UTAR Institutional Repository (Universiti Tunku Abdul Rahman)
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Exploring the intention of Malaysian to adopt green investment
Get PDFAs environmental concerns intensify globally, the concept of green finance has gained significant attention as a means to promote sustainable investment practices. This study investigates Malaysians’ desire to make green investment with the goal of identifying the variables that affect their propensity to use sustainable financial products. Green investment, which focus on environmentally responsible projects, have the potential to play a key role in addressing climate change and fostering economic sustainability. The main goal of this research is to determine the factors that shape Malaysians’ perceptions and intentions about green investment. Specifically, the study examines how factors such as environmental concern, perceived behavioural control, subjective norms and attitude towards green influence individuals’ investment decisions. A quantitative approach was employed, using a survey distributed to a diverse sample of 384 Malaysian citizens. The Statistical Package for the Social Sciences (SPSS) version 30.0 was employed for the data analysis. The results show that worries about the financial returns of green financial products and a lack of knowledge about them continue to be major obstacles. This study provides valuable insights into the adoption of green finance in Malaysia, suggesting ways for policymakers and financial institutions to increase public engagement with sustainable investment practices. By improving education, policy incentives and access to green financial products, Malaysia can strengthen its transition towards a more sustainable and zero net emission country.
Keywords: Green Investment, Environmental Concern, Attitude towards Green, Subjective Norms, Perceived Behavioural Control
Subject Area: HG4501-6051 Investment, capital formation, speculatio
The usage and gratification of ai on the development of communication skills: a case study on chatgpt among fci undergradutes in utar
Get PDFThe rapid rise of artificial intelligence (AI) tools such as ChatGPT has been introduced in the education field, particularly in communication skill development among university undergraduates. This study, The Usage and Gratification of AI on the Development of Communication Skills: a Case Study on ChatGPT among Faculty of Creative Industry (FCI) Undergraduates in University Tunku Abdul Rahman (UTAR), examines how ChatGPT as an AI tool is used by undergraduates and affects the development of their communication skills. The objectives of this study are guided by Uses and Gratification Theory, which are: (1) to
study the usage of ChatGPT on the development of communication skills among FCI undergraduates; (2) to study the ways ChatGPT gratifies FCI undergraduates in the aspect of communication skills development. This study adopted quantitative research, involving 102 FCI undergraduates as purposive sampling. The data is collected through an online survey questionnaire. The findings reveal that ChatGPT enhances students' writing and speaking skills by providing information and interpreting complex topics. In addition, the findings also show that ChatGPT fulfils the five needs mentioned in the Uses and Gratification theory. This study concludes that ChatGPT as an AI tool fulfils an important role in the development of communication skills.
Keywords: ChatGPT, Uses and Gratification Theory, communication skills, Artificial Intelligence, undergraduates
Smart companion robot for elderly care
Get PDFWith the aging population and an increasing number of elderly people living alone, their wellbeing,
health, and safety have been a big concern. Traditional monitoring systems such as
CCTV cameras and panic buttons are at a disadvantage since they are fixed and dependent on
human initiation. This project proposes the use of a multipurpose companion robot based on
TurtleBot3 with autonomous movement integration, AI-based fall detection, live monitoring,
and voice control. The robot will move autonomously throughout the home, continuously
keeping an eye on the health of the elderly via an AI-driven camera system to detect falls,
inactivity, or distress. It will also provide voice companionship and reminders to alleviate
loneliness and improve mental health. A basic web interface will enable caregivers to remotely
keep an eye on their loved ones, watch live video streams, and receive emergency alerts. The
project involves creating and deploying deep learning-based fall detection models, speech
recognition for two-way communication, and IoT integration for facilitating seamless data
transmission. By integrating AI, IoT, and robotics, the project aims to develop an intelligent,
cost-effective, and scalable solution that enhances elderly care, allows real-time safety
monitoring, and facilitates emotional interaction, thus bridging the gap between technology
and elderly care
Sensors substitution using AI for agriculture soil moisture monitoring
Get PDFThis project focuses on the growing trend of the Internet of Things (IoT) and Machine Learning (ML) in precision agriculture, specifically sensor substitution using AI for agriculture soil moisture monitoring. Traditional soil moisture sensors face challenges such as environmental degradation and maintenance costs, leading to the need for a more reliable and scalable solution. This project aims to develop an AI-powered soil moisture prediction system that enhances irrigation management by utilizing temperature and humidity data instead of direct soil moisture readings.
The system consists of IoT hardware (ESP32 microcontroller and DHT22 sensor), a cloud-based web application, and a trained machine learning model. The collected sensor data is sent to a real-time monitoring dashboard, where users can view live data trends and change to developer mode when data collection is needed for new plants. The AI model which is trained using ensemble method which contains random forest regressor and gradient boosting regressor to process the collected information to predict soil moisture levels and detect anomalies, providing smart irrigation recommendations.
The key novelty in this project is eliminating the need for direct soil moisture sensors through reliable AI estimation, integration of developer mode triggering for ESP32 via backend control and a modular dashboard design for visualizing data and database integration. The experimental results show promising accuracy in soil moisture predictions and support the efficient irrigation decision-making.
The systems improve the scalability, maintainability and also cost-effectiveness in smart farming, contributing toward AI-driven agriculture and better plant monitoring management
Fatigue detection system in cars
Get PDFFatigue among drivers continues to be one of the leading causes of road accidents particularly when people are driving for long hours, during the night, or in conditions that demand high focus. Even though technology has come a long way, a lot of cars still don’t come with proper systems that can detect when the driver is starting to lose focus or show early signs of drowsiness. That is what motivated this project – to come up with a solution that can identify fatigue symptoms through facial cues in real time and alert the driver before things get dangerous.
The project uses a lightweight CNN called Mobilenet to perform real-time image-based detection efficiently on a compact device like the Raspberry Pi. It integrates EAR and MAR to detect eye closure and yawning, while also tracking blink frequency, which can help identify early signs of drowsiness, Facial landmarks are used to extract these features from the driver’s face using a webcam. To further enhance reliability, the system includes head tilt detection by calculating pitch and roll angles to recognize unnatural head positions commonly associated with fatigue.
Special attention is given to real-world challenges such as low lighting conditions, reflection from spectacles, and obstructions that may affect facial visibility. These factors can impact detection accuracy, so the system is designed to be robust and adaptable in various driving environments.
Overall, this project is more than deep learning experiment. It integrates computer vision, facial analysis, real-time processing, and system integration to deliver a lightweight and practical solution. The system is cost-effective, easy to deploy, and reliable enough to help reduce the risk of accidents caused by driver fatigue
A study on the factors affecting perceived employee retention among Generation Z employees in banking institutions in northern Malaysia
Get PDFRetention among Generation Z employees remains a major challenge for all the banks and warrants increased attention from researchers. Technological advances have made it easier for Generation Z banking employees to compare their work, contributing to high turnover rates. Rewards and compensations, work environment and training and development are the factors that can significantly explain variance in employee retention. However, research conducted in Malaysian banks is limited. There is a growing number of research examining the impact of these factors on employee retention. Due to the inconclusive nature of the impacts of these factors on employee retention among Generation Z in banking institutions, this study aimed to explore the significant relationship between these factors and employee retention. A stratified sampling technique was used to select a sample of 375 participants from the population. This study employed a quantitative methodology, using a questionnaire as the primary data collection instrument. Data collected were subjected to multiple regression analysis using the Statistical Package for Social Sciences (SPSS). The study found that rewards and compensations, work environment and training and development have a significant relationship on Generation Z employee retention in banking institutions in Northern Malaysia. This study provides more insights for human resource practitioners in Malaysian banking institutions to help them design more effective Generation Z employee retention systems. Keywords: Employee retention; Rewards and Compensations: Work Environment; Training and Development: Banking institutions; Northern Malaysia; Generation Z employee Subject Area: HD28-70 Management. Industrial managemen
Behavioural biases and youths’ financial preparedness: Examining emergency savings in personal financial planning
Get PDFThis study examines how behavioural biases, financial literacy, social influence, self-control, and income level influence Malaysian youths' financial preparedness, particularly about emergency savings. Financial preparedness is critical for stability, yet many young people struggle owing to cognitive biases, poor information, peer pressure, and financial boundaries. This study used a quantitative cross-sectional design and was guided by the Theory of Planned Behaviour (TPB). An online questionnaire was issued to Malaysian youths aged 18-40, and 384 valid responses were evaluated using SPSS, which included descriptive statistics, reliability tests, normality test, and multiple regression. The findings indicate that all four independent factors have a substantial and positive relationship on financial preparation. Financial literacy was the most significant, highlighting the significance of knowledge and abilities in motivating savings behaviour. Social influence was significant, as supportive peer and family norms promoted readiness. The study found that self-control is crucial for resisting impulsive spending, whereas income level had a moderate but significant effect on saves capacity. The model accounted for 66.9% of deviation in financial preparedness. The report points out the importance of improved financial education, employer-led savings campaigns, and governmental measures that address behavioural and structural barriers. It contributes to personal financial planning research in emerging economies and enables policymakers, educators, and financial institutions useful information for increasing youths’ financial resilience. Keywords: Financial Literacy; Social Influence; Self-Control; Income Level; Financial Preparedness; Emergency Savings; Theory of Planned Behaviour Subject Area: HG 179 Personal Financ
Battle of the giants: Inspecting how the U.S.–China trade war is shaking up FDI inflows in the ASEAN+3
Get PDFThis study examines how tariffs and trade liberalization affect total FDI inflows to ASEAN+3 economies. Beyond this, it also highlights the main contribution of exploring the role of GVC integration and provides a closer analysis of sectoral FDI inflows, with particular attention to the manufacturing and services sectors. The analysis covers total trade data from 2010 to 2022 and sectoral data for the manufacturing and services sectors from 2017 to 2022. The research is motivated by the impact of the US–China Trade War, which has reshaped global trade dynamics and disrupted value chains. To fill in the research gap, this study focuses on how trade tensions have influenced sectoral FDI inflows during the recent period. This study employs six augmented Gravity Models to examine the determinants of total FDI inflows and to provide a deeper analysis of sectoral FDI. The empirical analysis indicates that tariffs are not the primary factor influencing total FDI inflows; however, their effects are more apparent in the manufacturing sector. In contrast, trade liberalization serves as the key driver of FDI across ASEAN+3, consistently driving inflows in the contexts of total trade as well as GVC integration. The study finds that rising U.S. tariffs affect sectoral FDI in ASEAN+3 in opposite ways. Manufacturing FDI may increase as firms relocate to countries with more costefficient production networks, while services FDI tends to decline. This difference reflects the nature of GVCs where manufacturing responds to production costs, whereas services depend on globally dispersed end-user demand rather than local production sites
The conditional interplay of green finance and governance in shaping carbon emissions: Evidence from G7 countries United Kingdom, United States of America, Japan, Canada, France, Italy, Germany
Get PDFThis research examines the interactive between the two key factors which how green finance and governance impact on carbon (CO₂) emissions in the G7 nations, Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States between year 2000 and 2022. The dependent variable, CO₂ emissions has been one of the main concerns of the developed economies that are known to contribute a huge portion of the world emissions. The use of green bonds, sustainable investments and funds to finance renewable energy is considered as a financial instrument to speed up the decarbonization, whereas effective governance is the ability to offer the regulatory framework, transparency and policy implementation that will hold accountability and progress. The study investigates the effects of these variables separately and in combination on the CO₂ emissions and empirically examines the issue of whether effective governance can amplify the effect of green finance in reducing carbon emissions. It is aligned with the Sustainable Development Goals (SDG), specifically SDG 7 (Affordable and Clean Energy), and SDG 13 (Climate Action) as the purpose is to make an impact on the global debate on the ways of reducing emissions. These findings highlight that the interactive between finance, governance, and CO₂ emissions that requires specific measures depending on the emission reduction objectives
Unraveling the determinants of transportation carbon emissions in China
Get PDFChina is the world’s largest carbon dioxide emitter and has recorded the fastest transportation growth rate among the top-emitting countries. The rapid expansion of its economies and urbanization have significantly increased the demand for transport services, leading to a surge in private car ownership and overall transport activity, thereby contributing to rising carbon emissions. This study investigates how explanatory variables such as urbanization, GDP per capita and renewable energy influence the transportation carbon emission, with particular focus on existence of the Environmental Kuznets Curve (EKC) hypothesis in China over the 38-year period from 1985 to 2023. Data for all variables were collected from the World Bank and Our World in Data. The study employs the Autoregressive Distributed Lag (ARDL) approach to examine the long-run relationship with robustness checks conducted using such as Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS) and Canonical Cointegration Regression (CCR). The empirical results showing that GDP per capita has significantly negative long-run relationship with transport carbon emission, indicating that the EKC hypothesis does not hold for China’s transport sector. Urbanization is found to be positively associated with emissions whereas renewable energy consumption shows a significant negative effect. All these results provide critical policy implications toward policymakers to achieve China’s dual carbon goals of peaking emissions by 2023 and reaching carbon neutrality by 2060