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

Shaher Falah Alroud,

Mishal Abdullah Aljabr,

Amjad Jameel Al-Shorafa

и другие.

EDPACS, Год журнала: 2024, Номер unknown, С. 1 - 40

Опубликована: Дек. 6, 2024

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

Язык: Английский

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

Shaher Falah Alroud,

Mishal Abdullah Aljabr,

Amjad Jameel Al-Shorafa

и другие.

EDPACS, Год журнала: 2024, Номер unknown, С. 1 - 40

Опубликована: Дек. 6, 2024

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

Язык: Английский

Процитировано

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