Evaluation of the Structural Model of Artificial Intelligence Acceptance in Digital Marketing Based on Customers’ Perspectives in the Online Retail Industry

Abstract

This study aimed to evaluate the structural model of artificial intelligence acceptance in digital marketing based on customers’ perspectives in the online retail industry. This applied, field-based, and descriptive study examined customers of online retail platforms, including Digikala, Digistyle, Plaza, Emalls, and Zanbil. Since the exact size of the statistical population was unknown, Cochran’s formula for an unlimited population was used, and 385 online retail customers were selected through random sampling. Data were collected using a researcher-made questionnaire developed based on the qualitative phase of the study and expert interviews. The questionnaire included three main dimensions and 11 items derived from 11 identified factors and was scored on a five-point Likert scale. The collected data were analyzed using partial least squares structural equation modeling through Smart-PLS software. The measurement model results indicated that the factor loadings of perceived usefulness, perceived security and trust, and interactional and communicative factors were significant and above the acceptable threshold. Composite reliability values for all constructs exceeded 0.70, and AVE values were greater than 0.50, confirming the reliability and convergent validity of the model. In the structural model, all main path coefficients had t-values greater than 1.96 and were significant at the 95% confidence level. The coefficient of determination for artificial intelligence acceptance was R² = 0.398, while the predictive relevance index was Q² = 0.306. The overall goodness-of-fit index was GOF = 0.517, indicating a strong and acceptable model fit. The findings demonstrated that perceived usefulness, perceived security and trust, and interactional and communicative factors are key determinants of artificial intelligence acceptance in digital marketing from the perspective of online retail customers. Accordingly, improving data security, algorithmic transparency, user trust, personalized usefulness, and interactive AI-based customer experiences can strengthen the acceptance and effective use of artificial intelligence in online retail marketing.