Despite a plethora of research on students’ experiences of electronic (e) learning amid COVID-19 in higher education institutions (HEI), limited research has recognized the differences between students based on their gender. This research aims to examine the differences between students regarding their e-learning experiences amid COVID-19, especially in a gender-segregated culture where female students do not have full access to conventional learning as their male counterparts do, albeit they often have more access to technology-based learning. A total of 1200 online questionnaires were analyzed from students (600 male and 600 female) in public universities in Saudi Arabia, which tend to use Blackboard to sustain their communication with students and e-learning amid COVID-19. The results of structural model and multi-group analysis using AMOS supported all the research hypotheses. The results showed that the path coefficients and significant values were higher among female students than among male students. Additionally, the explanatory power of the male structural model regarding the e-learning experience (0.58) was lower than that of the structural model of female students (0.85), reflecting a higher explanatory power to explain the e-learning experience. The research findings have numerous theoretical and practical implications, especially in gender-segregated cultures.

A new two-parameter model is proposed using the Kavya–Manoharan (KM) transformation family and Burr X (BX) distribution. The new model is called the Kavya–Manoharan–Burr X (KMBX) model. The statistical properties are obtained, involving the quantile (QU) function, moment (MOs), incomplete MOs, conditional MOs, MO-generating function, and entropy. Based on simple random sampling (SiRS) and ranked set sampling (RaSS), the model parameters are estimated via the maximum likelihood (MLL) method. A simulation experiment is used to compare these estimators based on the bias (BI), mean square error (MSER), and efficiency. The estimates conducted using RaSS tend to be more efficient than the estimates based on SiRS. The importance and applicability of the KMBX model are demonstrated using three different data sets. Some of the useful actuarial risk measures, such as the value at risk and conditional value at risk, are discussed.

This study takes a novel attempt to examine the impact of women’s empowerment in the Kingdom of Saudi Arabia, which has been prioritized recently by the country’s leadership as a part of the Saudi Vision 2030, on women’s intention towards entrepreneurship. A pre-examined survey was directed to the Saudi women working in KSA’s food and beverage businesses. The structural equation modeling results showed a significant positive impact of psychosocial, economic, and political empowerment on Saudi women’s intention to engage in entrepreneurship activities. However, the results confirmed a significant negative influence of social empowerment on entrepreneurship intentions. This is because Saudi women did not perceive the proper social empowerment by their community, which negatively influenced their entrepreneurship intention. Hence, interventions by decision-makers are crucial to adopt a media campaign regarding gender equality and the vital contribution of women in the labor market and entrepreneurship. Other implications were discussed for scholars and decision-makers.