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In almost every corner of the world, the COVID-19 pandemic disrupted daily life and work. During the pandemic, e-learning technologies were critical and were the ideal alternative to the traditional classroom setting. Throughout the COVID-19 pandemic, Malaysia’s education system placed heavy emphasis on online learning in connection with new technological advances as a mode of interaction to substitute direct conventional instruction. The aim of this research is to determine student acceptability of e-learning implementation following COVID-19 in a pondok school in Kelantan. This study is intended to ascertain the implications of students’ characteristics and technology-acceptance models as well as the moderation effect of familiarity with technology on their future behavioural intentions to use e-learning. This research used a quantitative technique and included 100 students from a pondok school in Kelantan. Self-administered questionnaires were used to gather data. Partial least squares structural equation modelling (PLS-SEM) was used in the data analysis. Outcomes from this study showed that students’ characteristics are positively affected by their motivation, mindset, and computer competency. Perceived ease of use and perceived usefulness positively affect technology adoption. On the other hand, economic deprivation negatively affects technology adoption. Furthermore, students’ characteristics and technology adoption positively affect the behavioural intent to continuously engage in e-learning in the future. However, familiarity with technology does not moderate the relationship between a student characteristics and intention nor between the technology acceptance model and a student’s intention to use e-learning.

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This study investigates the factors contributing to the mobile commerce usage among rural entrepreneurs in Peninsular Malaysia. By using the Unified Theory of Acceptance and Use of Technology (UTAUT), the relationship between performance expectancy, effort expectancy, social influence and facilitating conditions on the utilization of mobile commerce were examined. Face to face survey method was used for data collection. 360 samples were subsequently analysed using the Partial Least Square-Structural Equation Modelling (PLS- SEM) method. The study found that social influence was the most influential factor in mobile commerce utilization. The performance expectancy, effort expectancy, social influence, and facilitating conditions were positive and significantly influenced the use of mobile commerce among rural entrepreneurs in Peninsular Malaysia. The findings were added significantly in bridging the knowledge gap concerning the elements influencing the mobile commerce usage among rural entrepreneurs. This empirical study provides significant input to all stakeholders, including government, relevant stakeholders (e.g. entrepreneurs, supply chain industry, telecommunications industry, and ICT industry), and local communities.

Voltage source inverter (VSI) plays an important roles in electrical drive systems. Consistently, expose to hash environmental condition, the lifespan of the electronic component such as insulated-gate bipolar transistor (IGBT) may shorten and many faults related to the inverter especially switches can be occur. The present of VSI switches faults causing equipment failure and increased the cost of manufacturing process. Therefore, faults detection analysis is mandatory to identify the VSI switches faults. This paper presents the analysis of VSI switches faults using time-frequency distributions (TFDs) which are short times Fourier transform (STFT) and spectrogram. From time-frequency representation (TFR) obtained by using the TFDs, parameters of the faults signal are estimated such as instantaneous of average, root mean square (RMS), fundamental, Total Waveform Distortion (TWD), Total Harmonics Distortion (THD) and Total non-Harmonic Distortion (TnHD) of current signals. Then, based on the characteristics of the faults calculated from the signal parameters, VSI switches faults can be detected and identified. The performance of TFD for the faults analysis is also demonstrated to obtain the best TFD for switches faults detection and identification system. The results show that, STFT is the best technique to classify and identify VSI switches faults and can be implemented for automated system.