This study provides an analytical view of the correlation between several factors that influence the credibility of information available through various sources on the Internet. The most critical factors include information quality, source credibility, argument strength, message credibility, and average credibility. Additionally, the study explores the impact of gender and years of experience as demographic variables on the nature and size of these relationships. The study relied on a critical review of previous related literature. In addition, it adopted an analytical approach using a sample of 300 Internet users through a questionnaire designed based on the study hypotheses. Most of these relationships were found to be at an average level, except for the relationship between source credibility and information credibility. No statistical indicators were observed. Consequently, the researchers acknowledge the need for caution when generalizing these results to society. The study also found that the gender of the recipient and the number of years of experience did not necessarily play a mediating role in the relationship between the tested factors and the credibility of information received via the Internet.

The current study aims to determine the role of artificial intelligence (AI) in developing the accounting system (AS) in Jordanian Islamic banks. Currently, Islamic banks in Jordan are included in the research population. Using a quantitative research approach, 128 workers of Islamic banks in Jordan were chosen as a sample for this study. The study used a survey questionnaire instrument that was created based on past relevant literature and studies to collect the required data. The results indicated that there is an influence of AI (big data, intelligent agents, expert systems and automation processes) on the development of the AS in Jordanian Islamic banks. Accordingly, the study recommends that to improve AS tasks and reduce associated costs, accountants and accounting companies should always increase their understanding of artificial intelligence.

Breakthroughs and advances in translation technology by virtue of AI-powered MT tools and techniques contributed significantly to providing near-perfect translation. This study aims to evaluate the accuracy of three translation technologies (Google Translate, Gemini, and ChatGPT) in translating multidisciplinary Arabic research titles in the Humanities and Social Sciences into English. A corpus of 163 titles of Arabic research articles from various disciplines, including media studies, literature, linguistics, education, and political science, was extracted from a Scopus-indexed journal, namely Dirasat: Human and Social Sciences Series. The research methodology in the present study lends itself largely to Koponen’s (2010) translation error strategy framework. Based on the data analysis, the findings showed that the renditions provided by these programs were categorically marked with either sense or syntax errors, which often rendered the translations inaccurate. Many polysemous terms with multiple related senses were mistranslated. The results showed that the Gemini translations contained the least errors. In contrast, the human translations contained the least mistranslation and diction errors. Google Translate and ChatGPT, on the other hand, contained the highest number of equivalence-based errors. Unexpectedly, the human translations contained the highest number of syntactic errors, reflecting a lack of target language proficiency. The study's conclusions and findings would be beneficial to translators, students, and scholars who may consider translating their Arabic study research titles and abstracts through the most commonly used AI tools.

This research aims to analyze variable service quality on e-government user satisfaction and analyze information quality variables on e-government user satisfaction at the immigration office. The research method used in this research is associative quantitative research which aims to determine the relationship between two or more variables. In this way, we can build a theory that functions to predict and control a phenomenon. The population in this study were all immigration office employees. In this research, an analysis model is used, namely Partial Least Square-Structural Equation Modeling (PLS-SEM). In this study, the number of respondents was 876 immigration office employees who used e-government. The sampling technique used in this research is non probability sampling. In this research, the data collection method used was the questionnaire method. The instrument used to measure this research variable is a 7-point Likert scale. Data processing in this research uses SmartPLS software. The stages of data analysis in this research are the outer model test which includes convergent validity, discriminant validity and composite reliability as well as inner model analysis, namely hypothesis testing. The results of this research are that variable service quality has a positive and significant relationship to e-government user satisfaction at the immigration office and the information quality variable has a positive and significant relationship to e-government user satisfaction at the immigration office.

Social Networking Sites (SNS) play an important role in human psychological well-being. This research explores the effects of perceived overload on the passive usage intention of SNS among Malaysian users through SNS fatigue and anxiety by employing a Cognition-Affect-Conation (C-A-C) model. Cross-sectional survey research was carried out and the research acquired responses from 383 SNS users. Data was analyzed through PLS-SEM. The outcomes noted that perceived cognitive overload, perceived information overload, SNS fatigue, and anxiety are positive antecedents of SNS passive usage intention. The research brings valuable insights into the formation of passive usage intention of SNS and provides implications for service providers and users.

This study investigates the relationship between interaction, entertainment, trendiness, brand engagement, and purchase intention in the context of TikTok as a social media platform. Using a quantitative research approach, online questionnaires were distributed to 197 respondents who met specific criteria, including active usage of TikTok and being under 45 years old. The data collection period spanned from March 2023 to May 2023. Data analysis was conducted using variance-based structural equation modeling (PLS-SEM 4.0). The independent variables in the study are interaction (X1), entertainment (X2), and trendiness (X3), with brand engagement (Z) serving as the mediator variable and purchase intention (Y) as the dependent variable. The results revealed that interaction, entertainment, and trendiness significantly positively affect purchase intention, with brand engagement playing a moderating role in this relationship. This study sheds light on the influence of TikTok content attributes on consumers' purchase intention and underscores the importance of brand engagement as a key factor in shaping their purchasing decisions.

In this digital era, all human activities have moved towards digital. The digital era has provided significant changes in all aspects of life, one of which is the educational aspect. Digital technology has opened up new education opportunities but also presents challenges that must be faced. Almost all sectors, including education in the industry 5.0 era, have digitized, namely by utilizing sophisticated information technology. Era Society 5.0, is an era that will make it easier for human life to interact and transition to the digital era. Thus, the use of digital technology for every aspect of life, especially the education sector, is very necessary since it will reflect the level of competitiveness of a country. This research aims to analyze the relationship between e-learning and performance, digital leadership and performance, and the relationship between digital innovation and performance. This type of research uses quantitative research methods. The population in this research is all high school teachers who have used e-learning platforms and have carried out digital innovation. The sampling technique used in this research was a simple random sampling technique and the total sample of respondents from this research was 489 teachers. The type of data used in this research is primary data and the data search tool used is an online questionnaire using a Likert scale. The data analysis is to use structural equation modelling. The results show that e-learning had a positive and significant relationship with performance, digital leadership had a positive and significant relationship with performance and digital innovation had a positive and significant relationship with performance.

A large-scale cloud data center must have a low failure incidence rate and great service dependability and availability. However, due to several issues, such as hardware and software malfunctions that regularly cause task and job failure, large-scale cloud data centers still have high failure rates. These mistakes can have a substantial impact on cloud service dependability and need a large resource allocation to recover from failures. Therefore, it is important to have an efficient management of data recovery to protect organizations data from loss. This paper aims to study some factors that may improve the management of data recovery by using quantitative research design as a methodology. The results of hypothesis testing give strong evidence supporting the positive and significant correlations between the proposed hypotheses and the efficiency of data management recovery. This study finds that the presence of a data center in an organization demands the development of a solid plan for the most effective usage of a software program to handle data recovery.

Using the frameworks of the Theory of Use and Gratification (UGT) and the Unified Theory of Acceptance and Use of Technology (UTAUT2), this study explores the factors that influence individual behavior and behavioral goals in the adoption of digital banking. Partial Least Square-Structural Equation Modeling (PLS-SEM) is used in the analysis of research data using the program SmartPLS 3.2.9 professional. There are 432 people in the research sample that filled out questionnaires. The results show that behavioral intentions are strongly influenced by the integration of UGT-UTAUT2 by 60.3%. Performance and effort expectations are influenced by cognitive needs, effort expectations are influenced by affective needs, and social influence is impacted by social needs. Behavioral intentions for the use of digital banking are shaped by a combination of factors such as price value, hedonic motivation, habits, facilitating conditions, and effort expectations. The relationship between behavioral intentions, affective and cognitive needs is mediated by effort expectations. In the context of using digital banking, habits and behavioral intentions are important factors that influence behavior; in contrast, cognitive needs, affective needs, performance expectations, and social influence have no direct effect on behavioral intentions.

Information and Communication Technologies (ICT) affect all aspects of our daily lives. Using them is considered a symbol of modernization and social advancement. The global expansion and interconnection of ICT offers a significant opportunity to promote the advancement of humanity, bridge the digital gap and promote the growth of societies built on knowledge. In this study, we analyzed and identified the most influential factors in the adoption of ICT in education from the data set called “Final Survey-Digital Inclusion Teachers” of the Plurinational State of Bolivia, which consists of 871 instances and 189 columns. We performed feature selection by carefully combining the results of three feature selection methods: filter (chi-square, ANO-VA and mutual information), wrapper (RFE) and intrinsic (Classification And Regression Trees, Random Forest, Gradient Boosting and XGBoost). The results demonstrated that a teacher's motivation for curricular planning that includes ICT, teaching experience and the institutional environment are key factors in the adoption of these technologies in education. Furthermore, we identified that the Random Forest algorithm is the most appropriate for analyzing and predicting the adoption of ICT in education, we affirmed this after this algorithm obtained the highest values in four of the six metrics evaluated: a sensitivity of 77.7%, an F1 Score of 77.9%, a Cohen's Kappa coefficient of 60.8% and a Jaccard Score of 64.3%. These results suggest that Random Forest is the most effective algorithm to analyze the factors related to the adoption of ICT in educational environments.