Firms, recognizing their Corporate Social Responsibility (CSR), are becoming catalysts for societal change by integrating Environmental, Social and Governance (ESG) criteria into their activities. The fashion industry exemplifies this effort, with an increasing number of companies embracing sustainability and ethical practices. In this context, our purpose is to provide a clear and comprehensive picture of the link between sustainability and business performance in the fashion industry. This work presents a Multivariate Regression Analysis, scrutinizing both external perspectives through stock prices and internal perspectives via profitability indices. Our aim is to discern the intricate relationship between sustainability practices and financial performance within the fashion industry, aligning ESG criteria with long-term economic success. Our regression analysis reveals a significant positive correlation between ESG scores and stock prices, indicating investor recognition of ESG performance as a crucial investment criterion. However, when focusing internally on profitability, the ESG score does not exhibit statistical significance, suggesting a yet-to-be-established connection between ESG policies and corporate profitability. This study underscores the evolving role of companies as sustainability promoters, emphasizing the crucial role of ESG performance in shaping investor perceptions. Nevertheless, it also highlights the need for further exploration into the intricate relationship between sustainable policies and corporate profitability. As businesses increasingly embrace sustainability, in fact, it could become paramount for informed decision-making and fostering ethical societal and environmental progress.

This research explores the intricate relationship between digitalization, economic development, and non-cash payments in the ASEAN-7 countries over a ten-year period from 2011 to 2020. Focusing on factors such as commercial bank branches, broad money, and inflation, the study employs panel data regression analysis to investigate their impact on automated teller machine (ATM) usage. The findings reveal that commercial bank branches significantly influence ATM usage, emphasizing the role of accessibility, services, and technological preferences. Broad money also shows a significant impact on ATM transactions, reflecting the interplay between fund availability and non-cash transactions. However, inflation does not exhibit a direct influence on ATM usage. The research underscores the importance of maintaining service quality and security in the banking sector to enhance digital financial inclusion. Future research opportunities include exploring diverse non-cash payment methods and extending studies to countries with significant global economic impacts. This research contributes valuable insights to policymakers aiming to enhance digital financial inclusion policies, ultimately fostering economic growth through the digital economy in the ASEAN-7 region.

The objective of this work was to analyze the effect of the use of ChatGPT in the teaching-learning process of scientific research in engineering. Artificial intelligence (AI) is a topic of great interest in higher education, as it combines hardware, software and programming languages to implement deep learning procedures. We focused on a specific course on scientific research in engineering, in which we measured the competencies, expressed in terms of the indicators, mastery, comprehension and synthesis capacity, in students who decided to use or not ChatGPT for the development and fulfillment of their activities. The data were processed through the statistical T-Student test and box-and-whisker plots were constructed. The results show that students’ reliance on ChatGPT limits their engagement in acquiring knowledge related to scientific research. This research presents evidence indicating that engineering science research students rely on ChatGPT to replace their academic work and consequently, they do not act dynamically in the teaching-learning process, assuming a static role.

Despite the surge of publication of chatbots in the recent years in the field of education, we have little to know how this area has been researched so far, and the metrics of this type of research is still not known. To address such gap, this article offers a descriptive bibliometric study of chatbot research in education, aiming at presenting bibliometric analysis on articles on chatbots in education that were published in journals indexed in the Web of Science (WOS) database specifically Social Science Citation Index (SSCI) and Science Citation Index Expanded (SCIE) between 2016 and 2023. Descriptive bibliometric analysis was used to examine the data gathered from the chosen publications. including the annual number of articles and citations, the most productive author, countries with the highest publication output, productive affiliations, funding organizations, and publication sources. The bulk of the articles on chatbots in education, according to our dataset, were published between 2016 and 2023. The United States of America tops the list of countries regarding research productivity. The United Kingdom and China were ranked as most second and third productive countries, in terms of publication outputs. “Luke Kutszik Fryer emerged as the most productive author in this research domain in terms of the number of publications.” The University of Hong Kong had the highest number of publications among affiliations, indicating their significant contribution to the field. Additionally, the journal “Computers in Human Behavior” stood out with the highest number of publications per year, highlighting its relevance in publishing research on chatbots in education. This research offers valuable insights and a roadmap for prospective researchers, pinpointing critical areas where success can be attained in the study of chatbots in education.

The research aimed to: 1) analyze components and indicators of digital transformation leadership among school administrators, 2) assess their leadership needs, and 3) develop mechanism models to promote this leadership. A mixed-method approach was applied, involving three sample groups: 8 experts, 406 administrators, and 7 experts. Data collection tools included semi-structured interviews, leadership scales, needs assessments, and focus group discussions, with analysis performed through construct validity testing, needs assessment, and content analysis. The findings revealed: 1) The components and indicators of digital transformation leadership showed structural validity, as confirmed by the model's alignment with empirical data (Chi-Square = 82.3, df = 65, p = 0.072, CFI = 0.998, TLI = 0.997, RMR = 0.00965, RMSEA = 0.0256). 2) Among the leadership components, "innovative knowledge" ranked highest in need (PNImodified = 0.075), followed by "ideological influence" (0.066), "consideration of individuality" (0.055), "intellectual stimulation" (0.052), and "inspiration" (0.053). 3) Mechanism models for promoting leadership emphasized enhancing these five components to strengthen administrators' skills in applying technology, managing teaching and development plans, and fostering innovation. Administrators were encouraged to tailor strategies to individual needs, inspire personnel, and create a commitment to organizational change and development. These mechanisms aim to equip administrators to effectively lead transformations, motivate staff, and drive educational institutions to adapt and thrive in evolving environments.