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Impact of digital transformation on financial performance of licensed commercial banks in Sri Lanka pre and post COVID-19 pandemic
(National Science Foundation: Colombo, 2025-01-10) Herath, H. M. D. K.; Gamlath, Muthubandara
Enhancing the quality of goods and services through innovation and the integration of electronic applications is a fundamental aspect of digital transformation. This topic has increasingly captivated the attention of management and organizational scholars. This focus is especially relevant in improving organizational performance by digitalizing core operations. The global and Sri Lankan economies have faced severe disruptions due to the COVID-19 pandemic, which has compelled commercial banks to adopt innovative digital strategies as a means of mitigating the resultant risks and maintaining stability. This study explores the impact of digital transformation on the financial performance of 10 Licensed Commercial Banks in Sri Lanka, comparing outcomes before and after the pandemic’s onset. By employing a robust methodological framework that includes descriptive statistics, correlation analysis, and T-test analysis, the research investigates key variables to discern trends and patterns across different timeframes. The assessment of digital transformation is conducted through a multi-dimensional approach, focusing on metrics such as the volume of digital transactions, income from fees and commissions, and the proliferation of Automated Teller Machines (ATMs) and Cash Deposit Machines (CDMs). Financial performance, on the other hand, is gauged using critical indicators like Return on Assets (ROA) and Return on Equity (ROE). The analysis reveals nuanced insights: while the increase in ATMs and CDMs correlates positively with financial performance, this relationship lacks statisticalsignificance. In contrast, a substantial and positive effect on financial performance is observed from the volume of digital transactions and the income generated from fees and commissions. Significantly, the findings indicate that digital transformation initiatives have enabled commercial banks to not only weather the pandemic but also enhance their financial performance during this period of unprecedented challenge. These results emphasize the imperative for financial institutions to strategically integrate advanced technologies, such as artificial intelligence, into their digital transformation agendas. By doing so, they can secure and potentially elevate financial performance, even amidst unforeseen global crises like a pandemic.
Detection of sugar adulteration in black tea using multispectral imaging
(National Science Foundation: Colombo, 2026-04-25) Wickramasinghe, W.A.N.D.; Thilakarathne, G.; Ekanayake, E.M.S.L.B.; Wijesinghe, A.D.; Senarath, K.A.S.T.; Herath,H.M.V.R.; Godaliyadda, G.M.R.I.; Ekanayake, M.P.B.; Madhujith, T.; Mohotti, K.M.M.
Black tea, valued globally for its flavor, aroma, and nutritional benefits, is a major export commodity for countries such as China, India, and Sri Lanka. Rising demand has led to sugar adulteration to enhance color, twist, and weight, compromising quality and posing health risks, highlighting the need for rapid and reliable verification methods. This study presents a multispectral imaging (MSI) based approach for detecting and quantifying sugar adulteration in black tea. A custom-built system with thirteen narrow band LEDs (365 nm to 940 nm) sequentially illuminated powdered and brewed samples, capturing 26 spectral images in reflectance and transmittance modes, respectively. Spectral features corresponding to sugar induced color changes were extracted independently of natural tea variability. Preprocessing steps, including dark current subtraction, cropping, histogram equalization, and dimensionality reduction via linear discriminant analysis (LDA), ensured high quality data for analysis. Classification was performed using linear discriminant analysis (LDA), K nearest neighbors (K-NN), support vector machine (SVM), feed forward neural network (FFNN), and convolutional neural network (CNN), achieving accuracies above 93% across the tested models. These methods showed high sensitivity in detecting adulteration at levels as low as 5% (w/w) and strong specificity in distinguishing pure from adulterated brewed samples. Polynomial regression was applied to quantify sugar content, yielding R² values above 0.97 for polynomial orders from the first to the fifth. A third order polynomial was selected as it provided a slightly improved fit (R² = 0.9739) while maintaining low model complexity. These results demonstrate that multispectral imaging combined with machine learning enables reliable detection of sugar adulteration and continuous estimation of adulteration levels between 5% and 25%, supporting rapid and non-destructive monitoring of black tea authenticity
Behavioural intention to adopt mobile trading apps: an integrated theoretical and digital framework, privacy concerns, and information richness model
(National Science Foundation: Colombo, 2025-01-10) Itoo, Rais Ahmad; Jan, Anisa
This research investigates the transformative impact of mobile trading apps on the Indian financial landscape, particularly in the context of the unprecedented surge in DMAT (dematerialisation) accounts following the COVID-19 pandemic. Due to the advancement of online platforms and fast internet connectivity, stock exchanges across the globe have seen a dramatic inflow of retail investors and brokerage firms. The choice of using a particular mobile trading app draws significant importance because various factors determine the ability and ease of use of a specific app. The study underscores the role of FinTech services, particularly mobile trading apps, in revolutionizing stock trading by offering real-time access, increased trading activity, and enhanced features. Despite the proliferation of research on FinTech apps, a notable gap exists in understanding the adoption dynamics of mobile trading apps, especially in the Indian context. To address this gap, our research applies an adapted and extended version of the Unified Theory of Acceptance and Use of Technology (UTAUT-3) framework to examine the factors influencing investors’ intentions and usage of mobile trading apps. We introduce novel elements such as information richness and privacy concerns, which are crucial in the financial domain. A convenient sample size of 573 actively brokerage app-using respondents was selected to investigate and conclude the consumers’ behavioural intention to use mobile trading apps. The findings highlight the significant impact of practical value, effort expectancy, social influence, hedonic motivation, trust, information richness, privacy concerns, facilitating conditions, and personal innovativeness in IT (Information technology) on investors’ intentions to use trading apps. These factors influence behavioural intentions and mediate the relationship between various constructs, emphasizing their multifaceted roles in shaping user perceptions. Theoretical implications of the research contribute to extending the UTAUT-3 model and providing a comprehensive framework for examining technology adoption in the financial domain. Moreover, practical implications guide developers, financial institutions, and policymakers in creating secure, user-friendly, and information-rich mobile trading systems. While acknowledging sampling and self-reported data limitations, this research lays the groundwork for future longitudinal studies. It encourages the exploration of diverse FinTech services to gain a holistic understanding of adoption dynamics in the evolving financial technology landscape. This study adds empirical knowledge to mobile trading app adoption and catalyzes further research, shaping the trajectory of FinTech studies and practical applications in the ever-evolving financial ecosystem.
Effectiveness of National Innovation System of Sri Lanka: examining the roles of universities, S&T institutions and industry
(National Science Foundation: Colombo, 2025-01-10) Weerasinghe, R. N.; Jayawardane, A. K. W.; Huang, Qiubo
After resolving internal conflicts in 2009, Sri Lanka experienced significant economic growth through infrastructure development and other policy initiatives. However, the pace of growth has since decelerated, and the country now faces numerous challenges related to the sustainability of its progress. Scholars and policymakers have identified a primary issue: the need for a systematic approach to the country’s development models. Consequently, this study was conducted to comprehensively examine the policy inputs necessary for optimizing the National Innovation System (NIS) in Sri Lanka, focusing on the three main actors: universities, S&T institutions, and industry. A robust conceptual model was formulated by exploring the existing literature on NIS, their functions, and roles, with particular emphasis on the imperatives of NIS in developing countries. This study aims to understand the roles of these three main actors within NIS, evaluate the effectiveness of their functions, and assess the strength of the networking relationships among them. Based on this conceptual framework, the empirical phase of the study was structured and implemented. This research proposes several policy recommendations based on data analysis to enhance the three primary NIS actors’ role effectiveness and strengthen their network relationships, providing a solid foundation for future policy decisions.
Pathway for Industry 4.0 implementation in a Lean Manufacturing environment: evidence from Sri Lankan apparel sector
(National Science Foundation: Colombo, 2025-01-10) Nasra, M. R. F.; Bandara, A. M. A. S. M.
The fourth industrial revolution (I4.0) was based on several technological pillars developed over the years. Organizations are expected to embrace these technologies to realize the benefits associated with I4.0. However, manufacturing organizations that have optimized their operations through Lean management philosophies need a clear pathway to embrace the I4.0 technologies without disrupting the existing good practices.Therefore, this study aims to conceptualize a much-needed path for implementing I4.0 technologies in a Lean environment. To achieve that, researchers followed a qualitative approach and an exploratory framework. Researchers started with Apparel organizations with highest export revenue and employed a snowball sampling approach within each organization to identify the most suitable professionals for the study. The data collection was carried out through semi-structured interviews conducted through Zoom online platform. The collected data was analyzed through thematic analysis, allowing the identification of different themes. Our findings suggest that such a pathway involves four steps: (1) setting a Lean base, (2) strategic management, (3) human resource development, and (4) getting external support. Since the implementation of I4.0 technologies in a Lean environment is a relatively recent phenomenon, our study provides guidelines for managers and practitioners to help them prioritize efforts and narrow their attention more objectively to the proper mix of procedures and technologies.