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This study aims to uncover the ongoing discourse on generative artificial intelligence (AI), literacy and governance while providing nuanced perspectives on stakeholder involvement and recommendations for the effective regulation and utilization of generative AI technologies.

This study chooses generative AI-related online news coverage on BBC News as the case study. Oriented by a case study methodology, this study conducts a qualitative content analysis on 78 news articles related to generative AI.

By analyzing 78 news articles, generative AI is found to be portrayed in the news in the following ways: Generative AI is primarily used in generating texts, images, audio and videos. Generative AI can have both positive and negative impacts on people’s everyday lives. People’s generative AI literacy includes understanding, using and evaluating generative AI and combating generative AI harms. Various stakeholders, encompassing government authorities, industry, organizations/institutions, academia and affected individuals/users, engage in the practice of AI governance concerning generative AI.

Based on the findings, this study constructs a framework of competencies and considerations constituting generative AI literacy. Furthermore, this study underscores the role played by government authorities as coordinators who conduct co-governance with other stakeholders regarding generative AI literacy and who possess the legislative authority to offer robust legal safeguards to protect against harm.

In the era of m-learning environments, multiple factors have been considered to explain adult learners' continuance usage intention, but largely without considering the role of specific configurations of variables and how they may affect learners' intention. The purpose of this study is to show how cognitive need, subjective norms, perceived usefulness, satisfaction, confirmation, attitude and perceived ease of use combine to predict learners' frequent use intentions.

It is empirically validated through configurational analysis, using fuzzy-set qualitative comparative analysis (fsQCA) on 211 adult learners with experience in using Mobile learning applications (MLA).

The findings show learners' satisfaction of MLA usage combined with the cognitive need and attitude were found to be core conditions reinforcing learners' continuance intention.

This study was conducted in the context of adult learners MLA whereby the motivations for continued usage and the nature of technological innovation could differ. In this regard, findings from this study may not be generalizable to other technological contexts.

In the planning and development of learning apps, software developers should pay attention to practical functions and extend key features that are frequently required for solving a problem using the new skill. On the marketing side, MLA companies should emphasize the full functionality of their apps to cater efficiently to the different needs and expectations of the learners.

This study contributes by extending existing knowledge on how cognitive need, satisfaction and attitude combine to increase or mitigate continuance intention to use toward the development of new configurational theories. This study fills the gap in the literature by introducing adult learners' continuance intention to use MLA and introducing through a methodological approach of fsQCA in adult learners' context.

The purpose of this study is to introduce new tools to develop a more precise and focused bibliometric analysis on the field of digitalization in healthcare management. Furthermore, this study aims to provide an overview of the existing resources in healthcare management and education and other developing interdisciplinary fields.

This work uses bibliometric analysis to conduct a comprehensive review to map the use of the unified theory of acceptance and use of technology (UTAUT) and the unified theory of acceptance and use of technology 2 (UTAUT2) research models in healthcare academic studies. Bibliometric studies are considered an important tool to evaluate research studies and to gain a comprehensive view of the state of the art.

Although UTAUT dates to 2003, our bibliometric analysis reveals that only since 2016 has the model, together with UTAUT2 (2012), had relevant application in the literature. Nonetheless, studies have shown that UTAUT and UTAUT2 are particularly suitable for understanding the reasons that underlie the adoption and non-adoption choices of eHealth services. Further, this study highlights the lack of a multidisciplinary approach in the implementation of eHealth services. Equally significant is the fact that many studies have focused on the acceptance and the adoption of eHealth services by end users, whereas very few have focused on the level of acceptance of healthcare professionals.

To the best of the authors’ knowledge, this is the first study to conduct a bibliometric analysis of technology acceptance and adoption by using advanced tools that were conceived specifically for this purpose. In addition, the examination was not limited to a certain era and aimed to give a worldwide overview of eHealth service acceptance and adoption.