Predicting Online Purchase Intention from Consumer Behaviour: A Comparative Analysis of Regression and Machine Learning

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@article{1723243,
      author = {Hriday Das, Deepak Shyam},
      title = {Predicting Online Purchase Intention from Consumer Behaviour: A Comparative Analysis of Regression and Machine Learning},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {3},
      pages = {2385-2394},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723243.pdf},
      abstract = {Online purchase intention reflects multiple, overlapping behavioural factors, yet studies rarely compare how strongly such factors explain intention statistically against how strongly they predict it. This study examines seven behavioural constructs—Price Sensitivity, Review/Ratings Influence, Quality and Brand Trust, Website Convenience, Fulfilment and Security, Social Influence, and Previous Experience—as predictors of Online Purchase Intention among Indian online shoppers, comparing multiple regression with five machine-learning algorithms on the same survey data (N = 228; 227 usable). Six constructs showed significant positive bivariate relationships with purchase intention; Social Influence did not. The regression model was significant (R² = 0.1914, adjusted R² = 0.1656, F(7,219) = 7.4065, p < 0.001), with Quality and Brand Trust the only significant unique predictor (B = 0.211, p = 0.025). Among Linear Regression, Decision Tree, Random Forest, Support Vector Regression, and K-Nearest Neighbours, Support Vector Regression achieved the highest holdout R² (0.185), while Linear Regression achieved the highest mean cross-validated R² (0.123). Permutation importance ranked Previous Experience and Fulfilment and Security highest. Predictive performance was modest overall, and all multi-item constructs had Cronbach’s alpha below 0.70. Statistical significance, unique linear contribution, and predictive importance therefore capture different aspects of behavioural relevance and should be treated as complementary rather than interchangeable evidence in online consumer research.},
      keywords = {online purchase intention; consumer behaviour; electronic commerce; machine learning; multiple regression; predictive analytics; Cronbach’s alpha; India},
      month = {September},
  }