The practical application of business data analytics in human resource (HR) management activities is still limited and underutilized. The primary reason for this is the requirement for more analytical skills, which can be acquired through data literacy. These skills are necessary to ensure obtaining the potential benefits of human resource analytics linked to desired positive employee outcomes, such as fostering innovative behavior. To address this issue, this quantitative study has been conducted to delve into the prospective moderating role of data literacy in the interaction between human resource analytics and employees’ innovative behavior. The core objective of this study is to examine whether human resource professionals’ data literacy level significantly impacts the correlation between human resource analytics and the inclination of employees towards innovative behavior. A sample of 250 HR specialists from large companies in Jordan has been used to address this inquiry. A correlational-predictive design has been used in this study. Regression analysis using the SPSS macro-PROCESS software has been utilized to address the study hypotheses. The study reveals a positive connection between HR analytics and employees’ innovative behavior. Moreover, it uncovers a noteworthy moderating influence of data literacy on this association. These findings suggest that heightened data analysis proficiency among HR professionals amplifies the potential benefits derived from analytics, thereby enhancing employees’ innovative capabilities. As a result, the research suggests that companies upgrade their technical infrastructure for HR functions and concurrently utilize data analytics for informed HR decisions. Further, these insights do serve as necessary inputs in understanding HR analytics and their implications for prudent business strategy. In view of this dynamic nature of HR analytics, the contributions made by the study are immensely valuable; hence, further exploration is needed to fill in gaps within existing knowledge.

This qualitative study examined the impact of social media characteristics on the attitude of users toward the use of e-services by the Department of Lands and Survey. The study population comprised users of Department of Lands and Survey e-service, while the study sample comprised 407 users. Data from respondents were analyzed using SEM run using Amos (23). Results showed reliability, security, website design, ease of use, awareness, and digital divide as direct significant antecedents of user attitude toward the use of e-services from the Department of Lands and Survey. Also, all antecedents showed direct significant relationships with user attitude. Results showed a direct significant relationship between user attitude and the use of e-services from the Department of Lands and Survey. Through mediation of user attitude, five indirect significant relationships between the antecedents and the use of e-services from the Department of Lands and Survey were found. The inclusion of new antecedents such as privacy and site content may enhance the understanding of how users use e-services provided by the Department of Lands and Survey.

The world of the Internet of Things (IoT), even though it is continuously morphing as a fresh paradigm at the intersection of technology and education, is still struggling with several difficulties that prevent its absorption into the e-learning platforms of higher education institutions (HEIs). The breadth of Internet of Things implementation in developing countries, particularly Jordan, Malaysia, Iran, Saudi Arabia, Iraq, and Bangladesh, remains behind, even though industrialized nations have made significant advancements in their utilization of IoT, with the United Kingdom, the United States of America, China, and Japan acting as prominent examples. In the realm of research that focuses on the progression of the IOT integration into the e-learning systems of economically challenged countries, there is a substantial disparity. In particular, the focus of this research is on Jordan to shed light on the primary variables that are either facilitating or hindering the adoption of the IoT within the e-learning sector of Jordan's HEIs. A comprehensive analysis of previous research has been undertaken as a first stage in this investigation. The goal of this analysis is to identify important factors that are involved in the process of IOT adoption. Following that, we used an inferential technique, collecting data from 306 respondents who were enrolled in Jordanian higher education institutions. During our investigation, we discovered that the rate of the IOT integration was significantly influenced by factors such as accessibility, usability, technical assistance, and individual capabilities. In addition, our findings suggest that factors such as attitude, behavior, financial preparedness, dependability, and training have a substantial impact on the adoption of the IOT. On the other hand, the study seemed to indicate that characteristics such as class capacity, awareness, system resources, and course design had a minor influence on the adoption rates inside HEIs. In conclusion, this study provides tangible suggestions to strengthen the integration of the IoT inside Jordanian HEIs. These recommendations provide significant insights that can be used by policy architects, government entities, and higher education institutions to overcome the challenges that relate to the deployment of IoT in the higher learning sector.

Arabic handwritten script recognition presents an energetic area of study. These types of recognitions face several obstacles, such as vast open databases, boundless diversity in individuals' penmanship, and freestyle writing. Thus, Arabic handwriting requires effective techniques to achieve better recognition results. On the other hand, Multilayer Perceptron (MLP) is one of the most common Artificial Neural Networks (ANNs) which deals with various problems efficiently. Therefore, this study introduces a new technique called Block Density and Location Feature (BDLF) with MLP, namely BDLF-MLP, which aims to extract novel features from letter images and estimate the letter's pixel density and its location for each equal-sized block in the image. In other words, BDLF-MLP can deal with various styles of Arabic handwritten, such as overlapping letters. The BDLF-MLP starts with the Block Feature Extraction (BFE) of the image by dividing the image into sixteen parts. After that, it calculates the density and location of each block (i.e., BDLF) by finding the sum of all values inside blocks. Finally, it determines the position of the greatest pixel density to obtain better recognition accuracy. The dataset containing 720 images is used to evaluate the efficiency of the proposed technique. Also, 1440 letters are used for training and testing divided evenly between them. The experiment results illustrate that BDLF-MLP outperformed the other algorithms in the literature with an accuracy of 97.26 %.

This study investigates the relationship between business intelligence adoption (Business Intelligence (BI), Data Analysis and Reporting (DAR), Business Process Integration (BPI), and Continuous Improvement and Innovation (CII)) and marketing effectiveness in the Jordanian telecom industry. It specifically examines the mediating role of customer satisfaction in this relationship. A survey design method using the cross-sectional survey was utilized during the research process, involving quantification. The data was collected from 285 employees across the Jordanian telecom sector via electronic response forms. As the analysis's method, the partial least squares structural equation modeling (PLS-SEM) was utilized. The results, which showed BI, DAR and CII to have a positive direct effect on marketing effectiveness, whilst BPI displayed a negative direct impact, were compelling. But clear signs of positive influence on CS by the four dimensions of BI (BI, DAR, BPI, and CII) have also been observed, and this result has been proved to be a mediator between them and business effectiveness. The above study is a source of invaluable learning for the managers of telecom companies in Jordan is a tool that managers in the Jordanian telecom industry will greatly benefit from in the sense that it brings out all the importance of integrating BI, DAR, BPI, and CII practices that emphasize high quality customer service. By properly utilizing these assets and up-to-the-market, companies can improve the effectiveness of marketing and the whole organizational efficiency. The study enhances the existing theory of the interrelationships between the adoption of business intelligence, customer satisfaction, and marketing power which can be viewed from both resource-based view (RBV) theory and expectation-based theory (EDT). It affirms an idea that the RBV was rooted in that when organizational assets are valuable, firms have a competitive advantage and better performance. Also, an EDT states that customer satisfaction increases if the expectations of customers are met. Innovation - The essay is innovative and takes an original angle to explore the multifaceted interconnectivity among business intelligence adoption, customer satisfaction, and marketing effectiveness in the framework of the Jordanian telecom industry. It highlights the steps to customer satisfaction problem solutions and the caution comes with process integration efforts, hence, helping to arrive at a full comprehension of the aspirations for organizational performance in the field.

This study examines the relationship between the types of Artificial Intelligence (AI) technology employed and monitoring tax payments. A thorough literature review is conducted to examine different AI technologies in the context of tax administration. These include machine learning algorithms (MLA), natural language processing (NLP) technology, robotic process automation (RPA), explainable artificial intelligence (XAI), and advanced data analytics techniques (DAT). A variety of technologies, such as big data analytics, task automation, task automation, unstructured data analysis, and predictive modeling, are available to improve tax payment monitoring procedures. Recommendations for further study to expand our knowledge and use of AI in tax payment monitoring are included, along with the consequences of AI adoption for tax authorities, policymakers, and practitioners.

The widespread adoption of artificial intelligence (AI) technologies, including ChatGPT, into education, has become a focal point of attention in recent years. This research explores the connections among perceived usefulness (PU), perceived ease of use (PEOU), attitude toward using ChatGPT (ATUC), and intention to use ChatGPT (ITUC) within Jordanian universities. A survey was employed to gather information from 523 university students in Jordan, and the hypotheses were examined using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings revealed that perceived usefulness and perceived ease of use positively impacted attitude toward using ChatGPT and intention to use ChatGPT. Attitude toward using ChatGPT positively impacted intention to use ChatGPT. Implications from this research are crucial to provide developers, instructors, and institutions in Jordan with useful information to help them successfully incorporate ChatGPT into the educational process.

The fast fashion business is becoming more reliant on social media marketing (SMM) as SMM enables larger data collection and communication between the brand and its consumers. This study investigates the impact of SMM aspects such as customization, entertainment, interactivity, trendiness, and eWOM on fast fashion’s online and offline purchase intentions (PI). Similarly, it investigates the moderating influence of culture on the factors mentioned above and the relationship’s utilitarian and hedonic reasons. Additionally, 360 responses were obtained from three countries, Morocco, Jordan, and Spain, using an online questionnaire. The findings revealed that customization, amusement, and trendiness influence offline and online PI favorably. Culture was also shown to have a moderating influence on the link between SMM components and PI. Motivations were also discovered to be a mediator between eWOM, trendiness, customization, and PI.

The integration of blockchain technology into estate governance has the potential to revolutionize transparency, efficiency, and security in estate management. Traditional governance structures often grapple with inefficiencies, lack of transparency, and security issues in estate management. This paper comprehensively explores the impact of blockchain on estate governance, and then risk management and business sustainability. This research centers on the role of risk management on business sustainability to mediate and moderate the effect of estate governance on business sustainability. The results indicate that effective real estate governance positively affects risk management practices in real estate. However, both real estate governance and risk management contribute to business sustainability. Moreover, there are still gaps in the literature that require further investigation. Where policymakers and practitioners can develop informed strategies to strengthen governance structures, mitigate risks, and promote sustainable practices in real estate; Thus, promoting long-term success and resilience in the real estate industry. It is worth noting that future research should focus on empirical testing of the proposed hypotheses to provide a better understanding of these dynamics and their implications for risk management that can affect business sustainability.

Social media influencers have become important motivators in shaping tourist attitudes and behaviors. This study analyzed how exposure to influencer content impacts key outcomes for the destination Jordan. A survey of tourists who visited Jordan in the past 3 years measured their perceptions of influencer credibility, content quality, awareness/interest, trust/engagement, destination image, general tourism behavior, and intentions to revisit. Results of SEM analysis found significant positive effects of influencer marketing on both destination image and visit intentions. Awareness/interest and trust/engagement were most impactful, highlighting influencers' role in sparking early motivation. Content quality additionally predicted visit intentions by informing decisions. Perceived credibility made recommendations more persuasive. Furthermore, usage intensity positively moderated the mediated relationships, amplifying effects among heavy social media users. Findings provide theoretical validation of how influencers act as digital opinion leaders. By enhancing destination image through compelling portrayals, influencers shape audience travel interests and behaviors. Managerial implications suggest destinations should invest in influencer campaigns for reach and inspiration while ensuring content quality. Performance tracking informs optimal platform and demographic targeting. Overall, influencer marketing demonstrated significant persuasive appeal for potential tourists. This quantitative study pioneer’s measurement of influencer marketing's tangible impacts on key tourist metrics. The results empirically substantiate the ability of strategically leverage influencers to motivate visitation and guide decision-making. As practitioners refine partnerships for audience growth and branding, academic research must also advance a nuanced understanding of this emerging phenomenon at the confluence of social media and tourism consumer behavior.