This study aims at investigating the effects of e-HRM (operational, relational, and transformational) practices on the effectiveness of human resource service through user training. A questionnaire was used to collect data from a sample consisting of HR staff in fifteen food firms. The results showed that the influence of operational e-HRM on the effectiveness of human resource service was fully mediated by user training, and the influence of relational e-HRM on the effectiveness of human resource service was partially mediated by user training. Further, it was found that transformation has an insignificant effect on HR service effectiveness. The study contributes to the literature through identifying the effects of operational, relational, and transformational e-HRM practices on the effectiveness of human resource service and therefore help filling such a gap in literature. In addition to providing HR managers with results, acknowledge the importance of user training to implement e-HRM effectively.

Social media influencers have become a more effective modern marketing approach used by businesses to influence consumers' intention and attitude. This study explores this influence by involving several factors of influencer’s characteristics on both consumers’ attitude and intention. Also, a moderation role of vloggers as a new emerging marketing tool is also examined in this research. To conduct this research and achieve its key objective, the study uses a quantitative research method to collect data from TikTok users which has also become a more worldwide favorable web device for short videos. PLS-SEM method is conducted in the phase of analysis and the results show a significant influence of the hypothesized research model except the influence of source relatability on consumer attitude and the moderating role of vloggers on consumer intention. The research findings provided unsurprisingly implications and supported the existing related literature in this field but contribute to cover the research knowledge gap through the integrated new model including numerous variables that have not been examined previously together in a unique framework.

Intelligent Personal Assistant (IPA) adoption has ushered in a technological revolution in today's society. There are several well-known IPA agents, like Google Assistant and Cortana, which were both created by Microsoft. The benefits of IPAs' speech comprehension capabilities have resulted in an increase in IPA users year after year. The goal of the study is to look at how an IPA's speech comprehension capability could be improved in the future, making it easier for IPA users to handle their everyday routines. This study will employ a survey and an unstructured interview to learn about users' expectations for an IPA's speech comprehension capabilities and how IPA adaptation affects their daily routine management. To fulfill the research's goal, an IPA with a daily routine management system will be suggested using the methodology and project design that have been determined.

As a result of the advanced technological development, the businesses operations have been involved within modern marketing activities to promote their products and services. This study highlights the role of key various digital marketing channels. The study addressed the role of digital marketing channels by using some applications (such as online advertising, social media, emails marketing, and websites search engine) to be examined on the consumer buying decisions with a mediating effect of the eWOM. A quantitative research approach was used to achieve study objectives and examine the hypothesized research framework by using a customized survey questionnaire in the retailing sector. A total of 255 valid responses were considered for further analysis by using SmartPLS3 software to conduct the key analyses. The results revealed the significant effect and role of all digital marketing channels on the consumers buying decisions, with the moderated role of the eWOM on the effect of digital marketing channels on consumer buying decisions. The study offers additional contributions to the existing literature and supports the assumptions to provide a better grasp about the study topic.

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.

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.