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.

This quantitative study investigates the relationships between Management Information Systems (MIS), Business Intelligence (BI), and Strategic Planning (SP) within Jordanian Public Listed Companies, with a focus on the mediating role of BI. The target population comprises employees from the 108 public shareholding companies listed on the Amman Stock Exchange, totaling an estimated 1,080 senior managers involved in strategic planning. A random sample of 285 employees was surveyed to achieve a 95% confidence level with a 5% margin of error. Data were collected using a structured questionnaire with multi-item scales adapted from prior studies. Structural equation modeling (SEM) was employed to test the conceptual framework and hypothesized relationships, utilizing the two-step SEM approach with AMOS software. The results reveal significant positive relationships among MIS, BI, and SP. Specifically, MIS exhibits a statistically significant positive effect on SP, supporting previous research indicating MIS provides comprehensive data for informed planning. Furthermore, MIS significantly influences BI capabilities, underscoring the importance of robust MIS infrastructure for advanced BI analytics. BI, in turn, positively impacts SP, aligning with literature suggesting BI tools enhance planning agility and effectiveness through data-driven insights. Bootstrapping analysis demonstrates that BI partially mediates the relationship between MIS and SP. While BI acted as a significant mediating variable, MIS had a significant direct impact on SP, implying that though MIS has a direct impact on SP, it has an indirect impact, through BI, as well. Further analysis revealed that the constructs are interconnected, and that the mediation of BI is a necessary part of the process in Jordanian Public Listed Companies. As such, acknowledging the relevance of MIS, BI, and SP and the mediating role of BI, organizations can adapt their decision-making to achieve sustained competitive advantage within the dynamic business environment in Jordan.

The study aimed to identify the impact of digital transformation and analyze the obstacles and complexities facing the integration of digital technologies into Jordanian Customs and their subsequent impact on achieving strategic flexibility. The study aims to uncover challenges, evaluate their effects, and suggest recommendations to enhance adaptive strategies within Jordanian Customs considering digital transformations. The study included employees in Jordanian General Customs, including customs officials, IT staff, and administrative staff working on digital initiatives. A population of 670 individuals was identified, from whom a purposive sample of (465) directly involved in digital transformation efforts was selected to participate in the study. The study used the descriptive analytical approach to comprehensively investigate the challenges of digital transformation within Jordanian Customs. Surveys, interviews, analysis of regulatory documents formed data collection methods. Quantitative data were subjected to descriptive analysis and regression modeling, while qualitative insights were thematically analyzed to provide a comprehensive understanding of the challenges faced. The analysis revealed the results, the most important of which is that digital transformation has a positive, statistically significant impact in its four dimensions (strategy, organizational culture, transformational leadership, and human resources) in achieving strategic agility in Jordanian Customs, and reveals multi-faceted challenges prevailing within Jordanian Customs, including structural constraints. Infrastructure, resistance to change, cyber security vulnerabilities, and skills gaps among the workforces. The regression analysis highlighted the significant impact of these challenges in hindering the achievement of strategic flexibility within the Customs Department. Based on the results of the study, Jordanian Customs is recommended to take proactive measures to confront the challenges identified. This includes investing in a robust technology infrastructure, implementing targeted training programs to improve employee skills, promoting a culture of innovation, and establishing cross-departmental collaboration to enhance adaptability and strategic flexibility. The study recommended increasing attention to training workers and raising their capabilities to deal with digital transformation positively and improve services.

This research investigates the adoption of Information and Communication Technology (ICT) by Micro, Small, and Medium-Sized Corporations (MSMEs) in Jordan. The research formulates and examines hypotheses on factors affecting ICT adoption morals via the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Technology Acceptance Model (TAM). The suggested concept, which utilizes Structural Equation Modelling (ESM), highlights the critical responsibilities that social influence and perceived utility play in affecting choices to adopt ICT. It is noteworthy that MSMEs confront a multiple of obstacles when it comes to adopting ICT, encompassing restrictions in terms of infrastructure and resources, shortages of human capital, and hardships in the technology environment. Data were gathered from 305 MSMEs, and outcomes shed light on important factors to boost the impact of ICT integration in the special setting of Jordanian MSMEs.

This study investigates AI bias in financial technology (FinTech)-based supply chain management in Pakistan. The study employs Structural Equation Modeling (SEM) to analyze data from diverse respondents. Hypotheses examine the relationships between AI integration, algorithm diversity, employee training, data quality, regulatory compliance, organizational culture, and AI bias. The findings reveal that higher AI integration leads to increased AI bias, Algorithm diversity reduces AI bias, while employee training decreases bias, Quality and diversity of data negatively correlate with AI bias, and regulatory compliance lowers bias. In addition, organizational culture mediates the relationship between AI integration and AI bias. This research contributes a holistic understanding of AI bias factors, guiding ethical AI adoption. Policymakers can use these insights to shape regulations, and industry practitioners can make informed decisions.