Department of Information Management

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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.