The cars industry has undergone significant technological advancements, with data analytics and artificial intelligence (AI) reshaping its operations. This study aims to examine the revolutionary influence of artificial intelligence and data analytics on the cars sector, particularly in terms of supporting sustainable business practices and enhancing profitability. Technology-organization-environment model and the triple bottom line technique were both used in this study to estimate the influence of technological factors, organizational factors, and environmental factors on social, environmental (planet), and economic. The data for this research was collected through a structured questionnaire containing closed questions. A total of 327 participants responded to the questionnaire from different professionals in the cars sector. The study was conducted in the cars industry, where the problem of the study revolved around addressing artificial intelligence in its various aspects and how it can affect sustainable business practices and firms’ profitability. The study highlights that the cars industry sector can be transformed significantly by using AI and data analytics within the TOE framework and with a focus on triple bottom line (TBL) outputs. However, in order to fully benefit from these advantages, new technologies need to be implemented while maintaining moral and legal standards and continuously developing them. This approach has the potential to guide the cars industry towards a future that is environmentally friendly, economically feasible, and socially responsible. The paper’s primary contribution is to assist professionals in the industry in strategically utilizing Artificial Intelligence and data analytics to advance and transform the industry.

This study provides empirical data on the impact of generative AI in education, with special emphasis on sustainable development goals (SDGs). By conducting a thorough analysis of the relationship between generative AI technologies and educational outcomes, this research fills a critical gap in the literature. The insights offered are valuable for policymakers seeking to leverage new educational technologies to support sustainable development. Using Smart-PLS4, five hypotheses derived from the research questions were tested based on data collected from an E-Questionnaire distributed to academic faculty members and education managers. Of the 311 valid responses, the measurement model assessment confirmed the validity and reliability of the data, while the structural model assessment validated the hypotheses. The study’s findings reveal that New Approaches to Learning Outcome Assessment (NALOA) significantly contribute to achieving SDGs, with a path coefficient of 0.477 (p < 0.001). Similarly, the Use of Generative AI Technologies (UGAIT) has a notable positive impact on SDGs, with a value of 0.221 (p < 0.001). A Paradigm Shift in Education and Educational Process Organization (PSEPQ) also demonstrates a significant, though smaller, effect on SDGs with a coefficient of 0.142 (p = 0.008). However, the Opportunities and Risks of Generative AI in Education (ORGIE) study did not find statistically significant evidence of an impact on SDGs (p = 0.390). These findings highlight the potential opportunities and challenges of using generative AI technologies in education and underscore their key role in advancing sustainable development goals. The study also offers a strategic roadmap for educational institutions, particularly in Oman to harness AI technology in support of sustainable development objectives.

The global shortage of nurses has resulted in the demand for their services across different jurisdictions causing migration from developing to developed regions. This study aimed to review the literature on drivers of nurses’ migration intentions from source countries and offer future research directions. A search strategy was applied to ScienceDirect, Web of Science, and Scopus academic databases to find literature. The search was limited to peer-reviewed, empirical studies published in English from 2013–2023 resulting in 841 papers. The study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to conduct a systematic review of 35 studies after thorough inclusion and exclusion criteria. In addition, the VOSviewer software was utilized to map network visualization of keywords, geographic and author cooperation for bibliometric understanding. The findings revealed various socio-economic, organizational, and national factors driving nurses’ migration intentions. However, limited studies have been conducted on family income, organizational culture, leadership style, infrastructure development, social benefits, emergency service delivery, specialized training, and bilateral agreements as potential drivers for informing nurses’ migration intentions. Moreover, a few studies were examined from a theoretical perspective, mainly the push and pull theory of migration. This paper contributes to the health human resources literature and shows the need for future studies to consider the gaps identified in the management and policy direction of nurse labor migration.

This study is based on the theory of planned behaviour, and its aim is to understand the impact of doctoral pursuit intention on the doctoral preparatory behaviour of female teachers in independent colleges in China, as well as to determine the moderating effect of perceived risk between doctoral pursuit intention and doctoral preparatory behaviour. The participants in the study were female teachers from independent colleges in China, who were recruited between February and March 2024 based on convenience sampling. 776 valid questionnaires were obtained, and the data were analyzed using a hierarchical regression method. According to the results, a doctoral pursuit intention has a significant and positive predictive effect on doctoral preparatory behaviour, while the perceived risk has a significant and negative moderating effect between doctoral pursuit intention and doctoral preparatory behaviour. This indicates that female teachers with high doctoral pursuit intention more actively prepare to pursue a doctoral degree when the perceived risk is low, whereas the doctoral preparatory behaviour of those with high perceived risk shows a limited increase as their doctoral pursuit intention increases. Therefore, female teachers’ pursuit of a doctoral degree should be supported on an individual basis and analysed within the broader context of the transformation of independent colleges.

Urban areas are increasingly vulnerable to fire disasters due to high population density, sprawling infrastructure, and often inadequate safety measures. This study aims to analyze the capacity of the DKI Jakarta government in terms of human resource capabilities, asset readiness, and budget planning capabilities. Furthermore, it measures the government’s success as evidenced by the public response to the achievement of firefighter performance. This study uses qualitative analysis with a content analysis approach. Data sources come from annual performance report documents and the content of the DKI Jakarta Fire Department website containing city disaster information. Performance report and website data are analyzed and used as research data to support qualitative analysis. This research shows that command decisions are essential in the organizational structure of the fire brigade. Both laboratory services are carried out optimally as a concrete effort to map fire potential. The laboratory tests the safety and suitability of firefighting equipment. Available budgetary support provides broad operational powers for the fire service. The government’s strength in minimizing or overcoming fire problems has received a positive response from the public. The operational achievements of firefighting continue to be consistent and increase. Ultimately, this research provides scientific insight into disaster mitigation and reducing the fire risk in cities.

This work centres on the contribution of the Nigerian government’s Anchor Borrowers’ Programmes on rice production in the country. This study employs quantitative methodology and with a primary objective to dissect the efficacy of modern farming techniques facilitated by the Anchor Borrowers’ Programmes (ABP), evaluates the advantages and disadvantages inherent in rice production under this programme. Conducted within the agricultural landscape of Ebonyi State, Nigeria, this study adopts a cross-sectional survey approach to gauge the symbiotic relationship between rice production and the ABP. Targeting a cohort of rice smallholder farmers who have directly benefited from the program, the work employs stratified random sampling and purposeful selection techniques to guarantee comprehensive representation within a population of 400 respondents. This study utilizes the mixed-methods approach to data collection, including structured questionnaires administered to rice farmers in Ebonyi State, Nigeria. This research tests hypotheses by utilising statistical tools such as regression analysis. The outcome of this study underscores the imperative for continued support and refinement of the Anchor Borrowers’ Programme. Moreover, it elucidates the pivotal role of financial institutions and agricultural lending agencies in equipping farmers with the requisite skills and resources. Ultimately, this study affirms the crucial role of modern farming methodologies in propelling rice production within Ebonyi State, Nigeria. It recommends that young school leavers, especially those in the rural areas should also be encouraged to venture into agriculture through schemes such as the ABP, bank financing and innovative financing so as to help the Federal Government achieve its economic diversification drive.