The potential for transforming the estate management industry through the resolution of common inefficiencies, lack of transparency, and security concerns is presented by the use of blockchain technology into estate governance. The purpose of this article is to clarify how incorporating blockchain technology would affect estate operations and governance. This study is based on quantitative information that was collected from 317 estate management professionals using a 5-point Likert scale questionnaire. SmartPLS4 analysis demonstrates that blockchain governance has a statistically significant and robust influence on estate governance in Jordan. The impact of Blockchain Governance on Jordanian Estate Management appears to be negligible and unimportant. Furthermore, there appears to be a negligible and insignificant correlation between Jordanian estate management and estate planning methods. In-depth analysis of these theories is done in this article, which also offers insights into how blockchain technology affects estate governance dynamics and how it can affect Jordan's estate management procedures. The consequences go beyond theoretical understandings; they promote the use of blockchain technology in estate governance frameworks as a game-changing means of ensuring the safe, transparent, and effective administration of frozen estates in Jordan and elsewhere.

The objective of the study is to clarify the influence of digital capabilities on innovation capability, considering the direct and indirect impacts of digital leadership and digital culture in small and medium enterprises (SMEs) in Vietnam. The study used a mixed research method including qualitative with in-depth interviews and quantitative with Partial Least Squares Structural Equation Model (PLS-SEM) to analyze 271 responses. Research results show that digital capabilities have a positive impact on SMEs’ innovation capability. Digital leadership and digital culture also, directly and indirectly, affect firms’ digital capabilities and innovation capabilities. From there, the study confirms the theoretical contributions as well as proposes suggestions for SMEs to develop digital capabilities, and as a result, to develop innovation capabilities.

The objective of this research is to comprehend how these elements interact to influence young people's subjective happiness and to provide insightful information about their perspectives. Significant discoveries are shown by the study's findings. This research paper conducts a thorough analysis of the many factors that affect youth happiness, including GDP, loneliness, longevity, autonomy, generosity, and corruption. First off, GDP shows up as a significant and noticeably positive factor to young happiness. This emphasizes how crucial economic success is in raising young people's life happiness. Second, autonomy is recognized as another important factor, showing a significant and favorable influence on happiness. It emphasizes how important one's own independence and life control are for young wellbeing. Additionally, the study uncovers a diverse range of impacts among these indicators. Longevity and Generosity are found to positively influence happiness, emphasizing the role of health and social support in young people's contentment. Conversely, Loneliness and Corruption exhibit significant negative effects on happiness, underscoring the detrimental consequences of social isolation and institutional corruption on youth well-being. In conclusion, this research paper offers a holistic view of youth happiness, recognizing the multifaceted nature of its determinants. These findings have important implications for policymakers, highlighting the need to address not only economic aspects but also personal autonomy, social connectedness, and integrity in efforts to promote youth happiness and well-being in society.

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.

Accounting systems have experienced significant developments, including sophisticated software applications, automation, big data, data analytics, and so on. The purpose of this study is to examine the effect of digitalization of accounting systems on the quality of accounting information and the effectiveness of management decision making in small and medium enterprises (SMEs) in a developing country, Indonesia. Based on the innovation diffusion theory, the hypothesis states that digitalization of the accounting system can improve accuracy and timeliness, cost reduction, and decision making. Data were obtained from questionnaire surveys to SMEs who had implemented the digitalization of the accounting systems. The results showed that the digitalization of the accounting systems had a positive effect on the accuracy and timeliness of accounting information and cost reduction. Furthermore, these two variables had a positive effect on the effectiveness of SME management decision making.

Operational data is always huge. A preprocessing step is needed to prepare such data for the analytical process so the process will be fast. One way is by choosing the most effective features and removing the others. Feature selection algorithms (FSAs) can do that with a variety of accuracy depending on both the nature of the data and the algorithm itself. This inspires researchers to keep on developing new FSAs to give higher accuracies than the existing ones. Moreover, FSAs are essential for reducing the cost and effort of developing information system applications. Merging multiple methodologies may improve the dimensionality reduction rate retaining sensible accuracy. This research proposed a hybrid feature selection algorithm based on ScC and forward selection methods (ScCFS). ScC is based on stability and correlation while forward selection is based on Random Forest (RF) and Information Gain (IG). A lowered subset generated by ScC is fed to the forward selection method which uses the IG as a decision criterion for selecting the attribute to split the node of the RF to generate the optimal reduct. ScCFS was compared to other known FSAs in terms of accuracy, AUC, and F-score using several classification algorithms and several datasets. Results showed that the ScCFS excels other FSAs employed for all classifiers in terms of accuracy except FLM where it comes in second place. This proves that ScCFS is the pioneer in generating the reduced dataset with remaining high accuracies for the classifiers used.

Establishing new relations with new customers and managing customers’ relations with the existing ones is important and considered challenges for each firm nowadays. Thus, studying and understanding different psychological factors like consumers’ emotions and happiness, motivates and attitudes are important while it helps in how to do more business with customers and push them to stay longer especially when adopting e-government mobile applications. This study used a quantitative research approach with survey data collection from different users of government mobile applications (GMA). The sample is around 340 from different backgrounds and levels of using different government services mobile apps. The results revealed the standardized regression and coefficients interpret the direct association between the study variables, hence confirmed the hypothesized model that included several factors such as perceived usefulness, perceived ease of use, perceived skills readiness, and perceived security toward attitudes to GMA and use of GMA effects on customer happiness and positive emotion. Moreover, the conclusion and implications confirmed the literature of this research field and elucidated most of the stated factors that significantly influence the customers’ emotion and happiness. For further study, research directions are given to expand the extant understanding of new different factors with other outcomes of mobile applications use.

The current study aimed to examine marketing cyber-security (DDoS Attacks, Cross-Site Scripting, SQL Attacks, and passwords attacks) in the digital age by presenting evidence from digital marketing platforms. Depending on the quantitative approach and utilizing a questionnaire as a tool, (133) marketing managers in digital marketing companies in Jordan responded to an online questionnaire. SPSS was used to screen and analyze the gathered data. Results of the study accepted the main hypothesis, and it appeared that marketing cyber-security has a statistically positive influence on marketing platforms, in addition to that, it appeared that the highest influence of sub-variables was for the benefit of Structured Query Language (SQL) Attacks explaining 35.8% of the variation. This result meant that SQL attacks-security does have a statistically positive influence on marketing platforms. This hypothesis could be tested through various methodologies, for example, surveys, interviews, focus groups, and/or experiments. The study recommended that marketers should use role-based access to limit the data employees can access and regularly review their permissions. Further recommendations were presented in the study.

Active Queue Management (AQM) techniques are crucial for managing packet transmission efficiently, maintaining network performance, and preventing congestion in routers. However, achieving these objectives demands precise traffic modeling and simulations in extreme and unstable conditions. The internet traffic has distinct characteristics, such as aggregation, burstiness, and correlation. This paper presents an innovative approach for modeling internet traffic, addressing the limitations of conventional modeling and conventional AQM methods' development, which are primarily designed to stabilize the network traffic. The proposed model leverages the power of multiple Markov Modulated Bernoulli Processes (MMBPs) to tackle the challenges of traffic modeling and AQM development. Multiple states with varying probabilities are used to model packet arrivals, thus capturing the burstiness inherent in internet traffic. Yet, the overall probability is maintained identical, irrespective of the number of states (one, two, or four), by solving linear equations with multiple variables. Random Early Detection (RED) was used as a case study method with different packet arrival probabilities based on MMBPs with one, two, and four states. The results showed that the proposed model influences the outcomes of AQM methods. Furthermore, it was found that RED might not effectively address network burstiness due to its relatively slow reaction time. As a result, it can be concluded that RED performs optimally only with a single-state model.

This research attempts to better understand how students in Saudi Arabia benefit from narrative and story aspects in gamified learning environments. Data from a sample of 500 persons with varying levels of education are analyzed using quantitative methods such as descriptive statistics, correlation analysis, and multiple regression analysis. The findings point to strong positive correlations between the use of gamification in education, the influence of storytelling, narrative variables, and the acquisition of new skills. There has been a significant shift toward the use of narrative variables as measures of mastery in gamified classrooms. This study's results show that using gamified learning with story elements may increase students' interest, motivation, and knowledge retention. Efforts are now being made by Saudi Arabia to update its educational system and provide its youth with the tools they'll need to succeed in the country's emerging knowledge-based economy. The use of game-based learning and narrative-rich experiences has promising results in this setting.