Browsing by Author "Jayathilake, T"

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Open Access
Factors Impacting Job Satisfaction of Female Academic Staff in Sri Lankan State Universities
(SLIIT Business School, 2023-12-14) Edirisinghe, G; Serasinghe, I; Amarasinghe, S; Galappatti, D; Wisenthige, K; Jayathilake, T
The purpose of this research is to investigate the critical factors impacting job satisfaction of female academic staff in state universities. With a rising number of women entering academia, it is critical to identify the factors that contribute to their job satisfaction to build an inclusive and supportive work environment. Using a comprehensive literature review, survey questionnaires, this research investigates numerous factors that affect job satisfaction of female academic staff. The study was adopted a quantitative-method approach to gather data from a diverse sample of female academic staff members from various state universities with structured questionnaire is administered to a representative sample of female academic staff to gather quantitative data on their job satisfaction levels and identify key factors influencing their contentment at work. Using 330 sample size for the research according to the morgan table and using smart pls software for the data analysis. Data analysis method is structural equation modelling. According to the study's findings, several major factors have a substantial impact on the job satisfaction of female academic staff members in state universities. When it comes to their overall job satisfaction, work-life balance stands out as a key factor, with many participants citing difficulties juggling their professional and personal obligations. The study also emphasizes the value of inclusive and encouraging work environments and employee dedication. The results of this study have broad repercussions. Institutional leaders and politicians can conduct targeted interventions to improve working conditions and promote a more inclusive and supportive environment by studying the factors impacting job satisfaction among female academic staff at state universities. With a particular focus on the experiences of female academic workers, this study adds to the body of knowledge on job satisfaction in academia.
Open Access
Impact of Dynamic Capabilities on Business Performance of SMEs during an Economic Crisis with reference to Western Province
(SLIIT Business School, 2023-12-14) Haputhanthri, H; Jayawickrama, U; Lakma, L; Sellahewa, E; Wisenthige, K; Jayathilake, T
The current economic crisis which is experiencing has a significant impact on the development and performance of most SMEs, making their existence even more susceptible. In the face of the current economic crisis, Dynamic capabilities (DCs) can be utilized as a survival mechanism to help organizations to increase the value of their businesses, get competitive advantages, and increase business performance in a changing business environment. Therefore, the purpose of this paper is to analyze the impact of three dimensions of DCs which are sensing, seizing, and reconfiguring on the business performance of SMEs during this economic crisis. In addition, this study investigates how DCs could impact business performance through Information technology (IT) adoption as a moderator. The quantitative approach is adopted, where a cross sectional survey was utilized to collect primary data from SMEs. Findings of the study based on a sample of 380 SMEs in western province and stratified random sampling method was utilized to select participants. Structural Equation Modeling (SEM) was used to analyze data by using Smart PLS 4 software. The results revealed that only sensing and reconfiguring capabilities have a significant impact on SMEs’ performance and IT adoption moderates the relationship between DCs and business performance during the economic crisis. Therefore, this study provides a great effort to quantitatively investigate the impact of three procedures of DCs and the moderate effect of IT adoption during the economic crisis. Furthermore, it conveys a better understanding of how SMEs could deploy their DCs to ensure higher levels of performance in periods of crisis. The results of this research will pave a path for them to successfully take effective strategic decision on the SMEs.
Open Access
Wetland Water Level Prediction Using Artificial Neural Networks—A Case Study in the Colombo Flood Detention Area, Sri Lanka
(MDPI, 2023-01) Jayathilake, T; Sarukkalige, R; Hoshino, Y; Rathnayake, U
Historically, wetlands have not been given much attention in terms of their value due to the general public being unaware. Nevertheless, wetlands are still threatened by many anthropogenic activities, in addition to ongoing climate change. With these recent developments, water level prediction of wetlands has become an important task in order to identify potential environmental damage and for the sustainable management of wetlands. Therefore, this study identified a reliable neural network model by which to predict wetland water levels over the Colombo flood detention area, Sri Lanka. This is the first study conducted using machine learning techniques in wetland water level predictions in Sri Lanka. The model was developed with independent meteorological variables, including rainfall, evaporation, temperature, relative humidity, and wind speed. The water levels measurements of previous years were used as dependent variables, and the analysis was based on a seasonal timescale. Two neural network training algorithms, the Levenberg Marquardt algorithm (LM) and the Scaled Conjugate algorithm (SG), were used to model the nonlinear relationship, while the Mean Squared Error (MSE) and Coefficient of Correlation (CC) were used as the performance indices by which to understand the robustness of the model. In addition, uncertainty analysis was carried out using d-factor simulations. The performance indicators showed that the LM algorithm produced better results by which to model the wetland water level ahead of the SC algorithm, with a mean squared error of 0.0002 and a coefficient of correlation of 0.99. In addition, the computational efficiencies were excellent in the LM algorithm compared to the SC algorithm in terms of the prediction of water levels. LM showcased 3–5 epochs, whereas SC showcased 34–50 epochs of computational efficiencies for all four seasonal predictions. However, the d-factor showcased that the results were not within the cluster of uncertainty. Therefore, the overall results suggest that the Artificial Neural Network can be successfully used to predict the wetland water levels, which is immensely important in the management and conservation of the wetlands
Open Access
Wetland Water-Level Prediction in the Context of Machine-Learning Techniques: Where Do We Stand?
(MDPI, 2023-05) Jayathilake, T; Gunathilake, M. B; Wimalasiri, E.M; Rathnayake, U
Wetlands are simply areas that are fully or partially saturated with water. Not much attention has been given to wetlands in the past, due to the unawareness of their value to the general public. However, wetlands have numerous hydrological, ecological, and social values. They play an important role in interactions among soil, water, plants, and animals. The rich biodiversity in the vicinity of wetlands makes them invaluable. Therefore, the conservation of wetlands is highly important in today’s world. Many anthropogenic activities damage wetlands. Climate change has adversely impacted wetlands and their biodiversity. The shrinking of wetland areas and reducing wetland water levels can therefore be frequently seen. However, the opposite can be seen during stormy seasons. Since wetlands have permissible water levels, the prediction of wetland water levels is important. Flooding and many other severe environmental damage can happen when these water levels are exceeded. Therefore, the prediction of wetland water level is an important task to identify potential environmental damage. However, the monitoring of water levels in wetlands all over the world has been limited due to many difficulties. A Scopus-based search and a bibliometric analysis showcased the limited research work that has been carried out in the prediction of wetland water level using machine-learning techniques. Therefore, there is a clear need to assess what is available in the literature and then present it in a comprehensive review. Therefore, this review paper focuses on the state of the art of water-level prediction techniques of wetlands using machine-learning techniques. Nonlinear climatic parameters such as precipitation, evaporation, and inflows are some of the main factors deciding water levels; therefore, identifying the relationships between these parameters is complex. Therefore, machine-learning techniques are widely used to present nonlinear relationships and to predict water levels. The state-of-the-art literature summarizes that artificial neural networks (ANNs) are some of the most effective tools in wetland water-level prediction. This review can be effectively used in any future research work on wetland water-level prediction. © 2023 by the authors.