The impact of paid social Q&A on panic buying and digital hoarding at the stage of coexistence with COVID-19: The moderating role of sensitivity to pain of payment.

The worldwide disaster caused by COVID-19 and its variants has changed the behavior and psychology of consumers. Panic buying and hoarding of various commodities continue to emerge in our daily life. Meanwhile, many scholars have focused on the causes of panic buying and hoarding of physical products like daily necessities and food during the outbreak of COVID-19. In fact, the phenomenon of panic buying and digital hoarding of paid social Q&A and other digital content products is very prominent, both in the outbreak period of COVID-19 epidemic and the current coexistence stage. However, the existing literature lacks empirical research to explore this phenomenon, and the psychological mechanism behind it has not been clearly revealed. Therefore, at the current stage of coexistence with COVID-19, based on the SOBC framework, we developed a theoretical model and explored the causes of panic buying and digital hoarding in paid social Q&A. The data collected from 863 paid social Q&A users in China are empirically tested. The results show that the characteristics of paid social Q&A (usefulness, ease of use, professionalism and value) can cause emotional contagion among platform users, activate their willingness to pay, and finally lead to digital hoarding and panic buying behavior of COVID-19 co-existence stage. In addition, the sensitivity to pain of payment moderates the relationship between emotional contagion and willingness to pay. Compared with the spendthrifts, the tightwads are more willing to pay. The conclusions will have positive significance for improving the retail service of digital content platform and promoting the consumption of digital content.

The impact of paid social Q&A on panic buying and digital hoarding at the stage of coexistence with COVID-19: The moderating role of sensitivity to pain of payment

The worldwide disaster caused by COVID-19 and its variants has changed the behavior and psychology of consumers. Panic buying and hoarding of various commodities continue to emerge in our daily life. Meanwhile, many scholars have focused on the causes of panic buying and hoarding of physical products like daily necessities and food during the outbreak of COVID-19. In fact, the phenomenon of panic buying and digital hoarding of paid social Q&A and other digital content products is very prominent, both in the outbreak period of COVID-19 epidemic and the current coexistence stage. However, the existing literature lacks empirical research to explore this phenomenon, and the psychological mechanism behind it has not been clearly revealed. Therefore, at the current stage of coexistence with COVID-19, based on the SOBC framework, we developed a theoretical model and explored the causes of panic buying and digital hoarding in paid social Q&A. The data collected from 863 paid social Q&A users in China are empirically tested. The results show that the characteristics of paid social Q&A (usefulness, ease of use, professionalism and value) can cause emotional contagion among platform users, activate their willingness to pay, and finally lead to digital hoarding and panic buying behavior of COVID-19 co-existence stage. In addition, the sensitivity to pain of payment moderates the relationship between emotional contagion and willingness to pay. Compared with the spendthrifts, the tightwads are more willing to pay. The conclusions will have positive significance for improving the retail service of digital content platform and promoting the consumption of digital content.

Supply Chain Recovery Strategies for High Demand Products Based on the Impact of Capacity and Transportation Disruptions

The outbreak of the COVID-19 has had a huge impact on the manufacturing supply chain, especially the supply chain of high-demand products, and is mainly reflected in the double interruption of production capacity and transportation. The research aims to use system dynamics to explore how government subsidies can play a role in supply chain recovery when government subsidies are limited, which provides a new idea for improving supply chain management. In order to explore the impact of government subsidy strategies on supply chain recovery in the context of supply chain disruptions, this paper takes high-demand products during the epidemic as the research object, and takes the government's subsidy choices under the impact of production capacity and transportation disruptions as the entry point for recovery strategies. The cumulative total profit of chain members is used as a judgment indicator, and systems dynamics is used to conduct modeling and simulation to build a secondary supply chain for manufacturers and distribution centers and simulate eight scenarios of different levels of production capacity and transportation interruptions, clarifying the impact of government subsidies on supply the impact of chain recovery. The research results show that, for secondary supply chains, whether in the scenario of partial or complete transportation interruption, government subsidies to manufacturers make supply chain recovery more effective, government subsidies do not have an immediate recovery effect during production capacity and transportation interruptions, and that under the complete interruption of production capacity, the cumulative total value of the supply chain after increasing government subsidies has rebounded in a spiral.

Online Rumor Diffusion Model Based on Variation and Silence Phenomenon in the Context of COVID-19.

In the era of mobile internet, information dissemination has made a new leap in speed and in breadth. With the outbreak of the coronavirus disease 2019 (COVID-19), the COVID-19 rumor diffusion that is not limited by time and by space often becomes extremely complex and fickle. It is also normal that a piece of unsubstantiated news about COVID-19 could develop to many versions. We focus on the stagnant role and information variants in the process of rumor diffusion about COVID-19, and through the study of variability and silence in the dissemination, which combines the effects of stagnation phenomenon and information variation on the whole communication system in the circulation of rumors about COVID-19, based on the classic rumor SIR (Susceptible Infected Recovered) model, we introduce a new concept of "variation" and "oyster". The stability of the new model is analyzed by the mean field equation, and the threshold of COVID-19 rumor propagation is obtained later. According to the results of the simulation experiment, whether in the small world network or in the scale-free network, the increase of the immure and the silent probability of the variation can effectively reduce the speed of rumor diffusion about COVID-19 and is conducive to the dissemination of the truth in the whole population. Studies have also shown that increasing the silence rate of variation can reduce COVID-19 rumor transmission more quickly than the immunization rate. The interesting discovery is that at the same time, a higher rumor infection rate can bring more rumors about COVID-19 but does not always maintain a high number of the variation which could reduce variant tendency of rumors. The more information diffuses in the social group, the more consistent the version and content of the information will be, which proves that the more adequate each individual information is, the slower and less likely rumors about COVID-19 spread. This consequence tells us that the government needs to guide the public to the truth. Announcing the true information publicly could instantly contain the COVID-19 rumor diffusion well rather than making them hidden or voiceless.

Data-Driven Dynamic Adjustment and Optimization Model of Emergency Logistics Network in Public Health.

BACKGROUND AND AIM: In the long-term prevention of the COVID-19 pandemic, parameters may change frequently for various reasons, such as the emergence of mutant strains and changes in government policies. These changes will affect the efficiency of the current emergency logistics network. Public health emergencies have typical unstructured characteristics such as blurred transmission boundaries and dynamic time-varying scenarios, thus requiring continuous adjustment of emergency logistics network to adapt to the actual situation and make a better rescue. PRACTICAL SIGNIFICANCE: The infectivity of public health emergencies has shown a tendency that it first increased and then decreased in the initial decision-making cycle, and finally reached the lowest point in a certain decision-making cycle. This suggests that the number of patients will peak at some point in the cycle, after which the public health emergency will then be brought under control and be resolved. Therefore, in the design of emergency logistics network, the infectious ability of public health emergencies should be fully considered (ie, the prediction of the number of susceptible population should be based on the real-time change of the infectious ability of public health emergencies), so as to make the emergency logistics network more reasonable. METHODS: In this paper, we build a data-driven dynamic adjustment and optimization model for the decision-making framework with an innovative emergency logistics network in this paper. The proposed model divides the response time to emergency into several consecutive decision-making cycles, and each of them contains four repetitive steps: (1) analysis of public health emergency transmission; (2) design of emergency logistics network; (3) data collection and processing; (4) adjustment and update of parameters. RESULTS: The result of the experiment shows that dynamic adjustment and update of parameters help to improve the accuracy of describing the evolution of public health emergency transmission. The model successively transforms the public health emergency response into the co-evolution of data learning and optimal allocation of resources. CONCLUSION: Based on the above results, it is concluded that the model we designed in this paper can provide multiple real-time and effective suggestions for policy adjustment in public health emergency management. When responding to other emergencies, our model can offer helpful decision-making references.

The Implicit Aesthetic Preference for Mobile Marketing Interface Layout-An ERP Study.

Businesses and scholars have been trying to improve marketing effect by optimizing mobile marketing interfaces aesthetically as users browse freely and aimlessly through mobile marketing interfaces. Although the layout is an important design factor that affects interface aesthetics, whether it can trigger customer's aesthetic preferences in mobile marketing remains unexplored. To address this issue, we employ an empirical methodology of event-related potentials (EPR) in this study from the perspective of cognitive neuroscience and psychology. Subjects are presented with a series of mobile marketing interface images of different layouts with identical marketing content. Their EEG waves were recorded as they were required to distinguish a target stimulus from the others. After the experiment, each of the subjects chose five stimuli interfaces they like and five they dislike. By analyzing the ERP data derived from the EEG data and the behavioral data, we find significant differences between the disliked interfaces and the other interfaces in the ERP component of P2 from the frontal-central area in the 200-400 ms post-stimulus onset time window and LPP from both the frontal-central and parietal-occipital area in the 400-600 ms time window. The results support the hypothesis that humans do make rapid implicit aesthetic preferences for interface layouts and suggest that even under a free browsing context like the mobile marketing context, interface layouts that raise high emotional arousal can still attract more user attention and induce users' implicit aesthetic preference.

Exploring an Efficient Remote Biomedical Signal Monitoring Framework for Personal Health in the COVID-19 Pandemic.

Nowadays people are mostly focused on their work while ignoring their health which in turn is creating a drastic effect on their health in the long run. Remote health monitoring through telemedicine can help people discover potential health threats in time. In the COVID-19 pandemic, remote health monitoring can help obtain and analyze biomedical signals including human body temperature without direct body contact. This technique is of great significance to achieve safe and efficient health monitoring in the COVID-19 pandemic. Existing remote biomedical signal monitoring methods cannot effectively analyze the time series data. This paper designs a remote biomedical signal monitoring framework combining the Internet of Things (IoT), 5G communication and artificial intelligence techniques. In the constructed framework, IoT devices are used to collect biomedical signals at the perception layer. Subsequently, the biomedical signals are transmitted through the 5G network to the cloud server where the GRU-AE deep learning model is deployed. It is noteworthy that the proposed GRU-AE model can analyze multi-dimensional biomedical signals in time series. Finally, this paper conducts a 24-week monitoring experiment for 2000 subjects of different ages to obtain real data. Compared with the traditional biomedical signal monitoring method based on the AutoEncoder model, the GRU-AE model has better performance. The research has an important role in promoting the development of biomedical signal monitoring techniques, which can be effectively applied to some kinds of remote health monitoring scenario.

A Novel Emotion-Aware Hybrid Music Recommendation Method Using Deep Neural Network

Emotion-aware music recommendations has gained increasing attention in recent years, as music comes with the ability to regulate human emotions. Exploiting emotional information has the potential to improve recommendation performances. However, conventional studies identified emotion as discrete representations, and could not predict users’ emotional states at time points when no user activity data exists, let alone the awareness of the influences posed by social events. In this study, we proposed an emotion-aware music recommendation method using deep neural networks (emoMR). We modeled a representation of music emotion using low-level audio features and music metadata, model the users’ emotion states using an artificial emotion generation model with endogenous factors exogenous factors capable of expressing the influences posed by events on emotions. The two models were trained using a designed deep neural network architecture (emoDNN) to predict the music emotions for the music and the music emotion preferences for the users in a continuous form. Based on the models, we proposed a hybrid approach of combining content-based and collaborative filtering for generating emotion-aware music recommendations. Experiment results show that emoMR performs better in the metrics of Precision, Recall, F1, and HitRate than the other baseline algorithms. We also tested the performance of emoMR on two major events (the death of Yuan Longping and the Coronavirus Disease 2019 (COVID-19) cases in Zhejiang). Results show that emoMR takes advantage of event information and outperforms other baseline algorithms.

Propagation Model of Panic Buying Under the Sudden Epidemic.

The sudden outbreak of COVID-19 at the end of 2019 has had a huge impact on people's lives all over the world, and the overwhelmingly negative information about the epidemic has made people panic for the future. This kind of panic spreads and develops through online social networks, and further spreads to the offline environment, which triggers panic buying behavior and has a serious impact on social stability. In order to quantitatively study this behavior, a two-layer propagation model of panic buying behavior under the sudden epidemic is constructed. The model first analyzes the formation process of individual panic from a micro perspective, and then combines the Susceptible-Infected-Recovered (SIR) Model to simulate the spread of group behavior. Then, through simulation experiments, the main factors affecting the spread of panic buying behavior are discussed. The experimental results show that: (1) the dissipating speed of individual panics is related to the number of interactions and there is a threshold. When the number of individuals involved in interacting is equal to this threshold, the panic of the group dissipates the fastest, while the dissipation speed is slower when it is far from the threshold; (2) The reasonable external information release time will affect the occurrence of the second panic buying, meaning providing information about the availability of supplies when an escalation of epidemic is announced will help prevent a second panic buying. In addition, when the first panic buying is about to end, if the scale of the second panic buying is to be suppressed, it is better to release positive information after the end of the first panic buying, rather than ahead of the end; and (3) Higher conformity among people escalates panic, resulting in panic buying. Finally, two cases are used to verify the effectiveness and feasibility of the proposed model.