Research Publications Authored by SLIIT Staff

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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.

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2021 - 202621

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Author: search.filters.author.Rathnayake, N

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Now showing 1 - 10 of 21
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    PublicationEmbargo
    Unveiling the Current Extent of the Gig Economy Engagement in Developing Asian Countries
    (University of Nigeria Department of Mass Communication, 2025-05-21) Dilmith, C; Jayathilaka, R; Jayalal, S; Devhara, T; Rathnayake, N; Jayasuriya, N
    Background: The gig economy, driven by technological advancements, has shifted the labour market from traditional jobs to mainstream freelance and contract work via online platforms. Statistical evidence highlights the importance of examining gig economy engagement in developing Asian countries, which are key contributors to global platforms. Objective: This study sought to systematically analyse the rise of gig economy engagement in developing Asian countries and its implications for the future of work while providing insights for platform users. Methodology: This study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, drawing on past research and numerous reliable resources from 1999 to 2024. Results: Findings reveal a growing research focus on the gig economy, particularly since 2016, with a significant increase in publications from 2019 to 2024. This highlights gaps in understanding gig workers' well-being, including stress, quality of life, and gender-specific barriers. Conclusion: Scholars must pay adequate attention to the expanding contributions of the gig economy, considering its potential to reshape workforce dynamics and drive economic innovation. Unique contribution: This study presents a graphical representation that illustrates the evolution of existing scholarly contributions, highlighting key gaps that require further exploration, and emphasises the vital importance of investigating this area. Key Recommendation: Policymakers need to focus on adopting a fair work framework while addressing the underexplored areas of gig workers' experiences and challenges to foster equitable and sustainable growth of the gig economy in developing Asian countries. © 2025, University of Nigeria Department of Mass Communication.
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    PublicationOpen Access
    Efficient Hotspot Detection in Solar Panels via Computer Vision and Machine Learning
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025-07-15) Fernando, N; Seneviratne, L; Weerasinghe, N; Rathnayake, N; Hoshino, Y
    Solar power generation is rapidly emerging within renewable energy due to its cost-effectiveness and ease of deployment. However, improper inspection and maintenance lead to significant damage from unnoticed solar hotspots. Even with inspections, factors like shadows, dust, and shading cause localized heat, mimicking hotspot behavior. This study emphasizes interpretability and efficiency, identifying key predictive features through feature-level and What-if Analysis. It evaluates model training and inference times to assess effectiveness in resource-limited environments, aiming to balance accuracy, generalization, and efficiency. Using Unmanned Aerial Vehicle (UAV)-acquired thermal images from five datasets, the study compares five Machine Learning (ML) models and five Deep Learning (DL) models. Explainable AI (XAI) techniques guide the analysis, with a particular focus on MPEG (Moving Picture Experts Group)-7 features for hotspot discrimination, supported by statistical validation. Medium Gaussian SVM achieved the best trade-off, with 99.3% accuracy and 18 s inference time. Feature analysis revealed blue chrominance as a strong early indicator of hotspot detection. Statistical validation across datasets confirmed the discriminative strength of MPEG-7 features. This study revisits the assumption that DL models are inherently superior, presenting an interpretable alternative for hotspot detection; highlighting the potential impact of domain mismatch. Model-level insight shows that both absolute and relative temperature variations are important in solar panel inspections. The relative decrease in “blueness” provides a crucial early indication of faults, especially in low-contrast thermal images where distinguishing normal warm areas from actual hotspot is difficult. Feature-level insight highlights how subtle changes in color composition, particularly reductions in blue components, serve as early indicators of developing anomalies.
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    PublicationOpen Access
    A novel application with explainable machine learning (SHAP and LIME) to predict soil N, P, and K nutrient content in cabbage cultivation
    (Elsevier B.V., 2025-03-06) Abekoon, T; Sajindra, H; Rathnayake, N; Ekanayake, I, U; Jayakody, A; Rathnayake, U
    Cabbage (Brassica oleracea var. capitata) is commonly cultivated in high altitudes and features dense, tightly packed leaves. The Green Coronet variety is well-known for its robust growth and culinary versatility. Maximizing yield is crucial for food sustainability. It is essential to predict the soil’s major nutrients (nitrogen, phosphorus, and potassium) to maximize the yield. Artificial intelligence is widely used for non-linear predictions with explainability. This research assessed the predictive capabilities of soil nitrogen, phosphorus, and potassium levels with explainable machine learning methods over an 85-day cabbage growth period. Experiments were conducted on cabbage plants grown in central hills of Sri Lanka. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) were used to clarify the model’s predictions. SHAP analysis showed that high feature values of the number of days and plant average leaf area negatively impacted for nutrient predictions, while high feature values of leaf count and plant height had a positive effect on the nutrient predictions. To validate the results, 15 greenhouse-grown cabbage plants at various growth stages were selected. The nitrogen, phosphorus, and potassium levels were measured and compared with the predicted values. These insights help refine predictive models and optimize agricultural practices. A user-friendly application was developed to improve the accessibility and interpretation of predictions. This tool is a user-friendly platform for end-users, enabling effective use of the model’s predictive capabilities.
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    PublicationOpen Access
    Identifying the causes of adolescent malnutrition in Nuwara-Eliya District, Sri Lanka
    (Nature Research, 2025-05-06) Nandajeewa, S; Aluthwatta, S; Weerarathna, R; Rathnayake, N; Rajapakse, V; Wijesinghe, N; Liyanaarachchi, T
    Malnutrition, a persistent illness, significantly reduces fat, muscle and bone levels, harming internal organs. The economic crisis in Sri Lanka has led to widespread malnutrition among children, including adolescents experiencing growth spurts. This study identifies factors influencing malnutrition in grade 10 pupils in the Nuwara-Eliya District, with the highest rates of malnutrition and also a multicultural area with many estate sector residents. Using a cross-sectional, quantitative approach, the data was collected from 379 respondents via a Likert scale questionnaire. Structural Equation Model (SEM) analysis was conducted using Smart PLS 4.0. Key findings indicate that environmental factors, such as access to clean water and sanitation, significantly influence adolescent malnutrition. A comprehensive strategy incorporating education, healthcare, and environmental improvements is essential for this. Ongoing observation, community engagement, and cooperative tactics are crucial for sustainable solutions. Addressing environmental issues and promoting a holistic approach to health education and infrastructure improvements are vital to combat adolescent malnutrition in vulnerable populations
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    PublicationOpen Access
    Beyond compensation: effect of employee benefits on job motivation, performance, and turnover intention
    (Cogent OA, 2025-12-31) Peemanee, J; Weerarathna, R; Issarapaibool, A; Boonlua, S; Rathnayake, N
    This study investigates the influence of employee benefits on motivation, performance, and turnover intention within contemporary workplaces that increasingly emphasize employee well-being. Addressing a key gap in the literature, it employs Structural Equation Modeling (SEM) and analyzes data from 387 Generation Y and Generation Z employees in Small and Medium Enterprises (SMEs) in Thailand. The analysis examines how diverse benefit types influence employees’ motivation, performance, and decisions to remain with their organizations. The findings reveal a direct and positive link between employee benefits, enhanced motivation, and improved performance, which together significantly reduce turnover intention. Specifically, attraction and retention strategies, organizational support mechanisms, and a growth-oriented, well-being-focused environment emerged as critical factors in motivating employees and elevating their performance. Overall, the study demonstrates that strategically designed employee benefit packages—aligned with employee needs and workplace realities—foster engagement, productivity, and loyalty. This study contributes valuable insights for organizational leaders seeking to refine benefit systems and extends the academic understanding of the strategic importance of non-monetary benefits in promoting employee satisfaction and retention.
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    PublicationOpen Access
    Factors influencing migration intention of undergraduates in Sri Lanka: ‘About more than employment
    (Elsevier Ltd, 2026-01-26) Marawila, R; Weerarathna, R; Rathnayake, N; Guruge, R; Wehella, B; Udugahapattuwa, T; Weligodapola, M
    The objective of this study is to examine the factors influencing Sri Lankan undergraduates' intention to migrate. Persistent economic, social, and political challenges have driven many youngsters and professionals to leave their Country of Origin (COO). The economic collapse triggered by COVID-19 further intensified this trend, leading to a sharp increase in outward migration. Recently, a growing number of Sri Lankan undergraduates and skilled professionals have expressed a strong desire to relocate abroad, often immediately after completing secondary education. For this study, a sample of 385 undergraduates from state and non-state universities across Sri Lanka was analysed. Given the national concerns of brain drain and shortages of trained and skilled workers, the study specifically focused on understanding undergraduates' aspirations to migrate. Structural Equation Modeling (SEM) was applied to identify and test the variables influencing migration intentions within the Sri Lankan context. The findings provide a holistic picture of the drivers of undergraduate migration. These carry important implications not only for students but also for policymakers and Higher Education Institutions (HEIs), by informing policies and strategies that could encourage young people to realise their potential within Sri Lanka rather than abroad.
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    PublicationOpen Access
    Carbon emissions across income groups: exploring the role of trade, energy use, and economic growth
    (Springer Nature, 2025-07-10) Dharmapriya, N; Gunawardena, V; Methmini, D; Jayathilaka, R; Rathnayake, N
    This study investigates the interplay of trade openness, energy consumption, and gross domestic product (GDP) on carbon emissions across different income groups, analysing data from 163 countries from 2000 to 2019. Using panel regression and multiple linear regression techniques, the findings highlight energy consumption as the principal driver of carbon emissions across all income categories, underscoring its central role in environmental sustainability challenges. High-income countries, despite technological advancements, continue to exhibit substantial emissions due to their reliance on fossil fuels. In contrast low-income nations face difficulties in balancing economic growth with environmental sustainability, often lacking the resources to adopt cleaner energy alternatives. The study emphasises the urgent need for income-specific strategies to reduce carbon emissions, advocating for the widespread adoption of renewable energy sources and tailored policy interventions. These insights align with the United Nations Sustainable Development Goals, particularly SDG 13 (Climate Action), by promoting the integration of economic development with environmental stewardship. By addressing disparities across income levels, this research offers actionable recommendations for policymakers to support equitable and sustainable practices globally.
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    PublicationOpen Access
    Enhancing the Understanding of climate dynamics: analysis of global warming’s influence on Climatic changes across continents
    (Springer, 2025-07-14) Dharmapriya, N; Edirisinghe, S; Gunawardena, V; Methmini, D; Rathnayake, N; Jayathilaka, R
    Global warming, primarily due to increased atmospheric carbon dioxide, poses a significant threat to climate stability, yet research on its combined effects across different geographical areas is limited. In order to fill that gap, this study examines how carbon emissions (CE) are impacted by greenhouse gas emissions (GHG), agricultural nitrogen oxide (ANO), urban population (UP), and fossil fuel consumption (FFC) in 185 different nations between 2000 and 2019. With the exception of urban population, which was expressed as a percentage, all variables were standardised to metric tonnes per capita using panel regression analysis. The results draw attention to geographic disparities. Africa has the lowest carbon and greenhouse gas emissions due to its extensive forest cover and minimal industrial production. Although Oceania’s greenhouse gas emissions have decreased, the region continues to emit high amounts of agricultural nitrous oxide. Rapid industrialisation is the primary cause of Asia’s growing consumption of fossil fuels. Agricultural nitrous oxide and carbon emissions have a negative correlation in Asia, Oceania, and the globe, but a positive correlation in Africa, America, and Europe. Carbon emissions and the use of fossil fuels are strongly positively correlated in every region but Asia. These results highlight the complex, location-specific factors affecting carbon emissions. For policymakers to effectively cut emissions, they must develop customised, geographically specific initiatives. In order to accomplish Sustainable Development Goal 13: Climate Action by 2030, emission controls should be strengthened, and sustainable practices should be encouraged, particularly in the use of fossil fuels and farming.
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    PublicationOpen Access
    Understanding the interplay of GDP, renewable, and non-renewable energy on carbon emissions: Global wavelet coherence and Granger causality analysis
    (PLoS ONE, 2024-09-19) Caldera, Y; Ranthilake, T; Gunawardana, H; Senevirathna, D; Jayathilaka, R; Rathnayake, N; Peter, S
    This study examines the causality of Per Capita Gross Domestic Production (PGDP), Renewable Energy Consumption (REC), and Non-Renewable Energy Consumption (NREC) on Carbon dioxide (CO2) emissions at the global level utilising data gathered from 1995 to 2020 across various countries categorised based on income levels as High, Low, Upper Middle and Lower Middle and analysed through wavelet coherence. The findings reveal both bidirectional and unidirectional causality between the variables which have evolved. Globally, a bi-directional relationship is observed with a positive correlation between PGDP and NREC and in contrast, a negative correlation with REC. Furthermore, the analysis highlights varying causalities between CO2 emissions and PGDP, except for high-income and lower-middle-income country categories, all other shows one-way causality in different periods in the short term. Moreover, CO2 and REC, show unidirectional causality throughout the short-term, exceptionally medium & long term have both unidirectional and bidirectional causalities across all country categories with a positive correlation. In contrast, CO2 and NREC depict similar causalities to REC, however, with a negative correlation. A cross-country analysis was performed between CO2 and PGDP, CO2 and REC, and CO2 and NREC using Granger causality which shows mixed relationships. The findings hold significant implications for policymakers, providing valuable insights into the trade-offs between economic growth, energy consumption, and carbon emissions.
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    THE PERSPECTIVES OF UNIVERSITY ACADEMICS ON THEIR INTENTION TO PURCHASE GREEN SMARTPHONES IN SRI LANKA
    (Informing Science Institute, 15 June 2023) Janak, P; Kumarasinghe; Huang, Q; Rathnayake, N
    Aim/Purpose Most people use their phones for work and communication. Businesses today require sustainable mobile phones to limit the environmental impact of mobile phones. According to the Environmental Protection Agency (EPA), a green product uses less energy. Green smartphones need low radiation emission, are made from recyclable materials, and are designed to last longer than typical smartphones. Further, the manufacturing process needs to have a low environmental impact. The present study aims to identify the influence of variables (such as Green Awareness, Environmental Concern, Altruism, and Willingness to Pay) on green smartphone purchase intention among academics in the Sri Lankan higher education sector. Background With the swift technological advances, almost everyone has begun to use smartphones. Simultaneously, smartphone manufacturers have begun to release cutting-edge smartphone models to the general public. As a result, it has generated a significant amount of e-waste for the environment. As a result, therefore, the sustainability of green smartphones has become a major societal concern in the developed world, but this is not yet true in the developing world Methodology The study used a qualitative research method in which the authors attempted to acquire primary data by conducting in-depth interviews with academics from the Sri Lankan higher education sector using a semi-structured interview guide. Eight interviews were conducted, audio recorded, and word-to-word transcribed for content analysis. Researchers used content analysis to determine the presence, meanings, and linkages of specific words, themes, or concepts. Contribution The findings provide important environmental insights for smartphone makers and society, such as introducing waste reduction programs and energy-saving practices and creating awareness among people to change their consumption patterns. The study will provide valuable insights into the green smartphone phone purchasing intentions of academics in a developing country, especially helping green smartphone producers and marketers construct effective tactics with the insight of the current study based on university faculty members’ viewpoints. Findings The current study’s findings revealed that academics acknowledge the need for environmental protection with an awareness of the green concept and environmental concerns. According to the interviews, most participants intended to move from their present smartphone to an ecologically friendly phone, as they explained on altruism. This implies that even academics in underdeveloped countries are worried about environmental issues and have shown a more robust understanding of these issues and how environmentally aware individuals’ activities may assist the earth’s sustainability. Further, academics have a willingness to pay for a green smartphone. Recommendations for Practitioners Academics prioritize environmental conservation when making purchases. This implies that manufacturers and enterprises should focus on developing and innovating more environmentally friendly products. Recommendations for Researchers Using only academics as a sample approach is severely limited if the study’s population comprises people with various qualities. Nevertheless, this study presented only four independent variables, and more factors impacting green smartphone purchasing intention may exist. As a result, it is proposed that future research consider other factors. Impact on Society It was discovered that most participants displayed altruism in their product purchases, implying that policymakers must strengthen the moral practice of concern for the welfare and happiness of other humans, even in developing countries. Future Research A further in-depth study focusing on many perspectives, such as limits and motivations for purchasing green products in various socioeconomic groups with varying moderating factors such as gender, education, and rural-urban, would be advantageous. Individual (emotions, habits, perceived behavioral control, trust, values, personal norm, knowledge) and situational (availability, product attributes, subjective norm, brand, eco-labeling) variables should be included in future research