Pre-publication empirical research, methodological discussion papers, and policy analyses produced by CISR faculty fellows and research scholars at University of Rajshahi. Peer-vetted and faculty-approved.
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This study used narrative inquiry to explore the real-life business phenomena and empowerment barriers of 15 marginal women entrepreneurs in rural Bangladesh, highlighting grassroots resilience and institutional opportunities.
Explores how digital entrepreneurs' intention toward blockchain technology adoption, perception of reduced costs, and knowledge of Artificial Intelligence impact achieving UN's Sustainable Development Goals (SDGs).
While circular economic paradigms have been broadly examined in multinational conglomerates, manufacturing SMEs face distinct resource constraints, reverse logistics frictions, and capital hurdle rates. Based on 142 survey participants and 18 facility audits across Sweden, Denmark, and Finland.
This project assesses empirical pricing differentials between conventional corporate bonds and sustainability-linked debt issues across European capital markets. Utilizing transaction-level secondary market data from over 3,400 debt instruments, our team isolates the greenium and evaluates whether ESG rating divergence induces market mispricing.
Evaluates technical reliability, battery storage optimization, and community micro-tariff governance across 14 off-grid solar-hybrid mini-grid installations in coastal delta zones of Bangladesh.
A multi-institutional scoping review examining diagnostic assistance, predictive triage, and generative AI documentation workflows in resource-constrained health systems.
Bibliometric and systematic analysis examining the evolution of artificial intelligence, deep learning, algorithmic trading, fraud detection, and credit scoring across international financial institutions. Maps thematic clusters and identifies emerging regulatory governance gaps.
Reviewing automated feature extraction, semantic segmentation, and deep learning architectures applied to high-throughput microscopy. Compares convolutional models against vision transformers for automated anomaly detection and cell morphology tracking.
