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How to overcome informational conformity consumer behavior when faced with threats of death is a social problem in response to COVID-19. This research is based on the terror management theory, the need to belong theory and the materialism theory. It uses a theoretical model to determine the relationships between threats of death and informational conformity consumer behavior. From 1453 samples collected during outbreak of COVID-19 in China, we used a structural equation model to test multiple research hypotheses. The result shows that threats of death are positively associated with a need to belong, materialism and informational conformity consumer behavior. The need to belong and materialism can play a mediating role between threats of death and information conformity consumption behavior, and perceived social support can play a moderating role between threats of death and information conformity consumption behavior.

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In public health emergencies, people are more willing to save money rather than spending it, which is not conductive to economic development and recovery. Due to the absence of relevant research, the internal logic of this phenomenon is not clear. In the context of the COVID-19 pandemic, this study systematically explored whether and why public health emergencies stimulate consumers' preference for saving (vs. spending). We conducted two online surveys and used methods including stepwise regression analysis and bootstrapping to test the hypotheses. The first survey, with 1,511 participants from China in February 2020, indicates that the severity of emergencies has a significant positive impact on the populations' willingness to save (vs. spend). Risk perception plays a mediating role between the severity of emergencies and consumers' saving (vs. spending) willingness. Materialism plays a moderating role between risk perception and an individual's saving (vs. spending) willingness, individuals who are more materialistic have a lower saving (vs. spending) willingness when they perceive the risks of the pandemic. To verify the duration of the above effects, we conducted a follow-up survey consisted of 466 instances in August 2020. It is noteworthy that the above effects are not significant during the post-pandemic period. Thus, spending behavior in public health emergencies can be motived by reducing risk perception and increasing materialism. These findings can provide a valuable inspiration for public health, crisis management, and economic recovery during public health emergencies.

Objectives: During public health emergencies, people often scramble to buy scarce goods, which may lead to panic behavior and cause serious negative impacts on public health management. Due to the absence of relevant research, the internal logic of this phenomenon is not clear. This study explored whether and why public health emergencies such as the COVID-19 pandemic stimulate consumers' preference for scarce products.Methods: Applying the questionnaire survey method, two online surveys were conducted on the Credamo data platform in China. The first survey was launched in February and collected psychological and behavioral data from 1,548 participants. Considering the likelihood of population relocation due to the pandemic, a follow-up survey was conducted in August with 463 participants who had participated in the first survey and had not relocated to other cities between February and August. The hypotheses were tested with these data through stepwise regression analysis, bootstrapping, and robustness testing.Results: Pandemic severity was found to positively affect scarce consumption behavior and the effect was found to be situational; this indicates that the impact of the pandemic on scarce consumption was only significant during the pandemic. Further, it was found that materialism plays a mediating role in the relationship between pandemic severity and scarce consumption. Finally, the need to belong was found to play a moderating role between pandemic severity and materialism.Conclusion: This study findings imply that the scarce consumption behavior during public health emergencies can be reduced by decreasing materialism and increasing the need to belong. These findings may aid government leaders in managing public health emergencies.

Many countries have enacted a quick response to the unexpected COVID-19 pandemic by utilizing existing technologies. For example, robotics, artificial intelligence, and digital technology have been deployed in hospitals and public areas for maintaining social distancing, reducing person-to-person contact, enabling rapid diagnosis, tracking virus spread, and providing sanitation. In this paper, 163 news articles and scientific reports on COVID-19-related technology adoption were screened, shortlisted, categorized by application scenario, and reviewed for functionality. Technologies related to robots, artificial intelligence, and digital technology were selected from the pool of candidates, yielding a total of 50 applications for review. Each case was analyzed for its engineering characteristics and potential impact on the COVID-19 pandemic. Finally, challenges and future directions regarding the response to this pandemic and future pandemics were summarized and discussed.

With the popularity of deep learning (DL), artificial intelligence (AI) has been applied in many areas of human life. Artificial neural network or neural network (NN), the main technique behind DL, has been extensively studied to facilitate computer vision and natural language processing. However, malicious NNs could bring huge threats in the so-called coming AI era. In this paper, for the first time in the literature, we propose a novel approach to design and insert powerful neuron-level trojans or PoTrojan in pre-trained NN models. Most of the time, PoTrojans remain inactive, not affecting the normal functions of their host NN models. PoTrojans could only be triggered in very rare conditions. Once triggered, however, the PoTrojans could cause the host NN models to malfunction, either falsely predicting or falsely classifying, which is a significant threat to human society of the AI era. We would explain the principles of PoTrojans and the easiness of designing and inserting them in pre-trained deep learning models. PoTrojans doesn't modify the existing architecture or parameters of the pre-trained models, without re-training. Hence, the proposed method is very efficient. We verify the tacitness and harmfulness of the PoTrojans on two real-life deep learning models: AlexNet and VGG16.