Empirical investigation of generative artificial intelligence acceptability determinants among Moroccan chartered accountants: A structural equation modeling approach

Abstract

This study examines generative artificial intelligence (GAI) acceptability determinants among Moroccan chartered accountants using the Unified Theory of Acceptance and Use of Technology (UTAUT). Data collection from 262 professionals (31.9% response rate) applied a quantitative methodology based on the UTAUT framework. Partial least squares structural equation modeling assessed relationships between four determinants and GAI acceptability. Results indicate effort expectancy (β=0.538, f²=0.213) and social influence (β=0.498, f²=0.109) as primary determinants, while performance expectancy (β=0.319, f²=0.004) shows limited effect size despite statistical significance. Facilitating conditions demonstrate no substantial contribution (f²=0.000). The model explains 40.4% of GAI acceptability variance. These findings reveal distinct adoption patterns where ease of use and social factors predominate over performance considerations in the Moroccan accounting profession, contributing empirical evidence from non-Western professional contexts to technology acceptance literature.