VOLUME 13, ISSUE 8, AUGUST 2026

Abstract: Artificial Intelligence (AI) has emerged as a transformative force in business and management, promising unprecedented efficiency, innovation, and sustainability. Yet, its rapid integration into organizational practices raises critical questions about ethics, inclusivity, and long-term impact. This paper explores the triple challenge of AI in sustainable management balancing ethical responsibility, operational efficiency, and equitable access. Drawing on secondary data, industry cases, and policy frameworks, the study highlights how AI-driven solutions can optimize decision-making, streamline operations, and support environmental goals, while also exposing risks such as algorithmic bias, workforce displacement, and unequal access for small and rural enterprises. Special attention is given to the role of AI in empowering MSMEs and tourism based entrepreneurship in regions like Karnataka, where digital inclusion can bridge socio-economic divides. The paper argues that sustainable management in the AI era requires not only technological innovation but also strong ethical governance and equitable policies. By proposing actionable recommendations for academia, industry, and government, this research contributes to shaping a future where AI serves as a catalyst for inclusive growth and responsible development.

Keywords: Artificial Intelligence, Sustainable Management, Ethics, Efficiency, Equity, MSMEs, Rural Entrepreneurship, Digital Transformation, Inclusive Growth, Policy Frameworks

Abstract: This study examines how women in Assam’s Barak Valley perceive and benefit from private-sector CSR initiatives versus a major government welfare scheme (Orunodoi). A survey of 60 rural women (30 CSR beneficiaries, 30 Orunodoi recipients) was done and conduction of focus-group discussions to compare program accessibility, effectiveness, satisfaction, and livelihood outcomes. Findings suggest Orunodoi is generally seen as more accessible and immediately beneficial for household needs, whereas CSR programs (e.g. skill‐training or microcredit by companies) are valued for longer-term income improvements. Both interventions increased women’s sense of financial security, but differences in targeting and bureaucratic ease emerged. For example, Orunodoi recipients reported higher ease of enrollment (mean score ~3.6/5 vs. 2.8/5 for CSR), while CSR trainees reported slightly higher income gains (Table 2). Policy recommendations include better coordination of CSR and government efforts: for instance, linking CSR skill training with Orunodoi cash support to maximize impact. Despite a small sample, the study highlights that each approach has merits, and that women’s preferences hinge on both economic needs and social factors.

Keywords: Women’s empowerment; CSR initiatives; Orunodoi scheme; Barak Valley; livelihood outcomes.

Abstract: In the banking and financial services industry, loan approval is an important process that helps minimize credit risk and increase lending efficiency. Traditional machine-learning models can struggle to represent complex interactions among customer financial features and changes in financial activity over time. This paper presents a comparative study of two transformer-based deep-learning models - TabTransformer and Temporal Fusion Transformer (TFT) - for intelligent loan approval prediction. TabTransformer learns meaningful representations from structured customer data, whereas TFT is trained using twelve months of sequential financial data to model long-term financial patterns. For a fair comparison, both models are trained and tested using the same enhanced loan dataset, preprocessing pipeline and experimental setting. Performance is assessed using accuracy, precision, recall, F1-score, ROC-AUC and confusion- matrix analysis. The results show that TFT achieves 94.75% accuracy compared with 93.13% for TabTransformer, indicating that temporal financial information provides additional predictive value for automated loan application decision-making.

Keywords: Loan Approval Prediction, Tab Transformer, Temporal Fusion Transformer, Deep Learning, Credit Risk Assessment, Financial Data Analytics, Transformer Networks, Sequential Learning, Artificial Intelligence.

Abstract: The increasing demand for natural aggregates and the disposal of construction and demolition (C&D) waste and plastic waste have created significant environmental challenges. This study investigates the mechanical properties of M40 concrete by partially replacing natural coarse aggregate with recycled aggregate (RA) and fine aggregate with plastic granules (PG). Four concrete mixes were prepared: a control mix and three modified mixes containing 20% RA + 5% PG, 30% RA + 7% PG, and 40% RA + 9% PG. Fresh and hardened concrete properties were evaluated through slump, density, compressive strength, split tensile strength, and flexural strength tests. The results indicate that moderate replacement levels provide satisfactory strength while promoting sustainable construction and reducing environmental pollution.

Keywords: Recycled Aggregate, Plastic Granules, Sustainable Concrete, M40 Concrete, Mechanical Properties.

Abstract: The number of Internet of Things (IoT) applications and edge–cloud computing environments has expanded, and efficient and low-latency execution of tasks is desired. Cloud native orchestration solutions are often not well suited to deal with real-time needs due to the frequent movement of cloud workloads, changes in network and resource variations. In this study, we are interested in using two ensemble ML algorithms (Random Forest and XGBoost) to predict the time required for the execution of the tasks in the vmCloud workload dataset in a serverless edge–cloud environment. Seven workload attributes—CPU utilisation, memory usage, network latency, task size, cold-start delay, number of instances, and bandwidth—were used to train the models. RMSE, MAE, MAPE, R2 score and 5-fold cross-validation were used for the performance evaluation. The results of the experiments indicate that the performance of XGBoost is better than that of the RF, with Test R2 of 0.9209, RMSE is 10.08 ms, and MAPE is 14.57%, while RF’s Test R2 is 0.8898, RMSE is 11.89 ms, and MAPE is 18.38%. The feature importance analysis shows that network latency and cold start delay are the most important features that affect the time to execute. The findings indicate that XGBoost is a proper algorithm for latency-aware serverless function orchestration and can be applied to intelligently schedule in the edge– cloud computing environment.

Keywords: Edge Computing, Cloud Computing, IoT, Serverless Computing, XGBoost, Random Forest, Function Orchestration, Latency Prediction.

Abstract: Artificial Intelligence (AI)-powered personalization has become a cornerstone of modern e-commerce by enabling organizations to deliver customized product recommendations that enhance consumer experiences and influence purchasing decisions. However, the effectiveness of AI-driven personalization depends not only on the quality of recommendations but also on consumers' trust in AI systems. This study examines the influence of AI-powered personalization, perceived transparency, explainability of AI, and perceived privacy protection on purchase intention, with consumer trust serving as the mediating variable. A quantitative, cross-sectional research design was adopted, and data were collected from consumers who had prior experience with AI-enabled personalized recommendations on online shopping platforms. The proposed conceptual model was analyzed using Structural Equation Modelling (SEM) in Jamovi. The findings reveal that AI-powered personalization, perceived transparency, and explainability of AI significantly and positively influence consumer trust, whereas perceived privacy protection does not significantly affect trust. Consumer trust, in turn, exerts a significant positive influence on purchase intention. Furthermore, perceived privacy protection has a significant direct effect on purchase intention, while the direct effects of AI-powered personalization, perceived transparency, and explainability of AI on purchase intention are not statistically significant, indicating that their influence is primarily transmitted through consumer trust. The study highlights the pivotal role of trust as the underlying mechanism linking AI-enabled personalization characteristics to consumers' purchase intentions. The findings contribute to the growing literature on AI-driven consumer behaviour by extending the understanding of trust formation in AI-enabled retail environments and offer practical implications for e-commerce firms seeking to design transparent, explainable, and trustworthy AI recommendation systems that enhance consumer purchase intentions.

Keywords: AI-Powered Personalization; Consumer Trust; Purchase Intention; Explainability of Artificial Intelligence; Perceived Transparency

Abstract: The rapid increase in municipal solid waste (MSW) resulting from population growth, urbanization, and changing consumption patterns has emerged as a major environmental challenge in developing countries. Scientific landfill site selection is essential to minimize adverse environmental and public health impacts while ensuring sustainable waste management. The present study aims to identify suitable landfill sites in the Deogarh Municipal Area, Rajsamand District, Rajasthan, using Geographic Information System (GIS) and Multi-Criteria Decision Analysis (MCDA). Spatial datasets, including land use/land cover, road networks, water bodies, built-up areas, agricultural land, railway lines, and slope, were acquired through field surveys and secondary data sources and processed using QGIS. Buffer analysis and weighted overlay techniques were applied in accordance with the Solid Waste Management Rules, 2016, and the guidelines of the Central Pollution Control Board (CPCB) to evaluate landfill site suitability. The integrated analysis classified the study area into different suitability zones ranging from unsuitable to highly suitable. The results indicate that the existing dumping site is environmentally less suitable because of its proximity to residential areas, agricultural land, and public institutions, thereby posing potential environmental and public health risks. In contrast, the proposed alternative sites are located on barren land with favourable environmental conditions, adequate road accessibility, and greater compliance with landfill site selection criteria, making them more suitable for future landfill development. The study demonstrates that the integration of GIS and MCDA provides a robust, reliable, and scientifically sound decision-support framework for landfill site selection. The proposed methodology can assist municipal authorities and planners in developing sustainable solid waste management strategies and may serve as a replicable model for other urban areas facing similar waste management challenges. The final suitability analysis showed that 12.4% of the study area falls under highly suitable class while 28.6% was moderately suitable.

Keywords: Municipal Solid Waste (MSW), Multi-criteria Decision Analysis (MCDA), Buffer Analysis, Landfill Site Selection, (GIS), QGIS, Spatial Analysis

Abstract: Concrete is the most widely used construction material because of its strength, durability, versatility, and availability. At the same time, the construction sector consumes large quantities of natural aggregates and generates or receives large quantities of waste materials. Marble processing produces solid waste in the form of pieces and fragments, while plastic waste creates long-term environmental concerns because of its persistence and difficult disposal. This study investigates the possibility of using these two waste materials in concrete as partial replacements for conventional aggregates.

The experimental study considers M30 grade concrete. Marble pieces are used as a partial replacement for natural coarse aggregate, while plastic granules are used as a partial replacement for natural fine aggregate. The project methodology includes material testing, concrete mix proportioning, casting and curing of specimens, and strength evaluation after 7, 14, and 28 days. The synopsis specifies compressive strength, split tensile strength, and flexural strength as strength characteristics to be evaluated.

The reported results show that concrete strength generally increases with curing age. Among the replacement levels considered, the mixes containing 30% marble pieces and 7% plastic granules produced the highest reported strength performance. The synopsis concludes that higher replacement levels of 40% marble pieces and 9% plastic granules resulted in a reduction in strength, attributed to weaker bonding and increased voids.

The study therefore identifies 30% marble pieces with 7% plastic granules as the optimum combination among the investigated mixes. The findings indicate that appropriately proportioned marble and plastic waste can be incorporated into concrete while reducing dependence on natural aggregates and supporting more sustainable construction practices.

Keywords: M30 concrete, marble waste, plastic granules, coarse aggregate, fine aggregate, compressive strength, split tensile strength, sustainable concrete.