The aim of this study is to examine the impact of digital marketing on consumer behavior in the fast fashion industry in the Kingdom of Saudi Arabia (KSA). The fast fashion industry has been growing rapidly in the KSA, and digital marketing has played a significant role in changing consumer behavior in this industry. The study adopted a quantitative research design and used online surveys as the primary data collection method. The sample consisted of a convenient sample of participants who had purchased fast fashion products in the KSA. The data was analyzed using various statistical methods, including descriptive statistics, correlation analysis, and regression analysis. This study's findings shed new light on how digital marketing has affected customer behavior in the fast fashion sector in the KSA. Consumers in the KSA have a good reaction to digital marketing methods in the fast fashion industry, and this effect is significant.

The goal of this study was to find out how business intelligence systems, AI, and digital leadership affect how satisfied employees are with their jobs and how much value they add to companies in the Greater Amman Municipality. After the study samples were taken and looked at, a total of 246 samples were approved to be used in the PLS software-based analysis. The results of this study showed that putting in place business intelligence tools, artificial intelligence, and digital leadership all made employees happier with their jobs and gave businesses more value. The research showed that there are four key parts to digital leadership: commander, communicator, collaborator, and co-creator. The main parts of business intelligence are Data Warehouse, Data Mining, Business Process Management, and Competitive Intelligence. Findings show that digital transformation is made up of three key parts: changing processes, developing business models, and changing domains. The results also show that an employee's level of job satisfaction, which includes things like business success, work commitment, and job thinking, is linked to how much value they add to the company. Intriguingly, the current results go against those of earlier studies, which said that the variables of interest have no effect on how happy employees are with their jobs or how much value companies add for their customers. When the results of this study are looked at as a whole, they say that businesses should start doing things that make employees happier at work and increase the value of the business. The current study is innovative because it focuses on the most important parts of business intelligence, artificial intelligence, and digital leadership in order to improve employee satisfaction at work and the quality of business learning with added value in Greater Amman Municipality.

The banking sector around the globe has witnessed a huge development in its services and products. The electronic banking services are considered as a competitive advantage for the banking sector. The purpose of this paper is to evaluate the effectiveness of e-banking service quality on customer satisfaction in the context of Saudi Arabian commercial banks. Both quantitative and qualitative research methods were used in the study. A sample of 308 customers from the banking sector participated in this study. The researchers have developed a self-structured questionnaire to collect the relevant data. In addition, secondary data was gathered from published sources, including websites, journal papers, and publications of the chosen commercial banks. The findings of this study show that the eight service quality dimensions; reliability, transactional efficiency, customer support, service security, ease of use, performance, satisfaction with service quality and service content have a significant impact on the level of user's satisfaction with e-banking in the Saudi Arabian commercial banks.

The purpose of this study is to investigate the impact of using artificial intelligence applications on the performance of accountants and audit firms. The final sample for this study comprises 38 audit firms. This study uses a survey-based methodology in the context of Saudi Arabia. The results of the multiple regression revealed that the audit firms using artificial intelligence applications perceive them as useful instruments that increase the performance of accountants and audit firms. They can reduce the cost, effort, and time of the audit process, achieve a competitive advantage for the audit firms, help auditors better determine materiality, achieve a competitive advantage, improve the performance of the audit team, carry out the continuous audit process better than the traditional audit, enable auditors to select audit samples with high efficiency, improve the quality of control procedures on electronic transactions and files used by the client, contribute to the management of operations and tasks with more sophisticated and intelligent mechanisms, increase the efficiency and effectiveness of the audit process and the efficiency and effectiveness of planning and supervising the audit process, reduce uncertainty and audit risk. The results reported by this study can be valuable for the accounting and auditing professions, audit firms, and standards and auditing regulators to deeply understand the extent to which artificial intelligence applications influence the performance of accountants and audit firms.

This paper examines speakers’ systematic errors while speaking English as a foreign language (EFL) among students in Arab countries with the purpose of automatically recognizing and correcting mispronunciations using speech recognition, phonological features, and machine learning. Accordingly, three main steps are implemented towards this purpose: identifying the most frequently wrongly pronounced phonemes by Arab students, analyzing the systematic errors these students make in doing so, and developing a framework that can aid the detection and correction of these pronunciation errors. The proposed automatic detection and correction framework used the collected and labeled data to construct a customized acoustic model to identify and correct incorrect phonemes. Based on the trained data, the language model is then used to recognize the words. The final step includes construction samples of both correct and incorrect pronunciation in the phonemes model and then using machine learning to identify and correct the errors. The results showed that one of the main causes of such errors was the confusion that leads to wrongly utilizing a given sound in place of another. The automatic framework identified and corrected 98.2% of the errors committed by the students using a decision tree classifier. The decision tree classifier achieved the best recognition results compared to the five classifiers used for this purpose.

The primary purpose of this study is to investigate how group reference, perceived usefulness, and product quality influence online shoppers' desire to purchase and final purchase decisions. Multiple methods of quantitative analysis and processing of data are used to evaluate and validate the study's hypotheses. Using a Structural Equation Model, the study's theories were tested and analyzed. Customers of digital market websites in Jordan participated in this study by responding to an online poll. Data was compiled from 220 study questionnaires using a systematic sampling strategy. Data analysis with the Structural Equation Model revealed significant favorable effects of group reference, perceived usefulness, and product quality on both purchase intent and final online purchase decisions. The authors chose to center their study around the perspectives, comments, attitudes, and impressions of groups of individuals. Perceived usefulness can be gauged along the aspects of experience, enjoyment, and subjective norm. The words customer expectations, real product specifications, and the quality of the service received all shed light on the product's quality. The originality of this study rests in the model that was created to explain the relationship between group reference, perceived usefulness of products, and product quality as they relate to purchase intent and digital market website use in Jordan.

The expression "social media" refers to a software-based platform developed for users’ benefit. People use it to gain social power, market their products, conduct online business, and share information and ideas. This digital ecosystem has become helpful in various ways, but research indicates that it does not come for free. Addiction, depression, and anxiety are some of the adverse conditions discussed in many studies. The purpose of this study is to mark if there is a relationship between using social media networks and the numbering of people with anxiety or depression. Also, by addressing the need to learn more about what makes people use social networks and how that use affects anxiety and depression in Arabic-speaking users in Jordan, we can help people from different cultures understand each other better. This research uses TAM, telepresence, and survey data from 1050 people, mainly from Jordan. The research looks at how the usage of social media is related to supposed usefulness, supposed ease of use, trust, social influence, age, gender, level of education, marital status, the time spent on the internet, preferred social media network, and perceived usefulness of SNS. AMOS 20 methods of confirmatory factor analysis (CFA), structural equation modeling (SEM), and machine learning (ML), such as SMO, ANN, random forest, and the bagging reduced error pruning tree (RepTree), were used to test the proposed model hypotheses. According to the results, the researchers found high correlations between social network usage and depression and anxiety. The use of social networking sites is also affected by how useful they are seen to be, how easy they are to use, trust, social influence, and telepresence. Also, the moderator's age, gender, level of education, marital status, amount of time spent on the internet, experience with the internet, and favorite social networks all affect how they plan to use social networks.

Altman Z score is an effective tool for measuring a company's financial strength and the possibility of bankruptcy. In this study, the Z score formula was utilized to measure the Indian automobile industry's financial soundness. The financial data of ten automobile companies listed in the National Stoke Exchange (NSE) was taken to analyze both original and emerging market formulae of the Altman Z score. Results suggest that companies of the industry listed in the NSE are financially sound. The study concluded that the Indian automobile industry is sound and robust, and the automobile companies are not prone to financial distress or bankruptcy in the near future.

The Indian cement industry has adopted various environmental protection technologies, but adoption of these new environmental technologies and development of working model could not resolve many issues related to environmental concern among Stakeholders. This paper examined the current technologies used by the cement companies and the challenges they are facing in adoption of these technologies. This research describes the effects of cement manufacturing on global warming, water, coal and other pollution emissions during cement production process and involves environmental manufacturing technologies. The study measures the challenges of introducing environmental technologies by creating a model of challenges. including challenges in perceived usability of technology, challenges in perceived utility, challenges in user engagement, and challenges in intent to use behaviour. The study examines the challenges of introducing environmental technologies into the Indian cement industry to mitigate air, water, and energy pollution and to highlight the new environmental technologies and development of the model. The data from 1540 professionals responsible for using the environmental technology were gathered and analysed with t test and regression analysis. The final outcome of the research is the model expressing the challenges in adoption of environmental technologies in India.

This study aims to examine the effect of company experience on audit committee diligence among manufactured listed companies on Saudi Stock Exchange (Tadawul) for the period 2015-2017. The final sample of this study consists of 191 firm-year observations. Using the Pooled OLS regression, the study finds that there was a positive impact of company experience on audit committee diligence. The study provides insightful evidence to policy makers on the determinants influencing the audit committee diligence among manufacturing companies.