This study sought to determine how CAATS, corporate governance (CG), and EIAF positively and directly influence the quality of financial reports (QFR). In addition to identifying the moderating effect of EIAF on the links between CAATS, CG and QFR in Saudi commercial banks. To this end, data was collected from 293 participants, including administrative managers, board members, internal auditors, certified public accountants, and staff members from the audit and internal audit departments as well as the accounting departments. Using structural equation modelling (SEM) via SmartPls, data was analyzed. The study showed that QFR is positively and significantly affected by CAATS, CG, and EIAF. Moreover, EIAF does not moderate the effect of CAATS and CG on QFR. Hence, this study enriches accounting literature and has implications for both practitioners and policymakers.

The exponential expansion of the Internet of Things (IoT) has fundamentally transformed the way people, machines, and gadgets communicate, resulting in unparalleled levels of interconnectedness. Nevertheless, the growth of IoT has also brought up notable security obstacles, requiring the creation of strong intrusion detection systems to safeguard IoT networks against hostile actions. This study investigates the utilization of fog computing architectures in conjunction with machine learning approaches to improve the security of the IoT. The UNSW-NB15 dataset, containing an extensive range of network traffic characteristics, is used as the basis for training and assessing the machine learning models. The study specifically applies and evaluates the performance of various models, including linear regression, Ridge classifier, SGD classifier, and ensemble learning. Furthermore, the findings indicate that these models are capable of accurately identifying intrusions, with success rates of 94%, 97%, 96.60%, and 96.50%, respectively. The Ridge Classifier demonstrates exceptional accuracy, highlighting its potential for effective implementation in IoT security frameworks. The results emphasize the efficacy of combining machine learning with fog computing to tackle the distinct security obstacles faced by IoT networks. In the future, our work will prioritize optimizing these models for real-time applications, improving their scalability, and investigating more advanced ensemble strategies to enhance detection accuracy. The project intends to enhance these areas to create a comprehensive and scalable intrusion detection system that can offer strong security solutions for the growing IoT environment. This will guarantee the integrity and dependability of linked devices and systems.

This research attempts to investigate the effect of types of AI systems (assisted, augmented, and autonomous) on internal auditing in Saudi Arabia. A questionnaire was used to collect data from 150 internal auditors in Riyadh City. To confirm that the study's goals were met, the descriptive analytical method was used. The questionnaire data is analyzed, and hypotheses are tested, using the Smart pls application. The study’s results show that there is a clear positive effect of AI systems, but different impacts vary according to the kind of AI systems, which is high in augmented systems, moderate in autonomous systems, and weak in assistive intelligence systems. Further studies on this subject can be conducted with larger sample sizes, especially if they are conducted globally. Based on these findings, future research can concentrate on national and cultural conditions. Additionally, companies should adopt more comprehensive involvement methods in internal auditing issues and the development of AI adoption in all company activities.

Communication is essential for humanity today and in the past. However, some individuals lack verbal communication due to their innate disability and physical losses from accidents. There are sign-language communication methods developed for such people to communicate. Artificial intelligence solutions are offered to remove the disadvantaged situations of people with disabilities due to communication in daily life. Nowadays, rapidly developing image processing and artificial intelligence methods are proper solutions for the problem focused on in this study. Convolution neural network techniques, which have become very popular recently, offer solutions to many problems. On the other hand, the YOLO algorithm shows very high performance in real-time object detection. In this study, we proposed a method for identifying the alphabets which each gesture delivers. This work studied hand detection on images and classification according to hand movements. The American Sign Language (ASL) standard was used as the sign language. The most recent version of YOLO, known as YOLOv5x, is used for gesture detection. Concentrating on the Static Sign-language problem, a study was conducted on the definition of hand movements. The letters “J” and “Z” are not included in the data set because movable hand signals are required. Apart from these two letters, a total number of 24 letters are classified. The proposed model achieved a training performance of 99.45% mAP@.5. Moreover, the proposed model has a performance of 97.9% mAP@.5 on the test dataset. The results demonstrate that the model's object detection performance is excellent. A statistical analysis of the training time shows that the training time has been drastically decreased, 4.5 hours with the current model as compared to the existing models in the literature.

In this research integrated scheduling of machines and automated guided vehicles (AGVs) in a flexible job shop environment is addressed. The scheduling literature generally ignores the transportation of jobs between the machines and when considered typically assumes an unlimited number of AGVs. In order to comply with Industry 4.0 requirements, today’s manufacturing systems make use of AGVs to transport jobs between the machines. The addressed problem involves simultaneous assignment of operations to one of the alternative machines, determining the sequence of operations on each machine and assignment of transportation operations between machines to an available AGV. We present a Microsoft Excel® spreadsheet-based solution for the problem. Evolver®, a proprietary GA is used for the optimization. The GA routine works as an add-in to the spreadsheet environment. The flexible job shop model is developed in Microsoft Excel® spreadsheet. The assignment of AGV is independent of the GA routine and is done by the spreadsheet model while the GA finds the assignment of operations to the machines and then finds the best sequence of operations on each machine. Computational analysis demonstrates that the proposed method can effectively and efficiently solve a wide range of problems with reasonable accuracy. Benchmark problems from the literature are used to highlight the effectiveness and efficiency of the proposed implementation. In most of the cases the proposed implementation can find the best-known solution found by previous studies.

This research investigated how data privacy practices may impact reverse logistics in the automotive engineering sector, particularly by examining whether environmental efficiency plays a mediating role. This research uses the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT) to understand how data privacy-driven processes support environmental practices and reverse logistics optimization. Primary research is done through structured questionnaires of automotive engineering professionals. The partial least squares structural equation modeling (PLS-SEM) approach tested the relationships amongst data privacy, environmental efficiency, and reverse logistics. However, the results further clarify how key intermediate outcomes, after all, improved environmental efficiency, affected by robust data privacy practices, may enhance reverse logistics processes. The nexus of data privacy and environmental efficiency highlights the critical need to embed respect for private sector information into logistics strategies that achieve superior business performance and also protect corporate sustainability. The findings suggested that environmental consequences must be considered in the flexibility of data-privacy measures with important strategic implications for firms operating in a complex and more environmentally conscious market. This study makes a novel contribution to the extant literature by empirically detecting how environmental efficiency mediates data privacy practices and reverse logistics. These findings will be useful for industry practitioners to use data privacy to enable sustainable logistics management of business operations within automotive engineering.

The study aimed to clarify the most important interconnected factors in the tourism sector “especially in hotels” that contribute to enhancing the interaction of current customers of hotels operating in tourism sector in light of the presence of the customer’s hotel experience as a moderator variable, through an empirical study on hotels, based on an analysis of the opinions of a sample of hotels customers in this sector. The study has been done depending on a questionnaire designed for this purpose and the study employs SmartPLS to analyze the data collected. The study found that the variables: employee efficiency, improving service quality, re-engineering business processes, and enhancing customer relationship management affect the business output generated by current customers of hotels, but the impact of these factors varies on the customers repurchase intention. The factor (Employee’s competencies- EC) has the greatest degree of correlation with an impact coefficient of (-0.227), while the lowest degree of correlation is for the factor (Reengineering business process- RBP) with an impact coefficient of (-0.034). By adopting the variable (CSE) as a moderator variable, the relationships between the independent factors and the dependent factor showed a significant change from the inverse relationship to the direct relationship. The study also showed, through the coefficient of determination (16.1%), that the selected independent factors can be developed either by expanding the sample or by selecting other simple factors or a composite of more than one factor. The moderator variable showed a negative effect on the relationship between the dependent factor (CR) and both factors (ERM and ISQ), while its effect was positive on the relationship between the dependent factor and the other independent factors (RBP and EC).

This study investigates the direct impact of green marketing, green brands, green purchasing decisions, eco-label, and the theory of planned behavior on consumer behavior among women in Saudi Arabia, in addition to knowing the role of the theory of planned behavior as a moderating variable of the relationship between green marketing, green brands, green purchasing decisions, eco-label, and the theory of consumer behavior among women in Saudi Arabia. Questionnaires were distributed to 414 women in Saudi Arabia, and the data were analyzed using partial least squares structural equation modeling (PLS-SEM) for 377 valid questionnaires for analysis and hypothesis testing. This study evaluates a structural model to understand the impact of various factors on consumer behavior among Saudi women, focusing on environmental labels, green purchasing decisions, green brands, green marketing, and the Theory of Planned Behavior (TPB). The analysis confirms that environmental labels have a positive and significant effect on consumer behavior, supporting the hypothesis that they influence sustainable consumption. Conversely, green purchasing decisions, green brands, and green marketing do not show a statistically significant effect on consumer behavior, challenging assumptions that these factors alone drive sustainable choices. The TPB was found to moderate the impact of environmental labels and green brands but did not consistently influence consumer behavior or interact effectively with other factors like green purchasing decisions and green marketing. The study’s findings suggest that while environmental labels are crucial, green marketing and branding strategies need to be more comprehensive. Additionally, TPB's role as a moderator varies, indicating the need for context-specific approaches to understand and influence consumer behavior better. The study highlights the importance of tailored strategies and continued research to refine models and interventions for promoting sustainable consumption.

Green Banking Practices (GBP) would help to anass.hamad@gmail.com achieve Banking Environmental Performance (BEP). Therefore, the present research aims to find this nexus in the Saudi banking sector by applying the mediating role of Banking Economic Sustainability (BES). For this purpose, primary data was collected from 356 employees of the banking sector in Saudi Arabia, and Structural Equation Modelling (SEM) is applied for hypothesis testing. The findings disclose that GBP improves both BEP and BES and also BES enhances BEP. Thus, BES moderated the relationship between GBP and BEP. The research recommends the Saudi banking sector to further adopt environmentally sustainable practices to achieve their economic sustainability and environmental performance. Thus, the Saudi banking sector could play its role in achieving environmental sustainability in Saudi Arabia by reducing energy consumption and pollution emissions from banking operations.

Innovation in the manufacturing industry is viewed as crucial due to its substantial effects on performance. This view has led researchers to evaluate the importance of different types of innovation within manufacturing companies. The influence of marketing innovation on product innovation and overall innovative performance is examined in the present study. The study also aims to explore the influence of product innovation on innovative performance and to analyze the mediating role of product innovation in the relationship between marketing innovation and innovative performance. Questionnaires were distributed to 384 managers from Palestinian manufacturing firms through convenience sampling. Structural equation modelling was employed as the data analysis tool. According to the study's findings, marketing innovation directly and positively impacts both product innovation and innovative performance, while product innovation positively influences innovative performance. Additionally, product innovation partially mediates the relationship between marketing innovation and innovative performance. This study is different from previous research as it focuses on the interrelationships between various dimensions of firms' innovation and performance. It adds to the literature on manufacturing performance by further validating the scales of innovation and performance. This approach could offer new insights into existing models of innovation and performance, crucial for success, by examining the interrelationships among organizational innovation dimensions, specifically marketing innovation, product innovation, and innovative performance, within the Palestinian manufacturing sector, which operates in a developing country facing conflict.