Analysis of Factors Affecting Professional Accountants’ Resistance to the Implementation of Artificial Intelligence and Its Consequences for Financial Digital Transformation

Abstract

This study aimed to analyze the factors affecting professional accountants’ resistance to the implementation of artificial intelligence and to examine the consequences of this resistance for financial digital transformation. This applied quantitative study was conducted using a descriptive-correlational design with a structural equation modeling approach. The statistical population consisted of professional accountants working in Tehran in private companies, audit firms, tax and financial consulting institutions, and financial departments of industrial, commercial, and service organizations. A total of 384 professional accountants were selected through purposive sampling based on inclusion criteria, including relevant academic background, at least three years of professional experience, and direct involvement in accounting, auditing, financial reporting, taxation, or financial analysis processes. Data were collected using a structured questionnaire measuring perceived job insecurity, fear of skill obsolescence, perceived complexity of artificial intelligence systems, lack of trust in algorithmic outputs, data security and ethical responsibility concerns, perceived threat to professional judgment, insufficient organizational support and training, overall resistance to artificial intelligence implementation, and impairment of financial digital transformation. Data were analyzed using descriptive statistics, Pearson correlation coefficients, confirmatory factor analysis, and structural equation modeling. The inferential results showed that perceived job insecurity (β = 0.18, p < 0.001), fear of skill obsolescence (β = 0.21, p < 0.001), perceived AI complexity (β = 0.14, p = 0.001), lack of trust in algorithmic outputs (β = 0.16, p < 0.001), data security and ethical responsibility concerns (β = 0.10, p = 0.021), perceived threat to professional judgment (β = 0.09, p = 0.039), and insufficient organizational support and training (β = 0.24, p < 0.001) significantly predicted resistance to AI implementation. These variables explained 62% of the variance in resistance. Resistance to AI implementation significantly predicted impairment of financial digital transformation (β = 0.68, p < 0.001), explaining 46% of its variance. The findings indicate that resistance to artificial intelligence among professional accountants is shaped by employment, skill, technological, ethical, and organizational concerns and can substantially weaken financial digital transformation. Therefore, successful AI implementation in accounting requires systematic training, transparent communication, ethical governance, role redesign, and organizational support.