Predicting Study or Work from Home Satisfaction using Data Mining Technique: A Case Study in Malaysia

The COVID-19 pandemic has urged the government of Malaysia to implement Movement Control Order (MCO) which forces working people to work from home while students to study from home. People's satisfaction on work from home is crucial in determining their work productivity and efficiency whereas student's satisfaction on study from home is important for their learning effectiveness. There is no work has been done yet for exploring data mining techniques to build a model for predicting work or study from home satisfaction using Malaysia as a case study. This paper aimed to identify the best data mining model for predicting the work or study from home satisfaction. The prediction model is learned by analyzing the demographic, the personality traits, and the work from home experience collected from a group of Malaysia people. This study attempts to investigate four data mining techniques that are the decision tree, linear kernel support vector machine, polynomial support vector machine, and radial basis support vector machine. Experiment results show that the radial basis support vector machine outperformed other techniques in predicting the work or study from home satisfaction of Malaysia's community. © 2022 IEEE.

How a Successful Implementation and Sustainable Growth of e-Commerce can be Achieved in Developing Countries;a Pathway Towards Green Economy

Apart from the goal of the digital world and other benefits of e-commerce, it becomes the need of time during this COVID-19 pandemic. Successful implementation and sustainable growth of e-commerce in developing countries is a challenge. The goal of the digital world without the implementation and sustainable growth of e-commerce in developing countries is incomplete. Based on UTAUT theory, we have developed an integrated model to study the developing countries' consumers' adoption intentions towards e-commerce. We collected a valid useable sample of 796 respondents from a developing country, applied the SEM-ANN two-step hybrid approach to testing the proposed hypothesis, and ranked the antecedents according to their importance. Results revealed that Trust in e-commerce, Perceived risk of using e-commerce, Ease of use in e-commerce, Curiosity about e-commerce, Facilitating Conditions, and Awareness of e-commerce benefits influence the adoption intentions of developing countries' consumers. Sensitivity analysis results revealed that Ease of use in e-commerce platforms and awareness of e-commerce benefits are the two most crucial factors behind the adoption intentions in developing countries. The study's findings help authorities adopt sustainable e-commerce, multinational companies effectively market their goods online, and academics better understand how inhabitants of developing nations perceive e-commerce.