Journal of Technology Management and Technopreneurship (JTMT)

Environment Management Tools Towards Foods Waste Management in Malaysia: A Review for Clean Environment

Khoo Nee Kah — Tue, 31 Dec 2024 00:00:00 +0000

The major social issues faced by citizens in Malaysia are a lot of food wasted in the country such as agriculture which is not fresh, has been eliminated, and causes pollution and cases of diseases among citizens. From the information, Malaysia is in an untenable position and must act quickly to address its enormous food waste issue. Based on statistics, a total of 17,007 tons of food waste per day are reported in 2021. The substantial quantities of food and agricultural waste in Malaysia have been a persistent challenge, both locally and globally. This study is based on secondary data for researcher to analysis the issue and suggested Environment Management Tools (E.M.T) for overcome the issue. Due to the lack of strict regulations for waste management, land pollution has become a serious issue, impacting the ecosystem, environment, and communities. Without effective action from authorities, these problems continue to worsen. Farmers, agricultural SMEs, and local communities lack necessary skills and experience in implementing environmentally friendly waste treatment methods through (E.M.T). As a result, these wastes cause land, water and air pollution which results in soil contamination, harmful gas and the residue may be channelled into a water source thereby contaminating the water and aquatic ecosystem

The Future of Artificial Intelligence (AI) Powered Virtual Shopping Assistant Among Malaysian Retailers

Yasmin Dania Khairul Hisham, Nor Hazana Abdullah — Tue, 31 Dec 2024 00:00:00 +0000

Numerous sectors have been transformed by artificial intelligence (AI), and the retail sector is no exception. Virtual assistants with AI capabilities have made shopping more convenient, efficient, and personalised than ever before. These virtual assistants use artificial intelligence (AI) technology to help and direct customers through every step of the purchasing process, from product research to post-purchase assistance. However, to what extent virtual assistant would be prevalent in the future in unknown. Thus, this foresight study attempts to identify the future drivers of virtual assistant and their respective future scenarios from the customer perspective. This study utilized horizon scanning, STEEPV analysis, impact-uncertainty analysis, and scenario building. In order to derive the future images, drivers identified from the STEEPV analysis were used to analyze their respective impacts and uncertainties through the questionnaires distributed to the online retailers in Malaysia. Six merged key drivers were identified. Top two drivers had been identified which are ‘data security and privacy concerns’ and ‘The algorithms of AI virtual assistant’ in the impact-uncertainty analysis. These drivers were used for developing future images of the Al adoption in online retailers in the next 5 to 10 years. Four future scenarios had been discussed which are ‘trustworthy reputation’, ‘higher engagement levels’, ‘expectation of accuracy’ and ‘lack of trust’. This research would provide additional information about the future trends of Al powered virtual shopping assistant among Malaysian retailers.

The Impact of AI-Enabled Teaching Materials on Student Understanding in Islamic Financial Planning and Wealth Management

Tue, 31 Dec 2024 00:00:00 +0000

This study investigates the effects of AI-enabled teaching materials on student understanding in the course of Islamic Financial Planning and Wealth Management. By comparing pre- and post-course evaluations, we assess the improvement in students' comprehension and satisfaction. The results indicate that AI technologies significantly enhance learning outcomes, suggesting a broader potential for AI integration in educational curricula.

Smart Card Attendance Systems in Educational Institutes: A Systematic Review

AINI KHALIDA MUSLIM — Tue, 31 Dec 2024 00:00:00 +0000

Accurate attendance tracking is critical in boosting performance, regulating operational expenses, and improving accuracy of payments in different organizations. Using Radio Frequency Identification (RFID) or Near Field Communication (NFC) technology, smart card attendance has become a popular attendance mechanism that is faster and more reliable and secure, compared to its predecessors through manual and biometric. Although earlier research points to positive side effects of offloading administrative tasks, the evidence is also inconsistent and fragmented, and most researchers report on positive effects in studies of specific industries. To fill this gap, the current study proposes a productive systematic literature review (SLR) of the existing empirical studies conducted between the years 2000 and 2025 to support the effects of the implementation of smart card attendance systems on punctuality, administrative cost, and payroll accuracy. According to PRISMA 2020, literature was retrieved using Scopus, Web of Science, IEEE Xplore and ProQuest. The review would have a purpose of synthesizing the evidence in education, corporate, and in service industries so that they can come up with a consolidated evidence base. The uniqueness of the study consists in the combined analysis of the level of operational and financial success, not only providing new theoretical knowledge on the adoption of technology but also helping decision-makers in the process. The results will shape policy, minimize the number of people and improve accountability in the attendance monitoring devices.

Predictive Inventory Analytics and Demand Forecasting in Malaysian Electronics Manufacturing

Nurshamimah Samsuddin, Maliza Mohd Nor, Masri Sulaiman — Tue, 31 Dec 2024 00:00:00 +0000

This study investigates the role of predictive inventory analytics in enhancing demand forecasting accuracy within Malaysia’s electronics manufacturing sector. As global supply chains become increasingly volatile, electronics manufacturers face challenges in managing inventory for components with long lead times and fluctuating demand. Leveraging machine learning (ML) models such as XGBoost, LSTM, and Random Forest, this research evaluates how predictive analytics can reduce inventory waste, improve responsiveness, and support strategic planning. Findings from case studies and data analysis reveal that ML-driven forecasting significantly improves inventory performance, especially during periods of global disruption and product lifecycle transitions.