The spread of the Covid-19 pandemic and the interruption of personal communication between the teacher and students in higher education led to the need for finding solutions that enable the continuation of the educational process and ensure access to accurate information that improves the level of human capital in dealing with dynamic environments. Therefore, this research sought to analyse the impact of the application of artificial intelligence in entrepreneurship education in Qatari higher education institutions after the Corona pandemic. The measurement of artificial intelligence was based on dimensions (machine learning, natural language processing, expert systems, and machine vision), while entrepreneurship education was measured by dimensions of (entrepreneurial cognition, entrepreneurial competence, and innovation spirit). The research followed an experimental quantitative approach based on collecting data from Qatari university students using a questionnaire developed for the research purpose. Hence, the convenience sample used in the research was composed of 402 students from various Qatari universities, which represents a response rate of 67% from the distributed questionnaires. The statistical analysis of the research data was based on the covariance-based structural equation modeling technique (CB-SEM). The results of the research indicated that all dimensions of artificial intelligence had a positive impact on entrepreneurial education, with the highest impact being machine vision and the lowest impact being natural language processing. Accordingly, the results of the research revealed the need to invest in technological capabilities for supporting the educational system aimed at generating innovative human resources capable of coping with the uncertainty of the work environment.

The study examines the technological factors influencing the adoption of artificial intelligence (AI) technology. In addition, this study examines the mediating role of accounting automation on AI adoption in a small and medium-sized enterprise (SMEs) context. The owners and managers of SMEs were surveyed online using a convenience sampling technique. The proposed model was tested using SEM. The findings confirmed the relationships between the predictive variables and AI adoption. The results showed that accounting automation partially mediated the relationship between predictive variables and the adoption of AI. The results contribute to the TOE model by incorporating accounting automation into the TOE framework as a mediating variable. The study also contributed to the literature by including new variables in the model, such as saving time and efficiency-improving.

This study sought to investigate the impacts of big data, artificial intelligence (AI), and business intelligence (BI) on Firms' e-learning and business performance at Jordanian telecommunications industry. After the samples were checked, a total of 269 were collected. All of the information gathered throughout the investigation was analyzed using the PLS software. The results show a network of interconnections can improve both e-learning and corporate effectiveness. This research concluded that the integration of big data, AI, and BI has a positive impact on e-learning infrastructure development and organizational efficiency. The findings indicate that big data has a positive and direct impact on business performance, including Big Data External and Internal, Innovative Usage, Indexing, and Sources Accuracy. In addition, Artificial intelligence positively affects business performance, including Data Accuracy, Data Transparency, Data Speed, and Creative Thinking and Learning. Moreover, business intelligence has a direct and positive impact on business performance, including Data Warehouse, Data Mining, Business Process Management, and Competitive Intelligence. In addition, the findings indicate that e-learning which represents system quality, information quality, and self-efficacy has a positive relationship on enhancing business performance. Interestingly, the present findings are inconsistent with those of previous studies showing the variables of interest which have no effect on e-learning and business performance. Taken together, the findings of this study suggest that firms should begin to apply processes related with applying e-learning and developing business performance. The novelty of the present study lies in highlighting the key dimensions of big data, artificial intelligence, and business intelligence when it comes to enhancing e-learning and business performance at Jordanian telecommunications industry.

In recent years, technology adoption in Indonesia has begun to use advances in artificial intelligence (AI) to improve services. This change has had a significant impact on several institutions. This article provides an overview of the many institutions in Indonesia that are leveraging AI. A comprehensive review of 35 papers from the Scopus database was used to develop our methodology. Education, health care, ICT, licensing, transportation, and economic services are all well-represented in the existing literature, which puts AI into practice in various disciplines. We provide a framework for organizing governance research that identifies gaps in the existing literature and suggests future directions for research utilizing technology.

Artificial intelligence is user-friendly and possesses useful characteristics to share across the various services that are provided. By enhancing innovative contact, artificial intelligence applications (AIA) enable a more involved environment in governmental institutions. The goal of this study is to discover how users in the UAE feel about using AIA for educational reasons. Data collected from a survey of 387 university students were used to validate the model and hypotheses. The adoption features, such as perceived compatibility, trialability, relative advantage, ease of doing business, and technology export, are included in the conceptual model. The current study's practical implications are crucial in that they push the relevant educational authorities to comprehend the significance of each component and enable them to make plans and efforts in accordance with the order of the factors' relative importance. The managerial implications give educational sectors insight on how to apply AIA in their system to improve the growth of the provided service and to make the process easier for all users. The conceptual model of the paper, which links both traits of the individual and those of the technology, is what makes it new. The findings indicate that the diffusion theory variables outperform the other two variables of ease of doing business and technology export.

The aim of the present research was to identify the influence of Artificial Intelligence and Big data on loan decisions in Saudi Arabian banks. The study used a sample of thirteen banks located in Saudi Arabia for data analysis. The data was gathered using some questionnaires distributed to bank staff. The results indicate a significant connection among loan decisions and AI and Big data. Moreover, it was found that there was a statistically positive and significant relationship between the artificial intelligence and the quality of loan decision-making. There was also a positive association between experience, educational qualification and using big data and AI. In conclusion, the results show that AI and Big data could enhance innovation which could be applied in the banks of Saudi Arabia for loan decisions assessment.

In online shopping enterprises, AI technology has been widely used to provide accurate and fast personalized consumer services. This research demonstrates the use of AI technology in the e-commerce business, specifically online enterprises, to determine different effects. The study was conducted in Jordan and involved about 230 participants. The study evaluated different impacts of AI, such as e-payment and stimulating consumers' sentiments. The study used the Stimulus–Organism–Response model (SOR) empirical model, which states that the examination of human processes differs from that of the machine assessment. The model classified the AI technology experienced by the customers' when they visit online to do purchasing. Online purchasing behaviors can be influenced by insight, accuracy, and interaction experience. Also, the perceived value was used as a mediating variable from the prospects of perceived hedonic and utility value. The research integrated empirical research models such as SEM and SPSS to analyze the data on the effects of three-dimension. The results indicated that the AI technology accuracy, interactive experience, and insight significantly affected customers' perceived hedonic and utilitarian values.

The engagement between customers and brands is being transformed by artificial intelligence (AI). However, there has been little study into AI-powered customer experiences; hence, this research aims to examine how the incorporation of AI in purchasing might result in a better AI-powered customer experience. This research will develop a conceptual model based on the service quality model and trust-commitment theory. Further to this, an online questionnaire was distributed to individuals who had utilised an AI-powered service provided by a particular brand, and consequently, a total of 354 responses were analysed using Structural Equation Modelling (SEM). The results that were deduced from the responses demonstrated that relationship commitment has begun to substantially impact AI-powered customer experiences. In addition to this, the results also revealed that perceived sacrifice and trust both play an important role in mediating the impacts of perceived convenience, personalisation, and AI-powered service quality. This finding contributes to the previous literature by highlighting the mediating impacts of perceived sacrifice and trust, as well as the significant influence of relationship commitment on AI-powered customer experience. Furthermore, the research poses significant implications for merchants who use AI in services provided to their customers.

Advancement of banking and financial investment has led to the rapid expansion of services automation. The consistent increase of Artificial Intelligence (AI) usage in investment management implies the impending popularity of technology-based service. This study examined influencer endorsement and perceived security benefits as moderators to the relationship between perceived risk and financial AI services. Questionnaires were disseminated to 300 respondents who were customers with experience of using financial AI services in Jordan, and they were chosen through purposive sampling method. Structural equation modeling run using Smart-partial least squares (PLS 3.3.6) was employed in analyzing the data obtained from 220 completed questionnaires. The results show that perceived risk negatively affects financial AI services, while influencer endorsement and perceived security moderate the relationship between perceived risk and financial AI services. This study provides insight to companies on how to reduce perceived risk to encourage people to use business intelligence applications, as in the use of financial technology services.

The computer numerical control (CNC) machines are chiefly used for the production of jobs with high accuracy and high speed. The CNC machining centers perform the machining operations according to the given program instructions which are commonly programmed by a CNC programmer. In this paper, a procedure to develop an automatic CNC program for machining of different types of holes by using different machine learning algorithms is developed. The machine learning algorithms namely support vector machine (SVM) and restricted boltzmann machine algorithm (RBM) with deep belief network (DBN) are used for the au-tomatic development of CNC machining programs of different types of holes. Initially, the position and other parameters of machining operations are identified and thereafter the CNC machining program is developed by using the MATLAB application. The automatically de-veloped CNC programs are tested on a CNC simulator. It is found that the application of RBM machine learning algorithm with DBN outperforms the SVM machine learning algo-rithm for the development of automatic CNC machining program for the machining of blind holes, through holes, counterbores and countersink operations.