Fazilatulaili Ali

Electric vehicle (EV) adoption in emerging markets remains limited despite global policy efforts. This study examines the determinants of EV adoption intention (IT) in the Klang Valley, Malaysia by integrating the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM). The 437 valid responses from a survey conducted in the Klang Valley (comprising Kuala Lumpur, Selangor, Putrajaya and parts of Negeri Sembilan) from August to November 2025 were analyzed using a partial least squares structural equation modeling. The findings highlight that perceived behavioral control (PBC) is the strongest direct predictor of IT, while perceived usefulness and perceived ease of use influence IT indirectly through attitude, subjective norms, and PBC, with perceived ease of use having the largest total effect. The proposed integrated TPB–TAM framework explains 69.6% of the variance in IT, offering new insights into behavioral mechanisms underlying EV acceptance in an emerging economy. These results underscore the need for targeted policies and infrastructure development to accelerate EV transition in the Klang Valley and may offer preliminary insights for other developing markets, subject to further validation.

Supply–demand imbalance in bike-sharing systems undermines satisfaction. This preliminary study presents a micro-level explanation through a conceptual model that integrates the Attention Restoration Theory, Theory of Planned Behavior, cognitive appraisal theory, and environmental psychology. A cross-sectional survey (n = 186) was conducted using a self-designed questionnaire and analyzed with PLS-SEM. Findings indicate that built-environment perceptions affect adoption intention through sequential perceptual appraisals and subsequent attitudes toward shared parking facilities. Cycling infrastructure quality is a key driver, showing both direct effects and indirect effects mediated by perceived vehicle availability and perceived accessibility to parking lots. Attitude significantly mediates environmental influences, explaining 68.7 % of the variance in adoption intention. The results clarify psychological mechanisms and support operators’ demand-management and targeted environmental interventions to improve user satisfaction.

The purpose of this study is to examine the travel‐related perceptions and behaviours of the residents of the Klang Valley and categorise them into different groups. This research employs the analytic technique of hierarchical clustering analysis (HCA) and is focused on the area of environmental concern, economic viability, the existing infrastructure and the awareness of state‐sponsored subsidies. HCA was applied to the collected data of respondents from the Klang Valley, where data dimensionality reduction from 16 to 5 was applied, and the data reliability testing was conducted. The identified clusters were subjected to attitude and motivation analysis in order to determine the statistically significant differences. The study discovered that there are travel‐related customers groups who possess distinctly different travel‐related parameters. Some of them possess a high focus on travel costs, while others concern on the environment or have a greater awareness of the available technologies. The insights from this segmentation would be critical for policy formulation and strengthen electric vehicles (EV) companies, local authorities and policy makers in their marketing strategies. It adds to the body of literature by a localised context of how EV could become a tool toward sustainable transportation development in Malaysia with regards to EV adoption.

The work presented develops an autonomous system that identifies shifts in market instability through a synergistic integration of agentic artificial intelligence with statistical and deep learning techniques. The system operates through a multi‐agent architecture that streams real‐time market data, detects changes in volatility and produces corresponding trade position recommendations. Its analytical engine applies statistical methods such as principal component analysis and clustering together with deep learning models that use attention‐based recurrent neural networks. Using performance data from 2000 to 2023, the system achieved an accuracy of 87 percent in classification and generated average yearly risk‐adjusted returns that exceeded benchmark portfolio strategies by 2.3 percent. These outcomes highlight the growing capabilities of agentic artificial intelligence in the financial sector and demonstrate its adaptive predictive strength in enhancing clarity and supporting informed decision‐making.

Human error, particularly risky riding behaviour, dominates the causes of 30% of the global fatalities involving two-wheeled vehicles such as bicycles, motorcycles, e-bikes and e-scooters. Previous review studies failed to develop an extension of the theory of Planned behaviour to investigate two-wheeler riders’ risk-taking behaviour on the road and overlook the environment, such road conditions, and vehicle-relevant factors that lead to risk behaviours. The current studies focus on cyclists, motorcyclists and e-bike and e-scooter riders to explore the underlying mechanisms of riding behaviours. This review used riding behaviour keywords to filter existing studies in the Web of Science to provide better insights into the complex dynamics of two-wheeled vehicle rider behaviour. Riding simulator, observation, naturalistic riding, interview, and questionnaire methods can be recombined in the study on two-wheeler riders’ riding behaviour. Moral norm is a significant predictor of intention. Younger and lower-income two-wheeler riders were more inclined to engage in risky riding behaviours, while marital status, experience, and education factors have varying influences under cultural content differences. This paper develops additional factors such as environment, vehicle usage, and vehicle control beliefs from selected papers’ findings, and incorporates other factors and these novel variables into the TPB. The research agenda is demonstrated to conduct the conceptual E-TPB model. The environment-vehicle-human E-TPB model highlights the psychological and non-psychological factors influencing rider behaviours. This paper bridges the gaps between under-explored factors for data collection and data processing methods. This study provides a unique perspective for improving road safety and a theoretical contribution.

As university students, it is crucial for them to understand the process of selecting the most suitable student leader based on the current criteria. This research aims to gather input from the high committee of the student club, providing a set of guidelines for students to identify the appropriate criteria for selecting a fitting leader within the university. Additionally, this study serves to motivate aspiring candidates who wish to take up leadership roles, inspiring them to refine the criteria identified through the study. At Universiti Pertahanan Nasional Malaysia (UPNM), there exist several clubs that function as representatives of student leadership. Each club has shown their own trend leadership criteria in accordance with students’ obsecration. Employing the Analytic Hierarchy Process (AHP) methodology, this research aimed to establish a ranking of the top student clubs at UPNM. The outcomes can serve as a point of reference for managing the selection by Student Council Representatives (SRC) and simplifying student decision-making. The study encompasses crucial criteria distilled from the questionnaire, with communication, creativity, commitment, and teamwork emerging as the four paramount factors. The method commonly uses a multiple-choice question (MCQ) to select which criteria are most relevant then rank the criteria by using the AHP method. This study involves the high committee of the 5 clubs in UPNM as an expert to compare 3 of the selected clubs as an alternative with 60% of the respondents were male, and 40% were females. The result shows that communication (0.52) is the highest criteria followed by teamwork, commitment and creative with 0.26, 0.14 and 0.08 weightage respectively. Hence, this also affects the alternatives with the highest is Debate Club followed by Peers Leader and MPP UPNM respectively.

The purpose of this study is to find the resourceful links to use as monitoring resources (detectors/sensors) to collect traffic data in order to estimate an ac The purpose of this study is to find the resourceful links to use as monitoring resources (detectors/sensors) to collect traffic data in order to estimate an ac

This paper investigated the performance of Malaysian power plants from the year 2015 to 2017 using Malmquist Total Factor Productivity (TFP) index, which is based on Data Envelopment Analysis (DEA). This approach offers substantial advantages as compared to other existing methods as it can measure productivity changes over time for a variety of inputs and outputs. Moreover, it comprises two primary components: the technical efficiency change and the technological change indexes that provide clearer insight into the factors that are responsible for shifts in total factor productivity. This study uses a single input, installed generation capacity (MW), and two outputs, average thermal efficiency (%) and average equivalent availability factor (%). These output-input data included ten main power plants: TNB Natural Gas, SESB Natural Gas, SESB Diesel, SEB Natural Gas, SEB Coal, SEB Diesel, IPP Semenanjung Natural Gas, IPP Semenanjung Coal, IPP Sabah Natural Gas, and IPP Sabah Diesel. The results have two significant implications for fossil fuel power plants in Malaysia. First, technological change was the primary factor in boosting the TFP performance of the fossil fuel power plants in Malaysia. Meanwhile, the decline in TFP performance in Malaysian fossil fuel power plants may be attributed, in part, to a lack of innovation in technical components as the results found that the average technical efficiency changes in 2015 – 2016 were at 146% and then dropped significantly to 2% in 2016 – 2017. Second, the average scale efficiency changes rose dramatically from -53% to 3% providing a significant contribution to the improvement of technical efficiency changes. The fossil fuel power plants become efficient as the power plants’ size increases. This indicates that the size of a power plant positively impacts the performance of the TFP.