UUM e-journals System (Universiti Utara Malaysia)
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Evaluating the Effectiveness of Digital Product Advertisement Type using Machine Learning and Shapley Additive Explanations Analysis
No full textDigital advertising continues to grow rapidly, yet advertisers face persistent uncertainty in selecting the most effective ad format for different campaign objectives and audience segments. Prior studies often rely on limited metrics or lack interpretability, making it difficult to explain why certain formats perform better. This study addresses this gap by evaluating ad format effectiveness using explainable machine learning. This study evaluates the effectiveness of image and video advertisements (ads) across five key performance indicators (KPIs): reach, impressions, link clicks, cost per click (CPC), and cost per thousand impressions (CPM). A dataset of 4,526 campaigns from Meta Ads Manager (July 2021–August 2024) was analysed using machine learning models integrated with Shapley Additive Explanations (SHAP). Model performance was assessed using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²). The results showed that video ads achieved higher reach and impressions with lower CPM. Meanwhile, image ads delivered lower CPCs and stronger click performance, particularly among users aged 13–17 and 55+. Aside from confirming established format patterns, this study made certain contributions by applying explainable machine learning in a large-scale, non-Western context and by clarifying the mechanisms behind ad performance. The findings offered actionable guidance; for example, video ads were optimal for awareness and visibility objectives. Additionally, image ads were preferable for engagement-driven campaigns
Structural Time Series Modelling of Climate Change Effects on Mortality in a Tropical Developing Country
No full textHeat-related mortality has emerged as a critical public health issue, driven by the accelerating impacts of climate change. Most studies establish a direct causative relationship between high temperatures and mortality; however, there is scarce literature studying the climate change mortality ellipse. This study aims to fill this gap by examining the effects of climate change on mortality in the tropical region with consistently high year-round temperatures, specifically in Malaysia. Structural time-series models were applied to annual mortality data for the period 2005 to 2022, obtained from the Department of Statistics Malaysia. Monthly climate data, including temperature, rainfall amount, number of rainy days, and relative humidity, were sourced from the Malaysian Meteorological Department and subsequently aggregated into annual values to ensure consistency in the analysis. The model uses impulse indicator saturation, which makes it easier to spot structural breaks and extreme values, improving the reliability of the results. The analysis indicates that higher rainfall is strongly associated with increased mortality, reflecting the health risks linked to flooding and waterborne diseases. In contrast, periods of higher relative humidity tend to correspond with lower mortality rates. The fully saturated model identifies two key structural shifts in 2006 and 2018, likely caused by abrupt changes in temperature and rainfall. These findings provide a solid foundation for targeted interventions, such as heat-stress regulations and localised air-quality measures, and offer evidence to guide strategies to reduce climate-related health risks in the country, while also supporting broader public health planning
SELF-DIRECTED LEARNING USING THE QUADRIPARTITE CYCLEOF STRUCTURATION – A DESCRIPTIVE CASE STUDY
No full textPurpose – The purpose of this study is to explore how the internal elements and external conditions of learners shape their Self-Directed Learning (SDL). Notably, only a limited number of empirical studies have examined how learners interact between their internal and external structures within SDL environments. Accordingly, this study has employed the quadripartite cycle of structuration as a theoretical framework to analyze and describe the dynamic interplay between learners’ internal and external structures within the SDL context.
Methodology – A qualitative case study approach was employed. The study focused on a 15-year-old learner enrolled in an SDL school in Malaysia. Four semi-structured interviews, as well as informal interviews with the learner’s parent and facilitator were conducted. Documents related to the subject’s self-directed learning were collected to enhance validity and reliability. A constant comparison approach was used to analyze these data.
Finding – The SDL school’s structured guides scaffold the participant’s SDL, providing essential support and resources. Over time, the participant shifted focus to internal structures, driven by his interest, motivation, and a sense of satisfaction, while mirroring strategies learned from the structured guides to direct his own learning. This study illustrates how the participant interacted between his internal and external structures that helped shape his actions in SDL.
Significance – This study provides insights into how a self-directed learner interacts between his internal and external structures in his SDL journey. It advances our understanding of the relationship between self-direction and structures. It also informs a set of flexible structures and guidance for educators, curriculum designers, and policymakers to consider when creating environments that balance learner autonomy with the necessary scaffolding
ENTREPRENEURSHIP ECOSYSTEM IN THE COASTAL AREA OF SPECIAL REGION OF YOGYAKARTA TOWARD AN INTERSYSTEMIC POLICY FOR EDUCATION AND MICRO, SMALL, MEDIUM ENTERPRISE
No full textThis research explores how education fosters youth entrepreneurship and bolsters the coastal region to support the entrepreneurial ecosystem in the Special Region of Yogyakarta, including Gunungkidul Regency, Bantul Regency, and Kulonprogo Regency. It employs an explanatory sequential design with qualitative description, collecting data through focus group discussions and semi-structured interviews. The findings identify three interconnected barriers to effective vocational entrepreneurship: first, fragmented institutional collaboration among education, government, and Micro, Small, and Medium Enterprise (MSME) actors; second, curriculum-market misalignment that restricts experiential and place-based learning; and third, insufficient continuity of incubation and post-program support, which impedes the transition from entrepreneurial intent to viable enterprise. While institutional support and perceived market opportunities are strong predictors of entrepreneurial intent among students. The study concludes that, if Super’s vocational development theory is adapted to reflect the entrepreneurial ecosystem, it suggests an integrated policy to align curriculum, incubation, finance, and multi-actor governance. Policy recommendations include place-based curriculum localization, institutionalization of MSME-school relationships, diversification of financing mechanisms, and the development of regional monitoring dashboards. This research provides guidance for policymakers and educators aiming to transform vocational schools from a job-seeking pathway to a job-creating pathway in coastal local economies
SCIENCE MAPPING THE THEMES AND TRENDS OF SOCIAL INCLUSION AND TOURISM: A SCIMAT BIBLIOMETRIC STUDY
No full textThe significance of social inclusion for achieving sustainable development goals in tourism is widely recognized. However, there is a lack of bibliometric analysis of the scientific evidence in this domain. This study used the SciMAT bibliometric analysis software to examine 145 publications from 2001 to 2023. It has identified dominant themes and their evolution over time, creating strategic diagrams and performance indicators for various periods. The results showed that the research in this field has expanded beyond the limited aspects related to social inclusion, encompassing more advanced subjects such as disability, ecotourism, and tourism development. This study provides a comprehensive review of the existing literature on social inclusion and tourism research and predicts how this field will evolve in the future. It aims to contribute to the knowledge base by identifying common themes and gaps in the research domain, developing new research themes, and suggesting areas for further investigation
Analyzing the Impact of External Factors on Stock Market Performance in the Fast Food Industry During Boycott Movements Using Multiple Linear Regression
No full textThis study examines the impact of boycott movements on the stock market performance of McDonald’s and Pizza Hut from October 2023 to January 2025. Using Multiple Linear Regression (MLR), the research analyzes key factors affecting stock prices, including war-related news, boycott-related news, trading volume, and boycott duration. The findings indicate that historical stock prices significantly influence both companies’ stock performance, with McDonald’s showing an R-squared value of 83.0% and Pizza Hut 67.7%. War-related and boycott-related news were measured based on weekly article frequency, trading volume was obtained from official stock data, and boycott duration was calculated as the number of days since the boycott announcement, measured weekly. Trading volume was found not to be statistically significant in affecting McDonald’s stock price at the 99% confidence level. At the same time, other factors, such as boycott-related news and war-related news, do not have a significant impact on either company. The results suggest that investors prioritize historical stock trends over external socio-political events. This study provides insights into the financial consequences of consumer activism and can assist investors, policymakers, and business strategists in understanding market reactions to boycott movements
Ant Colony Optimization for Efficient Emergency Ambulance Routing in Urban Environments
No full textEfficient ambulance routing plays an important role in emergency medical services. However, solving the ambulance routing problem remains challenging. This study investigates the performance of ant colony optimization to solve the ambulance routing problem, aiming to improve the quality of route planning under constraints such as traffic, patient urgency, and ambulance capacity. To simulate realistic emergency scenarios, 27 benchmark instances from the classical vehicle routing problem were adapted to the ambulance routing context by mapping depots to ambulance stations, customer nodes to emergency sites, and incorporating patient urgency and ambulance capacity. The performance of ant colony optimization was compared with the genetic algorithm and particle swarm optimization. Each algorithm was independently applied to all instances, and route quality was evaluated based on best route cost, average cost, and standard deviations. The experimental results show that ant colony optimization consistently outperformed both genetic algorithm and particle swarm optimization across most instances. Specifically, ant colony optimization achieved shorter total route distances, which are measured as the cumulative route distance of ambulances required to serve the emergency sites. These improvements were accompanied by greater consistency in solution quality across multiple runs. These findings suggest that ant colony optimization is a robust and effective tool for ambulance routing optimization. This study contributes to the growing body of work on intelligent emergency logistics by demonstrating the practical advantages of ant colony optimization in critical decision-making. The findings are valuable for optimization researchers in enhancing ambulance routing efficiency
DIGITAL CONSUMPTION IN HIGHER EDUCATION: AN ANALYSIS OF ONLINE SPENDING AMONG LABUAN\u27S UNIVERSITY STUDENTS
No full textThe digital era has transformed consumer behaviour, with online shopping becoming increasingly common especially among university students who are often early have adopted the digital era of technology. This study investigates the factors influencing online shopping intentions among undergraduate students at University Malaysia Sabah, International Labuan, employing an integrated model that combines the Technology Acceptance Model and the Theory of Reasoned Action. Specifically, the study examines the impact of perceived usefulness, perceived ease of use, attitude, subjective norm, and trust on online shopping intention. Data were collected from a purposive sample of 210 Malaysian consumers via an online questionnaire. Structural equation modelling using SmartPLS-SEM and statistical analysis with SPSS version 27 were employed for data analysis. The results indicate that PU, PEOU, ATT, and SN positively influence online shopping intention among UMSKAL undergraduates, while, contrary to expectations, TR exhibits a negative influence. These findings offer valuable insights for consumers, researchers, and policymakers seeking to understand and enhance online shopping experiences, particularly within the context of UMSKAL students. This study contributes novel insights by integrating user and non-user perspectives to explore online shopping intentions within this population
UNDERSTANDING MILITARY DRIVING BEHAVIOUR: A SCIENTOMETRIC AND SCOPING REVIEW OF GLOBAL RESEARCH TRENDS AND GAPS
No full textThis study undertakes a comprehensive scientometric and scoping review of the global research landscape pertaining to driving behaviour within military contexts. The objective is to delineate research trajectories, theoretical underpinnings, and interventional strategies that directly impact driver safety and performance. Data were extracted from the Scopus and Web of Science databases, resulting in a corpus of 170 publications. These were analysed using ScientoPy and VOSviewer to assess publication trends, keyword co-occurrence patterns, and institutional contributions. A supplementary scoping review, informed by the SPIDER framework, synthesised evidence from intervention-focused studies. The scientometric analysis reveals a consistent increase in research output since 2005, with a notable acceleration post-2015, particularly in areas concerning driver performance, fatigue, driving outcomes among veterans, and safety interventions. However, the field exhibits conceptual limitations; only game theory and molecular dynamics theory were explicitly identified as foundational frameworks. This indicates a conspicuous absence of psychological, behavioural, and human factors theories to elucidate driver decision-making and performance under operational stress. The scoping review identified three primary intervention categories: digital educational, behavioural, and psychological. These interventions demonstrated quantifiable improvements in safety-related outcomes, including reductions in fatigue, sleepiness, road hostility, and crash risk. Notwithstanding these encouraging findings, the literature is marked by methodological heterogeneity, a paucity of longitudinal evaluations, and limited theoretical integration, thereby restricting the generalisability and long-term efficacy of the interventions. By explicitly connecting driver behaviour to occupational safety, human performance, and workforce sustainability, this study contributes to Sustainable Development Goal 8 (Decent Work and Economic Growth). It demonstrates how evidence-based military driving interventions can mitigate non-battle injuries, conserve skilled human capital, enhance operational productivity, and foster enduring organisational resilience within defence institutions
BENCHMARKING FRAMEWORK FOR PRODUCTIVITY FRONTIER FIRMS IN THE MALAYSIAN ELECTRICAL AND ELECTRONICS INDUSTRY
No full textThe objective of this study is to develop a benchmarking framework using Data Envelopment Analysis (DEA) to measure and identify frontier firms in Malaysia’s Electrical and Electronics (E&E) industry. This framework equips policymakers and business leaders with a decision-making tool to allocate resources efficiently and promote productivity improvements. The study assesses the relative efficiency of 21 Bursa Malaysia-listed E&E firms using firm-level productivity data from 2017 to 2019, identifies frontier firms as benchmarks, and quantifies inefficiencies for non-frontier firms. The Malmquist Productivity Index is then applied to analyse productivity growth, decomposing technical change, pure efficiency change, and scale efficiency change. Findings reveal fluctuating frontier firm compositions, with few consistently efficient firms and significant sectoral productivity contributions from frontier firms. Non-frontier firms can improve by aligning with benchmarks, although productivity trends vary across the subsector. The analysis combines quantitative DEA with a qualitative case study to provide a holistic assessment of productivity, capturing organizational and managerial factors beyond numerical data. This comprehensive approach supports targeted productivity-enhancement policies and strategic resource allocation at the enterprise level, providing valuable insights into firm-level performance dynamics in the Malaysian Electrical and Electronics industry