Recent Submissions
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, S
Purpose – 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.
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.A
This 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.
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. Y
Organizations 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.
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, R
This 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.
Simple Switch between Single Trip Vehicle Routing Problem and Multiple Trip Vehicle Routing Problem
(Sciendo, 2025-01-01) Samarakkody, T
omplexity 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.
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The collection comprises the research output of SLIIT staff and postgraduate research students, including research publications, conference and symposium papers, books, book chapters, theses, and other scholarly materials. Access to full texts may be restricted depending on the access and licensing terms.