Research Publications Authored by SLIIT Staff
Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/4195
This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.
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Publication Open Access Unlocking Gen Z engagement in phygital retailing: Evidence from a multi-mediated customer experience model(PloS one, 2026-08-06) Selvendran, M; Salman, S; Lakvidu, D; Dahanayaka, U; Wisenthige, K; Dayapathirana, NThis study investigates how phygital experience influences Gen Z customer loyalty in Sri Lanka's consumer electronics retail sector, addressing the limited evidence from emerging markets on hybrid retail approaches. The research emphasizes the growing relevance of integrating physical and digital touchpoints to create immersive consumer journeys and explores how such experiences foster loyalty among younger consumers. A quantitative research design was adopted using a structured 5-point Likert scale survey questionnaire was completed by 480 Gen Z customers of consumer electronics retailers in Sri Lanka. Data were analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine direct and indirect relationships between phygital experiences and loyalty. The study also evaluated a serial mediation mechanism to explain how immersive, technology-enabled environments and seamless physical-digital integration contributes to loyalty formation. Results demonstrate that phygital experiences significantly and positively influence customer loyalty. Gen Z consumers place high value on interactive, technology-driven retail environments that enhance satisfaction and strengthen their commitment to brands. The findings highlight the importance of co-creation, engagement, and perceived value in shaping loyalty, while underscoring the role of digital literacy in navigating hybrid retail contexts. The study provides actionable insights for retailers seeking to design effective phygital strategies that resonate with Gen Z consumers. It offers guidance for innovation and market expansion in emerging economies. However, the reliance on self-reported, cross-sectional data from a single sector and demographic limits generalizability. Future research should extend across industries, age cohorts, and cultural contexts, incorporating variables such as trust, emotions, and digital fatigue. This research contributes to the growing body of knowledge on phygital experiences by contextualizing the phenomenon in an emerging market setting. It advances theoretical understanding by empirically validating a mechanism linking phygital experience to loyalty and provides practical recommendations for retailers aiming to enhance customer journeys through hybrid strategies.Publication Open Access Recommending a Model to Forecast Sri Lanka Wholesale Price Index Using Big Data Analytics(IEEE, 2018-02-22) Thakshila, P. M. C; Asanka, P. P. G. DThe Whole Sale Price Index (WPI) is a main index, which is used to measure price variance before a product or service release to a consumer. WPI represents the basket of wholesale goods and services on market basket. Sri Lanka WPI is accumulated using Laspeyre's formula considering based year as 1974 and up till now not seasonally adjusted. Data collection, compilation, and Dissemination of WPI are done by Prices, Wedges, and Employment division of the Statistics Department of Central bank of Sri Lanka (CBSL) and releasing to public every month. Forecasting of WPI is necessary to understand the aid primary level economic impact of the country. Big data analysis and Data mining are using for data where it is hard to handle using traditional tools and techniques. Decision makers able to gain valuable insights analyzing that varied and rapidly changing data. Time series analysis compromise method for analyzing time series data in order to extract meaningful statistics and other characteristics of data. This review discusses the way to utilize big data analysis technology to systematically analyze time series based WPI data in Sri Lanka. The time series based forecast technologies ARIMA, ANN, VAR, Moving Average, AFARIMA etc. are reviewed based on previous findings. Based on the result will present the effective model to forecast WPIs in Sri Lanka and will critically evaluate selected WPIs. That selection will coordinate based on the weight and relationship to all items based WPI. WPI will compare with existing Sri Lankan Price Indices based on the relational factors.Publication Embargo A database optimization model with quantitative benchmark(IEEE, 2016-03-11) Murray, I; Jayakody, A; Herrmann, J; Lokuliyana, S; Kandawala, D. S. A; Nanayakkara, S. E. CQuery optimization and indexing have an immense impact on database optimization. This has been considered in many different perspectives which provide several different solutions in each case. The purpose of this paper is to primarily provide a comprehensive review and discussion of the core problems through the research on query optimization technology and indexing, based on a number of optimization techniques commonly used in the general approach of a query. A new database optimization model is designed and experiments show that this model can significantly reduce the amount of query execution time to improve the optimization efficiency. Regardless of research on the database optimization model, significant work has been done to comparatively evaluate three scenarios; non-optimized query, optimized query without following any optimization standard, and optimized query via proposed optimization model using the same experimental methodology. Further, this paper describes a benchmark that was developed specifically for the purpose of measuring the quantitative evaluations of the proposed database optimization model.