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.

Compliance to tax payment generally results in mitigating fiscal deficit and public debt, and in turn, provides funding to meet the economic and social development. However, regardless of the directed efforts of the government to increase sales tax compliance (or value added tax as it is referred to globally), Jordanian listed firms on the Amman Stock Exchange, compliance remains an issue in light of its negative impact on the government revenues. Literature dedicated to tax rate effects on sales tax compliance in the context of listed Jordanian firms on the Amman Stock Exchange has largely been lacking and as such, the study’s main objective is to examine the effect of tax rate on sales tax compliance among such firms. The study adopted a survey method with questionnaire copies distributed to 191 listed Jordanian firms. In the tabulation of data, only 169 questionnaire copies were deemed to be valid for the analysis. The formulated study hypotheses were tested using PLS-SEM and based on the results, tax rate has an insignificant effect on sales tax compliance. Future studies are recommended to provide further insight into the study determinants. The study contributes by furnishing information to and guiding policymakers and the listed Jordanian firms in enhancing sales tax compliance.

The objective of this study is to examine the role of external factors including MP, CP, and NP on the intention to adopt Cloud Based Accounting Information System (CB-AIS). The study proposes a theoretical framework based on institutional theory (INT). The data were collected from small and medium sized enterprise (SMEs) operating in Jordan. A total of 600 questionnaires were distributed to selected SMEs and only 142 were returned and used for the analysis. The empirical data were analyzed using the PLS-SEM modelling. The findings showed that MP, CP, and NP had significant direct associations with the CB-AIS intention to adopt. The results provide important insights to managers, researchers and policymakers to help them understand the importance of CB-AIS adopting to enhance firm performance.

This paper aims to contribute to the literature by examining whether audit committees' attributes affect risk disclosure practiced by Jordanian listed companies. Selecting a sample of 94 Jordanian companies listed on Amman Stock Exchange, the authors carried out a manual content analysis on annual reports to determine the level of risk disclosure. Random effect model was employed in the analysis. Empirical results show that the audit committee size had a positive effect on the level of risk disclosure. However, there was no evidence that the frequency of the audit committee meetings, expertise or overlapping of the audit committee membership were significantly related to the risk disclosure. The findings are important for standard setters to improve their comprehension about the influence of audit committee in disclosing risk information and reconsider the effective monitoring role played by audit committee.