Presentation of an Artificial Intelligence-Based Auditing Services Model with an Emphasis on Customer Trust: Theme Analysis and Single-Layer Perceptron Neural Network
Keywords:
Artificial Intelligence Auditing, Customer Trust, Thematic Analysis, Artificial Neural Network, Single-Layer PerceptronAbstract
With the rapid expansion of artificial intelligence (AI) technologies in the auditing profession, traditional models of auditing service delivery have encountered new challenges regarding customer trust. Accordingly, the present study was conducted to develop an AI-based auditing services model centered on customer trust and to determine the relative importance of its constituent components and sub-themes, thereby addressing the requirements of digital transformation and the evolving expectations of clients concerning audit quality, transparency, and service reliability. This study is applied in terms of purpose and employs an exploratory mixed-methods (qualitative–quantitative) research design. In the qualitative phase, data were collected through 10 semi-structured interviews with experts in auditing and artificial intelligence using purposive and snowball sampling techniques and were analyzed through thematic analysis. In the quantitative phase, data were gathered from 358 auditors and audit service clients using a researcher-developed questionnaire consisting of 103 items across 27 sub-themes, measured on a five-point Likert scale. The results of the thematic analysis revealed that the AI-driven auditing services model possesses a multilayered structure encompassing traditional trust-building challenges, customer expectations, design criteria, technical requirements, and ultimate benefits. The findings from the single-layer perceptron neural network indicated that themes associated with advanced analytics, process transparency, and model accuracy possess the highest relative importance. Customer trust in AI-based auditing emerges from a complex interaction among technical, perceptual, and procedural dimensions and cannot be achieved solely through the implementation of technology. By integrating thematic analysis and artificial neural network techniques, this study provides an innovative framework for explaining and prioritizing trust-related components in AI-driven auditing services.
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Copyright (c) 2025 Toktam Javidi (Author); Karim Nakhaei; Habibollah Nakhaei , Mohammadreza Gholamzadeh (Author)
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