Predicting Online Purchase Intention Among Urban Indian Millennials: Integrating Sentiment Analytics and TAM Framework

The rapid proliferation of digital commerce platforms in India has created an urgent need for predictive frameworks that can reliably decode consumer purchase behaviour in online environments. This study proposes and empirically validates an integrated model that combines the Technology Acceptance Model (TAM) with user-generated sentiment analytics to predict online purchase intention among urban Indian Millennials (aged 22–38 years). Employing a mixed-methods research design, the study collected primary quantitative data through a structured questionnaire (n=217) and conducted sentiment analysis on 1,200 product reviews from Flipkart and Amazon India using VADER and TextBlob. Results reveal that perceived usefulness (β=0.41), sentiment polarity (β=0.33), and perceived ease of use (β=0.29) are significant predictors of purchase intention (Adjusted R²=0.674, p<0.001). The integrated model explains 67.4% of variance in purchase intention, with sentiment polarity outranking ease of use — highlighting the decisive role of peer-generated content in Millennial purchase decisions.