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Subject: search.filters.subject.Sri Lanka

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    PublicationEmbargo
    Navigating economic crisis: Factors shaping resilience in Sri Lankan constructionSME supply chains
    (Taylor and Francis, 2024-10-05) Madhavika, N; Bandara, M; Manchanayake, M; Perera, C; Bandara, W; Jayasinghe, P; Ehalapitiya, S
    In today’s construction industry, supply chains are subject to much greater disruption than they were inthe past, resulting in a greater need for resilience. However, there is a gap in the literature that examinesthe resilience of construction small and medium scale Enterprises (SMEs) specifically focusing on develop-ing countries. This article is a step towards identifying the factors influencing the resilience of construc-tion SME supply chains taking the case of Sri Lanka: a developing country which is currently amidst amajor economic crisis. This research study adopted a mixed-method approach, employing 08 structuredinterviews with employees ranging from executive level to top level management of 08 constructionSMEs followed by a questionnaire survey considering a sample of 195 construction SMEs also with execu-tive level to top level management of each construction SME. The findings indicated that Collaboration,Entrepreneurial Orientation (EO), Internal Integration, and Outsourcing have a positive significant impacton the resilience of Sri Lankan construction SMEs’ supply chains during an economic crisis, while‘collaboration’ and ‘EO’ are the most influential factors respectively. Therefore, construction SMEs mustprioritize and enhance collaboration and EO when devising supply chain strategies to strengthen resili-ence during economic crises. This paper contributes to filling the research gap by investigating factorsinfluencing construction SME supply chains in a developing country during an economic crisis. Moreover,it contributes to the knowledge by being one of the latest empirical studies focusing on the constructionSME supply chains in Sri Lanka. The findings provide a valuable reference for both policymakers and prac-titioners seeking to improve the resilience of construction SME supply chains
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    PublicationOpen Access
    Security matters: Empowering e-commerce in Sri Lanka through customer insights
    (Springer Nature, 2024-12) Jayathilaka, R; Udara, I
    In the fast-paced, post-COVID digital world, e-commerce presents promising prospects for significant advancement. However, customers often feel uncertain due to persistent concerns about the robustness of security measures safeguarding e-commerce platforms. The primary objective of our study was to identify factors affecting the security of e-commerce platforms based on the perceptions of Sri Lankan customers. This research was conducted using data collected from Sri Lankan e-commerce users via both online and offline surveys. An ordered probit regression model was utilised, demonstrating that transaction security, privacy, vendor system security, and platform quality positively impact the perceived security of e-commerce. The e-commerce industry in Sri Lanka is expected to see growth and an increased user penetration rate. The findings of this study are anticipated to assist e-commerce business owners and policymakers in addressing critical security issues, namely vulnerabilities in transactional security, low privacy, inadequate system security, and poor e-commerce platform quality. These improvements are expected to build trust and credibility among consumers, maximising e-commerce success.
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    PublicationOpen Access
    Forecasting weekly dengue incidence in Sri Lanka: Modified Autoregressive Integrated Moving Average modeling approach
    (PLoS ONE, 2024-03-08) Karasinghe, N; Peiris, S; Jayathilaka, R; Dharmasena, T
    Dengue poses a significant and multifaceted public health challenge in Sri Lanka, encompassing both preventive and curative aspects. Accurate dengue incidence forecasting is pivotal for effective surveillance and disease control. To address this, we developed an Autoregressive Integrated Moving Average (ARIMA) model tailored for predicting weekly dengue cases in the Colombo district. The modeling process drew on comprehensive weekly dengue fever data from the Weekly Epidemiological Reports (WER), spanning January 2015 to August 2020. Following rigorous model selection, the ARIMA (2,1,0) model, augmented with an autoregressive component (AR) of order 16, emerged as the best-fitted model. It underwent initial calibration and fine-tuning using data from January 2015 to August 2020, and was validated against independent 2000 data. Selection criteria included parameter significance, the Akaike Information Criterion (AIC), and Schwarz Bayesian Information Criterion (SBIC). Importantly, the residuals of the ARIMA model conformed to the assumptions of randomness, constant variance, and normality affirming its suitability. The forecasts closely matched observed dengue incidence, offering a valuable tool for public health decision-makers. However, an increased percentage error was noted in late 2020, likely attributed to factors including potential underreporting due to COVID-19-related disruptions amid rising dengue cases. This research contributes to the critical task of managing dengue outbreaks and underscores the dynamic challenges posed by external influences on disease surveillance.