This study explores the impact of online assessments on students’ academic performance and learning outcomes at the University of Technology in South Africa. The research problem addresses the effectiveness and challenges of digital assessment platforms in higher education (HE), particularly their influence on student engagement, feedback quality, and academic integrity. A qualitative case study approach was employed, involving semi-structured interviews with ten undergraduate and postgraduate students from diverse academic backgrounds. The findings reveal that while online assessments provide flexibility and immediate feedback, they also pose challenges related to technical issues, feedback delays, and concerns about long-term knowledge retention. The study highlights the necessity of aligning assessment strategies with constructivist learning principles to enhance critical thinking and student-centered learning. Implications for theory include strengthening the application of constructivist learning in digital environments, while practical recommendations focus on improving assessment design, institutional support, and feedback mechanisms. Policy adjustments should consider inclusive and equitable access to online assessments. Future research should further investigate the long-term impact of digital assessments on professional readiness. This study contributes to ongoing discussions on online education by offering a nuanced understanding of digital assessment challenges and opportunities in higher education.

Credit risk assessment is one of the most important aspects of financial decision-making processes. This study presents a systematic review of the literature on the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in credit risk assessment, offering insights into methodologies, outcomes, and prevalent analysis techniques. Covering studies from diverse regions and countries, the review focuses on AI/ML-based credit risk assessment from consumer and corporate perspectives. Employing the PRISMA framework, Antecedents, Decisions, and Outcomes (ADO) framework and stringent inclusion criteria, the review analyses geographic focus, methodologies, results, and analytical techniques. It examines a wide array of datasets and approaches, from traditional statistical methods to advanced AI/ML and deep learning techniques, emphasizing their impact on improving lending practices and ensuring fairness for borrowers. The discussion section critically evaluates the contributions and limitations of existing research papers, providing novel insights and comprehensive coverage. This review highlights the international scope of research in this field, with contributions from various countries providing diverse perspectives. This systematic review enhances understanding of the evolving landscape of credit risk assessment and offers valuable insights into the application, challenges, and opportunities of AI and ML in this critical financial domain. By comparing findings with existing survey papers, this review identifies novel insights and contributions, making it a valuable resource for researchers, practitioners, and policymakers in the financial industry.

Biomass production (BIO) and its anomalies were modeled using MODIS satellite images and gridded weather data to test an environmental monitoring system in the biomes Atlantic Forest (AF) and Caatinga (CT) within SEALBA, an agricultural growing region bordered by the states of Sergipe (SE), Alagoas (AL), and Bahia (BA), Northeast Brazil. Spatial and temporal variations on BIO between these biomes were strongly identified, with the annual long-term averages (2007–2023) for AF and CT of 78 ± 22 and 58 ± 17 kg ha−1 d−1, respectively. BIO anomalies were detected through its standardized indexes—STD (BIOSTD), comparing the results for the years from 2020 to 2023 with the long-term rates from 2007 to each of these years. The highest negative BIOSTD values were in 2023, but concentrated in CT, indicating periods with the lowest vegetation growth, regarding the long-term conditions from 2007 to 2023. The largest positive BIOSTD values were for the AF biome in 2022, evidencing the highest vegetative vigor in comparison with the long-term period 2007–2022. The proposed BIO monitoring system is important for environmental policies as they picture suitable periods and places for agricultural and forestry explorations, allowing sustainable managements under climate and land-use changes conditions, with possibilities for replication of the methods in other environmental conditions.

This study provides a comparative analysis of Environmental, Social, and Governance (ESG) ratings methodologies and explores the potential of eXtensible Business Reporting Language (XBRL) to enhance transparency and comparability in ESG reporting. Evaluating ratings from different agencies, the research identifies significant methodological inconsistencies that lead to conflicting information for investors and stakeholders. Statistical tests and adjusted rating scales confirm substantial divergence in ESG scores, primarily due to differing data categories and indicators used by rating firms. Using a sample of 265 European companies, the study demonstrates that individual ESG agencies report markedly different ratings for the same firms, which can mislead stakeholders. It proposes that XBRL based reporting can mitigate these inconsistencies by providing a standardized framework for data collection and reporting. XBRL enables accurate and efficient data collection, reducing human error and enhancing the transparency of ESG reports. The findings advocate for integrating XBRL in ESG reporting to achieve higher levels of comparability and reliability. The study calls for greater regulatory oversight and the adoption of standardized taxonomies in ESG reporting to ensure consistent and comparable data across sectors and jurisdictions. Despite challenges like the lack of a standardized taxonomy and inconsistent adoption, the research contends that XBRL can significantly improve the reliability of ESG ratings. In conclusion, this study suggests that standardizing ESG data through XBRL could provide a viable solution to the unreliability of current ESG rating scales, supporting sustainable business practices and informed decision making by investors.

Given its insular geographic location, Taiwan inherently benefits from a natural advantage in developing its shipping industry, positioning it as a critical sector for the nation’s economic advancement. The shipping industry operates within a highly competitive maritime market, wherein ocean freight forwarders provide services on a global scale, thus classifying them within the international transportation and logistics industry. The global competition from logistics peers renders the services highly substitutable. This study breaks new ground by integrating the SERVQUAL scale with advanced methodologies such as the Analytic Hierarchy Process (AHP) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) to assess and enhance service quality in the shipping industry. By segmenting the five dimensions of SERVQUAL, the study delineates 19 specific evaluation indicators. The expert questionnaires developed and analyzed through AHP and DEMATEL reveal a previously unidentified link between specific service quality dimensions and customer satisfaction. The findings from this analysis offer crucial insights into the critical success factors (CSFs) of service quality and their causal interrelationships, thereby establishing a model for service standards. By leveraging the identified CSFs and understanding the causal relationships among these key factors, ocean freight forwarders can enhance and optimize their value propositions and resources. This proactive approach is expected to significantly improve service quality, fortify core competitiveness, and elevate customer support and satisfaction levels, ultimately leading to an increased market share and ensuring sustainable business operations.

Immeasurable basic and applied information has been evolved on all important floricultural crops through large-scale worldwide research on interdisciplinary aspects. The quantum and quality of work done on Chrysanthemum, among all other ornamentals, are par excellence. Conscientious attempt has been made to collect the whole multidisciplinary experimental results achieved world over. Despite remarkable achievements in knowledge and technology, a major part of present experimental research on chrysanthemum is largely a routine repeat of work. Assessment of past and present work is now significant for preparing target-oriented future research resolutions. This will help to secure the favored results within a justifiable period.