Phishing websites are characterized by distinguished visual, address, domain, and embedded features, which identify and defend such threats. Yet, phishing website detection is challenged by overlapping these features with legitimate websites’ features. As the inter-class variance between legitimate and phishing websites becomes low, commonly utilized machine learning algorithms suffer from low performance in overlapping feature cases. Alternatively, ensemble learning that combines multiple predictions intending to address low inter-class variations in the classified data improves the performance in such cases. Ensemble learning utilizes multiple classifiers of similar or different types with multiple deviations of the training data. This paper develops a framework based on random forest ensemble techniques. The limitations of the random forest are the inability to capture the high correlation between features and their join dependency on the label. The random forest is combined with k-means clustering to capture the feature correlation. The framework is evaluated for phishing detection with a dataset of 5000 samples. The results showed the proposed framework over-performed the random forest classifier, all other ensemble classifiers, and the conventional classification algorithms. The proposed framework achieved an accuracy of 98.64%, precision of 0.986, recall of 0.987, and F-measure of 0.986.

The online markets have been growing during the last near years, and many businesses have generated customer satisfaction and loyalty by developing their marketing activities. The main aim of this study is to review the related body of the literature to develop a thorough conceptual research framework including e-marketing in order to determine the respective antecedent factors of customer satisfaction and loyalty. Another study aim is examining the mediating role of the perceived value on the interrelationships between e-marketing, customer satisfaction and loyalty. Through a questionnaire survey, the study collected the data from various online shoppers in Jordan with about 482 participants. The study results indicated that e-marketing had a significant effect on customer satisfaction but did not generate customer loyalty, and the customer satisfaction had a significant effect on customer loyalty. Another key result is the perceived value mediated the relations between e-marketing and both customer satisfaction and customer loyalty. The study's respective implications offer new insights in marketing literature and conclude that the successful marketing activities should focus on the perceived value that is felt by the customers.

This study came to examine the impact of Digital transformation on sustainable performance. The research population consisted of employees of Industrial companies listed in Amman stock exchange that is 23 companies. Because of the difficulty of collecting data using a complete census, a random sample of 530 employees was determined. 397 responses were used for the purposes of the statistical analysis of this research. In order to find the impact of digital transformation on sustainable performance, the structural equation modeling (SEM) technique was used. Research results show that digital transformation dimensions had a positive impact on sustainable performance. Big data had the greatest impact. Accordingly, the researchers recommend Industrial companies invest more in manufacturing modern technologies, represented by Digital transformation technologies that give companies the ability to provide high-quality performance and prepare an infrastructure to operate these technologies.

Blockchain technology is one of the crypto-currency technologies that has received a lot of attention. It has also found use in various applications, including the Internet of Things (IoT) and Cloud computing. Nonetheless, Blockchain has a significant scalability issue, restricting its ability to support services with various transactions. On the other hand, cloud computing is the on-demand availability of shared computer system resources, although issues now beset it in automation, processes, management, policies, and human aspects. Combining cloud computing and blockchain technology into a single system can improve network control, task scheduling, data integrity, resource management, pricing, fair payment, and resource allocation. In this article, we offered a comprehensive and up-to-date survey of cloud computing and Blockchain integration, a critical service for business applications due to the benefits of privacy, security, and service support. The lack of a comprehensive assessment examining the significance of BaaS platforms used in cloud computing prompted this review. We focus on the various BaaS tools that are currently in use. This report also examines the most common BaaS platforms incorporating Blockchain as a cloud service, such as Alibaba, Oracle, Azure, Amazon, and IBM. Furthermore, this research highlighted some major technological issues associated with merging Blockchain with cloud computing.

The purpose of the paper is to select the mediating role of information integration between cloud computing and productivity. The research sample comprised 320 employees. Survey was utilized to gauge the cloud computing impact on productivity and information integration. 281 questionnaires returned for statistical analysis, adopting the quantitative approach. The study's findings indicated that productivity standards, applying information integration, and cloud computing usage were all highly prevalent. In addition, there was an impact for cloud computing on productivity and information integration. Recommendations for study were discussed.

The primary objective of this article is to present the findings of an experimental study on the use of a human-like “naturalistic” avatar (of both genders) for information presentations. The primary goal is to investigate how users' frame of mind, purchasing behavior, and satisfaction are affected by being presented with information by a naturalistic avatar that lacks expressive capabilities. Two different types of information presentations were developed and empirically tested on 48 participants, namely: (i) two dimensional static graphical and textual information presentation and (ii) non-expressive naturalistic avatar. For this comparative research study, a simplified version of the user experience model that was used in our earlier research (the EUX-DM) was selected to serve as the measurement model. Participants' perceived values of the measured qualities were found to be independent of both their own and the naturalistic avatar's gender, and the non-expressive avatar had a positive effect and a stronger encouragement on participants' intention to purchase, usage attitude, and satisfaction than other types of information presentations.

The current study aims at examining the differences between umbrella branding and house of brands on organizational market share. Various variables are adopted including reach, efficiency, image and ambiguity. A sample of 98 marketing managers or their representatives within the chemical industry sector in Jordan, SPSS is employed to screen and analyze gathered data. Results of study indicate that umbrella branding has a bigger and much deeper influence on market share compared to house of brands which is attributed to many factors including that umbrella branding has a wider reach, it is more efficient and more able to shed the light and increase the focus on organization's image, meaning that in umbrella marketing the focus is more on the marketing organization more than manufacturing organization which increases its reputation and market share. The study recommends the necessity for each brand to have a specific purpose to avoid overlapping or disintegration of brands.

The purpose of this study is to identify the key macroeconomic variables that affected stock price fluctuations in Amman Stock Exchange during the period 1980-2018. Using Augmented Dickey-fuller (ADF) test, it was found that the variables did not have the same degree of integration. According to Breusch-Pagan-Godfrey test, the residuals violated the constant variance assumption under Ordinary Least Square (OLS) model. Therefore, the study employed Generalized Autoregressive Conditional Heteroskedasticity (GARCH) methodology to analyze the model after taking the first difference of natural logarithm for all variables to be stationary at the same level and to show the fluctuation in the variables. It was found that fluctuations in portfolio investment and in industrial production index are statically significant to lead fluctuations in the stock price index in Amman Stock Exchange and they follow the same direction, whereas fluctuations in real effective exchange rate, real interest rate, and Brent crude oil prices were statically significant to lead fluctuations in the stock price index but in the opposite direction.

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.