Compliance with COVID-19 preventative health measures in the United Kingdom: a latent profile analysis.

Governments have adopted unprecedented measures to assist in slowing the spread of the COVID-19 pandemic, but compliance varies among individuals. This UK study uses latent profile analysis to identify four classes of individuals on factors believed to influence compliance. Those who sought health information from authoritative sources and actively sought information from multiple sources were most compliant. Profile differences in compliance and vaccination status were also primarily driven by trust in healthcare institutions over trust in government. These findings contribute to understanding compliance profiles and emphasise the importance of authoritative information and trust in healthcare systems.

AI-deploying organizations are key to addressing 'perfect storm' of AI risks.

We argue that a perfect storm of five conditions heightens the risk of harm to society from artificial intelligence: (1) the powerful, invisible nature of AI, (2) low public awareness and AI literacy, (3) rapid scaled deployment of AI, (4) insufficient regulation, and (5) the gap between trustworthy AI principles and practices. To prevent harm, fit-for-purpose regulation and public AI literacy programs have been recommended, but education and government regulation will not be sufficient: AI-deploying organizations need to play a central role in creating and deploying trustworthy AI in line with the principles of trustworthy AI, and taking accountability to mitigate the risks.

The Impact of COVID-19 Pandemic on Corporate Social Responsibility and Job Embeddedness in China.

This article aims to investigate the impact of employee perceptions of corporate social responsibility (CSR) on job embeddedness under the drastic circumstances of coronavirus disease 2019 (COVID-19). This study also investigated the role of organizational identification as a psychological mechanism linking employee perceptions of corporate social responsibility (CSR) to job embeddedness. Survey data were collected from 325 employees in banking industry of China and analyzed using partial least squares structural equation modeling (PLS-SEM). Results revealed that CSR to employees and organizational identification were positively and significantly related to job embeddedness, while CSR to customers, CSR to government, and CSR to society did not significantly predict job embeddedness. Organizational identification fully mediated the relationship between CSR to customers, CSR to government, CSR to society and job embeddedness, and partially mediated the relationship between CSR to employees and job embeddedness. The results suggest engaging in CSR activities can lead employees to identify themselves with the organization and enhance their embeddedness. The article concludes with several implications for practice and recommendations for future research.

Profiling adopters (and non-adopters) of a contact tracing mobile application: Insights from Australia.

OBJECTIVE: Many governments are using contact tracing mobile applications (CTMAs) yet public adoption of such systems has been relatively low. The main objective of this paper is to profile adopters (and non-adopters) of Australia's COVIDSafe CTMA. MATERIALS AND METHODS: We use latent profile analysis to examine predictors of CTMA download behaviour. Specifically, we draw on a representative Australian sample (N = 2575) to examine the interplay between age, education, income, dispositional desire for privacy and political ideology on download behaviour. We examine trust in government as a mediating mechanism between profiles and download behaviour. RESULTS: Our analysis produces seven profiles. Trust in government mediates the relationship between most profiles and download behaviour. A combination of wealth and education appear to be key explanatory factors of CTMA download behaviour. Two profiles -- comprising individuals with high income and education -- had the highest rates of download behaviour. Profiles with low download percentages comprised politically left-leaning participants with average to low income and education. CONCLUSION: Our findings clearly indicate the profiles of people who are (not) likely to download a CTMA. Practical ways to improve widespread adoption include providing structural support to the more vulnerable members of society, making clear the societal benefits of downloading CTMAs, and engaging in bipartisan promotion of such apps.