The COVID-19 pandemic has reshaped the lifestyle of Malaysians. The government has introduced various incentives to encourage contactless transactions. Malaysia has also expe- rienced a spike in e-wallet transactions during the COVID-19 pandemic. However, there is no consensus on the reasons behind the rapid increase in the usage of e-wallets. This study aims to fill a knowledge gap by incorporating government support, the perceived risk, and social influence as the potential factors affecting the use of e-wallets. Survey data were collated from 598 respondents using Google Forms and analyzed using covariance-based structural equation modeling (CB-SEM). The findings confirm that perceived usefulness, government support, the perceived risk, and social influence are positively related to the attitude toward the usage of e-wallets. This attitude is also positively related with the user’s intention of using the wallets. The outcomes of this study may assist policymakers to devise effective strategies that are able to capture the users’ intentions to use e-wallets during the COVID-19 pandemic. This study also recommends that the government increases the incentives to speed up the formation of a cash- less society. The related organizations should also enhance public awareness on the usefulness of e-wallets in preventing virus transmission.

E-wallet was an innovative payment instrument that arises under financial technology. E-wallet helps to ease the user's daily life, in which users can make their daily transactions without using the notes or coins. Indirectly, E-wallet also helps to reduce the risk of cash being stolen. Undeniably, E-wallet brings more benefits than disadvantages. The primary aim of this study is to examine the factors affecting the adoption of E-wallet services in Sarawak. The questionnaire, which consisted of 26 questions were distributed to the respondents and successfully collected 450 feedbacks. Firstly, this study applied factor analysis to construct all the variables. Also, Cronbach's α coefficient was computed to determine internal consistency reliabilities. Then, this study used regression analysis to test the relationship between the variables. The results of the regression analysis showed that the users would adopt E-wallet when they perceive that the E-wallet is useful and easy to be used. Meanwhile, the findings of this study also showed that rewards tend to attract users to use E-wallet. Besides that, this study also found that higher perceived risk may act as a barrier to stop users from using E-wallet. These results help the E-wallet service providers to identify the significant factors that influence the user's intention to use E-wallet services. Lastly, this study recommended the E-wallet service providers to take the security systems and rewards into consideration for the enhancement of their payment system.

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Forecasting stock market is always a challenge task for the investors. This study aimed to develop a new approach for forecasting the price movements of e-commerce stocks. The signals emitted by the technical indicators are used as the features for two machine learning algorithms in predicting the stocks movements. The technical indicators used in this study were Moving Average (MA), Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI) and Stochastic Oscillator (SO). Meanwhile, the machine learning algorithms used in this study were Random Forest (RF) and K-Neighbor Nearest (KNN). The findings of this study indicated that the inclusion of signals emitted by MA rule with 5-days short MA and 20-days long MA helps to reduce the error values for the prediction model. Besides that, this study also found that the signals from MA, MACD, RSI and SO fit the prediction model well. The investors are recommended to use machine learning algorithms to predict the price movements of e-commerce stocks. Lastly, investors are recommended to consider the signals from these four technical indicators, MA (5-days short MA & 20 long-MA), MACD, RSI and SO as the reference for their investment strategies in e-commerce stocks.

This study aims to test the ability of technical analysis in predicting the stock price and generating profits. This study employed two of the technical analysis indicators, which are (i) Variable Moving Average (VMA) rules and (ii) Elliot Wave Principle incorporated with Fibonacci numbers. Besides that, this study also examines the relationship between the signals emitted by VMA rules and the stock return by applying Ordinary Least Square (OLS) regression analysis. Among the 42 VMA rules tested, there were only 10 VMA rules shown that the mean returns generated from buy signals are significant higher than the unconditional return. While, the mean returns from sell signals are significant lesser than the unconditional return for all the VMA rules tested. As for Elliot Wave Principle incorporated with Fibonacci numbers indicator, the findings shows that impulsive wave is predictable, meanwhile the corrective wave is less predictable. Lastly, only the signals of 14 VMA rules had shown a significant relationship with the daily stock return. In conclusion, the VMA rules only able to generate profits for certain term of moving average, whereas the Elliot Wave Principle incorporated with Fibonacci numbers tools is useful in predicting the stock market trend.