Because of the Coronavirus Disease 2019 (COVID-19) outbreak, most universities were forced to choose Online Distance Learning (ODL). The study aimed to examine the response of university students to the new situation. A questionnaire was sent to the entire university student population. Based on responses from 606 students, it was revealed that use of all applications in ODL increased. However, only the use of MS Teams increased significantly, while the use of the other applications (email, Moodle, e-textbooks) increased in a range of low to medium in terms of effect sizes, and even nonsignificant for applications such as Padlet and Kahoot. Based on the replies of 414 respondents, a Model of Forced Distance Online Learning Preferences (MoFDOLP) based on Structural Equation Modeling was developed. With a chosen combination of predictors, we succeeded in predicting 95% of variance for Satisfaction, more than 50% for Continuance Preferences variance in MS Teams applications, and nearly 20% in the case of e-materials. Among hypothesized constructs, only Attitudes are a strong predictor of Satisfaction, while Organizational Support, Perceived Ease of Use and Learner Attitude toward Online Learning are not. Satisfaction is a good predictor of Continuance Preferences to use Information Technology after the lockdown ended.

Education based on social entrepreneurship (SE) practices is of importance at this time to shape the personality of students who are more responsible towards the surrounding environment. This SE requires high motivation from students to ensure success in education based on social entrepreneurship can be achieved. However, the factors that support the motivation for social entrepreneurship are still poorly identified, particularly in the setting of undergraduate students in Malaysia. Data were collected from 15 selected Malaysian universities involving undergraduate students who are actively involved in the Enactus program. A set of questionnaires was administered to 294 respondents online. The data analysis involved confirmatory factor analysis (CFA) to measure the construct validity of the measurement model, and covariance-based structural equation modelling (CB-SEM) to establish the relationship between the independent variables and dependent variables. The results revealed self-efficacy and entrepreneurship education provide a relationship in motivation toward social entrepreneurship by undergraduate students. However, social support does not relate to motivation toward social entrepreneurship. Overall, this study adds to the notion of factors that influence social entrepreneurship motivation by supplementing the literature in the areas of educational management and entrepreneurship. In practice, this study contributes significantly to the formation of government policies to further strengthen the motivation of social entrepreneurship that can enhance the community economy and local communities.

The primary objective of this study is to require the experts’ unanimous agreement on the e-learning antecedents and usage behavior towards e-learning performance. This study used the Fuzzy Delphi Method (FDM) to gather answers and feedback using a 7-point Likert scale. The survey (items) was reviewed and approved by eight panel members or experts. It was analyzed using Fuzzy Delphi Logic (FUDELO 1.0) software. The data were evaluated using triangular fuzzy numbering and the position (ranking) of each variable was established through defuzzification. The findings revealed that all of the items received high levels of expert agreement, significantly greater α-cut defuzzification values >.5, the overall value of the threshold (d) is less than .2 and had to comply with the overall percentage of percent consensus, which must be greater than 75%. All 45 recommended items were retained adequately and acceptable for a large-scale survey in this study. Finally, each item was prioritized (ranked) based on the defuzzification value, and then some additional items were added, as recommended by experts.

Online learning has become increasingly popular, making the learning process more attractive. One of the most popular learning media is artificial intelligence (AI). However, students do not accept this technology at all. Therefore, this study examined the factors influencing accounting students' acceptance of AI in learning. The survey was conducted with 147 higher-education students who use AI as a learning medium. The data were analyzed using SmartPLS 4.0 with the partial least square approach. The results showed that perceived usefulness influenced behavioral intention to use and satisfaction. However, perceived ease of use was only significant for satisfaction. Similarly, perceived confidence must be consistent with intention. Although it may influence perceived usefulness, other constructs, such as AI quality and personal innovativeness, can increase students' perceptions of the benefits and convenience of adopting AI in learning. Thus, this study contributes to the development of the technology acceptance model (TAM) and the information systems success model and is helpful to scholars, especially in applying AI in learning. They need to pay attention to the quality of AI, such as the accuracy of the information produced. Thus, the need to control the information from the AI only serves as a reference without requiring you to trust it completely.