What drives the vaccination intention against COVID-19? Application of EPPM, TAM, and theories of risk assessment

Vaccines are widely used to fight against COVID-19. However, COVID-19 vaccine hesitancy appears as some individuals are concerned with COVID-19 vaccines. This study investigates the vaccination intention against COVID-19 in China with the Extended Parallel Process Model (EPPM), Technology Acceptance Model (TAM), and theories of risk information assessment. Results showed that the formation mechanism of vaccination intention could be considered a psychological process, as subjective knowledge was a primary influence on correspondents' weighting of both the potentially positive (usefulness) and negative effects (threat) of vaccination. This unequal consideration then resulted in different levels of fear arousal between subjects. Driven by usefulness/threat perception and fear, people conducted different decision strategies, so-called analytical assessment, and experiential assessment to make vaccination decision. In addition to the direct effects of experiential assessment on vaccination intention, two decision strategies and fear arousal also affected people's vaccination intention through the mediation role of vaccination attitude. For policymakers and stakeholders, this study provides a knowledge base for confidence-building, and emotional guidance concerning against COVID-19 vaccination.

Chatbots for future docs: exploring medical students' attitudes and knowledge towards artificial intelligence and medical chatbots

Artificial intelligence (AI) in medicine and digital assistance systems such as chatbots will play an increasingly important role in future doctor - patient communication. To benefit from the potential of this technical innovation and ensure optimal patient care, future physicians should be equipped with the appropriate skills. Accordingly, a suitable place for the management and adaptation of digital assistance systems must be found in the medical education curriculum. To determine the existing levels of knowledge of medical students about AI chatbots in particular in the healthcare setting, this study surveyed medical students of the University of Luebeck and the University Hospital of Tuebingen. Using standardized quantitative questionnaires and qualitative analysis of group discussions, the attitudes of medical students toward AI and chatbots in medicine were investigated. From this, relevant requirements for the future integration of AI into the medical curriculum could be identified. The aim was to establish a basic understanding of the opportunities, limitations, and risks, as well as potential areas of application of the technology. The participants (N = 12) were able to develop an understanding of how AI and chatbots will affect their future daily work. Although basic attitudes toward the use of AI were positive, the students also expressed concerns. There were high levels of agreement regarding the use of AI in administrative settings (83.3%) and research with health-related data (91.7%). However, participants expressed concerns that data protection may be insufficiently guaranteed (33.3%) and that they might be increasingly monitored at work in the future (58.3%). The evaluations indicated that future physicians want to engage more intensively with AI in medicine. In view of future developments, AI and data competencies should be taught in a structured way during the medical curriculum and integrated into curricular teaching.

A smartphone-based online platform for clinical skills training and assessment with standardized patients: platform development and pilot study outcomes

There are limitations and difficulties in the management of traditional in-person standardized patient (SP) practice. The latest developments in online communication tools and the COVID-19 pandemic have promoted the needs for online clinical skills training objectively. However, existing commercial online platforms may not meet the requests for SP-based medical simulation. This paper described the methodology applied to develop a smartphone-based online platform for the management of clinical skills training and assessment with remote SPs, and aimed to determine whether this new platform is acceptable or useful through a pilot run in September 2020. The post-run survey including questionnaire inspired by technological acceptance model and determinants of the perceived ease of use was used to assess the acceptability and usefulness of the platform. Twenty four-year students of clinical medicine participated in the pilot study with twenty SPs and ten faculties. Data from the post-run survey showed that there was a general recognition that the platform is easy to use among all the users. Two questions regarding the usefulness of the platform showed significant differences between the SPs/faculties and the students. More SPs found the platform useful as a training method than the students did. The faculties showed more attempts than the students to use this platform for clinical skills training in the future. This smartphone-based online platform was widely accepted among the tested students, SPs and faculties, which meets the requests and challenges of the new era. It provides an effective approach for clinical skills training and assessment with remote SPs.

The pricing of European airbnb listings during the pandemic: A difference-in-differences approach employing COVID-19 response strategies as a continuous treatment

The COVID-19 pandemic has been a major shock to the global tourism industry. Given its peculiarity, this paper analyzes one of the most intriguing questions in the Airbnb literature - the pricing of Airbnb listings - by taking advantage of a difference-in-differences methodology that largely draws on variations in country-level policy responses to the pandemic. Relying on a dataset containing weekly information from 130,999 continuously active listings across 27 European countries from 2019 to 2020, this study first investigates the exogenous impact of response policies (proxied by the COVID-19 Stringency Index) on demand. Secondly, accounting for the endogeneity of both demand and prices, this research analyzes pricing responses to demand variations. Results show that: i) increases in the COVID-19 Stringency Index cause significant declines in Airbnb demand; ii) increases in demand cause, on average, increases in Airbnb prices; and iii) pricing strategies between commercial and private hosts differ substantially.

Examining thematic and emotional differences across Twitter, Reddit, and YouTube: The case of COVID-19 vaccine side effects

Social media discourse has become a key data source for understanding the public's perception of, and sentiments during a public health crisis. However, given the different niches which platforms occupy in terms of information exchange, reliance on a single platform would provide an incomplete picture of public opinions. Based on the schema theory, this study suggests a 'social media platform schema' to indicate users' different expectations based on previous usages of platform and argues that a platform's distinct characteristics foster distinct platform schema and, in turn, distinct nature of information. We analyzed COVID-19 vaccine side effect-related discussions from Twitter, Reddit, and YouTube, each of which represents a different type of the platform, and found thematic and emotional differences across platforms. Thematic analysis using k-means clustering algorithm identified seven clusters in each platform. To computationally group and contrast thematic clusters across platforms, we employed modularity analysis using the Louvain algorithm to determine a semantic network structure based on themes. We also observed differences in emotional contexts across platforms. Theoretical and public health implications are then discussed.

Exploring the impact of sentiment on multi-dimensional information dissemination using COVID-19 data in China

The outbreak of information epidemic in crisis events, with the channel effect of social media, has brought severe challenges to global public health. Combining information, users and environment, understanding how emotional information spreads on social media plays a vital role in public opinion governance and affective comfort, preventing mass incidents and stabilizing the network order. Therefore, from the perspective of the information ecology and elaboration likelihood model (ELM), this study conducted a comparative analysis based on two large-scale datasets related to COVID-19 to explore the influence mechanism of sentiment on the forwarding volume, spreading depth and network influence of information dissemination. Based on machine learning and social network methods, topics, sentiments, and network variables are extracted from large-scale text data, and the dissemination characteristics and evolution rules of online public opinions in crisis events are further analyzed. The results show that negative sentiment positively affects the volume, depth, and influence compared with positive sentiment. In addition, information characteristics such as richness, authority, and topic influence moderate the relationship between sentiment and information dissemination. Therefore, the research can build a more comprehensive connection between the emotional reaction of network users and information dissemination and analyze the internal characteristics and evolution trend of online public opinion. Then it can help sentiment management and information release strategy when emergencies occur.

The Impact of Post-Use Consumer Satisfaction on Smart Wearables Repurchase Intention in the Context of AI-Based Healthcare Information

In this chapter, using a combination of the expectation-confirmation theory (ECT) and the latent variable model, the authors have analyzed what factors contribute to one's intention to continue usage with conformation, performance expectancy, and satisfaction. Based on a sample of 678 smart wearable users, collected from Northern India, the authors have used structural equation modelling (Smart PLS4) and identified the impact of satisfaction post-adoption technology, willingness to pay a premium price, and health information accuracy on the continuing intention of smart wearable users. According to the findings, consumer satisfaction, health information accuracy, and willingness to pay the premium price are key variables that have a substantial impact on the long-term purpose to repurchase and use smart wearables. This study has many implications for smartwatch manufacturers and designers looking to increase users' intent to continue using their products.