This study examined students' motivations for using mobile learning applications in educational settings. Primary data were obtained through a survey involving 300 Jordanian university students with mobile learning use experience in their studies. The results showed a significant effect of self-efficacy on perceived usefulness, and a significant effect of perceived convenience on perceived usefulness. Results also showed a significant effect of social and emotional learning on behavioral intention to use mobile learning.

The increasing popularity of intelligence systems in accounting and auditing domains has led to the interest towards drivers of blockchain technology adoption. Consequently, this empirical study inspected the antecedent constructs with direct and indirect impact on blockchain technology adoption in the accounting domain. Data were gathered from 346 accountants employed in Information technology (IT) companies, through an online survey. Structural equation modeling with Smart PLS 4 was employed for research model testing. Out of thirteen proposed direct hypotheses, twelve were accepted. Mainly, the obtained empirical results confirmed effort expectancy impacting blockchain technology adoption the most. Furthermore, the obtained results also confirmed the mediation role of effort expectancy and performance expectancy.

This research investigates the adoption of Information and Communication Technology (ICT) by Micro, Small, and Medium-Sized Corporations (MSMEs) in Jordan. The research formulates and examines hypotheses on factors affecting ICT adoption morals via the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Technology Acceptance Model (TAM). The suggested concept, which utilizes Structural Equation Modelling (ESM), highlights the critical responsibilities that social influence and perceived utility play in affecting choices to adopt ICT. It is noteworthy that MSMEs confront a multiple of obstacles when it comes to adopting ICT, encompassing restrictions in terms of infrastructure and resources, shortages of human capital, and hardships in the technology environment. Data were gathered from 305 MSMEs, and outcomes shed light on important factors to boost the impact of ICT integration in the special setting of Jordanian MSMEs.

This research uses Technology Acceptance Model to explore the elements influencing students' attitudes toward using Chat Generative Pre-Trained Transformer (ChatGPT), a recently developed artificial intelligence (AI) tool, for learning and educational purposes. Using Amos version 23 structural equation modelling and 880 student survey responses, the suggested model was empirically tested. According to the report, students think well of ChatGPT utilization in the classroom. Credibility, Usefulness and ease of use, all influence how positively people feel about using this technology in a classroom setting. The study's findings, however, did not support the notion that students' adoption and use of ChatGPT was insignificantly influenced by perceived enjoyment. Moreover, the results conclude that attitude mediates the relationship between usefulness and intention to use ChatGPT. The research will help businesses, educational institutions, and the global community by providing insight into how students view the ChatGPT service within a learning environment. Additionally, the application boosts learners' confidence and interest, which improves general awareness and literacy. Finally, the research will facilitate developers of AI in the betterment of their product and service delivery and regulators in regulating the use of AI-based bots. Owing to its recentness, there is not much study currently available on ChatGPT use in education. This research adds significantly to the extant knowledge on the adoption of advanced education technologies by examining the adoption characteristics of ChatGPT, a novel AI-based tool involving students. Additionally, there is a dearth of research in the literature on students' adoption of ChatGPT for educational purposes. Such a gap was filled as this study identified the factors affecting students' adoption of ChatGPT in the classroom.

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.

This study investigated the relationship between the digital gap and the intention of Jordanians towards e-government usage. It focused on three components of the digital divide namely access, skills, and innovativeness. In addition, the research investigated how socio-demographic factors influence this connection. Data comprised 620 valid replies to questionnaires issued to 700 Jordanian citizens aged 22 and older who resided in urban and rural areas. Statistical Package for the Social Sciences (SPSS) version 26 and Analysis of Moment Structures (AMOS) version 24 were employed in data analyses and hypotheses testing. The results showed that all three aspects of the digital divide had a significant influence (access, skills, and innovativeness) on the intent of Jordanian citizens to utilize e-government, and the dimension of access imparted the strongest impact, followed by the dimension of skills and then the dimension of innovativeness. Additionally, it was discovered that gender, age, and education were the socio-demographic factors that could weaken the impact of the digital divide on the intentions of user to access e-government services. Contrariwise, the factor of income did not show a similar impact. Furthermore, perceived security significantly impacted the propensity towards e-government usage. Trust played a significant role in mediating the link between perceived security and the intention to engage in e-government.

This study examined the perception of investors toward the factors of Artificial Intelligence (AI) that impact behavioral intention of AI adoption. TAM was used with the addition of three factors of subjective norms, trust in service and service provider, and training. Jordanian stock market investors with basic investment knowledge were selected as the study participants using a convenience sampling method. The analyzed data were obtained from 610 responses. Results demonstrated the significant impact of trust in service and subjective norms on AI. Attitudes, perceived ease of use and perceived usefulness showed statistical significance toward intention to use AI. Results also showed significant impact of training on perceived ease of use.