ROLE OF TRUST IN AI-BASED MARKETING SYSTEMS WITH REFERENCE TO INDIA

Abstract

Artificial Intelligence has become a central force in contemporary marketing systems by enabling firms to automate customer interactions,
analyze large datasets, personalize offers in real time, and predict consumer behavior with increasing precision. Although such capabilities have
improved service responsiveness and marketing efficiency, the success of AI-driven marketing does not depend on technological sophistication
alone. Consumer trust remains a decisive factor that shapes whether AI-enabled interactions are perceived as useful, reliable, fair, and worthy of
continued engagement. In the context of a rapidly digitalizing economy such as India, this issue assumes special significance because consumers
vary widely in digital literacy, prior exposure to automated systems, and sensitivity to privacy and transparency concerns. The present study
examines the role of trust in AI-based marketing systems by analyzing the relationships among transparency, personalization, customer
satisfaction, purchase intention, and trust as a mediating construct. The study adopts a quantitative research design and uses structured
questionnaire data collected from 480 consumers across metropolitan cities in India. Structural Equation Modeling (SEM) through AMOS is
employed to test the proposed conceptual framework and to estimate the direct and mediating effects among the study variables. The findings
indicate that transparency exerts a strong positive effect on trust (β = 0.69), personalization significantly enhances customer satisfaction (β =
0.63), and trust substantially influences purchase intention (β = 0.58). Customer satisfaction also contributes positively to purchase intention (β
= 0.61), confirming that both cognitive confidence and experiential evaluation matter in AI-led marketing environments. The model demonstrates
strong fit, with CFI = 0.964 and RMSEA = 0.039, while AVE and Composite Reliability values meet accepted thresholds. The study contributes
to theory by integrating trust within an AI-based customer experience and marketing framework, and it offers practical guidance for organizations
seeking to balance personalization, automation, and transparency in order to build durable consumer confidence and stronger market outcomes.