Determinants of AI Image Generator Adoption Among Marketing Agencies: The Mediating Effects of Perceived Usefulness

This study aims to investigate the factors influencing the adoption of AI image generators among marketing agencies in Bangladesh, focusing particularly on the mediating role of perceived usefulness (PU). Employing a quantitative approach, data was collected from 170 respondents working in various marketing roles across different agencies in Bangladesh. Structural Equation Modeling (SEM) was utilized to analyze relationships among the constructs such as performance expectancy (PE), effort expectancy (EE), facilitating conditions (FC), attitude towards technology (ATT), perceived risk (PR), social influence (SI), perceived usefulness (PU), and behavioral intention (BI) to Adopt AI image generators. Findings reveal that PE, FC, and ATT positively influence PU, which subsequently predicts BI significantly. Conversely, PR negatively impacts PU, while EE and SI exhibit weaker effects. The study underscores the pivotal role of perceived usefulness in shaping the adoption intentions of AI image generator among marketing professionals in Bangladesh. Limitations include the study's cross-sectional design and industry-specific focus, suggesting potential avenues for future research to explore longitudinal effects and compare adoption dynamics across diverse industries and regions. Practical implications highlight the importance for marketing agencies to emphasize performance benefits, Social Influence, and positive attitudes towards AI while addressing perceived risks to foster successful adoption of AI image generators. Socially, the study underscores AI's potential to drive innovation and enhance competitiveness to commercialize AI technologies in developing economies. This research contributes to literature by applying established technology adoption models to a new context, offering insights into AI technology adoption within the marketing sector of a developing nation.