Modelling the significance of website quality and online reviews to predict the intention and usage of online hotel booking platforms.

Innovative technologies are paving the way for the online purchase of products and services. The COVID-19 also harness online shopping and hotel booking in the post-COVID-19 era. Online hotel booking is becoming the norm among young consumers. The six determinants of the intention to use online hotel booking platforms (OHBPs) are information quality, integrity, perceived risks, perceived benefits, system quality, and service quality was utilized in the study. Cross-sectional data were collected through the online survey, and 884 valid responses were utilised for the data analysis. The analysis results showed that the perceived benefits, system quality, and service quality significantly predicted the intention to use the OHBPs. Meanwhile, the usefulness and quantity of online reviews and the intention to use OHBPs have a positive and significant effect on the usage of the OHBPs. The study results confirmed the insignificant moderating role of the usefulness and quantity of online hotel booking reviews in the relationship between the intention to use and the usage of OHBPs. The intention to use OHBPs had low predictive power, while the usage of OHBPs had high predictive power. This study's findings have offered valuable theoretical and managerial contributions. The management of OHBPs needs to concentrate on information quality and integrity to help manage the associated risks of online hotel booking. Previous customers' reviews are vital to encourage the usage of OHBPs. Finally, social media usage and promoting the OHBPs by sharing satisfied users' experiences improve the intention and usage of the OHBPs.

COVID-19 preventive behavior among university students in Southeast Asia: Effects of knowledge, concern, awareness, and perceived risk.

The persistent rise of pandemics across the globe in recent times has led to the prescription of several collaborative preventive strategies to reduce the effect that the pandemic has on public health. Consistent monitoring and surveillance appear to be the only available approach to detecting and classifying the issues of public health threats. Global pandemic threats demand public co-operation to take preventive actions at a personal level so that the risk of infectious diseases can be contained. Said that, this study explored the influence of awareness of precaution measures (APM), concerns about coronavirus disease 2019 (COVID-19) (CAC), knowledge of COVID-19 (KOC), and perceived risk (PRK) on preventive behavior (PRB), as well as the effect of age and gender on the relationships among the studied variables. Quantitative data were collected from 551 university students across Malaysia and Vietnam through field survey and online survey, respectively. The data collection was performed from 13 March to 23 March 2020. Partial least square structural equation modeling (PLS-SEM) was employed for data analysis. The multiple group analysis (MGA) technique was applied to compare the data retrieved from the respondents based on age and gender. The results revealed that APM, CAC, KOC, and PRK on PRB significantly influenced PRB toward COVID-19. In light of the two personal factors, age and gender, significant variances were noted for age and KOC, while PRK on PRB on the PRB toward COVID-19. Based on the study outcomes, APM emerged as the most significant predictor of PRB, followed by PRK on PRB, and CAC. Since a large fraction of the world reside in rural areas and have high-level interaction with animals, the provision of education at all level can harness the attitude to adopt PRB toward COVID-19. As such, policymakers need to work with the young generation so that the latter may serve as change agents to spread the message of taking precautions and adopting effective PRB toward COVID-19.

Modelling the mass adoption potential of wearable medical devices.

Digital technologies empower users to manage their health and reduce the burden on the public health system. The mass adoption of wearable medical devices (WMDs) promotes the ageing population's confidence besides facilitating users. Thus, the current study aims to empirically evaluate the formation of perceived product value (PPV) with the WMDs' computability, usefulness, cost, and accuracy, the intention to use WMDs influenced by health consciousness (HCS), health anxiety (HAY), product value, and perceived critical mass (PCM), and later the adoption of WMDs among Chinese adults. The study examined the mediating effect of PPV on the relationship between the intention to use WMDs and perceived compatibility (PCT), perceived cost (PCO), perceived usefulness (PUS), and perceived technology accuracy (PTA). This study adopted a cross-sectional approach and used an online survey to collect quantitative data from 1,160 Chinese adults. Data analysis was performed using the partial least squares structural equation modelling (PLS-SEM). Results showed that PCT, PUS, and PTA significant positive effect on PPV. Meanwhile, HCS, PCM, and PPV has a significant positive effect on intention to use WMDs, and the intention to use WMDs and PCM influenced the adoption of WMDs. Consequently, the analysis confirmed that PPV mediated the relationships between the intention to use WMD and PCT, PUS, and PTA. The WMD cost must be reduced to enhance the value of WMDs. Finally, the study's implications, limitations, and suggestions for future studies are discussed.

Predicting the Mass Adoption of eDoctor Apps During COVID-19 in China Using Hybrid SEM-Neural Network Analysis.

Technology plays an increasingly important role in our daily lives. The use of technology-based healthcare apps facilitates and empowers users to use such apps and saves the burden on the public healthcare system during COVID-19. Through technology-based healthcare apps, patients can be virtually connected to doctors for medical services. This study explored users' intention and adoption of eDoctor apps in relation to their health behaviors and healthcare technology attributes among Chinese adults. Cross-sectional data were collected through social media, resulting in a total of 961 valid responses for analysis. The hybrid analysis technique of partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) analysis was applied. The obtained results revealed the significant influence of eDoctor apps in terms of usefulness, compatibility, accuracy, and privacy on users' intention to use eDoctor apps. Intention and product value were also found to suggestively promote the adoption of eDoctor apps. This study offered practical recommendations for the suppliers and developers of eHealth apps to make every attempt of informing and building awareness to nurture users' intention and usage of healthcare technology. Users' weak health consciousness and motivation are notable barriers that restrict their intention and adoption of the apps. Mass adoption of eDoctor apps can also be achieved through the integration of the right technology features that build the product value and adoption of eDoctor apps. The limitations of the current study and recommendations for future research are presented at the end of this paper.