IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0296336.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0296336
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0296336&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0296336?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    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. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Anastasia Theodorou & Leonidas Hatzithomas & Thomas Fotiadis & Anastasios Diamantidis & Antonios Gasteratos, 2023. "The Impact of the COVID-19 Pandemic on Online Consumer Behavior: Applying the Theory of Planned Behavior," Sustainability, MDPI, vol. 15(3), pages 1-17, January.
    2. Ruijie Zhu & Guojing Zhao & Zehai Long & Yangjie Huang & Zhaoxin Huang, 2022. "Entrepreneurship or Employment? A Survey of College Students’ Sustainable Entrepreneurial Intentions," Sustainability, MDPI, vol. 14(9), pages 1-15, May.
    3. Andreas Falke & Nadine Schröder & Claudia Hofmann, 2022. "The influence of values in sustainable consumption among millennials," Journal of Business Economics, Springer, vol. 92(6), pages 899-928, August.
    4. Matteo Migheli, 2021. "Green purchasing: the effect of parenthood and gender," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(7), pages 10576-10600, July.
    5. Sabakun Naher Shetu, 2025. "Determinants of generation Z consumers’ mobile online shopping apps continuance intention to use during COVID-19 and beyond—an empirical study," Future Business Journal, Springer, vol. 11(1), pages 1-18, December.
    6. Nketiah, Emmanuel & Song, Huaming & Cai, Xiang & Adjei, Mavis & Adu-Gyamfi, Gibbson & Obuobi, Bright, 2022. "Citizens’ intention to invest in municipal solid waste to energy projects in Ghana: The impact of direct and indirect effects," Energy, Elsevier, vol. 254(PC).
    7. Osakwe, Christian Nedu & Ogunmokun, Oluwatobi A. & Elgammal, Islam & Kwarteng, Michael Adu, 2025. "Individuals' attitudes and their adoption intentions of central bank digital currency: Combining theories and analytics for deeper insights," Technological Forecasting and Social Change, Elsevier, vol. 210(C).
    8. Rambabu Lavuri & Abhinav Jindal & Umair Akram & Bhukya Koteswara Rao Naik & Alrence Santiago Halibas, 2023. "Exploring the antecedents of sustainable consumers' purchase intentions: Evidence from emerging countries," Sustainable Development, John Wiley & Sons, Ltd., vol. 31(1), pages 280-291, February.
    9. Costa Synodinos, 2019. "Environmental purchase behaviour concerns of African Generation Y students in South Africa," Proceedings of International Academic Conferences 9912363, International Institute of Social and Economic Sciences.
    10. Narwal, Preeti & Rai, Shivam, 2022. "Individual differences and moral disengagement in Pay-What-You-Want pricing," Journal of Business Research, Elsevier, vol. 149(C), pages 528-547.
    11. Cheung, Millissa F.Y. & To, W.M., 2019. "An extended model of value-attitude-behavior to explain Chinese consumers’ green purchase behavior," Journal of Retailing and Consumer Services, Elsevier, vol. 50(C), pages 145-153.
    12. Chen, Junhong & Nian, Yefan & Gao, Zhifeng, 2022. "Value, Attitude/Belief, and Sustainable Food Consumption," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322485, Agricultural and Applied Economics Association.
    13. Yu Hao & Yingting Wang & Qiuwei Wu & Shiwei Sun & Weilu Wang & Menglin Cui, 2020. "What affects residents' participation in the circular economy for sustainable development? Evidence from China," Sustainable Development, John Wiley & Sons, Ltd., vol. 28(5), pages 1251-1268, September.
    14. Heleen Dreyer & Nadine Sonnenberg & Daleen Van der Merwe, 2022. "Transcending Linearity in Understanding Green Consumer Behaviour: A Social–Cognitive Framework for Behaviour Changes in an Emerging Economy Context," Sustainability, MDPI, vol. 14(22), pages 1-27, November.
    15. Jana Hojnik & Mitja Ruzzier & Tatiana S. Manolova, 2020. "Sustainable development: Predictors of green consumerism in Slovenia," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 27(4), pages 1695-1708, July.
    16. Zaharah Mohamed Rani & Anida Ismail & Noraini Rahim & Siti Rohimi Mohamed Apandi & Ferial Farook, 2024. "The Impact of Environmental Knowledge on Food Waste Reduction and Sustainability Practices among Hospitality Students in Malaysia," Information Management and Business Review, AMH International, vol. 16(3), pages 51-58.
    17. Maria Rodrigues & João F. Proença & Rita Macedo, 2023. "Determinants of the Purchase of Secondhand Products: An Approach by the Theory of Planned Behaviour," Sustainability, MDPI, vol. 15(14), pages 1-18, July.
    18. Bora Ly & Romny Ly, 2024. "A quantitative analysis of factors shaping attitudes toward green products among Cambodian public employees," Journal of Environmental Studies and Sciences, Springer;Association of Environmental Studies and Sciences, vol. 14(2), pages 287-299, June.
    19. Duong Cong Doanh & Katarzyna Gadomska-Lila & Le Thi Loan, 2021. "Antecedents of green purchase intention: a cross-cultural empirical evidence from Vietnam and Poland," Oeconomia Copernicana, Institute of Economic Research, vol. 12(4), pages 935-971, December.
    20. Nik Masdek Nik Rozana & Wong Kelly Kai Seng & Mohd Nawi Nolila & Sharifuddin Juwaidah & Wong Wang Li, 2023. "Antecedents of sustainable food waste management behaviour: Empirical evidence from urban households in Malaysia," Management & Marketing, Sciendo, vol. 18(1), pages 53-77, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0296336. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.