This study proposes a Fusion, Feature-Level, Face Recognition System (FFLFRS) that is based on the Multi-Resolution, Singular Value Decomposition (MSVD) fusion technique. Face recognition in the FFLFRS is achieved via four processes: face detection, feature extraction, feature fusion, and face classification. In this system, the most significant face features (that is, the eyes, nose, and mouth) are first detected. Then, local and global features are extracted by the Local Binary Pattern (LBP) and Principal Component Analysis (PCA) extraction approaches. Afterwards, the extracted features are fused by the MSVD method and classified by the Artificial Neural Network (ANN). The proposed FFLFRS was verified on 10,000 face images drawn from the face images database of the Olivetti Research Laboratory (ORL). Face recognition performance of this system was contrasted with levels of performance of three state of the art, fusion-level, face recognition systems (FRSs) depending on the Frequency Partition (FP), Laplacian Pyramid (LP), and Covariance Intersection (CI) fusion methods. Ten-thousand images were employed to test the proposed model and assess its performance, which was evaluated in terms of changes in pose, illumination, and expression, besides low resolution and presence of occlusion. The face recognition results of the proposed FFLFRS are encouraging. This system proved to be effective in dealing with images having challenges to face recognition and it could achieve a recognition accuracy as high as 97.78%.

Having a citizen's opinion is considered the most important method to evaluate a public institution's performance. As a contemporary theory regarding public institutions and private organizations, public value theory provides an alternative approach to evaluating organizations' performance. The current research has provided a new insight to assess the Mobile-government applications (m-gov app) by proposing a new model entitled ‘Mobile Government Public Value (MGPV)’ to measure the performance of m-gov apps in developing country settings, specifically Jordan. Depending on several theories engaged with information technology, many determinants have been selected to draw the line for evaluating MGPV in Jordan. Measuring the level of m-gov apps’ usage was estimated depending on its perceived need, awareness, perceived security, social influence and self-efficacy to gauge the weather of creating or increasing the public value of the m-gov app from a citizen's perspective. In the current research, Structural Equation Model (SEM) was selected to obtain the research objectives. The results have indicated that the m- gov apps perceived need, m- gov apps' awareness, m- gov apps perceived security, and m- gov apps social factors played an essential role in creating public value of m- gov apps through the mediation role of m- gov apps use factor. While m-gov apps' self-efficacy factor did not provide a positive effect on creating the public value of m-government apps.

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.

This paper aims to explore the intentions to use FinTech and its important role in the banking industry in Jordan. Accordingly, this study analyzes the nature of the relationship between intention to use financial technology and each of: Processing Unit (PU) perceived usefulness, social impact (SI), customer’s trust (TRU) and perceived ease of use (PEU). Previous research related to financial technology is still under development and which is still being researched by providing an alternative approach to understanding how different business levels have stimulated the emergence of innovation-focused fintech companies, and what are the motives of success. Therefore, the main contribution of this research is to fill the gap in previous research related to financial technology that is still under development and which is still being researched by providing an alternative approach to understanding how different business levels have stimulated the emergence of innovation-focused fintech companies, and what are the motives of success. Results show a positive relation between intention to use financial technology and Processing Unit (PU), social impact (SI), customer’s trust (TRU) and perceived ease of use (PEU). The main contribution of this research is to fill the gap in previous research related to financial technology that is still under development and which is still being researched by providing an alternative approach to understanding how different business levels have stimulated the emergence of innovation-focused fintech companies, and what are the motives of success.

One of the most used social media platforms is TikTok, which is widely and increasingly used due to the short-video interactive music. Very few studies about why people prefer to use TikTok applications were carried out. Therefore, the objective of the current research is to examine the effect of perceived usefulness, perceived ease of use, perceived enjoyment, sense of belonging, and user-generated content on the adoption of TikTok application, using the TAM model. Quantitative research has been applied as a methodological approach and was successfully carried out through an online survey, gathering a total of 255 filled surveys to test the applicability of the developed research model. The results show that the user-generated content has the highest significant positive influence on the intention to use TikTok. Followed by the perceived enjoyment, then the sense of belonging, the perceived ease of use, and the perceived usefulness, consequently. Also, results show that the independent variables explain 47.8% of the variance in the intention to use TikTok. Finally, to assure the generalizability of the study results the study recommends conducting further research in different countries and communities.

For the development of technologies and networks that have been evolving and expanding day by day. In this generation, these facilities allow users to interact more efficiently. Safety is therefore very important to consider preserving the network and database and the need to detect a potential attack before an attack occurs. Network security has become the main issue, particularly in the industries, and there are many techniques to be used to protect network systems. One of them is called Honeypot, a software that is used to detect unauthorized misuse of information systems and to evaluate the actions and behavior of the attacker. Honeypot is, in other words, a trap for any attackers to log in to the network. If the behavior of these attackers is detected, the information will be used to improve network security. Using Honeypot does not cause attackers to notice they are being detected. This research is primarily focused on Honeypot, which is a ground-breaking new technology that has the potential to provide security communities with protection and how it is important for its use to enhance the network security framework.

The study aim is to examine the impact of industry 4.0 on sustainability of industrial organizations in Jordan. The population of the study consists of employees at various administrative levels of industrial organizations in Jordan. Due to the large population and the spatial and temporal limitations of the research, it was difficult to collect data using the comprehensive method. Therefore, the random sampling method was applied to collect data from the research population. The structural equation modeling (SEM) technique was used to test the impact of industry 4.0 dimensions on sustainability. The study results confirmed that all dimensions of industry 4.0 had an impact on sustainability. The greatest effect was for cyber-physical systems. Based on this result, researchers recommend the management of industrial organizations to invest in information technology to provide a large variety of data in a very short time and providing appropriate programs for analyzing big data and producing accurate and reliable information that can be used by employees.

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.

The aim of this research is to study the acceptance of university students to use Microsoft Teams e-Learning system and their intention to use it as a Learning Management System (LMS) for education during the COVID-19 pandemic in Jordan. An ex-tended Technology Acceptance Model (TAM) with a blend of external factors that are used together for the first time was developed and used for the purpose of this study. TAM was used because of its wide use and success during the past few years for evaluating the influence of different factors affecting the acceptance and intention to use e-Learning platforms within educational institutes. However, all the studies were examining the variables and factors affecting the behavioral intention and acceptance to use LMSs when normal and conventional classroom study is available. In this research, seven external variables, in addition to the four TAM variables, were introduced in a model including one external variable, Internet Connectivity (IC), used for the first time in the field of education. A model is constructed by extending TAM with the introduced external variables, hypotheses are constructed and a questionnaire for 396 students at two universities in Jordan is conducted. Reliability, confirmatory factor, model fit, and hypothesized structural model analyses are presented. Results show that all the variables tested affect, either directly or indirectly, the acceptance and intention to use MS Teams during the pandemic. 21 hypotheses were tested between the constructs and found significant except the relations between (Social Norm - Perceived Usefulness) and (Technical Support - Perceived Usefulness).

The aim of the study is to examine the impact of online consumer reviews on purchasing intention through product mental image. The primary sources represented by the instrument were distributed to the study population consisting of consumers of cosmetics in Jordan. Recently, the phenomenon of selling cosmetics through Internet websites and social media platforms has spread widely in Jordan. Therefore, Google Forms was used to design the research instrument and ask consumers of these products to respond to it by publishing it on the official websites of the sellers and with their approval. Structural equation modeling (CB-SEM) technique using the AMOS software was used to test the study hypotheses. The study results show that product mental image mediates the relationship between online consumer review and purchasing intention. Based on the results of the study, the researchers provided recommendations.