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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Author: search.filters.author.Rathnayake, NStart date: 2020

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Now showing 1 - 10 of 27
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    PublicationOpen Access
    Beyond compensation: effect of employee benefits on job motivation, performance, and turnover intention
    (Cogent OA, 2026) 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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    PublicationEmbargo
    Exploring Food Waste Management Practices: Insights from a Coastal Hotel in Sri Lanka
    (University of Nigeria Department of Mass Communication, 2025-01) Panapitiya, C; Dias, A; Aluthge, K; Ahamed, A; Weligodapola, M; Rathnayake, N
    Background: Food waste is immense, accounting for over one-third of worldwide food production for human consumption, totalling 1.3 billion tons annually. Additionally, Sri Lanka's estimated daily food waste is 4000 tons, with the hotel and hospitality sector responsible for a notable percentage. Therefore, it is essential to understand how this amount of waste is generated and what strategies are being employed to manage it. Objective: The research intends to identify the causes of food waste and understand the main waste generation points and food waste mitigation techniques employed within the hotel. Methodology: This exploratory study employs a qualitative approach to examine food waste management practices at a coastal hotel in Sri Lanka’s western province. Researchers used purposive sampling, semi-structured interviews with four key informants, and participant and non-participant observations. Thematic analysis was used to analyse the data. Results: The findings demonstrate that the hotel implements various food waste management techniques, including operational, quality, and standards elements. These techniques vary from internal initiatives to external measures. Conclusion: Effective practices can reduce the waste generated throughout the hotel's food supply chain. This will ultimately result in better economic, environmental, and societal outcomes. Unique Contribution: This research provides useful insights and lays the groundwork for future research by addressing this timely issue prevailing within both the local and global hotel industry. These findings can be applied to other settings, such as households, retail, and emerging hotels and resorts.
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    PublicationEmbargo
    Enhancing the Understanding of climate dynamics: analysis of global warming’s influence on Climatic changes across continents
    (Springer Science and Business Media, 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
    How effective are incentives in driving green behavior? Analyzing monetary and non-monetary incentives in the hospitality industry
    (Cogent OA, 2025-09-16) Dilmi, K.A; Sannasgala, S; Weerarathna, R; Rathnayake, N; Pitipanaarachchi, S.M; Dushmanthi, N; Rajapakse, V
    This study employs a cross-sectional survey of 383 Sri Lankan hospitality employees to examine the impact of monetary and non-monetary incentives on Green Employee Behavior (GEB). Using Structural Equation Modeling (SEM), the study tested relationships between incentives and workplace sustainability actions, distinguishing between in-role and extra-role behaviors. Findings show both incentive types significantly enhance GEB. Monetary rewards, explaining 36.3% variance, primarily drive compliance with green policies, whereas non-monetary rewards exert a stronger influence on voluntary, value-driven behaviors that build long-term green culture. These results highlight the complementary role of incentives: monetary rewards secure short-term adherence, while non-monetary rewards foster sustained commitment to environmental practices. The study provides practical guidance for managers and policymakers in designing dual-incentive strategies that balance immediate compliance with enduring green engagement. By integrating such schemes, hospitality firms can reduce their environmental footprint and align with broader sustainability goals. Beyond managerial implications, the study adds to the growing literature on workplace sustainability by empirically demonstrating how incentive structures distinctly shape in-role and extra-role green behaviors. This evidence emphasizes the importance of tailoring incentive programs to nurture both compliance and proactive contributions to organizational sustainability.
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    PublicationEmbargo
    Shadows on the ivory tower: the unseen scars of academic bullying in South Asia
    (Emerald Publishing, 2025-08-18) Jayasinghe, P.S.K; Kevitiyagala, L; Joshep, K; Rajapaksha, S; Illangamtilake, K; Rathnayake, N
    Purpose Workplace bullying (WB) is increasingly recognised in academic literature. This study aims to investigate the relationship between WB and turnover intentions among academics in Sri Lanka’s higher education sector, focusing on the mediating role of supervisor support (SS). Design/methodology/approach Data were collected via a structured questionnaire from 346 academics using simple random sampling. Structural equation modelling using Smart PLS was used to test the hypothesised relationships. Findings This study reveals a strong positive relationship between WB and employee turnover intention, indicating that academics subjected to bullying are more likely to consider leaving their institutions. Furthermore, SS significantly mediates this relationship, emphasising its role in mitigating the adverse effects of bullying. Research limitations/implications The cross-sectional nature of this study limits the ability to capture long-term dynamics. Future research should incorporate longitudinal or qualitative approaches to explore additional mediating or moderating factors. Originality/value This research fills a critical gap in the literature by focusing on the underexplored Sri Lankan higher education sector, which has received limited scholarly attention. In contrast to prior research that focused on developed nations and other industries, this study provides new insights into the types and impacts of WB in academic institutions. It also underscores the value of SS and offers practical recommendations for creating supportive and retention-friendly work environments.
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    PublicationOpen Access
    Bridging the chatbot connection: the role of AI-driven chatbot affordances in e-commerce purchase intentions
    (Emerald Publishing, 2025-10-05) Yatawara, K.M.B; Sampath, T; Kalupahana, P.L; Rathnayake, S.O; Jayasuriya, N; Rathnayake, N
    Purpose – This paper explores how AI-driven chatbots, using social support and affordance theories, influence consumer behavior and purchase intentions (PIs) in e-commerce, addressing challenges like limited understanding and security concerns. Design/methodology/approach – A quantitative study was conducted to collect data from 385 customers who had interacted with AI-driven chatbots on e-commerce platforms. Purposive sampling was employed to ensure the relevance of the respondents. Partial least squares-structural equation modeling (PLS-SEM) analysis was performed using SmartPLS 4 to test the hypothesized relationships and analyze the data. Findings – The results show a small direct effect of chatbot affordances on PI, with stronger indirect effects via customer satisfaction (CS) and trust. Notably, trust does not directly influence PI, suggesting a reliance on word-of-mouth. Customer engagement (CE) plays a minor mediating role, highlighting the importance of emotional and experiential factors. Originality/value – This pioneering study within the Sri Lankan context addresses the underexplored area of AI-driven chatbots and their influence on PI. Additionally, the study provides nuanced insights into the mediating mechanisms of CS, trust and engagement in the relationship between chatbot affordances and PI.
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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