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This study presents a systematic literature review analysing the impact of artificial intelligence (AI) on higher education, focusing on its methods, results, and implications. By synthesising a diverse range of academic papers, the review explores how AI technologies influence educational standards and practices in higher education institutions. Findings reveal that AI has the potential to enhance the quality of higher education by diversifying teaching responsibilities, customising learning experiences, and employing intelligent, adaptive teaching strategies. These capabilities position AI as a transformative tool for improving educational delivery and outcomes. However, the study also highlights significant challenges associated with integrating AI into higher education. These challenges include delineating the appropriate scope of AI use, addressing inequalities in access to digital resources, and ensuring adequate training and support for educators and students. The review underscores the importance of understanding these complexities to guide the development of effective strategies and policies that optimise AI's potential while mitigating its limitations. The review offers critical insights into the dual role of AI in higher education, where it can either advance or hinder educational standards depending on how it is implemented. By examining the advantages, limitations, and broader consequences of AI-powered instructional tools, this study provides a comprehensive perspective on the intricate relationship between AI and educational quality. The findings aim to inform educators, policymakers, and stakeholders about the opportunities and challenges of adopting AI in higher education, contributing to the development of inclusive, innovative, and sustainable educational practices.

The utilization of powerful machine learning models in artificial intelligence offers novel prospects for the identification of fraudulent activities. Artificial Intelligence (AI) is a revolutionary technological tool that enhances the ability to detect and prevent fraud by improving efficiency and effectiveness. This research offers a thorough examination of the utilization of artificial intelligence technology in the realm of procurement fraud prevention and detection. Additionally, it highlights the obstacles that arise when employing machine learning techniques for the purpose of identifying and preventing fraudulent activities. A mixed methods approach was employed in this study, wherein data was collected through an unstructured interview and questionnaire. We conducted a comprehensive review of relevant scholarly articles and online resources. The findings indicate that fraudsters are progressively advancing in their skills, which poses a significant challenge in detecting fraudulent activities. The advent of AI in fraud detection has demonstrated its transformative impact. AI has achieved unparalleled precision and velocity in crime detection and prevention, surpassing the capabilities of any human. Artificial intelligence (AI) enhances the capacity for automation. Accessing unstructured data in the form of spreadsheets, digital documents, and email inboxes poses a significant issue for the procurement function. In order to achieve a successful procurement transformation, businesses should prioritize the creation of essential tools, guarantee acceptance through the establishment of a superior user experience, and integrate both novel and pre-existing technology. It is imperative to disseminate knowledge to the general public regarding the escalating sophistication of artificial intelligence (AI) in the realm of fraud detection and prevention. This includes elucidating the potential benefits of AI in identifying patterns of suspicious activities, assessing its efficacy in predicting potential threats or fraudulent activities prior to their manifestation, and exploring its utility in analyzing historical data pertaining to both familiar and unfamiliar forms of fraudulent behavior.

Financial inclusion, especially the adoption of fintech, has emerged as a crucial avenue for removing the financial access divides in developing countries, including Bangladesh. Following the comprehensive analysis, this article intends to outline Bangladesh's recent experiences with fintech-based financial inclusion, identify the short-comings and hence make policy suggestions. Being a descriptive research, cross-sectional and observational research approaches are applied for the explanatory analysis of secondary data. Results suggest an urban-rural gap in the implementation of digital financial services, with urban areas consistently having higher rates of fully online branches and a greater number of financial machines. Despite this, the sharp rise in the issuance of active cards is a positive sign suggesting an expansion of consumer base and an adoption of electronic modes of payment. Moreover, inter-bank electronic fund transfers and online banking have experienced increasing uptake, revealing a progressing trend towards digital transactions within the banking industry. Additionally, the study identifies a speedy growth in the number of mobile financial services accounts in both urban and rural areas. This study adds to the exchange of ideas regarding financial inclusion by carrying out a detailed study of Bangladesh's digital financial transformation, exemplifying the accomplishments, obstacles, and future research directions. It highlights the need for targeted policies and ongoing research efforts to build inclusive financial systems to serve the entire society’s interest. Nevertheless, the observations will have both theoretical and practical significance, contributing towards building capability in harnessing technology for sustainability, community uplifting, and shaping a brighter future.  

The 21st century has witnessed a rapid surge in industrial pollution, accompanied by the depletion of natural resources, prompting a collective call to action. As a result, the greening of corporations and ecological sustainability has emerged as a matter of paramount importance for decision-makers worldwide. This study, thereby, aims to examine the implementation of environmentally responsible (Green) human resource management practices in the health sector and their influence on organizational sustainability performance. Methodologically, a descriptive approach was employed, utilizing a questionnaire survey to collect data from 250 healthcare employees within Pakistan's healthcare sector. The study's statistical outcomes indicate that green hiring positively influences economic performance but not social and environmental performance. However, green training, involvement, performance management, and compensation are all positively related to organizational sustainability performance, encompassing social, economic, and environmental aspects in Pakistan’s healthcare sector. By summing up, this study concludes the significance of customizing green HR practices for enhancing sustainability performance in the healthcare sector. It underscores the crucial role played by green human resource management practices and their impact on organizational sustainability performance in a developing country context, specifically in Pakistan.

This paper aims to examine the financial performance of Bangladeshi listed commercial banks. The study analyzed the financial performances of banks categorized into three generations—first, second, and third—based on their year of commencement of operations in the country. The data of a total of 24 banks, 8 from each generation, listed in the Dhaka Stock Exchange (DSE), has been collected from audited financial statements for the time period of 13 years, from 2010 to 2022. Data has been analyzed using different ratios of profitability, liquidity, capital adequacy, operational efficiency, and credit risk. The ANOVA test also has been used to make a comparison of profitability—the return on assets (ROA), and return on equity (ROE) of the banks of different generations. According to the results, the average profitability ratios in terms of ROA, and ROE of the second-generation banks showed their credibility than banks of other generations; while first generation banks kept higher liquidity, and capital adequacy with higher credit risk than the banks of other generations. The first-generation banks also showed a lower cost-expense ratio, and a higher efficiency in terms of operation, than the banks in the second and third generations. The research also discovered that the profitability of banks of different generations significantly varies from one another.  

Partial Least Squares Structural Equation Modeling has gained considerable attention across diverse academic fields, including business, social sciences, marketing, and management. A key challenge in utilizing PLS-SEM is balancing explanatory and predictive power when selecting the most suitable model from competing alternatives. This paper explores the effectiveness of various quality criteria for evaluating causal-predictive models, with a focus on resistance to change in Vietnamese SMEs. The study emphasizes the importance of both in-sample and out-of-sample predictions, using metrics such as R², BIC, AIC, Q², RMSE, MAE, and CVPAT. The findings reveal that traditional criteria like R² may not be sufficient for identifying the best model, while PLSpredict, CVPAT, BIC, and AIC offer superior performance in determining the optimal balance between explanatory and predictive capabilities. These insights provide practical implications for researchers and practitioners, highlighting the need to tailor model selection to specific objectives, such as theory development or real-world forecasting. For practitioners, the study underscores the benefits of leveraging simpler, more generalizable models for robust decision-making in dynamic or resource-constrained environments.

Indigenous communities possess rich traditions of conflict resolution rooted in their cultural values, collective decision-making, and community involvement. However, the imposition of Western legal frameworks and colonial histories has often disrupted these practices, leading to increased tensions and unresolved conflicts. This study explored conflict resolution within Indigenous communities, emphasizing the role of social work in facilitating culturally appropriate processes. The study employed a qualitative approach, incorporating interviews with Indigenous leaders, social workers, and community members, alongside a review of relevant literature on traditional practices and social work interventions. Social workers play a crucial role as facilitators, helping to navigate tensions and bridging gaps between traditional practices and contemporary needs. Challenges include a lack of cultural competency and the need for ongoing community engagement. The integration of Indigenous knowledge systems into conflict resolution practices enhance outcomes and foster healing. Social workers prioritize cultural humility and active listening, ensuring that Indigenous voices lead the resolution process. By supporting the reclamation of traditional conflict resolution methods and advocating for culturally sensitive practices, social workers empowered Indigenous communities, promote resilience, and facilitate more effective conflict resolution. This perspective underscores the necessity of collaboration, respect, and recognition of Indigenous sovereignty in all social work interventions.

This study presents a systematic literature review analysing the impact of artificial intelligence (AI) on higher education, focusing on its methods, results, and implications. By synthesising a diverse range of academic papers, the review explores how AI technologies influence educational standards and practices in higher education institutions. Findings reveal that AI has the potential to enhance the quality of higher education by diversifying teaching responsibilities, customising learning experiences, and employing intelligent, adaptive teaching strategies. These capabilities position AI as a transformative tool for improving educational delivery and outcomes. However, the study also highlights significant challenges associated with integrating AI into higher education. These challenges include delineating the appropriate scope of AI use, addressing inequalities in access to digital resources, and ensuring adequate training and support for educators and students. The review underscores the importance of understanding these complexities to guide the development of effective strategies and policies that optimise AI's potential while mitigating its limitations. The review offers critical insights into the dual role of AI in higher education, where it can either advance or hinder educational standards depending on how it is implemented. By examining the advantages, limitations, and broader consequences of AI-powered instructional tools, this study provides a comprehensive perspective on the intricate relationship between AI and educational quality. The findings aim to inform educators, policymakers, and stakeholders about the opportunities and challenges of adopting AI in higher education, contributing to the development of inclusive, innovative, and sustainable educational practices.

The main aim was to determine how the supply chains of the FMCG industry may enhance their resilience through the adoption of digital technologies. Additionally, enhancing visibility and transparency through the augmentation of operational efficiency to elevate overall performance. This analysis utilised the Uncertainty Framework theory, which provides a systematic approach for identifying, categorising, and managing uncertainties within the supply chain. Organisations can formulate strategies to effectively manage and mitigate risks by understanding the many dimensions of uncertainty within the supply chain and their interrelations. The research utilised a qualitative methodology, gathering data via semi-structured interviews with 25 supply chain specialists and senior executives possessing extensive experience in the FMCG sector. The study's findings indicate that digital transformation enhances agility, transparency, and data-driven decision-making, which is crucial for bolstering the resilience of the FMCG supply chain. Six themes emerged from the findings: data-driven decision-making, supply chain visibility and transparency, automation and robots, collaborative platforms and ecosystems, predictive analytics and forecasting, and traceability and sustainability. The research will enhance our understanding of the implementation of digital transformation initiatives throughout FMCG supply chains. This involves analysing the application of digital technologies in supply chain operations. The study may ascertain the impact of these technologies on various resilience criteria, including flexibility, agility, visibility, and responsiveness, by analysing the role of digital transformation in FMCG supply chains. The research findings can assist supply chain managers in making educated decisions on investments and strategic planning for digital transformation initiatives.Research on digital transformation in the FMCG sector is limited, particularly on these unprecedented upheavals. Moreover, there is little study connecting digital transformation with sustainability and resilience in FMCG supply chains, which is essential for comprehending how these projects might collaboratively enhance robust supply systems.

Small and Medium Enterprises (SMEs) in South Africa previously faced challenges due to limited resources, restricted access to technology, and the need to constantly adapt to a dynamic business environment. The introduction of Generative Artificial Intelligence (AI) emerged as a potential solution to these issues, promising to enhance operational efficiency and strategic decision-making. As a representative of developing economies, South Africa experienced a growing interest in AI technologies. This study was conducted to explore the impact of generative AI on SME productivity in South Africa, an area which had been underexplored. Employing a qualitative methodology, the study evaluated the current state and implications of generative AI in South African SMEs. It involved in-depth interviews to gather perceptions, experiences, challenges, and benefits from SME owners and managers regarding the adoption of generative AI technologies. The findings analysed via R Statistical Software revealed significant insights into the specific areas where generative AI substantially impacted SME productivity. It also identified the challenges and opportunities associated with the adoption of generative AI by SMEs, as well as the potential long-term implications. Key findings included notable improvements in data-driven decision-making, operational efficiencies, and market expansion strategies. However, the study also highlighted barriers such as the lack of technical expertise, initial setup costs, and concerns over data security. Overall, the impact of generative AI on SMEs in South Africa was found to be predominantly positive, paving the way for further technological advancements and adoption in the sector.