Citation:

Pankaj Kumar Tiwari, Vikrant Veer Singh Pathania. "Predicting Consumer Purchase Intention on Social Commerce Platforms: A Hybrid Machine Learning and Advanced Econometric Approach" International Research Journal of Economics and Management Studies, Vol. 5, No. 4, pp. 154-161, 2026. Crossref. http://doi.org/10.56472/25835238/IRJEMS-V5I4P119

Abstract:

The rapid proliferation of social commerce platforms has fundamentally transformed consumer buying behaviour, making the prediction of purchase intention a critical challenge for digital marketers. This study integrates advanced machine learning (ML) algorithms with structural econometric techniques to model and predict consumer purchase intention on Instagram and other social commerce platforms in the Indian context. Using a primary cross-sectional dataset of 412 respondents collected via a structured questionnaire across four Indian metropolitan areas, we apply a Probit regression model with average marginal effects (AMEs) alongside a comparative suite of six ML classifiers—Logistic Regression, Decision Tree, Random Forest, Gradient Boosting (XGBoost), Support Vector Machine, and Multilayer Perceptron Neural Network. Predictor variables encompass social media engagement, influencer credibility, perceived value, brand trust, advertisement personalisation, and price sensitivity, supplemented by demographic controls. Descriptive statistics and a Pearson correlation matrix confirm construct validity prior to modelling. Results reveal that the Gradient Boosting classifier achieves the highest predictive accuracy (89.1%) and AUC-ROC (0.941), while the Probit model identifies social media engagement (β = 0.482, p < 0.001; AME = 0.152) and influencer credibility (β = 0.374, p < 0.001; AME = 0.118) as the strongest determinants of purchase intention. A SHAP-Probit convergence analysis establishes a near-perfect Spearman rank correlation of ρ = 0.91 across all eight predictors, confirming methodological complementarity. This hybrid framework provides marketers, platform managers, and policymakers with actionable, evidence-based insights for targeted digital marketing strategy formulation in emerging markets.

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Keywords:

Consumer Purchase Intention, Gradient Boosting, Influencer Marketing, Machine Learning, Probit Regression, SHAP Analysis, Social Commerce.