The objective of this study was to showcase the influence of Business Intelligence (BI) tools, such as Online Analytical Processing (OLAP), Extract, Transform, Load (ETL) processes, Data Mining (DM), Relational Database Management Systems (RDBMS), and Document Management Systems (DMS), on maintaining the quality of financial reports in Jordanian commercial banks listed on the Amman Stock Exchange. Two approaches were employed to achieve the research objectives: a descriptive-analytical approach involving the development of a questionnaire to gather primary data on the independent variables associated with BI tools (OLAP, ETL, DM, RDBMS, DMS), and an applied approach to evaluate the dependent variable represented by the sustainability of financial report quality, utilizing the financial statements of commercial banks listed on the Amman Stock Exchange from (2016 to 2021). Data analysis and hypothesis testing were conducted using statistical software (SPSS) through multiple regression analysis. The results of the statistical data analysis and input from the research community indicated that the sustainability of financial report quality, as a valuable asset for banks, relies on the utilization of Business Intelligence tools. IT professionals in commercial banks perceive a statistically significant impact of BI tools on maintaining the quality of financial reports. Consequently, the management of commercial banks listed on the Amman Stock Exchange should prioritize the effective utilization of Business Intelligence tools, as their potential lies in aiding the accounting process to achieve its objectives, which ultimately contribute to the sustainability of financial reports. By employing these tools accurately and efficiently in accounting practices, all stages of the accounting process can be influenced, enabling the transformation of available data into information that benefits decision-makers both internally and externally within the banking environment.

This paper investigates the driving factors behind sustainable supply chains in Jordan, focusing on the implementation of blockchain technology, customs ports practices, and technological infrastructure. The primary data for the study was collected through questionnaires distributed to employees working in the Jordanian customs. A random sampling method was employed to select participants, and a total of 184 valid questionnaires were retrieved for analysis. The collected data was analyzed using the statistical software Smartpls PLS4. The results of quantitative research reveal that the implementation of blockchain technology and technological infrastructure positively affects the driving of sustainable supply chains in Jordan, also customs ports practices also have a positive impact on driving sustainable supply chains, emphasizing the significance of efficient and resilient customs operations for sustainability. Additionally, compliance with environmental regulations enhances the effectiveness of blockchain technology in achieving sustainability objectives. Moreover, underscoring the role of robust technological capabilities in supporting sustainable operations within customs ports. The study contributes to the understanding of the key drivers of sustainable supply chains in Jordan, providing valuable insights for policymakers, supply chain managers, and other stakeholders involved in promoting sustainability within the customs ports industry. The findings can guide decision-making and inform strategies aimed at enhancing eco-efficiency and resilience in supply chain operations.

The study explored trust in the accounting information system and tested its compatibility with the credit granting decision level. The study also hypothesized that whenever the confidence in the current accounting information system is high, the level of the decision to grant credit is high, and vice versa. To test this assumption, the study used partial least squares structural equation modelling (PLS-SEM). This method is usually preferred when the goal of the research is to develop theory and explain variance or predict structures. The survey design approach was adopted in the commercial banks in Jordan, where 225 valid questionnaires were retrieved for statistical analysis. The results demonstrate a direct and significant effect on GCD in commercial banks. Moreover, the results of this paper show that cloud computing (CC) mediates the relationship between the independent variable (Accounting Information System Trust (AIS-Trust) with the dependent variable (Grant Credit Decision (GCD)).

Recreational tourism as a niche market has prompted researchers to take a variety of approaches to investigate the elements that influence travelers’ decisions to engage in leisure activities. The reasons why Muslim visitors select leisure-based travel are commonly discussed in the tourism literature, although little research has been done in this area. By analyzing the significance of travel motivation, emotional response, and satisfaction in determining attitudinal loyalty for Muslim leisure travelers, this study aims to bridge this knowledge gap. This study employed Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) using AMOS version 23 to answer the research questions. According to the findings, emotional experiences and extrinsic motives have a good direct impact on satisfaction and an indirect impact on the inclination to return. Having a deeper understanding of leisure-based travel from the perspective of Muslim tourists will benefit recreation managers.

This study explores the role of taxation and customs strategies in promoting sustainable design and environmental responsibility in supply chain management. The research examines their direct effects on sustainable and environmental responsibility in Jordan, as well as the moderating influence of socioeconomic factors. It also investigates the mediating effect of sustainable practices adoption. Quantitative analysis using Smart PLS4 software was conducted. The results highlight the positive influence of taxation strategies on sustainable and environmental responsibility. Effective taxation strategies contribute to a stronger commitment to sustainable practices and environmental concerns within supply chains. Similarly, customs strategies positively influence sustainable and environmental responsibility, demonstrating that efficient customs management aligns with sustainable practices. In addition, socioeconomic factors were identified as significant moderators. The relationship between taxation strategies and sustainable responsibility, as well as environmental responsibility, varied based on the socioeconomic context. This suggests that the effectiveness of these strategies depends on the specific circumstances in which organizations operate. Furthermore, sustainable practices adoption was found to moderate the relationship between taxation and environmental strategies, and sustainable responsibility. This indicates that the successful implementation of sustainable practices enhances the impact of taxation and environmental strategies in promoting sustainability within the supply chain. This paper emphasizes the importance of developing and implementing effective taxation and customs policies that align with sustainable practices. Organizations should consider the influence of socioeconomic factors and prioritize the sustainable practices adoption to maximize the impact of these strategies. The findings provide valuable insights for strategic decision-making and encourage businesses to integrate sustainability into their supply chain management, contributing to environmental conservation and long-term success.

The study examines whether audit committee (AC) characteristics influence firm performance, and whether this relationship is moderated by board of director’s (BOD) ownership. The sample is listed manufacturing firms in Jordan. AC characteristics, as an indicator of corporate governance mechanism, include its size, meeting frequency, independence, and experience. Firm performance is proxied by return on assets (ROA). Thirty firms are included in the sample. Data are collected from 2015 to 2021 for a total of 210 observations. The first model indicates that AC meetings and independence positively and significantly influence firm performance. On the other hand, AC size is not a significant predictor of firm performance. The second model shows that the interaction effects (AC size, AC independence, and AC experience) are significant and positive on firm performance. The results provide insights on how to improve AC effectiveness so as to improve the performance of listed manufacturing firms in Jordan. They also suggest the significance of BOD ownership in enhancing internal corporate governance mechanisms, particularly the AC. Jordanian policy makers must therefore ensure the effectiveness of these mechanisms, especially AC, through relevant regulations and recommendations.

This research examines the synergistic influence of inventory competition, artificial intelligence (AI) adoption, and digital marketing intensity on quality improvement decisions within contemporary supply chains. With a focus on enhancing product and service quality, we investigate the intricate relationships among these variables. A quantitative approach involving 380 supply chain professionals reveals that heightened inventory competition, increased AI adoption, and robust digital marketing significantly contribute to quality enhancement initiatives. The study builds upon prior research by empirically validating these connections and offers practical insights for supply chain practitioners. The findings underscore the strategic imperative of organizations to strategically balance these factors to optimize their quality management strategies, fostering customer satisfaction and competitiveness. While offering valuable contributions, the study acknowledges limitations in terms of self-reported data and a specific respondent group. Future research could extend this investigation to diverse industries and geographical contexts. In the end, this study sheds light on the complex interplay that exists between inventory competition, the use of AI, digital marketing, and judgments about quality improvement. As a result, a road map has been provided for efficient quality management of supply chains in the digital age.

Although the critical success factors might be different between the pre and post-adoption stages of mobile wallets, there have been few studies conducted to examine those factors for the post-adoption stage when compared to the number of studies conducted to examine those factors for the pre-adoption stage. Yet, the post-adoption stage of mobile wallets is crucial to the success and sustainability of the mobile wallets’ ecosystem. Thus, this study developed and examined a model by integrating relevant factors into the Technology Acceptance Model 2 (TAM2). Data were collected from 578 mobile wallet users in Jordan using an electronic questionnaire. A structural equation modelling approach was utilized to analyze the data. The results revealed that perceived usefulness and perceived ease of use have statistically significant positive direct effects on the intention to continuous use of mobile wallets, while subjective norm does not. In addition to that, results indicated that trust, security, and ubiquity have statistically significant positive direct effects on perceived usefulness and perceived ease of use, and, in turn, on the intention to continuous use of mobile wallets. Moreover, this study found that perceived ease of use and subjective norms have statistically significant positive direct effects on perceived usefulness, and, in turn, on the intention of continuous use of mobile wallets. While risk does not have a significant effect on perceived usefulness, it has been found to have a statistically significant negative direct effect on perceived ease of use, and, in turn, on the intention to continuous use of mobile wallets. The findings of this study should help stakeholders to develop more effective consumer retention tactics and formulate appropriate marketing decisions.

Dementia is a neuropsychiatric brain disorder that usually occurs when one or more brain cells stop working partially or at all. Diagnosis of this disorder in the early phases of the disease is a vital task to rescue patients’ lives from bad consequences and provide them with better healthcare. Machine learning methods have been proven to be accurate in predicting dementia in the early phases of the disease. The prediction of dementia depends heavily on the type of collected data which usually are gathered from Normalized Whole Brain Volume (nWBV) and Atlas Scaling Factor (ASF) which are normally measured and corrected from Magnetic Resonance Imaging (MRIs). Other biological features such as age and gender can also help in the diagnosis of dementia. Although many studies use machine learning for predicting dementia, we could not reach a conclusion on the stability of these methods for which one is more accurate under different experimental conditions. Therefore, this paper investigates the conclusion stability regarding the performance of machine learning algorithms for dementia prediction. To accomplish this, a large number of experiments were run using 7 machine learning algorithms and two feature reduction algorithms namely, Information Gain (IG) and Principal Component Analysis (PCA). To examine the stability of these algorithms, thresholds of feature selection were changed for the IG from 20% to 100% and the PCA dimension from 2 to 8. This has resulted in 7×9 + 7×7= 112 experiments. In each experiment, various classification evaluation data were recorded. The obtained results show that among seven algorithms the support vector machine and Naïve Bayes are the most stable algorithms while changing the selection threshold. Also, it was found that using IG would seem more efficient than using PCA for predicting Dementia. These promising results open the door to a new era of early prognosis of Alzheimer’s Disease and Related Dementias (ADRD).

This paper presents a case study on how Six Sigma methodology can be used to improve student academic performance in three main subjects (Math, Arabic, and English) in a private secondary school located in the Middle East. The Six Sigma methodology of Define, Measure, Analyze, Improve and Control (DMAIC) was used to improve student performance along with several improvement tools such as Fishbone diagram, Pareto charts, and Critical-To-Quality (CTQ) tree. Introducing quality improvement concepts and tools resulted in a series of improvements leading to an increase in student academic achievement quantified by Sigma Quality Level (SQL). The SQL improved in Math from (2.01) to (2.37) and in Arabic from (1.86) to (2.38). On the other hand, English grades did not increase from baseline. The results of this study provide a clear, step-by-step approach to systematically approaching and solving educational problems using the DMAIC model. Practically speaking, the results of this study provides clear evidence that the DMAIC can be used in the educational sector by school administrators and teachers to improve student grades.