Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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Consumer behavior in immersive virtual reality retail environments: A systematic literature review using the stimuli-organisms-responses (S-O-R) model
(Wiley, 2024-07-10) Erensoy A; Mathrani A; Schnack A; Elms J; Baghaei N
With the rising popularity of immersive virtual reality (iVR) technologies, retailers are increasingly seeking innovative ways to create unique digital shopping experiences for their consumers. However, existing literature lacks a unified and comprehensive review that examines the interplay between virtual stimuli and consumer behavior in iVR shopping environments. To fill this gap, we conducted a systematic literature review, employing the Stimulus-Organisms-Responses (S-O-R) model as the underlying theoretical framework. This review analyzed empirical research on consumer behavior in iVR retail environments by focusing on experimental studies. Following the thematic analysis, we categorized the outcomes into descriptive themes to better comprehend consumer behavior within each theme. Our findings provide valuable insights for retailers and marketers aiming to enhance the consumer shopping experience using iVR technologies and suggest directions for future research.
Use of Predictive Analytics within Learning Analytics Dashboards: A Review of Case Studies
(Springer Nature BV, 2023-09-01) Ramaswami G; Susnjak T; Mathrani A; Umer R
Learning analytics dashboards (LADs) provide educators and students with a comprehensive snapshot of the learning domain. Visualizations showcasing student learning behavioral patterns can help students gain greater self-awareness of their learning progression, and at the same time assist educators in identifying those students who may be facing learning difficulties. While LADs have gained popularity, existing LADs are still far behind when it comes to employing predictive analytics into their designs. Our systematic literature review has revealed limitations in the utilization of predictive analytics tools among existing LADs. We find that studies leveraging predictive analytics only go as far as identifying the at-risk students and do not employ model interpretation or explainability capabilities. This limits the ability of LADs to offer data-driven prescriptive advice to students that can offer them guidance on appropriate learning adjustments. Further, published studies have mostly described LADs that are still at prototype stages; hence, robust evaluations of how LADs affect student outcomes have not yet been conducted. The evaluations until now are limited to LAD functionalities and usability rather than their effectiveness as a pedagogical treatment. We conclude by making recommendations for the design of advanced dashboards that more fully take advantage of machine learning technologies, while using suitable visualizations to project only relevant information. Finally, we stress the importance of developing dashboards that are ultimately evaluated for their effectiveness.
Methodological Aspects in Study of Fat Stigma in Social Media Contexts: A Systematic Literature Review
(MDPI (Basel, Switzerland), 2022-05-17) Wanniarachchi V; Mathrani A; Susnjak T; Scogings C; Moreno, A
With increased obesity rates worldwide and the rising popularity in social media usage, we have witnessed a growth in hate speech towards fat/obese people. The severity of hate content has prompted researchers to study public perceptions that give rise to fat stigma from social media discourses. This article presents a systematic literature review of recent literature published in this domain to gauge the current state of research and identify possible research gaps. We have examined existing research (i.e., peer-reviewed articles that were systematically included using the EBSCO discovery service) to study their methodological aspects by reviewing their context, domain, analytical methods, techniques, tools, features and limitations. Our findings reveal that while recent studies have explored fat stigma content in social media, these mostly acquired manual analytical methods regardless of the evolved machine learning, natural language processing and deep learning methods. Although fat stigma in social media has gained enormous attention in current socio-psychological research, there exists a gap between how such research is conducted and what technologies are being applied, which limits in-depth investigations of fat stigma discussions.
Supporting Students’ Academic Performance Using Explainable Machine Learning with Automated Prescriptive Analytics
(MDPI (Basel, Switzerland), 2022-12) Ramaswami G; Susnjak T; Mathrani A
Learning Analytics (LA) refers to the use of students’ interaction data within educational environments for enhancing teaching and learning environments. To date, the major focus in LA has been on descriptive and predictive analytics. Nevertheless, prescriptive analytics is now seen as a future area of development. Prescriptive analytics is the next step towards increasing LA maturity, leading to proactive decision-making for improving students’ performance. This aims to provide data-driven suggestions to students who are at risk of non-completions or other sub-optimal outcomes. These suggestions are based on what-if modeling, which leverages machine learning to model what the minimal changes to the students’ behavioral and performance patterns would be required to realize a more desirable outcome. The results of the what-if modeling lead to precise suggestions that can be converted into evidence-based advice to students. All existing studies in the educational domain have, until now, predicted students’ performance and have not undertaken further steps that either explain the predictive decisions or explore the generation of prescriptive modeling. Our proposed method extends much of the work performed in this field to date. Firstly, we demonstrate the use of model explainability using anchors to provide reasons and reasoning behind predictive models to enable the transparency of predictive models. Secondly, we show how prescriptive analytics based on what-if counterfactuals can be used to automate student feedback through prescriptive analytics.
Learning analytics dashboard: a tool for providing actionable insights to learners
(BioMed Central Ltd, 2022-02-14) Susnjak T; Ramaswami G; Mathrani A
This study investigates current approaches to learning analytics (LA) dashboarding while highlighting challenges faced by education providers in their operationalization. We analyze recent dashboards for their ability to provide actionable insights which promote informed responses by learners in making adjustments to their learning habits. Our study finds that most LA dashboards merely employ surface-level descriptive analytics, while only few go beyond and use predictive analytics. In response to the identified gaps in recently published dashboards, we propose a state-of-the-art dashboard that not only leverages descriptive analytics components, but also integrates machine learning in a way that enables both predictive and prescriptive analytics. We demonstrate how emerging analytics tools can be used in order to enable learners to adequately interpret the predictive model behavior, and more specifically to understand how a predictive model arrives at a given prediction. We highlight how these capabilities build trust and satisfy emerging regulatory requirements surrounding predictive analytics. Additionally, we show how data-driven prescriptive analytics can be deployed within dashboards in order to provide concrete advice to the learners, and thereby increase the likelihood of triggering behavioral changes. Our proposed dashboard is the first of its kind in terms of breadth of analytics that it integrates, and is currently deployed for trials at a higher education institution.
Rural–Urban, Gender, and Digital Divides during the COVID-19 Lockdown: A Multi-Layered Study
(MDPI AG, 9/05/2023) Mathrani A; Umer R; Sarvesh T; Adhikari J
This study explores digital divide issues that influenced online learning activities during the COVID-19 lockdown in five developing countries in South Asia. A multi-layered and interpretive analytical lens guided by three interrelated perspectives—structure, cultural practices, and agency—revealed various nuanced aspects across location-based (i.e., rural vs. urban) and across gendered (i.e., male vs. female) student groups. A key message that emerged from our investigation was the subtle ways in which the digital divide is experienced, specifically by female students and by students from rural backgrounds. Female students face more structural and cultural impositions than male students, which restricts them from fully availing digital learning opportunities. Rich empirical evidence shows these impositions are further exacerbated at times of crisis, leading to a lack of learning (agency) for women. This research has provided a gendered and regional outlook on digital discriminations and other inequalities that came to the forefront during the COVID-19 lockdown. This study is especially relevant as online learning is being touted as the next step in digitization; therefore, it can inform educational policymaking and help build inclusive digital societies and bridge current gender and regional divisions.
Clustering Analysis on Sustainable Development Goal Indicators for Forty-Five Asian Countries
(28/03/2023) Mathrani A; Wang J; Li D; Zhang X; Sacco, PL
This paper draws upon the United Nations 2022 data report on the achievement of Sustainable Development Goals (SDGs) across the following four dimensions: economic, social, environmental and institutional. Ward’s method was applied to obtain clustering results for forty-five Asian countries to understand their level of progress and overall trends in achieving SDGs. We identified varying degrees of correlation between the four dimensions. The results show that East Asian countries performed poorly in the economic dimension, while some countries in Southeast Asia and Central and West Asia performed relatively well. Regarding social and institutional dimensions, the results indicate that East and Central Asian countries performed relatively better than others. Finally, in the environmental dimension, West and South Asian countries showed better performance than other Asian countries. The insights gathered from this study can inform policymakers of these countries about their own country’s position in achieving SDGs in relation to other Asian countries, as they work towards establishing strategies for improving their sustainable development targets.
Interpreting academic integrity transgressions among learning communities
(BioMed Central Limited, 2021-12) Mathrani A; Han B; Mathrani S; Jha M; Scogings C
Educational institutions rely on academic citizenship behaviors to construct knowledge in a responsible manner. However, they often struggle to contain the unlawful reuse of knowledge (or academic citizenship transgressions) by some learning communities. This study draws upon secondary data from two televised episodes describing contract cheating (or ghostwriting) practices prevalent among international student communities. Against this background, we have investigated emergent teaching and learning structures that have been extended to formal and informal spaces with the use of mediating technologies. Learners’ interactions in formal spaces are influenced by ongoing informal social experiences within a shared cultural context to influence learners’ agency. Building upon existing theories, we have developed an analytical lens to understand the rationale behind cheating behaviors. Citizenship behaviors are based on individual and collective perceptions of what constitutes as acceptable or unacceptable behavior. That is, learners who are low in motivation and are less engaged with learning may collude; more so, if cheating is not condemned by members belonging to their informal social spaces. Our analytical lens describes institutional, cultural, technological, social and behavioral contexts that influence learner agency.
Fat stigma and body objectification: A text analysis approach using social media content
(SAGE Publications, 15/08/2022) Wanniarachchi V; Scogings C; Susnjak T; Mathrani A
This study investigates how female and male genders are positioned in fat stigmatising discourses that are being conducted over social media. Weight-based linguistic data corpus, extracted from three popular social media (SM) outlets, Twitter, YouTube and Reddit, was examined for fat stigmatising content. A mixed-method analysis comprising sentiment analysis, word co-occurrences and qualitative analysis, assisted our investigation of the corpus for body objectification themes and gender-based differences. Objectification theory provided the underlying framework to examine the experiential consequences of being fat across both genders. Five objectifying themes, namely, attractiveness, physical appearance, lifestyle choices, health and psychological well-being, emerged from the analysis. A deeper investigation into more facets of the social interaction data revealed overall positive and negative attitudes towards obesity, which informed on existing notions of gendered body objectification and weight/fat stigmatisation. Our findings have provided a holistic outlook on weight/fat stigmatising content that is posted online which can further inform policymakers in planning suitable props to facilitate more inclusive SM spaces. This study showcases how lexical analytics can be conducted by combining a variety of data mining methods to draw out insightful subject-related themes that add to the existing knowledge base; therefore, has both practical and theoretical implications.
Hate Speech Patterns in Social Media: A Methodological Framework and Fat Stigma Investigation Incorporating Sentiment Analysis, Topic Modelling and Discourse Analysis
(Australasian Association for Information Systems and Australian Computer Society, 8/02/2023) Wanniarachchi V; Scogings C; Susnjak T; Mathrani A
Social media offers users an online platform to freely express themselves; however, when users post opinionated and offensive comments that target certain individuals or communities, this could instigate animosity towards them. Widespread condemnation of obesity (fatness) has led to much fat stigmatizing content being posted online. A methodological framework that uses a novel mixed-method approach for unearthing hate speech patterns from large text-based corpora gathered from social media is proposed. We explain the use of computer-mediated quantitative methods comprising natural language processing techniques such as sentiment analysis, emotion analysis and topic modelling, along with qualitative discourse analysis. Next, we have applied the framework to a corpus of texts on gendered and weight-based data that have been extracted from Twitter and Reddit. This assisted in the detection of different emotions being expressed, the composition of word frequency patterns and the broader fat-based themes underpinning the hateful content posted online. The framework has provided a synthesis of quantitative and qualitative methods that draw on social science and data mining techniques to build real-world knowledge in hate speech detection. Current information systems research is limited in its use of mixed analytic approaches for studying hate speech in social media. Our study therefore contributes to future research by establishing a roadmap for conducting mixed-method analyses for better comprehension and understanding of hate speech patterns.

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