Institutional-Repository, University of Moratuwa
Welcome to the University of Moratuwa Digital Repository, which houses postgraduate theses and dissertations, research articles presented at conferences by faculties and departments, university-published journal articles and research publications authored by academic staff. This online repository stores, preserves and distributes the University's scholarly work. This service allows University members to share their research with a larger audience.
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Recent Submissions
item: Conference-Full-text
Innovative energy recovery system design for locomotives: advancing technology management for sustainable rail transportation
(Business Research Unit (BRU), 2025) Madhumal, HKKK; Subasinghe, LU; Jayasekara, JGAS; Sapumanage, NC
In Sri Lankan railway, dynamic braking energy from Diesel-Electric Multiple Units (DEMUs) are currently wasted as heat, unlike electrified railways around the world, which recover and reuse this energy. This research proposes to design an innovative and sustainable system to harvest and reuse dynamic braking energy in S14 DEMUs operating on the upcountry railway line, planning to reduce operational costs and support environmental sustainability. Based on past research studies, energy losses during a 20-hour round trip from Badulla to Colombo were estimated around 3.5 MWh. Braking intervals and energy peaks were analyzed to identify optimal energy recovery points. A Lithium Iron Phosphate (LFP) battery system was selected for its proven safety, reliability and feasibility in transport applications. The suggested innovative design integrates a 1 MWh battery pack, capable of utilizing dynamic braking energy, primarily during the downhill section. The stored energy is then reused to power up the auxiliary systems during the return journey. This solution provides significant economic benefits. Over a 10-year period of battery lifetime, each train could save more than Rs. 87 million and reduce diesel consumption by approximately 550,800 liters. These savings not only reduce fuel costs but also reduce carbon emissions, contributing to a greener, more sustainable rail transport system. This novel system's expansion over the upcountry railway fleet could have a multiplied positive impact on the economy and environment. The results show that innovative energy recovery in non-electrified railroads is both economically feasible and ecologically sound.
item: Conference-Full-text
The Impact of FinTech adoption and green banking practices on bank sustainability performance: green financing as a mediator
(Business Research Unit (BRU), 2025) Suruthishagaran, S; Munasinhe, MADMO; Rajakarunanayake, S
This study explores the influence of FinTech adoption and green banking practices on the sustainability performance of banks in Sri Lanka, with a particular focus on the mediating role of green financing. It seeks to address the research gap related to the interaction between digital transformation and sustainability in the Sri Lankan banking industry. This study uses a positive research approach and a survey-based methodology to gather data from 250 managerial-level staff members of Sri Lanka's systemically important institutions. Partial least squares structural equation modeling (PLS-SEM) is used to examine the gathered data in order to evaluate the connections among green banking practices, FinTech uptake, and sustainability performance. The findings reveal that both green banking practices and FinTech adoption significantly positively influence bank sustainability performance. Moreover, green financing serves as a mediator in these relationships, amplifying the effects of green banking and FinTech adoption on sustainability outcomes. The study offers valuable insights for Sri Lankan banks, urging them to integrate FinTech and green banking practices into their operations to improve sustainability. The findings suggest that green financing is crucial for supporting sustainable projects for attracting socially responsible investors and enhancing bank performance in line with sustainability goals. This study is among the first to examine the combined effects of green banking, green financing, and FinTech adoption in Sri Lanka. By extending the Resource-Based View (RBV) theory and offering strategic advice for financial institutions operating in emerging economies, it adds to the body of knowledge in academia.
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Applications of sustainable practices in maritime logistics sector
(Business Research Unit (BRU), 2025) Silva, CMUD; Kavirathna, CA
Maritime logistics is a cornerstone of international trade, yet the transition toward sustainable operations remains difficult, especially in developing and emerging economies. Although many global studies discuss green technologies and regulatory initiatives, there is still limited understanding of how these ideas are implemented in regions of the Global South and what obstacles they face. Typical challenges include high capital requirements, fragmented or unstable regulatory environments, and insufficient infrastructure to support sustainable technologies. This study conducts a systematic examination of how sustainable practices are applied within the maritime logistics sector. It pursues four objectives: (1) to identify and classify the barriers that influence the application of sustainable practices; (2) to map the indicators used to evaluate the extent of sustainability adoption; (3) to review trends and descriptive patterns in the implementation of sustainable practices; and (4) to uncover major research gaps in the existing literature. A systematic literature review (SLR) was carried out following the PRISMA 2020 guidelines, covering 43 peer-reviewed publications issued between 2015 and 2025. The review shows that financial constraints, technological and operational limitations, and regulatory issues are the most frequently reported barriers to sustainable practices in maritime logistics. It also highlights key performance indicators such as reductions in carbon emissions, improvements in energy efficiency, and the uptake of renewable energy as central measures of sustainable performance. Moreover, the findings point to an increasing emphasis on digitalization, cleaner energy solutions, and other green technologies, particularly in large port settings. Overall, the study offers an integrated picture of current sustainable practices in maritime logistics and outlines directions for future research aimed at strengthening the sector’s transition toward more sustainable operations across different regions.
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The Influence of artificial intelligence on consumers’ impulse buying behavior in online
(Business Research Unit (BRU), 2025) Hewapathirana, DSD; Ranaweera, HRAT
This study investigates the influence of Artificial Intelligence (AI) on consumers' impulse buying behavior in online shopping in Sri Lanka. Grounded in the Technology Acceptance Model (TAM) and the Stimulus-Organism-Response (S-O-R) framework, it examines how AI-driven features including personalized recommendations, emotional triggers, promotional campaigns, perceived electronic word-of-mouth (eWOM), chatbot assistance, and consumer comfort affect impulsive purchasing. Data from 279 active online shoppers were analyzed using multiple regression and moderation analysis, with consumer awareness as a moderator. Results indicate that emotional triggers, promotional campaigns, chatbot assistance, and comfort significantly influence impulse buying, whereas personalized recommendations and eWOM do not. Importantly, consumer awareness strengthens several AI-behavior relationships.
item: Conference-Full-text
Benchmarking comparison between serverless vs. vm- architectures for AI workloads
(Business Research Unit (BRU), 2025) Fernando, HTV; Wijayanayake, J
The rapid growth of Artificial Intelligence (AI) has led to an increased demand for cloud computing systems capable of handling diverse AI workloads. Serverless computing and VM-based cloud architectures are two dominant models for deploying AI applications, but choosing the appropriate model depends on performance factors such as latency, scalability, and cost-efficiency. This Systematic Literature Review (SLR) compares these two architectures, focusing on their effectiveness for AI tasks like real-time inference and AI model training. The review evaluates the performance metrics associated with each model, including cold-start latency, resource utilization, and scalability. Studies from 37 selected papers were analyzed using the PRISMA methodology, and key insights into the strengths and weaknesses of both models were extracted. Findings reveal that serverless systems excel in elastic scaling and cost-efficiency for bursty AI workloads but struggle with latency issues during cold starts. In contrast, VM-based systems offer predictable performance for long-duration tasks but often suffer from resource underutilization and higher costs. The review highlights the need for task-specific benchmarks and suggests the use of hybrid cloud models that combine the advantages of both approaches. Overall, this review contributes to the trade-offs between Serverless and VM-based cloud models for AI workloads and provides practical recommendations for future research and cloud deployment strategies.