UTAR Institutional Repository (Universiti Tunku Abdul Rahman)
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Determinants of green financing adoption among small and medium-sized enterprises (SMEs) in Malaysia
Get PDFSmall and medium-sized enterprises (SMEs) play an important role in Malaysia's economic development, but their use of green financing remains restricted, despite rising awareness of climate change and sustainability. Previous research has mostly focused on the impediments to green finance adoption, but there is little understanding of the factors influencing SMEs' financing decisions. As a result, the purpose of this research is to look at the factors that influence Malaysian SMEs' adoption of green finance. The independent factors are government support, financing information, technology, environmental commitment, corporate governance, production, and company size. Primary data were gathered using an online questionnaire distributed to 413 SME financial decision-makers in Malaysia. The data were analysed using descriptive statistics, reliability and multicollinearity tests, and logistic regression with the Likelihood Ratio Test and Wald Test. The findings reveal that financing information, environmental commitment, and company size all have a substantial impact on SMEs' adoption of green financing, however government support, technology, corporate governance, and production do not. These findings emphasize the need of excellent financial communication and strong environmental values in guiding SMEs toward sustainable financing practices. This paper provides significant insights for policymakers, financial institutions, SMEs and academics by outlining measures for increasing green finance adoption and supporting Malaysia's transition to a low-carbon economy. Keywords: small and medium-sized enterprises (SMEs); green financing; sustainability; financing adoption; Malaysia Subject Area: HG1641 – 1643 Bank loans. Bank credit. Commercial loan
Impact of exchange rate, inflation and GDP per capita on tourism revenue: A case study in Thailand
Get PDFThailand's economy depends heavily on tourism, which is one of the primary sources of foreign exchange revenue in the nation. However, changes in macroeconomic factors like GDP per capita, inflation and exchange rates frequently have an impact on the performance of the tourism industry. The purpose of this study is to look into how these three important factors affect Thailand's tourism industry in the short and long term between 1993 and 2023. This study employs econometric methods such as the Granger Causality test, Autoregressive Distributed Lag (ARDL) model, Error Correction Model (ECM) and Augmented Dickey-Fuller (ADF) test to ascertain the relationships between the variables using annual time series data obtained from the World Bank. By making goods and services comparatively cheaper for international tourists, exchange rate depreciation generally increases Thailand's competitiveness as a travel destination, boosting inbound tourism income. However, high inflation reduces this benefit by raising tourism-related expenses, decreasing demand and making Thailand less competitive in terms of pricing when compared to nearby travel destinations. In the meantime, it has been demonstrated that GDP per capita and tourist revenue are positively correlated, indicating that higher income levels promote more discretionary spending on travel-related activities. When implementing strategies to increase Thailand's resilience against external economic shocks, governments, tourist stakeholders, and business owners can all benefit from the insightful information this study offers. This research advances our understanding of how macroeconomic factors interact to influence revenue from tourism in a developing but tourism-dependent country like Thailand by covering research gaps and combining GDP per capita, inflation, and exchange rates into an overall analytical framework
The power play: Geopolitical influence on emerging market stock performance
Get PDFIn this era of globalization, emerging markets have become more closely linked with global trade and capital flows, make it high growth potential but also heightened vulnerability to external shocks. Geopolitical risks are particularly important due to unpredictability and the absence of standard policy tools for mitigation. This study investigates the effect of disaggregated geopolitical risk categories, geopolitical threats and acts, and the global GPR index on sectoral and overall stock market volatility in Malaysia, Thailand, and the Philippines. Using quarterly data from 2012Q1 to 2024Q3, OLS estimation is applied with control variables such as foreign portfolio investment, lagged volatility, and market portfolio. Diagnostic tests such as normality, heteroscedasticity, and serial correlation tests are conducted to ensure the reliability and accuracy of the results. The regression results indicate that the majority of disaggregated GPR categories show statistically insignificant relationship with sectoral and overall volatility across the three markets. Only limited significant results are observed in this study, suggesting that geopolitical risks exert weaker and less systematic effects in ASEAN emerging markets compared to global evidence. Lastly, this study contributes to the literature by providing comparative sectoral evidence for ASEAN economies, offering insights for investors and academic researchers as well as deepen understanding of the complex linkages between geopolitical risks and sectoral stock market volatility. Keywords: geopolitical risk; sectoral stock market volatility; Malaysia, Thailand, Philippines; foreign portfolio investment; ordinary least squares Subject Area: HB135-147 Mathematical economics Subject Area: HG4538 Foreign investment
Evaluate the performance of university timetabling problem with various artificial intelligence techniques
Get PDFUniversity timetabling is a complex and critical task in higher education institutions as it involves the assignment of courses, lecturers, and students to available timeslots and venues while satisfying various constraints. The project focuses on developing an automated university course timetable scheduling tool using Genetic Algorithm (GA). University course timetable scheduling (UCTTP) is a well-known optimization problem due to its NP-hard nature and the complexity of the problem increases exponentially with eh addition of constraints. Over time, numerous algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and other approaches have been introduced to address the challenges of optimizing class schedules. While each university or institution have its own unique constraints, this project aims to improve existing timetabling systems by introducing a new constraint, the ‘Proximity and Travel Minimization Constraint’ which optimizes class schedules to minimize travel distances between venues scheduled in adjacent time slots. By implementing this new constraint, the project addresses the gap in traditional timetabling methods, which often overlook the impact of travel distances on the efficiency and experience of both lecturers and students. Hence, through the application of GA, this project aims to develop an efficient university class timetabling tool that integrates the newly introduced constraint
Terra: An IoT-enabled monitoring system for Japanese musk melon cultivation
Get PDFPrecision agriculture has become increasingly important in modern farming, with the integration of Internet of Things (IoT) technologies playing a crucial role in improving efficiency, resource management, and crop yield. Monitoring environmental parameters such as temperature, humidity, and soil moisture is essential for optimizing plant health, particularly for high-value crops like Japanese musk melons, which require stringent environmental control for quality production. Traditional farming relies on manual observation and fixed irrigation schedules, leading to inefficiencies in water usage and inconsistent environmental control. Farmers need real-time data to make proactive decisions, reducing labour dependency and resource wastage through automated monitoring and control. This project, “Terra: An IoT-Enabled Monitoring System for Japanese Musk Melon Cultivation” aims to develop an IoT-enabled monitoring system that collects and analyses real-time environmental data to optimize Japanese musk melon cultivation. By leveraging IoT technology, the system enhances environmental monitoring, improves decision-making, and enables remote farm management. The project introduces an affordable IoT-based monitoring system, reducing manual data collection and improving irrigation efficiency. It integrates IoT automation for real-time environmental monitoring and establishes a foundation for future enhancements such as predictive analytics and advanced automation. The system employs ESP32-based architecture with environmental sensors to collect real-time data, displayed on an OLED interface and transmitted to a cloud platform. It integrates with an existing greenhouse irrigation setup, using IoT-controlled solenoid valves to automate precise water distribution based on soil moisture levels. The system is expected to improve efficiency in Japanese musk melon cultivation by providing actionable insights, optimizing irrigation control, and reducing resource wastage. By automating environmental monitoring and integrating data-driven decision-making, the project aims to enhance crop health and yield while promoting sustainable agricultural practices
3D character reconstruction from 2D orthogonal images
Get PDFThe demand for 3D content in games and films has driven the need for more efficient character modelling workflows. Traditionally, 3D character creation from concept art requires time-consuming manual modelling and sculpting, followed by retopology, texturing, and rigging. These processes may need weeks to complete for a single character. While recent AI-based 3D generation models offer faster alternatives, but lack of fine control over the output and are often requires extensive post processing to ensure the quality of the mesh.
This research is motivated by the need to support artists in traditional 3D character modelling workflow that refine the details of mesh from primitive shapes. Instead of fully automating the modelling process, this project aims to introduce an algorithm that reconstruct primitive cubes of stylized 3D character from front, left, back and right orthogonal views images by integrating interactive image segmentation and blender scripting method. This enables a rapid starting point for geometry detail adjustment using box modelling or digital sculpting techniques.
The project objectives are to: (1) review the existing state-of-arts for solving 3D generation and reconstruction, (2) design an algorithm capable of reconstructing primitive shapes of 3D characters from four orthogonal images, optimized for limited computational resources.
This research contributes a flexible and artist-friendly reconstruction algorithm that capable of generating part-level primitive shapes, which is efficient for generating high-fidelity mesh using box modelling and digital sculpting techniques. The algorithm can generate primitive shape from at least two orthogonal view images. The proposed algorithm is organized into modular stages, allowing artists and developers to further improving the pipeline by integrating computer vision, AI models and 3D modelling and scripting techniques. Finally, the model’s performance will be evaluated based on topology quality, editability, and usability. This work not only improves the character modelling pipeline, but also supports future studies in 3D reconstruction and digital content creation
Factors affecting Malaysians continuance intention towards financial robo-advisors
Get PDFAs the adoption of financial robo-advisors continues to increase in the Malaysian financial environment, there is a need to ensure that the significance of informing the continuance intention has become relevant. In this study, the intention of Malaysian working adults to use financial robo-advisors more will be analysed, and the impact of confirmation, perceived usefulness, and satisfaction on these issues will be assessed based on the Expectation Confirmation Model (ECM). The quantitative research approach was applied, and the results were collected by means of an online questionnaire of 200 participants. It was modelled using the partial least squares structural equation modelling (PLS-SEM). The results indicate that confirmation positively affects both perceived usefulness and satisfaction. The study describes the best practices that developers of fintech and financial service providers can adopt to retain more users using post-adoption experiences. Keywords: Malaysian, Financial Robo-advisors, Expectation Confirmation Model, Continuance Intention, Structural Equation Modelling Subject Area: HG4621 Stockbrokers. Security dealers. Investment adviser
Impact of gamification in activewear marketing on purchasing intention among Gen-Z in Malaysia
Get PDFThis study investigates the impact of gamification in activewear marketing on purchasing intention among Generation Z consumers in Malaysia using the Technology Acceptance Model (TAM) as the theoretical foundation. The model was extended with Perceived Social Influence (PSI) and Perceived Enjoyment (PE) to account for the social dynamics of gamified marketing. The framework includes Perceived Usefulness (PU), Perceived Ease of Use (PEoU), PE, and PSI as independent variables, Attitude (ATT) as the mediating variable, and Purchase Intention (PUI) as the dependent variable. A quantitative approach was adopted, with data collected from 384 Malaysian Gen Z respondents who had prior exposure to gamified marketing campaigns in the activewear industry. Partial Least Squares Structural Equation Modelling (PLSSEM) was used to analyze the data and evaluate the proposed relationships. The findings demonstrate that PU, PEoU, PE, and PSI significantly influence ATT, which in turn positively affects PUI. The mediating effect of ATT was also confirmed, highlighting the importance of gamification in shaping attitudes and driving purchase intentions. These results provide both theoretical contributions and practical insights for marketers and activewear brands seeking to engage Gen Z consumers more effectively. Keywords: Gamification; Perceived Usefulness (PU); Perceived Ease of Use (PEoU); Perceived Enjoyment (PE); Perceived Social Influence (PSI); Attitude (ATT); Purchase Intention (PUI); Generation Z; Activewear Marketing; Technology Acceptance Model (TAM) Subject Area: HF5410-5417.5 Marketing. Distribution of product
Devising photocatalytic fuel cell with dual photoelectrode for simultaneous energy harvesting and rubber wastewater depollution
Get PDFThe rapid growth of Malaysia’s rubber glove industry has led to the discharge of large volumes of wastewater containing high concentrations of ammonia, suspended solids, organic pollutants, and heavy metals. Conventional treatment methods are often insufficient, underscoring the need for advanced oxidation technologies such as photocatalytic fuel cells (PFCs). In this study, a novel PFC system was developed by integrating a solar-sensitized cell (SSC) into a ZnO/FTO nanorod array photoanode paired with a NiFe₂O₄/FTO photocathode, enabling simultaneous wastewater degradation and energy generation. The photoelectrodes were synthesized and characterized using Field Emission Scanning Electron Microscopy (FESEM), Energy Dispersive X-ray Spectroscopy (EDX), X-Ray Diffraction (XRD), Transient Photocurrent Response (TPR), UV-Vis Diffuse Reflectance Spectroscopy (UV–Vis DRS), and Mott–Schottky (M-S) analysis, which confirmed their crystalline structure, purity, and suitable bandgap energies (3.24 eV for ZnO and 1.87 eV for NiFe₂O₄). Under optimal operating conditions of 0.3 M Na₂SO₄ electrolyte, a wastewater dilution ratio of 1:5, and natural pH (6.71), the constructed PFC system achieved a chemical oxygen demand (COD) removal efficiency of 97.56%, zinc removal of 91.92%, and a peak power density of 0.0864 mWcm- ². Radical scavenging experiments identified hydroxyl (•OH), superoxide (•O₂⁻), and sulfate (•SO₄⁻) radicals as the dominant oxidative species driving pollutant degradation. Toxicity assessment using Zebrafish (Danio rerio) further supported treatment effectiveness: the untreated wastewater (1000 mg/L) caused high mortality, while the treated wastewater (100 mg/L) showed vii markedly reduced toxicity, with combined probit analysis yielding an LC₅₀ value of 916.65 mg/L. Recyclability tests demonstrated electrode durability, while cost analysis indicated economic feasibility, with fabrication costs estimated at RM 53.54. Overall, the developed PFC system shows strong promise as a sustainable post-treatment option for real industry wastewater, offering both environmental remediation and renewable energy generation. Keywords: Photocatalytic fuel cell; Dual-Photoelectrode; Rubber wastewater; Heavy metal removal; Toxicity Subject Area: QD701-731 Photochemistr
Design of low-noise amplifier for MEMS sensors
Get PDFThis project focuses on minimizing low-frequency noise and enhancing signal integrity from a common industrial underwater pressure sensor (MS5803-14BA), whose small signal output makes it vulnerable to noise interference. In
recent years, the demand for accurate and low-noise signal acquisition in MEMS-based pressure sensing applications has grown significantly. This is especially critical in underwater environments, where the design of an efficient
analog front-end is essential to ensure signal integrity. To address this, the primary objective of this project is to design a low-noise, chopper-stabilized amplifier capable of interfacing with this sensor. The proposed amplifier is
implemented using Generic Process Design Kit (GPDK) 90nm CMOS
technology in the Cadence Virtuoso environment. Chopper modulation is employed to suppress flicker noise by modulating the low-frequency input to a higher frequency where noise is less dominant, followed by demodulation and
filtering. The design process involves modelling the sensor’s electrical characteristics, determining the required gain to fully utilize a 24-bit ADC, and constructing a signal chain that includes a Wheatstone bridge, NMOS chopper switches, a differential amplifier, and a low-pass filter. Transient simulation and post-layout analysis confirm the amplifier’s effectiveness, achieving a gain of 20.01 dB and excellent common-mode rejection of 115.4 dB. Besides, the noise analysis shows a significant reduction in input-referred noise, from 6.33019 µV to 0.712369 µV while maintaining a low average power consumption of 17.8 µW. The output is compatible with the ADC's full dynamic range, ensuring optimal resolution. This work demonstrates a complete analog front-end solution that meets the performance requirements
for high-precision MEMS pressure sensor applications. Future work may focus on integrating the digital processing blocks on-chip to streamline signal processing and minimize reliance on external components.
Keywords: Low-Noise Amplifier (LNA), Chopper Stabilization, MEMS Pressure Sensor, CMOS Circuit Design, Flicker Noise Reduction.
Subject Area: TK7800-8360 Electronic