The influence of artificial intelligence on electronic audit evidence: exploring the mediating role of digital transformation: evidence from Jordanian export firms

Abstract

This study investigates the impact of Artificial Intelligence (AI)—specifically Machine Learning (ML), Natural Language Processing (NLP), and Expert Systems (ES)—on Electronic Audit Evidence (EAE) and the mediating role of Digital Transformation (DT) within the Jordanian Export Firms. A survey of 347 employees was conducted, and data analysis was performed using Structural Equation Modeling (SEM) and Confirmatory Factor Analysis (CFA). The results show that ML → DT (Path Coefficient = 0.431, T-Statistic = 5.2, p = 0.000**) and ES → DT (Path Coefficient = 0.302, T-Statistic = 4, p = 0.002**) significantly contribute to DT. However, NLP → DT was not supported (p = 0.064). DT → EAE (Path Coefficient = 0.107, T-Statistic = 1.8, p = 0.002) positively influences EAE, and ML → EAE (Path Coefficient = 0.329, T-Statistic = 4, p = 0.001**) has a significant positive effect on EAE. Conversely, NLP → EAE (Path Coefficient = -0.189, T-Statistic = -3.1, p = 0.002**) shows a negative impact on EAE. DT → ML → EAE (Path Coefficient = 0.125, T-Statistic = 2.5, p = 0.008*) is supported, demonstrating that DT mediates the relationship between ML and EAE. However, DT → ES → EAE was not supported (p = 0.085). These findings underscore the importance of ML and ES in driving digital transformation and EAE adoption in the sector.