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Objective: The objective of the article is to provide a comprehensive identification and understanding of the challenges and opportunities associated with the use of generative artificial intelligence (GAI) in business. This study sought to develop a conceptual framework that gathers the negative aspects of GAI development in management and economics, with a focus on ChatGPT. Research Design Methods: The study employed a narrative and critical literature review and developed a conceptual framework based on prior literature. We used a line of deductive reasoning in formulating our theoretical framework to make the study’s overall structure rational and productive. Therefore, this article should be viewed as a conceptual article that highlights the controversies and threats of GAI in management and economics, with ChatGPT as a case study. Findings: Based on the conducted deep and extensive query of academic literature on the subject as well as professional press and Internet portals, we identified various controversies, threats, defects, and disadvantages of GAI, in particular ChatGPT. Next, we grouped the identified threats into clusters to summarize the seven main threats we see. In our opinion they are as follows: (i) no regulation of the AI market and urgent need for regula- tion, (ii) poor quality, lack of quality control, disinformation, deepfake content, algorithmic bias, (iii) automation- spurred job losses, (iv) personal data violation, social surveillance, and privacy violation, (v) social manipulation, weakening ethics and goodwill, (vi) widening socio-economic inequalities, and (vii) AI technostress. Implications Recommendations: It is important to regulate the AI/GAI market. Advocating for the regula- tion of the AI market is crucial to ensure a level playing field, promote fair competition, protect intellectual property rights and privacy, and prevent potential geopolitical risks. The changing job market requires workers to continuously acquire new (digital) skills through education and retraining. As the training of AI systems becomes a prominent job category, it is important to adapt and take advantage of new opportunities. To mitigate the risks related to personal data violation, social surveillance, and privacy violation, GAI developers must prioritize ethical considerations and work to develop systems that prioritize user privacy and security. To avoid social manipulation and weaken ethics and goodwill, it is important to implement responsible AI practices and ethical guidelines: transparency in data usage, bias mitigation techniques, and monitoring of generated content for harmful or misleading information. Contribution Value Added: This article may aid in bringing attention to the significance of resolving the ethical and legal considerations that arise from the use of GAI and ChatGPT by drawing attention to the controversies and hazards associated with these technologies.

Generative Artificial Intelligence (GAI) has brought revolutionary changes to the world, enabling businesses to create new experiences by combining virtual and physical worlds. As the use of GAI grows along with the Metaverse, it is explored by academics, researchers, and industry communities for its endless possibilities. From ChatGPT by OpenAI to Bard AI by Google, GAI is a leading technology in physical and virtual business platforms. This paper focuses on GAI’s economic and societal impact and the challenges it poses. Businesses must rethink their operations and strategies to create hybrid physical and virtual experiences using GAI. This study proposes a framework that can help business managers develop effective strategies to enhance their operations. It analyzes the initial applications of GAI in multiple sectors to promote the development of future customer solutions and explores how GAI can help businesses create new value propositions and experiences for their customers, and the possibilities of digital communication and information technology. A research agenda is proposed for developing GAI for business management to enhance organizational efficiency. The results highlight a healthy conversation on the potential of GAI in various business sectors to improve customer experience.

Sea urchins (Tripneustes gratilla) are among the most highly prized seafood products in Vietnam because of their nutritional value and medicinal properties. In this research, lipid classes and the phospholipid (PL) molecular species compositions from the body and eggs of T. gratilla collected in Hon Tam, Nha Trang, Khanh Hoa, Vietnam, were investigated. Hydrocarbon and wax (HW), triacylglycerol (TG), mono- and diacylglycerol (MDAG), free fatty acid (FFA), sterol (ST), polar lipid (PoL), and monoalkyl-diacylglycerol are the major lipid classes. In PL, five main glycerophospholipid classes have been identified, in which 137 PL molecular species were detected in the body and eggs of T. gratilla, including 20 inositol glycerophospholipids (PI), 11 serine glycerophospholipids (PS), 22 ethanolamine glycerophospholipids (PE), 11 phosphatidic acids (PA), and 73 choline glycerophospholipids (PC). PI 18:0/20:4, PS 20:1/20:1, PE 18:1e/20:4, PA 20:1/20:1, and PC 18:0e/20:4 are the most abundant species with the highest content values of 38.65–48.19%, 42.48–44.41%, 41.21–40.03%, 52.42–52.60%, and 7.77–7.18% in each class of the body–eggs, respectively. Interestingly, PL molecules predominant in the body sample were also found in the egg sample. The molecular species with the highest content account for more than 40% of the total species in each molecular class. However, in the PC class containing 73 molecular species, the highest content species amounted to only 7.77%. For both the body and egg TL samples of the sea urchin T. gratilla, a substantial portion of C20:4n polyunsaturated fatty acid was found in PI, PE, and PC, but C16, C18, C20, and C22 saturated fatty acids were reported at low levels. The most dominant polyunsaturated fatty acid in PI, PE, and PC was tetracosapolyenoic C20, while unsaturated fatty acid C20:1 was the most dominant in PS and PA. To our knowledge, this is the first time that the chemical properties of TL and phospholipid molecular species of the PoL of Vietnamese sea urchin (T. gratilla) have been studied.

Objective: The article aims to offer a thorough examination and comprehension of the challenges and pro‐ spects connected with artificial intelligence (AI) prompt engineering. Our research aimed to create a theoret‐ ical framework that would highlight optimal approaches in the field of AI prompt engineering.Research Design Methods: This research utilized a narrative and critical literature review and established a conceptual framework derived from existing literature taking into account both academic and practitioner sources. This article should be regarded as a conceptual work that emphasizes the best practices in the domain of AI prompt engineering.Findings: Based on the conducted deep and extensive query of academic and practitioner literature on the subject, as well as professional press and Internet portals, we identified various insights for effective AI prompt engineering. We provide specific prompting strategies.Implications Recommendations: The study revealed the profound implications of AI prompt engineering across various domains such as entrepreneurship, art, science, and healthcare. We demonstrated how the effective crafting of prompts can significantly enhance the performance of large language models (LLMs), gen‐ erating more accurate and contextually relevant results. Our findings offer valuable insights for AI practition‐ ers, researchers, educators, and organizations integrating AI into their operations, emphasizing the need to invest time and resources in prompt engineering. Moreover, we contributed the AI PROMPT framework to the field, providing clear and actionable guidelines for text‐to‐text prompt engineering.Contribution Value Added: The value of this study lies in its comprehensive exploration of AI prompt engineer‐ ing as a digital competence. By building upon existing research and prior literature, this study aimed to provide a deeper understanding of the intricacies involved in AI prompt engineering and its role as a digital competence.

Many educators and professionals in different industries may need to become more familiar with the basic concepts of artificial intelligence (AI) and generative artificial intelligence (Gen-AI). Therefore, this paper aims to introduce some of the basic concepts of AI and Gen-AI. The approach of this explanatory paper is first to introduce some of the underlying concepts, such as artificial intelligence, machine learning, deep learning, artificial neural networks, and large language models (LLMs), that would allow the reader to better understand generative AI. The paper also discusses some of the applications and implications of generative AI on businesses and education, followed by the current challenges associated with generative AI.

How K-12 education will handle generative artificial intelligence (GAI) is dependent upon educators’ preparedness and support structures. This study explores educational professionals’ perceptions regarding GAI and their support needs. 452 educational professionals, representing all years levels and educational roles in K-12, participated in a professional development (PD) intervention focused on GAI in education. Drawing on this cross-sectional design we adopted a mixed-methods approach. Data from polls, surveys, and collegial reflections were analysed using descriptive statistics, thematic analysis and Transformative Learning Theory. One of the key findings revealed a notable gap between positive attitudes toward GAI and an actual confidence in using it: although only 31% had used AI chatbots, 88% were inclined to adopt them. These results signal simultaneous enthusiasm and hesitation, and emphasise the need for comprehensive PD and organisational support that extends beyond technical skills to advocate for responsible, sustainable and ethical GAI use. This study offers insights for policymakers aiming to achieve effective, whole-school GAI integration, highlighting how design and innovation perspectives can guide the process.

National levels of personal health-care access and quality can be approximated by measuring mortality rates from causes that should not be fatal in the presence of effective medical care (ie, amenable mortality). Previous analyses of mortality amenable to health care only focused on high-income countries and faced several methodological challenges. In the present analysis, we use the highly standardised cause of death and risk factor estimates generated through the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) to improve and expand the quantification of personal health-care access and quality for 195 countries and territories from 1990 to 2015. We mapped the most widely used list of causes amenable to personal health care developed by Nolte and McKee to 32 GBD causes. We accounted for variations in cause of death certification and misclassifications through the extensive data standardisation processes and redistribution algorithms developed for GBD. To isolate the effects of personal health-care access and quality, we risk-standardised cause-specific mortality rates for each geography-year by removing the joint effects of local environmental and behavioural risks, and adding back the global levels of risk exposure as estimated for GBD 2015. We employed principal component analysis to create a single, interpretable summary measure–the Healthcare Quality and Access (HAQ) Index–on a scale of 0 to 100. The HAQ Index showed strong convergence validity as compared with other health-system indicators, including health expenditure per capita (r=0·88), an index of 11 universal health coverage interventions (r=0·83), and human resources for health per 1000 (r=0·77). We used free disposal hull analysis with bootstrapping to produce a frontier based on the relationship between the HAQ Index and the Socio-demographic Index (SDI), a measure of overall development consisting of income per capita, average years of education, and total fertility rates. This frontier allowed us to better quantify the maximum levels of personal health-care access and quality achieved across the development spectrum, and pinpoint geographies where gaps between observed and potential levels have narrowed or widened over time. Between 1990 and 2015, nearly all countries and territories saw their HAQ Index values improve; nonetheless, the difference between the highest and lowest observed HAQ Index was larger in 2015 than in 1990, ranging from 28·6 to 94·6. Of 195 geographies, 167 had statistically significant increases in HAQ Index levels since 1990, with South Korea, Turkey, Peru, China, and the Maldives recording among the largest gains by 2015. Performance on the HAQ Index and individual causes showed distinct patterns by region and level of development, yet substantial heterogeneities emerged for several causes, including cancers in highest-SDI countries; chronic kidney disease, diabetes, diarrhoeal diseases, and lower respiratory infections among middle-SDI countries; and measles and tetanus among lowest-SDI countries. While the global HAQ Index average rose from 40·7 (95% uncertainty interval, 39·0–42·8) in 1990 to 53·7 (52·2–55·4) in 2015, far less progress occurred in narrowing the gap between observed HAQ Index values and maximum levels achieved; at the global level, the difference between the observed and frontier HAQ Index only decreased from 21·2 in 1990 to 20·1 in 2015. If every country and territory had achieved the highest observed HAQ Index by their corresponding level of SDI, the global average would have been 73·8 in 2015. Several countries, particularly in eastern and western sub-Saharan Africa, reached HAQ Index values similar to or beyond their development levels, whereas others, namely in southern sub-Saharan Africa, the Middle East, and south Asia, lagged behind what geographies of similar development attained between 1990 and 2015. This novel extension of the GBD Study shows the untapped potential for personal health-care access and quality improvement across the development spectrum. Amid substantive advances in personal health care at the national level, heterogeneous patterns for individual causes in given countries or territories suggest that few places have consistently achieved optimal health-care access and quality across health-system functions and therapeutic areas. This is especially evident in middle-SDI countries, many of which have recently undergone or are currently experiencing epidemiological transitions. The HAQ Index, if paired with other measures of health-system characteristics such as intervention coverage, could provide a robust avenue for tracking progress on universal health coverage and identifying local priorities for strengthening personal health-care quality and access throughout the world. Bill & Melinda Gates Foundation.

The neoliberal transformation of higher education in the UK and an intertwined focus on the productive efficiency and prestige value of universities has led to an epidemic of overwork and precarity among academics. Many are found to be struggling with lofty performance expectations and an insistence that all dimensions of their work consistently achieve positional gains despite ferocious competition and the omnipresent threat of failure. Working under the current audit culture present across education, academics are thus found to overwork or commit to accelerated labour as pre-emptive compensation for the habitual inclemency of peer-review and vagaries of student evaluation, in accommodating the copiousness of ‘invisible’ tasks, and in eluding the myriad crevasses of their precarious labour. The proliferation of generative artificial intelligence (GAI) tools and more specifically, large language models (LLMs) like ChatGPT, offers potential relief for academics and a means to offset intensive demands and discover more of a work-based equilibrium. Through a recent survey of n  = 284 UK academics and their use of GAI, we discover, however, that the digitalisation of higher education through GAI tools no more alleviates than extends the dysfunctions of neoliberal logic and deepens academia’s malaise. Notwithstanding, we argue that the proliferating use of GAI tools by academics may be harnessed as a source of positive disruption to the industrialisation of their labour and catalyst of (re)engagement with scholarly craftsmanship.

Background Generative artificial intelligence (GAI) has developed rapidly and been increasingly used in scholarly publishing, so it is urgent to examine guidelines for its usage. This cross-sectional study aims to examine the coverage and type of recommendations of GAI usage guidelines among medical journals and how these factors relate to journal characteristics. Methods From the SCImago Journal Rank (SJR) list for medicine in 2022, we generated two groups of journals: top SJR ranked journals ( N  = 200) and random sample of non-top SJR ranked journals ( N  = 140). For each group, we examined the coverage of author and reviewer guidelines across four categories: no guidelines, external guidelines only, own guidelines only, and own and external guidelines. We then calculated the number of recommendations by counting the number of usage recommendations for author and reviewer guidelines separately. Regression models examined the relationship of journal characteristics with the coverage and type of recommendations of GAI usage guidelines. Results A higher proportion of top SJR ranked journals provided author guidelines compared to the random sample of non-top SJR ranked journals (95.0% vs. 86.7%, P  < 0.01). The two groups of journals had the same median of 5 on a scale of 0 to 7 for author guidelines and a median of 1 on a scale of 0 to 2 for reviewer guidelines. However, both groups had lower percentages of journals providing recommendations for data analysis and interpretation, with the random sample of non-top SJR ranked journals having a significantly lower percentage (32.5% vs. 16.7%, P  < 0.05). A higher SJR score was positively associated with providing GAI usage guidelines for both authors (all P  < 0.01) and reviewers (all P  < 0.01) among the random sample of non-top SJR ranked journals. Conclusions Although most medical journals provided their own GAI usage guidelines or referenced external guidelines, some recommendations remained unspecified (e.g., whether AI can be used for data analysis and interpretation). Additionally, journals with lower SJR scores were less likely to provide guidelines, indicating a potential gap that warrants attention. Collaborative efforts are needed to develop specific recommendations that better guide authors and reviewers.