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.
AZIKI et al. (Sun,) studied this question.