The role of uncertainty and affect in decision-making on the adoption of AI-based contact-tracing technology during the COVID-19 pandemic.

Objective: This study explores how negative affect, perceived net equity, and uncertainty influence the public's privacy decision-making regarding the adoption of contact-tracing technology based on artificial intelligence (AI) during the COVID-19 pandemic. Methods: Four hundred and eighteen adults in the US participated in the study via Amazon Mechanical Turk in August 2020. Statistical analyses were performed using the PROCESS macro. Indirect effects and their significance were estimated using bias-corrected bootstrap confidence intervals (CIs) with resampling set to n = 5000. Results: Perceived net equity was positively associated with low levels of perceived uncertainty regarding a COVID-19 contact-tracing application and intention to adopt it. Low levels of perceived uncertainty were positively associated with intentions to adopt such an application, thereby suggesting that a perceived level of uncertainty mediates the association between perceived net equity and adoption intentions. Anxieties regarding AI technology and COVID-19 risks both moderate the associations among perceived net equity, perceived level of uncertainty, and intentions to adopt the contact-tracing technology. Conclusions: Our findings highlight how the differing sources of emotion influence the associations among rational judgment, perceptions, and decision-making about new contact-tracing technology. Overall, the results suggest that both rational judgments and affective reactions to risks are important influencers of individuals' perceptions and privacy-related decision-making regarding a new health technology during the pandemic.

Increase the Number of Views and Shares of COVID-19 Videos: Content Relevance and Emotional Consistency with Virus Variant Topics.

(1) Background: The coronavirus variants have posed serious challenges for the prevention and control of the COVID-19 pandemic. Individuals selectively watch and forward videos that help them reduce the damage caused by the virus. Therefore, the factors influencing video viewing and sharing in the context of the COVID-19 pandemic caused by virus variation must be explored. (2) Method: Based on a combination of uncertainty reduction theory and functional emotion theory, this paper designed hypotheses regarding how content relevance and emotional consistency affect video views and shares. We used the support vector machine (SVM) classification algorithm to measure the content relevance between videos and virus variant topics. We performed sentiment analysis of video text to evaluate the emotional consistency between videos and virus variant topics. Then, we used empirical analysis to build the model. (3) Results: The trained SVM classifier was effective in judging whether the video text was related to virus variant topics (F = 88.95%). The content relevance between COVID-19 videos and virus variant topics was generally low. The results showed that the higher the content relevance, the more views (IRR = 1.005, p = 0.017) and shares (IRR = 1.008, p = 0.009) the video received. Individuals were more willing to view (IRR = 1.625, p < 0.001) and share (IRR = 1.761, p < 0.001) COVID-19 videos with high emotional consistency with virus variant topics. (4) Conclusions: The results of empirical analysis showed that content relevance and emotional consistency between videos and virus variant topics significantly positively impacted video views and shares. The trained SVM classifier can support public health departments in monitoring and assessing the COVID-19 pandemic. Our study provides management advice while helping individuals reduce harm and inform next-step decisions.

The Role of Transparency, Trust, and Social Influence on Uncertainty Reduction in Times of Pandemics: Empirical Study on the Adoption of COVID-19 Tracing Apps.

BACKGROUND: Contact tracing apps are an essential component of an effective COVID-19 testing strategy to counteract the spread of the pandemic and thereby avoid overburdening the health care system. As the adoption rates in several regions are undesirable, governments must increase the acceptance of COVID-19 tracing apps in these times of uncertainty. OBJECTIVE: Building on the Uncertainty Reduction Theory (URT), this study aims to investigate how uncertainty reduction measures foster the adoption of COVID-19 tracing apps and how their use affects the perception of different risks. METHODS: Representative survey data were gathered at two measurement points (before and after the app's release) and analyzed by performing covariance-based structural equation modeling (n=1003). RESULTS: We found that uncertainty reduction measures in the form of the transparency dimensions disclosure and accuracy, as well as social influence and trust in government, foster the adoption process. The use of the COVID-19 tracing app in turn reduced the perceived privacy and performance risks but did not reduce social risks and health-related COVID-19 concerns. CONCLUSIONS: This study contributes to the mass adoption of health care technology and URT research by integrating interactive communication measures and transparency as a multidimensional concept to reduce different types of uncertainty over time. Furthermore, our results help to derive communication strategies to promote the mass adoption of COVID-19 tracing apps, thus detecting infection chains and allowing intelligent COVID-19 testing.

Negotiating Desire and Uncertainty on Tinder During the COVID-19 Pandemic: Implications for the Transformation of Sexual Health Communication.

Research on the impact of the COVID-19 pandemic on communication on Tinder is presented in this article. This research examines reported changes in communication about sex and health during COVID-19 and the potential implications of those changes on hookup culture and sexual health communication beyond the pandemic. Data from 29 in-depth qualitative interviews with 20 female and 9 male college students are reported. Results include discussion of how college students are managing uncertainty, particularly uncertainty about health status, changes in communication and information-seeking strategies, different COVID-19 safety measures they have employed, and the future impact on communication about sexual health post-COVID-19. Participants in this study use uncertainty reduction communication strategies to gauge whether the risks of remaining on Tinder or meeting in person are worth the benefit. Results show that the quality and quantity of communication around first time in-person meetings have been modified, as the traditional public spaces for meeting have been reduced or eliminated and replaced by private spaces. Participants stated that they communicate more frequently and more directly about health, sexual health, and infection transmission. Finally, results indicate that participants have a strong desire to be more cautious and cognizant of health and safety in the future by using some of the communication strategies they developed during COVID-19.

Uncertainty risks and strategic reaction of restaurant firms amid COVID-19: Evidence from China.

To contain the pandemic of coronavirus (COVID-19), social distancing, self-isolation and travel restrictions have been imposed globally. To shed light on how COVID-19 is affecting restaurant industry, this study aims to explore the factors that affect restaurant firms' financial turnaround for their sustainability after the business shutdowns amid the COVID-19 pandemic. By utilizing a total of 86,507 small- and medium-sized restaurant firms' sales data collected from nine cities in Mainland China, the exploratory approach of this study successfully determines positive impacts of three aspects of operational characteristics (i.e., delivery, discounts, and service type) and brand effects as uncertainty minimizing factors amid distinctive business shutdowns and restrictions. This is the first empirical study in the management realm on the impacts of COVID-19 on restaurant industry. This paper strengthens the extant literature by highlighting the impact of COVID-19 on the restaurant industry after the business restrictions are lifted.