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Based on an experimental study on English for Specific Purposes (ESP) students, at the Business Department at the University of Bisha, the purpose of the study is to examine the effect of chatbot use on learning ESP in online classrooms during COVID-19 and find out how Dialogflow chabot can be a useful and interactive online platform to help ESP learners in learning vocabulary well.

The research paper is based on an experimental study of two groups, an experiential group and a controlled group. Two tests were carried out. Pre-tests and post-test of vocabulary knowledge were conducted for both groups to explore the usefulness of using the Dialogflow chatbot in learning ESP vocabulary. A designed chatbot content was prepared and included all the vocabulary details related to words' synonyms and a brief explanation of words’ meanings. An informal interview is another tool used in the study. The purpose of using the interview with the participants was to elicit more data from the participants about using the chatbot and about how and in what aspects chatbot using the conversational program was useful and productive.

The findings of the study explored that the use of chatbots plays a major role in enhancing and learning ESP vocabulary. That was clear as the results showed that the students who used the chatbot Dialogflow in the experimental group outperformed their counterparts in the control group.

The study displays an important pedagogical implication as the use of chatbots could be applied in several settings to improve language learning in general or learning ESP courses in particular. Chatbot creates an interesting environment to foster build good interactions where negotiation of meaning takes place clearly seems to be of great benefit to help learners advance in their L2 lexical development.

Examining and exploring whether the use of chatbots plays a major role in enhancing and learning ESP vocabulary in English as Foreign Language setting.

Substantial changes in the education system and the shift to online classes during the lockdown have raised teachers’ attention to the idea of practicing cooperative learning in online environments. Cooperative learning activities enhance academic skills if designed effectively. This study aims to explore students’ attitudes toward cooperative learning in online learning environments.

This study is a descriptive study. A survey was administered to 50 graduate and postgraduate students of English as a Foreign Language.

Results indicated that the students had positive attitudes toward the integration of cooperative tasks in online environments. They also revealed no significant differences in the attitudes toward cooperative learning based on the students’ level (i.e. undergraduate or postgraduate) and that the students preferred small groups.

Cooperative learning is a widely researched topic, especially in higher education. However, most of the previous studies reported results of the implementation of cooperative learning in traditional classrooms. This study aimed to fill this gap by examining the impact of online tools on cooperative learning.

Relational autonomy proposes that persons are socially embedded, with decisions being made within social relationships. Through this theoretical lens, this article explores how the healthcare professional–patient relationship can affect pregnant women’s decisions to accept pertussis and influenza vaccines.

Hackney was chosen as the study site as it has very low vaccine uptake rates. In-depth interviews were conducted with 40 pregnant and recently pregnant women, as well as 10 healthcare professionals. Interviews explored experiences of the UK's National Health Service (NHS) health care and views towards vaccination in pregnancy. An observation of a consultation between a pregnant patient and her General Practitioners (GPs) was also conducted in order to understand how the vaccination discussion takes place.

The findings of this study indicate that advice from friends and family can greatly influence a pregnant woman’s vaccination decisions. The patient’s social context, including influences on her decisions, must be understood by healthcare professionals, so that discussions about concerns can take place. If close relationships with patients are formed, healthcare professional advice is more likely to be trusted. With support from healthcare professionals, patients feel competent, empowered to make the right decision for them, and are more likely to vaccinate.

This research will help to inform contextualised policies aimed at increasing vaccination acceptance and reducing inequality in access to vaccination during pregnancy in Hackney.

To the author’s knowledge, this chapter is the first to apply the theory of relational autonomy to views towards maternal vaccination and decision making. It provides valuable insights into how healthcare professionals’ interactions with their pregnant patients can influence vaccination acceptance. The chapter contains advice on how both healthcare professionals and policy-makers can include mothers in vaccine decision-making processes in more personalised ways, by adopting a dialogue that appreciates and understands the social processes around vaccination concerns.

Blockchain and distributed ledger technologies are increasingly prominent, yet their adoption remains complex. This paper addresses the common misalignment between blockchain technology and actual needs, often leading to project failure. It introduces a decision-making framework focused on the technological aspects of blockchain adoption.

We designed the framework by analyzing key decision drivers from existing literature and applied it to a real-world use case in the electric vehicle supply chain. The blockchain solution was tested with live production data.

Blockchain is beneficial for use cases requiring decentralized governance, but it often needs to be supplemented with additional technologies in industrial applications.

The framework provides a set of managerial-level questions that simplify the decision-making process for those without deep technical expertise, helping determine when blockchain is appropriate, valuable and superior to other technologies.

This study aims to investigate the mental health treatment journeys of stigmatized consumers using user-generated content (UGC) while also examining the role of UGC in the journey.

This study offers valuable insights from 68 distinct, stigmatized consumers through a qualitative content analysis of 73 YouTube product review videos related to ten antidepressants. Data is coded, combining inductive coding with theory to provide a nuanced interpretation. Applying the Common-Sense Model of Self-Regulation to traditional consumer journey concepts, the analysis of UGC is structured by a unique mental health treatment journey.

The findings show that consumers use UGC to destigmatize their mental health treatment by engaging in dynamic reflection throughout their journey, rather than following traditional feedback models. Unlike typical consumption patterns, where search is limited to the initial stage, these consumers search at every journey phase while sharing insights that offer valuable support to others which, sometimes they report, is reciprocated by viewers.

Theoretically, this study introduces an innovative framework blending psychological and marketing theories to address a gap in health-care service marketing literature concerning long-term mental health treatment journeys. By introducing the concept of dynamic reflection, it demonstrates how consumers actively engage in and share insights throughout their treatment process, differing from traditional feedback models, and highlights the impact of UGC on health-care service provision.

Findings could inform potential health-care provider interventions that may improve treatment effectiveness.

Although stigmatized consumers’ experiences have been examined, their treatment experiences have not been framed within a journey framework.

This paper aims to assess an integrative model of origins and sequences of employee intrapreneurial behaviour in hospitality companies.

The research sample comprises 321 frontline employees in four-star to five-star hotels in Ho Chi Minh City of Vietnam, using a structural equation model (SEM).

The paper shows that employee behavioural factors (subjective norms, attitude towards intrapreneurship and perceived behavioural control) and perception of firm entrepreneurial orientation dimensions (innovativeness, proactiveness and risk-taking) have a significant impact on the two intrapreneurship activities of employee strategic renewal behaviour and venture behaviour, which in turn, beneficial impact firm performance. The influence of demographic variables is also assessed in the research model, and education level, income level and management level are found to be prestigious.

Hospitality firms need to establish the architecture and system related to entrepreneurship orientation and organisational climate. Additionally, there is a need for providing resources and knowledge, thereof, could provide support employees in appreciation of corporate entrepreneurship and authorisation to conduct intrapreneurial behaviour.

The findings grant influencing mechanisms of employee individuality and dimensions of entrepreneurial orientation on firm performance through employee strategic renewal behaviour and venture behaviour within the hospitality firms.

Transformative generative artificial intelligence (AI) tools, such as ChatGPT, have revolutionized various sectors, including higher education. Since its release in November 2022, it has gathered attention from academia, scholars, students, regulators and education policymakers. Opinions diverge on whether ChatGPT’s use in academia should be circumscribed or regulated. To offer insights into some of these, this study synthesizes 139 contributions (articles and blogs) authored by instructors, publishers, professors, editors and education journalists in the education domain.

The study uses sentiment analysis and topic modelling to examine the 139 articles and blogs. Drawing on their insights, it firstly explores emotional dimensions of the content. Subsequently, using critical discourse analysis, it integrates them with emerging concerns (what and why), and explicates key processes (how) to inform academia, research, practice and policy.

Drawing on the results obtained from sentiment analysis, the study identifies that the overall sentiments surrounding generative AI tools such as ChatGPT in higher education are more positive (33%) than negative (11%). Using topic modelling, the study further highlights four thematic areas to explore benefits and challenges: perceptions and biases, pedagogical impact, ethical considerations and implementation and adoption.

Limitations include the absence of student perspectives, which may offer deeper insights into perception, pedagogical impacts, and integrity-related issues. Additionally, while findings are applicable across various domains, specialized areas may present differing insights that could refine the conclusions.

Overall, contributors acknowledge the capabilities of generative AI tools like ChatGPT in enhancing students’ productivity. The findings suggest that it is likely to offer significant gains in the education domain, providing several benefits to both teachers and students. Nevertheless, they also consider its limitations, discrimination and bias, copyright infringement, plagiarism, fabricated unauthentic textual content and assessment bias.

This study aims to explore how artificial intelligence (AI) can be used to overcome the challenges associated with implementing electronic health record (EHR) systems in primary health-care facilities in Tanzania. It aims to assess the technological, organisational and environmental barriers to EHR system implementation and investigate the role of AI in optimising these systems for more effective health-care delivery.

The study adopts a qualitative approach, using case studies from five regions in Tanzania: Dar es Salaam, Mwanza, Morogoro, Singida and Pwani. Data were collected through 26 semi-structured interviews with health-care providers, including medical doctors, nurses, pharmacists and IT personnel. The study applied the diffusion of innovation (DOI) theory and the technology-organisation-environment framework to assess the factors affecting EHR implementation and the potential integration of AI to enhance these systems.

Key challenges include unreliable network connectivity, frequent power outages, insufficient training and complex system usability issues. Despite these challenges, EHR systems have improved patient data accessibility and workflow efficiency. AI presents opportunities to address these challenges, mainly through predictive analytics, AI-driven encryption for data security and personalised training modules. AI integration can enhance system reliability, usability and security, ultimately improving health-care outcomes.

This study provides valuable insights into integrating AI to optimise EHR systems in resource-constrained environments like Tanzania. It addresses a gap in the literature by focusing on how AI can be adapted to low-resource settings and provides a framework for future EHR system implementations in similar contexts. The findings contribute to the global discourse on health-care informatics and the role of AI in improving health-care systems in developing countries.