The main aim of this study is to assess the effectiveness of entrepreneurship quality education in higher education institutions in Indonesia on entrepreneurial goal intentions with the mediating effect of entrepreneurial training. The research followed the quantitative research design as the researcher collected data based on numeric facts. The sample size was 314 participants and a test was carried out on Smart PLS. The results were based on the measurement model to test the factor loadings, validity and reliability, path assessment, and the blindfolding technique which were mainly used for assessing the predictive relevance of the model. The findings of the study suggest that entrepreneurial training mediates entrepreneurship quality education programs in higher education institutions in Indonesia with entrepreneurial goal intentions.

This research paper aims to find out the impact of entrepreneurial education and entrepreneurial training on the performance of women entrepreneurs in Indonesia by considering the moderating role of personal characteristics. A quantitative research method has been adopted in this research. The instrument used for collecting primary data was a Likert-scale based survey questionnaire which included close-ended questions. A sample of 376 women was drawn from the population. The statistical analysis has been carried out via Smart PLS where structural equation modelling has been used. There are different techniques used to effectively interpret the data and test the hypothesis of the research paper including confirmatory factor analysis, discriminant validity, and path analysis. The current study confirms that there is a significant and positive impact of entrepreneurial education on the performance of women entrepreneurs. However, the study contradicts the findings of the prior studies when it was determined that there had been no significant impact of entrepreneurial training on the performance of women entrepreneurs in the context of the Indonesian entrepreneurial sector. The moderating effect of personal characteristics for this research was found to be insignificant.

An entrepreneurial mindset requires a solid foundation of various components. These compo-nents may possibly affect the entrepreneurial mindset and intention of the entrepreneurs either negatively or positively. Hence, to analyze such effect, this study attempts to examine the association among entrepreneurial education and the components of the Theory of Planned Behavior (TPB), i.e., social norms, attitude and self-efficacy, and their effects on the Community College of Abqaiq students’ entrepreneurial intention. The structural equation modeling based on partial least square (PLS) was employed for analyzing the data of the study. The findings confirmed that the entrepreneurship education construct had a significant effect on attitude towards entrepreneurship. Furthermore, there was a statistically significant positive effect of attitude and self-efficacy on entrepreneurial intention. Finally, social norms did not disclose any significant effect on entrepreneurial intention.

This study identifies the factors affecting user satisfaction with AI solutions in the Jordanian health sector, especially in the cities of Amman and Zarqa. It focuses on the usage of ChatGPT, which has emerged as one of the most common AI technologies. A descriptive analytical approach was adopted. Questionnaires were distributed to 584 ChatGPT users, and 541 valid questionnaires were completed for analysis. The results indicated a positive impact of anthropomorphism, perceived interactivity, electronic word of mouth, and performance expectations on ChatGPT user satisfaction in the Jordanian health sector. The study also found a negative impact of perceived trust, facilitating conditions, and perceived technological anxiety on user satisfaction to use ChatGPT in the Jordanian health sector. The results extend the UTAUT and advance understanding about the use of AI in the health sector. They inform both academics and practitioners on how to ensure user satisfaction with AI in the health sector.