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Predictive modeling of consumer purchase behavior on social media: Integrating theory of planned behavior and machine learning for actionable insights

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  • Md Shawmoon Azad
  • Shadman Sakib Khan
  • Rezwan Hossain
  • Raiyan Rahman
  • Sifat Momen

Abstract

In recent times, it has been observed that social media exerts a favorable influence on consumer purchasing behavior. Many organizations are adopting the utilization of social media platforms as a means to promote products and services. Hence, it is crucial for enterprises to understand the consumer buying behavior in order to thrive. This article presents a novel approach that combines the theory of planned behavior (TPB) with machine learning techniques to develop accurate predictive models for consumer purchase behavior. This study examines three distinct factors of the theory of planned behavior (attitude, social norm, and perceived behavioral control) that provide insights into the primary determinants influencing online purchasing behavior. A total of eight machine learning algorithms, namely K-nearest neighbor, Decision Tree, Random Forest, Logistic Regression, Naive Bayes, Support Vector Machine, AdaBoost, and Gradient Boosting, were utilized in order to forecast consumer purchasing behavior. Empirical findings indicate that gradient boosting demonstrates superior performance in predicting customer buying behavior, with an accuracy rate of 0.91 and a macro F1 score of 0.91. This holds true when all factors, namely attitude (ATTD), social norm (SN), and perceived behavioral control (PBC), are included in the analysis. Furthermore, we incorporated Explainable AI (XAI), specifically LIME (Local Interpretable Model-Agnostic Explanations), to elucidate how the best machine learning model (i.e. gradient boosting) makes its prediction. The findings indicate that LIME has demonstrated a high level of confidence in accurately predicting the influence of low and high behavior. The outcome presented in this article has several implications. For instance, this article presents a novel way to combine the theory of planned behavior with machine learning techniques in order to predict consumer purchase behavior. This integration allows for a comprehensive analysis of factors influencing online purchasing decisions. Also, the incorporation of Explainable AI enhances the transparency and interpretability of the model. This feature is valuable for organizations seeking insights into factors driving predictions and the reasons behind certain outcomes. Moreover, these observations have the potential to offer valuable insights for businesses in customizing their marketing strategies to align with these influential factors.

Suggested Citation

  • Md Shawmoon Azad & Shadman Sakib Khan & Rezwan Hossain & Raiyan Rahman & Sifat Momen, 2023. "Predictive modeling of consumer purchase behavior on social media: Integrating theory of planned behavior and machine learning for actionable insights," PLOS ONE, Public Library of Science, vol. 18(12), pages 1-26, December.
  • Handle: RePEc:plo:pone00:0296336
    DOI: 10.1371/journal.pone.0296336
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    References listed on IDEAS

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    1. Seyed Nasir Ketabi & Bahram Ranjbarian & Azarnoush Ansari, 2014. "Analysis of the Effective Factors on Online Purchase Intention through Theory of Planned Behavior," International Journal of Academic Research in Business and Social Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Business and Social Sciences, vol. 4(4), pages 374-382, April.
    2. Hansen, Torben & Møller Jensen, Jan & Stubbe Solgaard, Hans, 2004. "Predicting online grocery buying intention: a comparison of the theory of reasoned action and the theory of planned behavior," International Journal of Information Management, Elsevier, vol. 24(6), pages 539-550.
    3. Yadav, Rambalak & Pathak, Govind S., 2017. "Determinants of Consumers' Green Purchase Behavior in a Developing Nation: Applying and Extending the Theory of Planned Behavior," Ecological Economics, Elsevier, vol. 134(C), pages 114-122.
    4. David Juárez-Varón & Victoria Tur-Viñes & Alejandro Rabasa-Dolado & Kristina Polotskaya, 2020. "An Adaptive Machine Learning Methodology Applied to Neuromarketing Analysis: Prediction of Consumer Behaviour Regarding the Key Elements of the Packaging Design of an Educational Toy," Social Sciences, MDPI, vol. 9(9), pages 1-23, September.
    5. Hsiu‐Yi Lin & Meng‐Hsiang Hsu, 2015. "Using Social Cognitive Theory to Investigate Green Consumer Behavior," Business Strategy and the Environment, Wiley Blackwell, vol. 24(5), pages 326-343, July.
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    1. Ecel J. Juaton & Angelie Enaga & John Mark B. Lazaro & Helaria B. Carmona, 2025. "Social Media Influence, Brand Reputation, and Electronic Word-of-Mouth Marketing: A Structural Equation Model on Consumer Purchase Decision Making on Apparel Businesses," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(5), pages 1519-1537, May.

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