Authors: Md. Ashif Karim Assistant Professor, Kunal Rathore, Tanuja Bhakne
Abstract: The development and adoption of Artificial Intelligence (AI) technology in the digital commerce arena have radically transformed the consumer decision-making process in today’s era. The touchpoints such as Personalized Recommendation Engine, Conversational Chatbots, Predictive Analytics, Visual Search, and Automated Pricing Algorithms have come into play to influence the consumers at every step of their purchase process. Although there is extensive use of such AI interventions in various retail and service businesses, there still remains a gap in the empirical knowledge regarding the effect of AI touchpoints in affecting consumer’s cognition, affective evaluation, and purchase behavior. This empirical research aims to explore the structural effect of Artificial Intelligence touchpoints on consumer purchase behavior in pre-purchase, post-purchase evaluation of alternatives, purchase decisions, and customer journey. Building on the theories of Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT) and Stimulus-Organism-Response (S-O-R) model, this paper proposes a quantitative cross-sectional research approach based on primary survey data collected from a stratified sample of 320 respondents actively using AI enabled e-commerce platforms. Statistical analyses were performed using IBM SPSS Statistics (Version 28.0) including reliability analysis using Cronbach’s Alpha coefficient, descriptive statistics, Pearson bivariate correlation and Multiple Linear Regression models Recommendation algorithms became the driving force behind impulse buying and basket growth, while conversational artificial intelligence made a substantial contribution to the effectiveness of the pre-purchase assessment stage. On the other hand, the privacy concerns and lack of transparency were recognized as moderating factors that undermine consumers' trust. In light of these results, an integrated management framework is suggested together with recommendations for marketers and developers.
