Browsing by Author "Weerasinghe K"

Now showing 1 - 3 of 3
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    Introduction to the Judgement, Big Data-Analytics and Decision-making Minitrack
    (University of Hawai‘i at Mānoa, 2021-01-05) Pauleen D; Weerasinghe K; Taskin N; Intezari A; Bui TX
    2021 is the first year that the Judgement, Big Data-Analytics and Decision-making mini-track has been offered. The track's objective is to monitor and advance our knowledge of the convergent technologies of Big Data and analytics and their role in augmenting knowledge for better management decision-making. The track attracted seven submissions of which five were accepted. The papers form a diverse group, offering case studies of big data analytics projects and critical analysis of various factors that impact the successful or unsuccessful use of data/analytics in organizational settings.
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    Making sense of COVID-19 over time in New Zealand: Assessing the public conversation using Twitter
    (PLOS, 2021-12-15) Jafarzadeh H; Pauleen DJ; Abedin E; Weerasinghe K; Taskin N; Coskun M; Mehmood R
    COVID-19 has ruptured routines and caused breakdowns in what had been conventional practice and custom: everything from going to work and school and shopping in the supermarket to socializing with friends and taking holidays. Nonetheless, COVID-19 does provide an opportunity to study how people make sense of radically changing circumstances over time. In this paper we demonstrate how Twitter affords this opportunity by providing data in real time, and over time. In the present research, we collect a large pool of COVID-19 related tweets posted by New Zealanders-citizens of a country successful in containing the coronavirus-from the moment COVID-19 became evident to the world in the last days of 2019 until 19 August 2020. We undertake topic modeling on the tweets to foster understanding and sensemaking of the COVID-19 tweet landscape in New Zealand and its temporal development and evolution over time. This information can be valuable for those interested in how people react to emergent events, including researchers, governments, and policy makers.
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    Risks of e-commerce Recommender Systems: A Scoping Review
    (Australasian Association for Information Systems, 2024-11-25) Kathriarachchi E; Alam S; Weerasinghe K; Pauleen D; Kautz K
    While recommender systems (RS) used in e-commerce have improved significantly providing customers with a personalised shopping experience, scholars have constantly raised concerns over the risks associated with e-commerce RS. However, a lack of methodological synthesis of risk-generating events associated with e-commerce recommender systems has curtailed systematic investigation of the risks of e-commerce RS. This paper presents a scoping review aimed at addressing this gap by synthesising different risk-generating events involved with the use of e-commerce RS as reported in the literature that could affect the welfare of customers who use those systems. Accordingly, peer-reviewed research studies published from 2003-2023 were extracted from the SCOPUS database and EBSCOhost platform for review. Sixty-two publications with evidence on risk-generating events of e-commerce RS were considered for the review. Twenty risk-generating events were identified through the review. These events were mapped with the corresponding risks based on existing frameworks on risks of e-commerce. We were able to identify several risk-generating events that had not previously been considered in conceptualising the risks of e-commerce RS. Further, we identified the plurality of the outcomes of risk-generating events which could provide guidance for the evaluation of e-commerce recommender systems from a multistakeholder perspective.