Individual and Technological Factors Affecting the Adoption of AI-Powered Remote Auditing in the Jordanian Banking Sector

Salah Turki Alrawashdeh, Khaleel Ibrahim Al Daoud, Badrea Al Oraini, Ibrahim Mohammad Suleiman, Asokan Vasudevan, Lian Xiao and Rakan Alshbiel

Data and Metadata, 2024, vol. 3, .408

Abstract: Introduction Artificial intelligence technologies have recently contributed to the field of remote auditing and have led to significant improvements in the efficiency and outcomes of the audit process. However, this professional technological integration remains unexplored in the Jordanian banking sector. Accordingly, understanding the mechanism of integration between these factors is essential to keep pace with the evolving work environment. This study aims to examine how these factors affect the adoption of remote auditing supported by artificial intelligence in Jordanian banks. Methods A quantitative approach consistent with a cross-sectional design was used to collect primary research data. A structured questionnaire was distributed to 158 decision-makers in various commercial banks in Jordan. The questionnaire measured individual factors (e.g., skill level of users and Attitude towards technology) and technological factors (e.g., technology readiness, data security and privacy, and integration capabilities). Structural equation modeling (SEM) was used to test the relationships between these factors and intention to adopt AI-powered remote auditing using SMART PLS. Results The results depicted that all factors, including individual and technological factors, significantly influenced the adoption of AI-powered remote auditing. Attitude towards technology and integration capabilities were the strongest predictors. Additionally, technology readiness, data security and privacy, and skill level of users had moderate but significant, effects on adoption intention. Conclusion The findings emphasize that both individual perceptions and technological robustness are crucial for adopting AI-powered remote auditing in Jordanian banks. Improving system reliability and showcasing the benefits of AI tools can significantly boost adoption rates

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:datame:v:3:y:2024:i::p:.408:id:1056294dm2024408

DOI: 10.56294/dm2024.408

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