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 evaluated the efficiency and productivity of the manufacturing industries of Singapore. Singapore is one of the world’s most competitive countries and manufacturing giants. All 21 manufacturing industries as classified by Singapore’s Department of Statistics were included in the study as decision-making units (DMUs). Using the Malmquist DEA on data spanning 2015–2021, we found that excerpt for the Paper and Paper product industry, all industries recorded positive total factor productivity (TFP). TFP ranged from 0.977 to 1.481. In terms of technical efficiency, 14 out of 21 industries showed positive efficiency change. The highest TFP was recorded in 2020 and the lowest in 2016. By measuring and improving efficiency, industries in Singapore can achieve cost savings, increase output, and enhance their competitiveness in the global marketplace. In addition, efficiency measurement can help policymakers identify potential areas for improvement and develop targeted policies to promote sustainable economic growth. Given these benefits, performance measurement is inevitable for industries and policymakers in Singapore to achieve economic objectives. Manufacturing industries need to find ways to manage the size and scale of operations as we flag this as an area for improvement.

The integration of Big Earth Data and Artificial Intelligence (AI) has revolutionized geological and mineral mapping by delivering enhanced accuracy, efficiency, and scalability in analyzing large-scale remote sensing datasets. This study appraisals the application of advanced AI techniques, including machine learning and deep learning models such as Convolutional Neural Networks (CNNs), to multispectral and hyperspectral data for the identification and classification of geological formations and mineral deposits. The manuscript provides a critical analysis of AI’s capabilities, emphasizing its current significance and potential as demonstrated by organizations like NASA in managing complex geospatial datasets. A detailed examination of selected AI methodologies, criteria for case selection, and ethical and social impacts enriches the discussion, addressing gaps in the responsible application of AI in geosciences. The findings highlight notable improvements in detecting complex spatial patterns and subtle spectral signatures, advancing the generation of precise geological maps. Quantitative analyses compare AI-driven approaches with traditional techniques, underscoring their superiority in performance metrics such as accuracy and computational efficiency. The study also proposes solutions to challenges such as data quality, model transparency, and computational demands. By integrating enhanced visual aids and practical case studies, the research underscores its innovations in algorithmic breakthroughs and geospatial data integration. These contributions advance the growing body of knowledge in Big Earth Data and geosciences, setting a foundation for responsible, equitable, and impactful future applications of AI in geological and mineral mapping.

This research conducts a comparative urban analysis of two coastal cities with analogous tourism models situated in distinct geographical regions: Balneário Camboriú in Brazil and Benidorm in Spain. The study delves into two critical urban phenomena impacting the sustainability of tourist cities, utilising social network data to gather insights into economic and urban activities (Google Places) and spatio-temporal patterns of citizen presence (Twitter). The spatial analysis explores the municipal and, to a more detailed extent, the coastal strip extending 500 m inland from the coastline, spanning the entire length of each city to their municipal boundaries. The analysis uncovers both similarities and differences between the two destinations, offering insights that could inform future development strategies aimed at fostering sustainable urban environments in these well-established coastal tourist areas.

Strategically managing production systems is crucial for creating value and enhancing the competitive capabilities of companies. However, research on organizational culture within these systems is scarce, particularly in the Colombian context. This research aims to evaluate cultural profiles and their impact on the performance of production systems in Colombian firms. The regional focus is vital as cultural and contextual factors can vary significantly between regions, influencing organizational behavior and performance outcomes. To achieve this, we make a study in a sample of Colombian companies, with participation from working students of the Universidad Nacional Abierta y a Distancia (UNAD). We used a data analytics approach to collected data. The results will be relevant to both the scientific community and business practitioners. This research seeks to determine whether the perception of the work environment within a company influences the perceived performance of the company. The findings will provide a deeper understanding of the relationship between organizational culture and production system performance, offering a foundation for business decision-making and enhancing competitiveness in Latin American context.

The purpose of this study is to investigate the correlation between sponsorship and the performance and development of early career athletes transitioning from junior level to professional sports, because this issue has not been fully explored in the Czech Republic. The reason is the almost absolute absence of financial or material support for such early-career athletes, when their transition from junior categories and the entire junior category is almost always exclusively financed and supported by their parents and families. We also emphasise the absolute absence of legislative provisions that would give supporters of such athletes at least a tax or other advantage. The research is based on research of Cardenas (2023), Hong and Fraser (2023) and Moolman and Shuttleworth (2023) and aims to assess how financial and material support provided by sponsors can enhance an athlete’s performance and long-term career trajectory. A mixed method approach was adopted, combining quantitative analysis through surveys and performance data with qualitative interviews. Data from 173 early career athletes from various disciplines were analysed using t-tests and ANOVA statistical methods to assess financial stability, access to better training, and community participation. Results indicate that sponsorship significantly contributes to better performance metrics, with sponsored athletes showing a 20% improvement in competition results compared to nonsponsored athletes. Furthermore, sponsorship financial support improved training opportunities and access to elite facilities, which was shown to increase athletes’ performance by 15%. However, some challenges related to sponsorship obligations, such as marketing commitments, were highlighted by athletes, underscoring the pressures that sponsorship can introduce. The implications of this study suggest that effective sponsorship strategies can play a vital role in an athlete’s career development, offering not only financial stability but also opportunities for personal branding and increased community engagement. Another implication is a possible consideration for legislators in the context of preparing a legislative framework enabling tax or other benefits for companies and organisations sponsoring or supporting these young athletes. More research is recommended to explore the long-term impact of sponsorship on athlete mental health and career sustainability, as well as the differences in sponsorship effects across various sports disciplines.