Research Publications Authored by SLIIT Staff
Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/4195
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.
Browse
131 results
Search Results
Publication Embargo The digital bridge: how digital transformation mediates the innovative culture-resilience nexus in IT firms(Emerald Publishing, 2025) Kodithuwakku, T; Samaraweera, I; Mathew, M; Samarakkody, T; Thelijjagoda, S; Gamage, SPurpose – This study aims to identify the impact of innovative culture on organizational resilience in the Sri Lankan information technology (IT) sector, with a specific focus on the mediation role of digital transformation. Design/methodology/approach – Using a quantitative approach, data were collected from over 274 participants who were managerial or above-level employees in the IT industry via surveys. Partial least squares structural equation modeling was used to analyze the data and test the hypothesized relationships between variables. Findings – The findings of this study revealed that innovative culture has a significant positive impact on the adoption of digital transformation, as the innovative mindset that is ingrained encourages continuous growth, creativity and risk-taking, thereby strengthening digital transformation initiatives. Both innovative culture and digital transformation have a significant positive impact on organizational resilience. Digital transformation significantly mediates the effect of innovative culture on organizational resilience. Practical implications – The findings offer valuable guidance to industry leaders and policymakers for the strategic utilization of technology and the design of appropriate business models. Originality/value – This study emphasizes the importance of developing innovative culture and digital transformation in the IT industry to ensure sustainable business processes.Publication Embargo Exploring deceptive behavior in intra-organizational activities of teleworkers in the IT sector in Sri Lanka(Springer Science and Business, 2025-07-04) Rajapakshe, W; Bangsajayah B.S.AThis study aims to explore and validate a conceptual framework based on socio-technical systems and information manipulation theories to understand how deficiencies in IT infrastructure and interpersonal distrust lead to communication breakdowns and foster deceptive behavior. The research examines this phenomenon, which became particularly pronounced as companies shifted to remote work during the COVID-19 pandemic. The study employs moderated regression analysis (MRA) utilizing the PROCESS macro model 7 to assess hypotheses concerning the mediated moderation effect of deception. Data was collected from a judgmental sample comprising 200 remote IT workers to probe their motivations for deceptive practices within virtual work environments. Research findings demonstrate that the moderated mediation index (the interpersonal trust index) is −.1894. Moderated mediation is statistically significant, not including zero, as indicated by the 95% confidence interval (−.2380 to −.1385). Interpersonal trust moderates the indirect effect of IT infrastructure on communication deception. These findings imply that teleworkers can effectively communicate information if companies provide the infrastructure. Interpersonal trust can increase communication even in inappropriate household environments. Employers should prioritize managing trust and maximizing human capital to create a win–win situation for the company and teleworkers. This study sheds light on the role of interpersonal trust in shaping the relationship between communication and deception, filling a gap in the empirical literature on virtual work environments in the post-pandemic landscape. It provides novel insights by demonstrating how organizational trust moderates communication dynamics and mediates the influence of both deceit and IT infrastructure provision.Publication Open Access Enhancing Organizational Threat Profiling by Employing Deep Learning with Physical Security Systems and Human Behavior Analysis(Science and Information Organization, 2025) Senevirathna D.H; Gunasekara W.M.M; Gunawardhana K.P.A.T; Ashra M.F.F; Fernando, H; Abeywardena, K. YOrganizations need a comprehensive threat profiling system that uses cybersecurity methods together with physical security methods because advanced cyber-threats have become more complex. The objective of this study is to implement deep learning models to boost organizational threat identification via human behavior assessment and continuous surveillance activities. Our method for human behavior analysis detects insider threats through assessments of user activities that include logon patterns along with device interactions and measurement of psychometric traits. CNN, together with Random Forest classifiers, has been utilized to identify behavioral patterns that indicate security threats from inside the organization. Our model uses labeled datasets of abnormal user behavior to properly differentiate between normal and dangerous user activities with high accuracy. The physical security component improves surveillance abilities through the use of MobileNetV2 for real-time anomaly detection in CCTV video data. The system receives training to detect security breaches and violent and unauthorized entry attempts, and specific security-related incidents. The combination of transfer learning and fine-tuning methodologies enables MobileNetV2 to deliver outstanding security anomaly detection alongside low power requirements, thus it fits into Security Operations Centers operations. Experiments using our framework operate on existing benchmark collection sets that assess cybersecurity, together with physical security threats. Experimental testing establishes high precision levels for detecting insider threats along with physical security violations by surpassing conventional rule-based methods. Security Operation Centers gain an effective modern threat profiling solution through the application of deep learning models. The investigation generates better organization defenses against cyber-physical threats using behavioral analytics together with intelligent surveillance systems.Publication Embargo Focus on Middle East and Central Asia: rationale of IMF assistance seeking(Springer Science and Business, 2025-11-08) Wisenthige, K; Pathiranage, H.S.K; Jayathilaka, RThis study delves into the rationale behind the tendency of nations in the Middle East and Central Asia (MECA) to seek aid from the IMF. The IMF supports global financial stability, aiming to foster economic growth and prosperity across its member countries by promoting policies that encourage monetary cooperation and financial resilience. The study employs a conditional fixed-effects logit model, the analysis spans 22 years of data from twenty-five MECA countries to identify the factors driving these nations to seek IMF assistance. It focuses on six determinants: Current Account Balance (CAB), Inflation (INF), Corruption (CORR), General Government Net Lending and Borrowing (GGNLB), General Government Gross Debt (GGGD), and Gross Domestic Product Growth (GDPG). The fixed-effects logit shows that slower GDP growth raises the odds of an IMF programme, while short-run changes in corruption control and public debt ratios are not significant once country and year effects are absorbed. Inflation is weakly positive; the current account balance is still insignificant. A post-GFC and an income-group robustness check confirm the pattern. Furthermore, the study identifies Lebanon, a lower-middle-income country, as a leading example of seeking IMF assistance during the study period. Overall, this research highlights the importance of policymakers understanding the dynamics and rankings within the MECA region to effectively address economic challenges, provide financial support, and foster a more sustainable economic structure.Publication Open Access Simple Switch between Single Trip Vehicle Routing Problem and Multiple Trip Vehicle Routing Problem(Sciendo, 2025-01-01) Samarakkody, Tomplexity of the mathematical model development for vehicle routing problems increases with the addition of more variables, constraints, and instances to single trip vehicle routing models. In this study, mathematical model was developed for the single trip vehicle routing problem in the initial phase and then it was converted to a multiple trip vehicle routing model using a simple approach. Novelty is brought to the study through simple three index formulation developed for the multiple trip vehicle routing problem, with a fewer constraint. Both the models were developed with Mixed integer linear programing techniques and were tested with the real-world data set using Cplex optimizer. Output of the experimental analysis showed a clear reduction in distance travel and the number of vehicles used. It implies that the optimization algorithm proposed in the study is applicable to real world cases to enjoy cost benefits and easiness in scheduling and optimizations. This study contributes to the existing knowledge gap through the development of novel and simple mathematical model, and the model’s testing and validation serve to the industrial applications as well.Publication Open Access The role of platform interactivity in enhancing trust: unlocking purchase intentions for skincare products on Facebook(Cogent OA, 2025-10-07) Jayasingha, N; Kavindiya, W; Ranjith, D.P; Pathiranage, S.N; Wisenthige, K; Dayapathirana, NSocial commerce, which integrates social media with e-commerce, has transformed how consumers engage with brands and make purchasing decisions. In Sri Lanka, the skincare product market on Facebook has seen significant growth, emphasizing the need to understand the factors influencing consumer purchase intention. This study explores how social media, perceived usefulness and platform interactivity influence trust in the seller and, in turn, affect social commerce purchase intention. Using purposive sampling, the study targeted active Facebook users who purchase skincare products. An online questionnaire was administered to 384 such users. Using structural equation modelling, the study found that perceived usefulness and platform interactivity significantly enhance trust in sellers. Additionally, trust in the seller plays a mediating role between these factors and purchase intention. The study offers theoretical contributions by extending the Technology Acceptance Model (TAM) into a high-involvement product context. The findings highlight that a more interactive and engaging platform experience increases consumer confidence in online sellers, ultimately encouraging purchase behavior. Social media platforms like Facebook not only provide a space for product promotion but also serve as trust-building environments through user engagement and perceived usefulness. This study finds that useful and interactive Facebook posts build trust and lead to more skincare product purchases. Brands should post better content to earn trust and boost sales. For businesses, especially skincare brands operating in social commerce environments, this study offers practical insights into developing strategies focused on enhancing platform interactivity and trust to drive consumer engagement and intention to purchase.Publication Open Access Improved Path Planning for Multi-Robot Systems Using a Hybrid Probabilistic Roadmap and Genetic Algorithm Approach(Department of Agribusiness, Universitas Muhammadiyah Yogyakarta, 2025-03-24) Jathunga, T; Rajapaksha, SThis study focuses on the development and application of an improved Probabilistic Roadmap (PRM) algorithm enhanced with Genetic Algorithms (GA) for multi-robot path planning in dynamic environments. Traditional PRM-based methods often struggle with optimizing path length and minimizing turns, particularly in complex, multi-agent scenarios. To address these limitations, we propose a hybrid PRM-GA approach that incorporates genetic operators to evolve optimal paths for multiple robots in real-time.The research contribution is an enhanced PRM-GA framework that improves efficiency in multi-robot navigation by integrating evolutionary techniques for dynamic obstacle handling and optimized path generation.The research methodology involves testing the algorithm in various environments, including varying robot numbers and environmental complexities, to evaluate its scalability and effectiveness. Our results demonstrate that the PRM-GA algorithm successfully reduces both path lengths and turn counts compared to standard PRM-based methods, ensuring collision-free and smooth paths. The algorithm showed robust performance across different scenarios, effectively handling dynamic obstacles and multi-agent coordination. However, in highly dynamic environments with rapidly changing obstacles and constraints, the algorithm may occasionally produce paths with turn counts and distances similar to or slightly higher than those of simpler approaches due to the need for frequent re-optimization. Future research can explore incorporating additional factors such as energy consumption and time optimization, alongside distance and turns, to further enhance the algorithm's efficiency in real-world applications. Overall, the PRM-GA approach advances the state of the art by offering a more adaptable and scalable solution for multi-robot path planning, with applications in logistics, industrial automation, and autonomous robotics.Publication Open Access Real Time Accident Detection and Emergency Response Using Drones, Machine Learning and LoRa Communication(Science and Information Organization, 2025) Bandara H.M.S.I.D; Maduhansa H.K.T.P; Jayasinghe S.S; Samararathna A.K.S.R; Fernando, H; Lokuliyana, SRoad accidents and delayed emergency responses remain a major concern in urban environments, contributing to over 1.4 million fatalities globally each year. With rapid urbanization and increasing vehicle density, timely detection and efficient traffic management are critical to reducing the impact of such events. This study proposes a real time Accident Detection and Emergency Response System with integrating Machine Learning IoT enabled drones and LoRa communication. The system combines real time accident detection using CCTV, drone assisted fire detection for post accident scenarios, crime activity monitoring and automated traffic management to reduce congestion and improve public safety. LoRa ensure long range, energy-efficient communication. ML models improve detection accuracy across accidents, fires, crimes and vehicles. Figures and sensor data are analyzed in real time to trigger alerts and assist emergency responders. The system supports scalable integration with existing urban infrastructure, promoting the development of smart city safety frameworks. By minimizing emergency response time, limiting secondary incidents and improving situational awareness, the proposed solution addresses critical gaps in current urban safety systems. It offers a practical, intelligent and adaptive approach to accident mitigation and traffic control in smart cities.Publication Open Access Alcohol Consumption and Stroke Mortality: Global Patterns, Risks and Public Health Implications(Springer, 2025-05-07) Kolonne, T; Mudalige, K; Dissanayaka, G; Rathnayake, K; Jayathilaka, R; Rajamanthri, L; Wickramaarachchi, CGlobally, stroke remains a leading cause of mortality and disability, while alcohol consumption continues to vary widely across regions, prompting concern over its health impacts. This study examines the association between different alcoholic beverages and stroke mortality, using secondary data from 1990 to 2020. Alcohol consumption and stroke death rates across 189 countries were categorized into five levels, from very high to very low, and averaged over two periods (1990–1999 and 2011–2020). Multiple Correspondence Analysis (MCA) was applied to assess relationships among four categorical variables. The findings indicate a significant association between very high alcohol consumption and increased stroke mortality, with eight countries showing elevated death rates. Conversely, moderate beer consumption was linked to reduced stroke mortality, suggesting nuanced effects based on beverage type and quantity. These insights offer a foundation for targeted public health policies and emphasize the need for further investigation into the mechanisms driving alcohol-related stroke risks.Publication Embargo Addressing Child Labour in SAARC: The Synergy of Education, Health and Economic Growth Towards SDGs(John Wiley and Sons, 2025-11-09) Muthugala, H; Magammana, T; Perera, A; Bandara, A; Jayathilaka, RChild labour remains a critical socio-economic challenge in the SAARC region, closely linked to sustainable development goals (SDGs). This study investigates the determinants of child labour by examining the roles of education, health and economic growth using a robust methodological framework. The analysis captures the non-linear country-specific relationships between these variables and child labour, employing advanced methodological approaches, including multiple polynomials, stepwise and simple polynomial regression. The findings reveal a complex interplay of factors, with each variable showing positive and negative effects on child labour in country-specific contexts. Improved access to education generally reduces child labour, but disparities in quality and affordability can have the opposite effect. Health improvements significantly lower child labour rates, yet unequal healthcare access perpetuates exploitation among vulnerable groups. Economic growth shows dual effects: it promotes adult employment and alleviates poverty, yet unregulated expansion in specific sectors can heighten the demand for child labour. This study makes a novel contribution by integrating socio-economic determinants with child labour within a regional framework, providing actionable insights while aligning with SDGs 3, 4, 8 and 8.7. Key policy recommendations include fostering regional collaboration, ensuring access to free education, enacting and enforcing new laws, improving healthcare infrastructure and promoting inclusive and sustainable economic growth. These measures align with global SDG commitments but aim to secure a brighter future for the region's children by achieving these goals by 2030.