The current manuscript aimed to explain the impact of big data on the financial reporting quality of the industrial sector in Jordan. To achieve the manuscript goals and validate hypotheses, a field study was conducted by distributing a questionnaire to 325 financial managers in industrial companies listed on the Amman Stock Exchange during a specific period. Gathered data were analyzed using structural equation modeling (SEM). The manuscript concluded that the big data dimensions, including variety, volume, and velocity, had a positive impact on financial reporting quality. Therefore, a set of recommendations were provided to managers of the industrial companies in Jordan to put in place an extensive data governance system to as-sure data quality, security, privacy, and compliance. To ensure the integrity and dependability of financial reporting, define data ownership, create data quality standards, and develop processes for data access, use, and preservation.

This study aimed to demonstrate the impact of big data analytics techniques on reducing information asymmetry in industrial companies listed on the Amman Stock Exchange from the point of view of workers in Jordanian financial intermediation companies. Two approaches have been adopted to achieve the target of this research. The first approach is the analytical descriptive approach through a survey to collect primary data that measures the elements of the independent variable related to big data analytics techniques (Volume, Velocity, Variety, and Veracity). The second approach is an applied approach that measures the dependent variable of information asymmetry based on the financial statements of industrial companies listed on Amman Stock Exchange for the period (2015-2021). The statistical program (SPSS) has been used to analyze data and test the hypotheses through multiple regression testing. Based on the results of the statistical analysis of the data and the opinions of the research community, it was found that the huge volume of big data has become difficult to process using traditional data processing applications. Furthermore, there is a statistically significant relationship between big data analytics techniques and the reduction of information asymmetry from the point of view of employees in intermediation firms in Jordan. Consequently, it is necessary for those in charge of the industrial companies listed on the Amman Stock Exchange to develop modern techniques capable of analyzing big data with high efficiency. It can also assist in providing target groups including investors, stakeholders, and other beneficiaries with reliable and efficient data required to make rational decisions, as well as to reduce the risks of information asymmetry.

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.

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 the current era of information technology Big Data has gained significant importance in almost all the industries throughout the world. Big Data is now renowned for having the capability of effective decision making. Now companies around the globe are using Big Data for market analysis, customer analysis, however, the utilization of Big Data is much higher in the financial sector, yet publications on Big Data and finance are limited because of having significant challenges. Even though utilization of Big Data is highest in the financial sector and its importance cannot be ignored, the studies and analysis are inadequate. Considering the importance of Big Data in the financial sector this paper is an attempt to conduct a comprehensive literature review in the field of Big Data and finance. Thus, the study will contribute to the body of knowledge by providing horizons for empirical research in the field of Big Data and finance.

The aim of the research is to test the impact of big data on the quality of decision making. The necessary data was collected from telecommunication companies operating in Jordan. Three companies provide telecommunications services in Jordan: Zain, Umniah, and Orange. Non-probability purposive sampling was used. Hence, the study instrument was distributed to managers at the senior and middle levels of these companies. The structured equation modeling (SEM) technique was employed to test the study hypotheses. The results confirmed that big data impacts decision quality. Besides, it was determined that the highest impact was on velocity. The researchers suggest that Jordanian telecom companies invest in information technology that allows them to provide data with accuracy, reliability, and high quality, as well as improve the qualifications of their employees.

With the inception of high-speed internet data services and ever-growing technical advancement in manufacturing technology, the integration of production systems and the internet of things to produce different types of jobs via cloud computing has become possible. The internet-enabled advanced automatic production systems can be referred to as the cyber-physical production systems (CPPS). The use of CPPS via cloud computing and the internet of things (IoT) can offer high productivity and high flexibility for the production of jobs in a dynamic production environment with varying specifications. The aim of this paper is to present a generalized synergic framework between different production facilities locating at different geographical locations to realize an energy-saving and efficient cyber-physical production system for the production of different types of jobs in the context of the industry 4.0 and beyond. In addition to the above, the study also identifies a need to address large scale multi-objective optimization issues to make the best decisions for different combinatorial production scenarios by using CPPS that are functioning in smart production facilities at different geographical locations.

The purpose of this research is to investigate the status and the evolution of the scientific studies for the effect of social networks on big data and usage of big data for modeling the social net-works users’ behavior. This paper presents a comprehensive review of the studies associated with big data in social media. The study uses Scopus database as a primary search engine and covers 2000 of highly cited articles over the period 2012-2019. The records are statistically analyzed and categorized in terms of different criteria. The findings show that researches have grown exponentially since 2014 and the trend has continued at relatively stable rates. Based on the survey, decision support systems is the keyword which has carried the highest densities followed by heuristics methods. Among the most cited articles, papers published by researchers in United States have received the highest citations (7548), followed by United Kingdom (588) and China with 543 citations. Thematic analysis shows that the subject nearly maintained an important and well-developed research field and for better results we can merge our research with “big data analytics” and “twitter” that are important topics in this field but not developed well.