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This study aimed to investigate Jordanian university students' interaction, Internet self-efficacy, self-regulation and satisfaction regarding online education during the COVID-19 pandemic.

A correlational cross-sectional design was utilized using convenience sampling to include 702 undergraduate students from Jordanian universities using an online self-administered questionnaire. Descriptive statistics, T-tests, one-way ANOVA and multiple regression analyses were used to analyze the data.

The mean score of students' satisfaction was low (m = 45.14, SD = 25.62). Regarding student's interaction, learner-instructor interaction had the highest total mean score (m = 58.53, SD = 24.51), followed by learner-learner interaction (m = 47.50, SD = 22.64). Learner-content interaction had the lowest total mean score (m = 45.80, SD = 24.60). Significant differences in students' satisfaction were identified according to the level of education, university type and marital status. Significant predictors of students' satisfaction with online education were self-regulated learning, Internet self-efficacy, learner-content interaction, learner-learner interaction and the number of e-learning theoretical courses.

Online education is not well-established in developing countries. This study contributed to the limited knowledge of university students’ preparedness and satisfaction with online education during the early stage of COVID-19 pandemic.

The purpose of this paper is to assess the perceived level of Jordanian nurses’ competencies in offering care to the community during a disaster.

A correlational descriptive design was used to assess nurses’ competencies in offering care for the community during a disaster.

A total of 370 nurses (55 % males) aged 25−55 agreed to participate. The mean score of competencies of nurses who offer care to the community during the disaster was 2.11 (SD = 0.59) points. The results of correlation coefficient tests revealed a significant positive correlation between stated competencies level and nurses’ sex, receiving disaster education and training with rpb (371) = 0.13, p < 0.01; rpb (598) = 0.15, p = 0.004; rpb (598) = 0.21, p < 0.001, respectively. Furthermore, the “care of communities” subscale had a weak positive correlation with the.

Nurses play a critical role in disaster response. However, there was a gap in nurses’ competencies for disaster, which shows there is a crucial need to include disaster management courses in the nursing curriculum and update disaster management courses in hospitals based on nurses’ needs to improve their competencies during disasters.

Online learning gained ground during the pandemic and has continued to be used in the post-Covid era. Items related to online learning should be included in service quality assessment. The purpose of this study is to examine the influence of service quality, which includes the online learning dimension, on student satisfaction in higher-education in a blended learning environment.

A total of 452 valid questionnaires were collected from business undergraduate students enrolled in public universities in Greece. A modified version of HEdPERF is used to evaluate service quality. Due to the extensive use of online learning, an additional dimension was added to the HEdPERF scale which focuses on online education, a field that has not yet been widely examined. Structural equation modeling is used to examine the relationships between service quality, and student satisfaction.

The research findings verify the six-structure scale of the HEdPERF instrument (non-academic aspects, academic aspects, reputation, access and programs issues and online learning), providing satisfactory results in terms of reliability and validity tests. Service quality dimensions such as academic aspects, access, program issues and online learning are the influential dimensions of student satisfaction in a blended learning context.

To the best of the authors’ knowledge, no previous study has expanded traditional service quality instruments to include the dimension of service quality of online learning.

Misinformation on social media has become a great threat across the globe. Therefore, the authors aim to provide a comprehensive understanding of social media users' misinformation combating behavior, especially during the COVID-19 pandemic. Specifically, the authors merged the uses and gratifications theory, social cognitive theory and theory of prosocial behavior into one theoretical framework (e.g. information seeking, status seeking, entertainment and norms of reciprocity) to understand their effect on users' prosocial media sharing experience and misinformation self-efficacy to combat misinformation.

The authors collected data from 356 social media users through “Google Forms” during the third wave of coronavirus in Pakistan. Further, the authors applied structural equation modeling for hypotheses testing.

The authors noted that entertainment and perceived norms of reciprocity positively affect social media users' prior experience and misinformation self-efficacy to enhance their misinformation combating intention. However, information seeking positively affects social media users' prior experience and insignificantly affects their misinformation self-efficacy. Similarly, status seeking was noted to be insignificantly associated with social media users' prior experience and misinformation self-efficacy.

The authors tested this model of misinformation combating intention in a developing country during the COVID-19 pandemic and noted that entertainment and status seeking motives are context-specific. Therefore, this study may likely benefit researchers, academicians and policymakers to understand the causal relationship between motivations and the behavior of combating misinformation on social media within a developing country.

In this study the authors merged three theories (e.g. uses and gratifications theory, social cognitive theory and theory of prosocial behavior) to understand information seeking, status seeking, entertainment and norms of reciprocity as the main motives for social media users' misinformation combating intention.

The onus of this study was to find out the role played by virtual learning environment factors on students' satisfaction during the COVID-19 period in Nigeria. A survey was carried out on students in higher education institutions in Nigeria to actualize this purpose.

Simple random sampling techniques with the aid of Krejcie and Morgan's (1970) sample determinant and the Snowball sampling technique were adopted to sample 270 students in higher education institutions in Nigeria. An adapted questionnaire was used. Cronbach alpha coefficients were calculated for the two sections of the independent and dependent variables. Virtual learning environment factors yielded 0.89, while students' satisfaction yielded 0.87. Data were analyzed using descriptive statistics, Pearson product–moment correlation, two-way ANOVA and linear regression analysis.

The results of the ANOVA, F (df 4, 265) = 50.905, p < 0.000, indicate a statistically significant relationship (stronger than 0.05) between the independent variables (virtual learning environment factors) and the dependent variable (students' satisfaction). It was found among others that instructors' support and collaboration factors predicted students' satisfaction with virtual learning experiences during the COVID-19 period.

Virtual learning during COVID-19 caught both lecturers and students unprepared. Most developing countries especially Africans were used to the traditional face-to-face learning, more so, the use of virtual means to learn was still at a nascent stage. This study, therefore, contributed to the role of the learning environment in virtual learning satisfaction.

Although many universities have begun to provide artificial intelligence (AI)-related courses for students, the influence of the course on students' intention to participate in the development of AI-related products/services needs to be verified. In order to explore the factors that influence students' participation in AI services and system development, this study uses self-efficacy, AI literacy, and the theory of planned behaviour (TPB) to investigate students' intention to engage in AI software development.

The questionnaire was distributed online to collect university students' responses in central Taiwan. The research model and eleven hypotheses are tested using 151 responses. The testing process adopted SmartPLS 3.3 and SPSS 26 software.

AI programming self-efficacy, AI literacy, and course satisfaction directly affected the intention to participate in AI software development. Moreover, course playfulness significantly affected course satisfaction and AI literacy. However, course usefulness positively affected course satisfaction but did not significantly affect AI literacy and AI programming self-efficacy.

The model improves our comprehension of the influence of AI literacy and AI programming self-efficacy on the intention. Moreover, the effects of AI course usefulness and playfulness on literacy and self-efficacy were verified. The findings and insights can help design the AI-related course and encourage university students to participate in AI software development. The study concludes with suggestions for course design for AI course instructors or related educators.

The COVID-19 pandemic compelled educational institutions to shift from classroom interactions to electronic learning (e-learning). This study uses eTAM with usability dimensions to examine the use of e-learning for core academic activities during the COVID-19 pandemic. Leveraging the switching value framework, it investigates students’ preference for extending the use of e-learning to extracurricular activities post-pandemic. The study also integrates eTAM and the switching value framework to conceptualize students’ e-learning usage behavior in a synchronous learning environment.

Structural equation modeling was used to test the proposed research framework using a cross-sectional design. Data were collected through a survey conducted at two premier management institutes with broad national representation in India.

The result revealed that emotional cost had a more significant impact on perceived switching value compared to learning, sunk and reduced performance costs. Also, perceived switching value had a stronger influence on e-learning than the eTAM factors. Interestingly, learning support negatively influenced perceived ease of use and usefulness.

The study provides evidence that while eTAM largely explains e-learning usage during the pandemic, perceived switching value significantly influences its extended use post-pandemic. Our study challenges the belief that eTAM factors influence the continuous use of e-learning post-pandemic. The study insights can guide educational institutions, educators and policymakers in expanding the application of e-learning beyond the pandemic.