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Title: GDP, Unemployment, Government Expenditure in Bangladesh: Testing the Relevance of Okun’s Law

Abstract: This study examines the dynamic relationship among Gross Domestic Product (GDP), unemployment, and government expenditure in Bangladesh from 2010 to 2025. The analysis highlights a period of robust economic performance, with GDP growth consistently averaging above 6%, driven primarily by manufacturing, garment exports, and the service sector. Despite the significant disruption caused by the COVID-19 pandemic in 2020, which saw growth dip to 3.45%, the economy demonstrated structural resilience and a rapid recovery. Concurrently, government expenditure has expanded significantly as a fiscal tool for development, particularly through large-scale infrastructure projects and social safety nets. Public spending rose from BDT 1,217 billion in 2016 to over BDT 2,218 billion by 2025. However, this expansionary stance has not fully addressed labor market inefficiencies. Unemployment has remained relatively stable between 3% and 5%, suggesting that the link between GDP growth and job creation is weak, thereby offering only limited support for Okun’s Law in the Bangladeshi context. The study identifies persistent structural challenges, including high youth unemployment and skill mismatches, and concludes that inclusive growth policies and labor market reforms are essential to translate macroeconomic gains into sustainable employment generation.

By S.K.S. Yadav, Imran Hosen, Dhwani Gupta
In Volume: 15,Issue: 1
Title: A Qualitative Analysis of 40 Case Studies on BUBT Students' Admission Experience and Satisfaction

Abstract: Based on in-depth case studies of forty students from all academic departments, this study offers a thorough qualitative examination of the admission experiences and satisfaction of students at Bangladesh University of Business and Technology (BUBT). This study finds important factors impacting admission decisions, student happiness, and retention intentions using NVivo 14 software for rigorous qualitative data analysis. The results show a complicated paradox: although 90% of students selected BUBT mainly because of its reasonably priced tuition (20,000–25,000 BDT per semester), they also voiced serious discontent with the qualifications of the instructors, classroom conduct, bullying on Facebook, and subpar housing. With 92.5% of female students satisfied, the proctorial system was found to be the most favorable element. Nonetheless, 65% of students said they had witnessed or experienced instructor annoyance in the form of yelling, canceling classes, or acting insultingly, and 85% of students desired senior teachers with PhDs. The fact that 80% of students said they would be willing to pay an additional 5,000–10,000 BDT per semester if BUBT hired internationally renowned PhD staff and addressed behavioral concerns with teachers is also remarkable. Additionally, 45% of students saw their institution rating as a direct advantage for their own careers, according to the report. Eight evidence-based recommendations, with projected implementation timescales ranging from immediate to two years, are included in the research's conclusion. These recommendations include urgent PhD faculty recruitment, teacher training programs, hostel expansion, and official Facebook group moderating.

By S.K.S. Yadav, Tasfiunnoor Pinky, Dhwani Gupta
In Volume: 15,Issue: 1
Title: Impact of AI-Powered Personalized Learning on Self-Regulated Learning, Academic Engagement, and Learning Autonomy among Secondary School Students

Abstract: There have been numerous ways in which Artificial Intelligence (AI) has revolutionized the educational process through its ability to deliver adaptive instruction, intelligent tutoring, feedback mechanisms, learning analytics, and learning through Generative AI. AI-driven personalized learning is able to provide personalized education for students, thus not only enhancing the academic achievements but also the processes involved in regulating and engaging in learning. Several studies have found that AI is able to facilitate self-regulated learning (SRL), depending on the technological design, instructional context, length of intervention and level of teacher and learner participation (Zhu & Sari, 2026; Xu et al., 2026). The current paper focuses on the possible effect of AI-driven personalized learning on three related learner outcomes including self-regulated learning, academic engagement, and learning autonomy among secondary school students. This research brings together international literature along with Indian literature such as Babbar, Raju and Kumari’s studies regarding Generative AI and Learning Efficiency in the Indian educational setting. The structured research approach and quantitative method are introduced, and then illustrated with statistical findings. The literature indicates that personalisation through AI can be effective in terms of helping learners to monitor and regulate their activities; however, it will not help in case the dependence on technology becomes too high and there is no pedagogical scaffolding present. That is why the paper concludes in favour of human-centered approach and AI as a tool rather than substitution of teachers and cognitive processes.

By Rajesh Kumar Raju, Monika Kumari
In Volume: 15,Issue: 1
Title: Innovative Business Models in Supplying Energy Finance as a Service and Leasing in México

Abstract: This research report examines innovative business models in energy finance as a service (EFaaS) and leasing mechanisms within the Mexican energy sector. As México transitions toward renewable energy and sustainable development goals, novel financing structures have emerged to overcome traditional barriers to energy infrastructure investment. Results demonstrate that energy leasing and service-based models have achieved significant market traction, particularly in commercial solar photovoltaic installations and energy efficiency projects. Findings reveal that successful models incorporate flexible payment structures, comprehensive maintenance services, and performance guarantees that align incentives between service providers and clients. The discussion addresses regulatory enablers, financing innovations, and technology integration strategies that facilitate model adoption. The report concludes that EFaaS and leasing models represent transformative mechanisms for accelerating Mexico's energy transition, with implications for policy development, financial sector engagement, and sustainable infrastructure deployment.

By José G. Vargas-Hernández, Dra. Patricia Villasana-Ramos, M. C. Omar C. Vargas-González
In Volume: 15,Issue: 1
Title: Digital Payments and Fraud in India: A Systematic Review of UPI's Growth and Machine Learning-Based Detection Frameworks

Abstract: India's Unified Payment Interface (UPI) can be stated as the most revolutionary payment system of the twenty-first century. It processed over 21 billion transactions worth ?27.97 lakh crore in December 2025 alone. These figures dictate both its remarkable reach and the security challenges it presents. Since its launch in April 2016 by the National Payments Corporation of India (NPCI), UPI has outpaced traditional digital payment systems, and now over 85% of digital transactions in India are done using UPI. Though the growth is remarkable, it allows new kinds of frauds and scams to arise. Studies show that digital fraud has surged by 346% during COVID-19. This paper examines the empirical and review studies from 2017 to 2026 related to the intersection of UPI’s exponential growth and evolving landscape of digital payment frauds in India. The study critically evaluates the most dominant types of fraud typologies, like phishing, QR code manipulation, KYC impersonation, and social engineering scams, along with the methods used for the prevention of such types of fraud, like machine learning and deep learning architectures. The review finds that machine learning and deep learning architectures achieved over 99% accuracy in the detection of fraud. There are still persistent challenges like class imbalance, data privacy, and adaptive fraudulent behavior. The paper argues that effective fraud mitigation and prevention require more than algorithmic advancement. A multi-layered response integrating system, regulatory dimensions, and user awareness is still required to tackle and prevent fraudulent activities.

By Abhishek Mishra and Dr. Mahendra Pal Singh Yadav
In Volume: 15,Issue: 1

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