In this study, the impact of COVID-19 lockdown on Jordanian high school students’ reading habits and attitudes was examined. COVID-19 has indeed affected education systems all over the world; education institutions all over the world were compelled to implement innovative technological approaches so that education could still be delivered to students, fulfilling the academic expectations, while the Sustainable Learning and Education (SLE) ideals are consistently embraced. One of these approaches has been the use of mobile learning applications (MLA). These applications (MLAs) employ some prominent features of mobile apps, to allow students to collaborate and participate in purposeful online learning. Still, the success of any technology is dictated by the acceptance of the user, in this context, the acceptance of students. In other words, student acceptance of MLA will determine the success of MLA. Accordingly, the effect of COVID-19 lockdown on the information behavior of high school students was examined in this study, with MLA being used by these students. Data were gathered from 394 high school students in Jordan. These students were chosen randomly, and they were all mobile phone users. The data covered the 2022–2023 fall term and were analyzed using Structural Equation Modeling (SEM). Based on the analyses results: Self-Efficacy and Perceived Compatibility had significant impact on Perceived Performance Expectancy and Perceived Effort Expectancy; Perceived Convenience and Perceived Effort Expectancy had significant impact on Perceived Performance Expectancy; Perceived Enjoyment had significant impact on the Behavioral Intention to use MLA; COVID-19 had significant impact on the Behavioral Intention to use MLA; Perceived Compatibility showed no significant impact on Perceived Enjoyment; and Perceived Effort Expectancy, Perceived Performance Expectancy and Perceived Compatibility showed no significant impact on the Behavioral Intention to use MLA. The outcomes of this study demonstrate a practical indication in support of digital information behavior among high school students in this era.

Crises possess a remarkable propensity to serve as catalysts for change, particularly within the sphere of education. They catalyze change in several key dimensions, including Accelerated Adoption of Technology, Highlighting Inequalities, Global Collaboration, Adaptation of Assessment Methods, Engagement with Online Resources, and Reimagining Education. In essence, crises compelling educational institutions to reevaluate long-standing norms and embrace innovative solutions. They shine a spotlight on challenges that may have remained overlooked and stimulate the formulation of strategies aimed at cultivating a more robust, adaptable, and inclusive educational landscape. This study provides a comprehensive strengths, weaknesses, opportunities, and threats (SWOT) analysis of E-learning at The University of Jordan amid the COVID-19 pandemic, offering valuable insights for strategic planning during crises. Based on data collected from 379 undergraduate students in the year 2022, it reveals strengths in terms of convenience and flexibility but highlights weaknesses such as low bandwidth and unstable internet connections. Additionally, it recognizes the opportunity for E-learning effectiveness during lockdowns while identifying threats like unreliable power supply and inconsistent internet access. This analysis enriches our understanding of the educational landscape and equips decision-makers with essential insights to enhance E-learning resilience and effectiveness during challenging times.

The onset of the COVID-19 pandemic has wrought a profound transformation in the higher education landscape, compelling universities worldwide to pivot towards E-learning as the primary mode of instruction. This study, which was performed on a sample of 330 undergraduate students at The University of Jordan, undertook an in-depth exploration of student satisfaction with E-learning at The University of Jordan during this unprecedented shift, examining it through four demographic variables: gender, residence, faculty type, and academic year. The researchers employed rigorous statistical analyses, including calculations of mean and standard deviation, as well as one-way ANOVA tests, to thoroughly examine the data. The findings indicate a strong overall satisfaction level among students regarding E-learning, with discernible variations between items. There is a noteworthy correlation observed between students' academic faculty and their academic year in relation to their satisfaction level, with a p-value below 0.05, indicating statistical significance. This underscores the nuanced influence of these demographic factors in shaping students' perceptions and contentment levels as they navigate the evolving landscape of higher education during this extraordinary period. While the gender and the residence have no effect on the satisfaction level.

The COVID-19 pandemic has led to the end of in-person classes at universities and schools and the beginning of digital advancements in higher education. Flipped learning is very different from traditional teaching methods and necessitates some shifts in the roles of the teacher and the student. The purpose of this study is to investigate the effect of using flipped learning on students' achievement and measure their attitudes towards learning through it during the Corona pandemic period. A quasi-experimental study design was adopted through the pretest and posttest measurements. Two groups were randomly assigned one to be experimental, and the other a control. The present study showed that there was no statistically significant difference between the mean of the pre-measurement and post-measurement tests of the experimental group’s motivation towards learning using the flipped learning strategy. The findings from the quantitative data revealed that flipped learning contributed to the academic success of students and their attitudes toward learning during the corona pandemic period. Hence, further studies with more extended periods are recommended to examine the effect of Flipped learning on self-directed learning and other related variables.

The aim of this study was to examine how the implications of the Covid-19 pandemic moderate the impact of Artificial Intelligence (AI) on the effective application of Cyber Governance (CG) in Islamic banks. A total of 93 questionnaires from branch heads of Islamic banks were used in this study, and the data were analyzed using the Statistical Package for Social Sciences (SPSS) through descriptive-analytical methods. The findings indicated that AI has a significant influence on the effective application of CG in Islamic banks. The study also revealed that the Covid-19 Pandemic positively moderates the influence of AI on the effective application of CG in Islamic banks. The results of this study have implications for regulators and decision-makers in proposing new legislation to effectively apply CG in the Islamic banking sector, which can help protect public funds and limit cyber-attacks. This study is the first to investigate the moderating effect of the Covid-19 pandemic on the influence of AI on the effective application of CG in Islamic banks.

This work estimates the relationship between the economic expectations given by Tobin's q variables, terms of exchange and 3-month economic expectations with private gross investment for the period from the first quarter of 2010 to the first quarter of 2020, under the stress scenario of the COVID-19 pandemic. Through an econometric model, results were obtained such as that the variation in private gross investment VIBP has an inertial behavior with a coefficient of 0.258791, shadow cost of capital or q of Tobin QTC with a coefficient of -0.03213, as well as terms of TI exchange with a coefficient of 0.805618. This indicates that the private investment decision by the companies analyzed depends on factors such as economic expectations, the inertial effect of investment and the shadow cost of capital expressed in Tobin's q.

The COVID-19 outbreak has affected the entire global financial market in an unprecedented way. Due to disruptions in the global market, the Jordanian financial market also responded to the pandemic and observed sudden volatility. The outbreak of the virus has led the management of the Jordanian market (Amman Securities Exchange / ASE) to halt trading on the secondary market during the period 17 March 2020 – 9 May 2020. Hence, using daily closing prices of listed firms, this paper empirically examines the market’s liquidity cost before its closure (2 January 2020 – 16 March 2020) and after (10 May 2020 – 31 December 2020). The premise of this objective rests on the fact that the trading activity on the secondary market, following the resumption of trading is carried- out within uncertain circumstances. The data used in this study comes from the daily trading reports published by ASE. All listed companies are included in the analysis. Based on the daily closing bid and ask prices, we calculate the daily spreads during two sub-periods (2 January 2020 – 16 March 2020 and 10 May 2020 - 31 December 2020). We then regress the daily spreads on daily stock prices, number of daily contracts, risk, and where the companies list their shares (first or second market). The main findings of this paper are threefold. First, liquidity cost in the ASE is relatively high. Second, following the resumption of trading on the secondary market, liquidity cost has increased. Third, other known determinants of liquidity cost are significant and have the expected coefficient signs. The fact that liquidity cost in the ASE is high, and higher even after the resumption of trading, necessitates some clear policy measures. These include a reduction in the currently used minimum tick.

One of the leading industries that affect economic growth in Indonesia is the palm oil industry. The role of this industry depends on the level of exports from European Union countries. Based on the COVID-19 pandemic situation, international trade activities are hampered and could affect industry performance from a stock perspective. Therefore, this study aims to explore the impact of the COVID-19 cases that occurred in the European Union and related macroeconomic variables on the stock performance of the oil palm industry in Indonesia. This research also examines the impact of COVID-19 on certified sustainable companies and companies that are not certified. Panel regression was applied in this study with Eviews 11 Software.This research's observations are 13 palm oil companies in Indonesia which are listed on the Indonesia Stock Exchange (IDX) from March 2, 2020, to August 31, 2020. This study's results reveal that the world CPO prices and market capitalization affect the activities shares of palm oil companies in Indonesia.Meanwhile, from the grouping of certifications within companies, the impact of COVID-19 in the European Union was more substantial on companies that were certified as sustainable. Based on these results, The COVID-19 case in the European Union must be a concern for palm oil companies in Indonesia.

The spread of the Covid-19 pandemic and the interruption of personal communication between the teacher and students in higher education led to the need for finding solutions that enable the continuation of the educational process and ensure access to accurate information that improves the level of human capital in dealing with dynamic environments. Therefore, this research sought to analyse the impact of the application of artificial intelligence in entrepreneurship education in Qatari higher education institutions after the Corona pandemic. The measurement of artificial intelligence was based on dimensions (machine learning, natural language processing, expert systems, and machine vision), while entrepreneurship education was measured by dimensions of (entrepreneurial cognition, entrepreneurial competence, and innovation spirit). The research followed an experimental quantitative approach based on collecting data from Qatari university students using a questionnaire developed for the research purpose. Hence, the convenience sample used in the research was composed of 402 students from various Qatari universities, which represents a response rate of 67% from the distributed questionnaires. The statistical analysis of the research data was based on the covariance-based structural equation modeling technique (CB-SEM). The results of the research indicated that all dimensions of artificial intelligence had a positive impact on entrepreneurial education, with the highest impact being machine vision and the lowest impact being natural language processing. Accordingly, the results of the research revealed the need to invest in technological capabilities for supporting the educational system aimed at generating innovative human resources capable of coping with the uncertainty of the work environment.

E-learning results from the integration of technology and education and has become an effective learning medium today. E-learning courses and systems with various services are on the rise owing to its importance. E-learning systems should be evaluated to assure successful delivery, effective usage, and positive impacts on learners. A holistic model that identifies various levels of success on a vast range of success determinants was proposed. The model was empirically validated using data obtained from 724 e-learning student users in Jordan. Structural Equation Modelling (SEM) was used in data analyses. Results showed that perceived usefulness of information systems, user training, system quality, and management support have positive effects on user’s behavioral intention; whereas perceived ease of use has not. Also, SEM displayed that user behavioral intention has a positive effect on information systems use, use on student satisfaction, and the latter on student loyalty. Machine Learning (ML) methods produce high correlation values reaching up to 80% in predicting Behavior Intention (BI) from the input factors, and student loyalty from student satisfaction factors. This indicates that the ML are promising techniques to forecast the future targets based on the input independent features.