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.

As an important type of knowledge, standards are key factors in economic development and technological innovation. To analyze the impact of participation in standards development on enterprise performance, this study takes China’s high-tech industry as an example. We use the operating data of listed enterprises in the industry in 2019 and conduct the propensity score matching method matching analysis on the entire sample and the classification. The conclusion shows: From an overall point of view, the participation of enterprises in the development of standards has a positive impact on the enterprise’s return on total assets. Specifically, participating in the development of over three standards can also improve the return on total assets. Large enterprises can increase the return on total assets of the enterprise and the return on invested capital. The state-owned enterprises have a positive effect on the return on total assets of the enterprise. Enterprises in the western, central, and eastern region enterprises can increase their net profit, enterprise value and net profit, return on total assets and enterprise value respectively. The enterprises in Beijing-Tianjin-Hebei region, Guangdong-Hong Kong-Macao Greater Bay Area can improve their return on invested capital and enterprise value, average rate of return respectively. The participation in the development of national standards, industry standards and local standards can help increase their return on total assets, the return on total assets and enterprise value, enterprise value respectively. Finally, we suggestions are put forward to enhance enterprises’ enthusiasm to take part in standards development.

This study conducted a systematic literature review on current and emerging trends in the use of artificial intelligence (AI) for community surveillance, using the PRISMA methodology and the paifal.ai tool for the selection and analysis of relevant sources. Five main thematic areas were identified: AI technologies, specific applications, societal impact, regulations and public policy. Our findings revealed exponential growth in the development and implementation of AI technologies, with applications ranging from public safety to environmental monitoring. However, this advancement poses significant challenges related to privacy, ethics and governance, driving a debate on the need for appropriate regulations. The analysis also highlighted the disparity in the adoption of these technologies among different communities, suggesting a need for inclusive policies to ensure equitable benefits. This study contributes to the understanding of the current scenario of AI in community policing, providing a solid foundation for future research and developments in the field.

In Ecuador, although regulations on curricular adaptations are clearly defined, Physical Education teachers face challenges at the micro-curricular level in adapting their classes to meet the needs of students with disabilities, specific learning difficulties, and vulnerable situations. The objective of this study was to analyze the presence and characteristics of specific curricular adaptations for Physical Education on a global scale. A scoping review was conducted following the PRISMA-ScR guidelines, covering studies from the Scopus database. A total of 112 articles were identified, and 16 that met the inclusion criteria were selected. These studies addressed curricular adaptations in Physical Education across five dimensions: teaching methodology, inclusive assessment, access to resources, accessible environments, and learning content, with a focus on students with disabilities. It was concluded that the combination of access adaptations, methodological strategies, and curricular content modifications enhances the inclusion and participation of students with disabilities. Interventions with these simultaneous adaptations achieved levels of satisfaction, self-efficacy, and holistic development, influenced by the geographical and cultural context.

This study analyzes the role of innovation in the development of smart cities in Latin America. It focuses on how emerging technologies and sustainable strategies are being integrated into urban planning and urban development. In this sense, this study seeks to contribute to the smart city literature by answering the following research questions: (i) To what extent smart city innovative initiatives have been addressed in Latin America? and (ii) To what extent scholars have addressed sustainable innovation strategies in the smart city literature? To this end, this is the first comprehensive bibliometric analysis of smart city research in Latin America, with a structured and systematized review of the available literature. This methodological approach allows cluster visualization and detailed analysis of inter-node relationships using the VOSViewer software. The research comprises 4 stages: (a) search criteria; (b) selection of documents; (c) software and data extraction; and (d) analysis of results and trends. Results indicate that studies on the Latin America region began to develop in 2012, with Brazil as a leader in this field and the tourism sector as the most relevant. Nevertheless, strong international collaboration was identified in co-authoring studies, underscoring a cooperative approach to solving common urban problems. The most active research area is technological innovation and sustainability, with focus on solutions for urban mobility, quality of life and smart governance. Finally, this work underlines the need to continue exploring the integration of technology in urban development, suggesting an agenda to guide future research to evaluate the sustainability and long-term impacts of smart city initiatives in Latin America. From the policy perspective, smart city initiatives need to be human-centered to boost smart solutions adoption and to guarantee long term local impacts.

Introduction: Chatbots are increasingly utilized in education, offering real-time, personalized communication. While research has explored technical aspects of chatbots, user experience remains under-investigated. This study examines a model for evaluating user experience and satisfaction with chatbots in higher education. Methodology: A four-factor model (information quality, system quality, chatbot experience, user satisfaction) was proposed based on prior research. An alternative two-factor model emerged through exploratory factor analysis, focusing on “Chatbot Response Quality” and “User Experience and Satisfaction with the Chatbot.” Surveys were distributed to students and faculty at a university in Ecuador to collect data. Confirmatory factor analysis validated both models. Results: The two-factor model explained a significantly greater proportion of the data’s variance (55.2%) compared to the four-factor model (46.4%). Conclusion: This study suggests that a simpler model focusing on chatbot response quality and user experience is more effective for evaluating chatbots in education. Future research can explore methods to optimize these factors and improve the learning experience for students.