of the classification model to characterize the health behaviors of the public through the lens of social media. In future studies, we intend to extend the model to study public perceptions of other important interventions by public health authorities.
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Project description:BackgroundThe adoption of nonpharmaceutical interventions and their surveillance are critical for detecting and stopping possible transmission routes of COVID-19. A study of the effects of these interventions can help shape public health decisions. The efficacy of nonpharmaceutical interventions can be affected by public behaviors in events, such as protests. We examined mask use and mask fit in the United States, from social media images, especially during the Black Lives Matter (BLM) protests, representing the first large-scale public gatherings in the pandemic.ObjectiveThis study assessed the use and fit of face masks and social distancing in the United States and events of large physical gatherings through public social media images from 6 cities and BLM protests.MethodsWe collected and analyzed 2.04 million public social media images from New York City, Dallas, Seattle, New Orleans, Boston, and Minneapolis between February 1, 2020, and May 31, 2020. We evaluated correlations between online mask usage trends and COVID-19 cases. We looked for significant changes in mask use patterns and group posting around important policy decisions. For BLM protests, we analyzed 195,452 posts from New York and Minneapolis from May 25, 2020, to July 15, 2020. We looked at differences in adopting the preventive measures in the BLM protests through the mask fit score.ResultsThe average percentage of group pictures dropped from 8.05% to 4.65% after the lockdown week. New York City, Dallas, Seattle, New Orleans, Boston, and Minneapolis observed increases of 5.0%, 7.4%, 7.4%, 6.5%, 5.6%, and 7.1%, respectively, in mask use between February 2020 and May 2020. Boston and Minneapolis observed significant increases of 3.0% and 7.4%, respectively, in mask use after the mask mandates. Differences of 6.2% and 8.3% were found in group pictures between BLM posts and non-BLM posts for New York City and Minneapolis, respectively. In contrast, the differences in the percentage of masked faces in group pictures between BLM and non-BLM posts were 29.0% and 20.1% for New York City and Minneapolis, respectively. Across protests, 35% of individuals wore a mask with a fit score greater than 80%.ConclusionsThe study found a significant drop in group posting when the stay-at-home laws were applied and a significant increase in mask use for 2 of 3 cities where masks were mandated. Although a positive trend toward mask use and social distancing was observed, a high percentage of posts showed disregard for the guidelines. BLM-related posts captured the lack of seriousness to safety measures, with a high percentage of group pictures and low mask fit scores. Thus, the methodology provides a directional indication of how government policies can be indirectly monitored through social media.
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Project description:The spread of false and misleading information on social media is largely dependent on human action. Understanding the factors that lead social media users to amplify (or indeed intervene in) the spread of this content is an ongoing challenge. Prior research suggests that users are not only more likely to interact with misinformation that supports their ideology or their political beliefs, they may also feel it is more acceptable to spread. However, less is known about the influence of newer, issue-specific beliefs. Two online studies explored the relationship between the degree of belief-consistency of disinformation on users' moral judgements and intentions to spread disinformation further. Four disinformation narratives were presented: disinformation that supported or undermined the UK Government's handling of COVID-19, and disinformation that minimised or maximised the perceived risk of COVID-19. A novel scale for measuring intentions to contribute to the spread of social media content was also used in study 2. Participants reported greater likelihood of spreading false material that was consistent with their beliefs. More lenient moral judgements related to the degree of belief-consistency with disinformation, even when participants were aware the material was false or misleading. These moral judgements partially mediated the relationship between belief-consistency of content and intentions to spread it further on social media. While people are concerned about the spread of disinformation generally, they may evaluate belief-consistent disinformation differently from others in a way that permits them to spread it further. As social media platforms prioritise the ordering of feeds based on personal relevance, there is a risk that users could be being presented with disinformation that they are more tolerant of.
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