Analyzing Consumer Behavior in the Context of Online Social Media Marketing
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Abstract
The rapid expansion of digital marketing across social media has fundamentally transformed how consumers interact with online sellers. In Facebook Commerce (F-Commerce), understanding consumer behavior is vital for enabling targeted promotion, personalized engagement, and efficient negotiation. This study presents a machine-learning-driven framework to analyze consumer intentions using a survey dataset of 656 Facebook users. To address high dimensionality and improve predictive performance, Principal Component Analysis (PCA) is applied at multiple explained variance thresholds (75%, 80%, 85%, 90%, 95%). We evaluate three models — Artificial Neural Network (ANN), Random Forest (RF), and k-Nearest Neighbors (KNN) — on both original and PCA-transformed data. The Random Forest model yields the highest accuracy (0.98), outperforming the ANN and KNN models across most metrics. A Current Reality Tree (CRT) is constructed to identify root causes behind non-purchasing behaviors. The study contributes a novel, PCA-enhanced predictive framework for F-Commerce decision modeling and provides actionable insights for digital marketers.
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K. M. Alhendawi, A. A. Al-Janabi, and J. Badwan. Predicting the quality of mis characteristics and end-users perceptions using artificial intelligence tools: Expert systems and neural network. In Proceedings of the International Conference on Intelligent Computing and Optimization, Hua Hin , Thailand, 2019, pages 18–30. Springer.
J. Ali, R. Khan, N. Ahmad, and I., Maqsood. Random forests and decision trees. In International Journal of Computer Science Issues (IJCSI), 9(5):272, 2012.
S. A. Bhat, K. Kansana, and J. Khan. A review paper on e-commerce. In Asian Journal of Technology and Management Research [ISSN: 2249–0892], 6(1), 2016.
R. R. Chowdhury, M. A. Adnan, and R. K. Gupta. Real-time principal component analysis. ACM Transactions on Data Science, 1(2):1–36, 2020.
M. Fishbein and I. Ajzen. Predicting and changing behavior the reasoned action approach. OCLC: 1190691560, 2015.
P. G. Gill, M. A. Purnell, N. Crumpton, K. R. Brown, N. J. Gostling, M. Stampanoni, and E. J. Rayfield. Dietary specializations and diversity in feeding ecology of the earliest stem mammals. Nature, 512(7514):303–305, 2014.
P. Gloor, A. Fronzetti Colladon, J. M. de Oliveira, and P. Rovelli. Put your money where your mouth is: Using deep learning to identify consumer tribes from word usage. In International Journal of Information Management, 51:101924, 2020.
D. I. Hawkins and D. L. Mothersbaugh. Consumer behavior: building marketing strategy. McGraw-Hill Education, New York, NY, thirteenth edition, 2016.
R. Hecht-Nielsen. Theory of the backpropagation neural network. Elsevier, 1992.
T. K. Ho. The random subspace method for constructing decision forests. In IEEE transactions on pattern analysis and machine intelligence, 20(8):832–844, 1998.
S. Hossain, M. M. Hasan, and T. Hossain. An Analytical Study of Influencing Factors on Consumers’ Behaviors in Facebook using ANN and RF. In International Conference on Intelligent Computing & Optimization, pages 744–753. Springer, 2020.
S. M. Islam, M. S. A. Forhad, M. A. Uddin, M. S. Arefin, S. M. Galib, and M. A. Khan. Developing an intelligent system for recommending products. In International Conference on Intelligent Computing & Optimization, pages 476–490. Springer, 2020.
Z. Jaadi. “A Step-by-Step Explanation of Principal Component Analysis (PCA)”, 2022 [Online]. Available: https://builtin.com/data-science/step-step-explanation-principal-component-analysis.
I. T. Jolliffe. and J. Cadima. Principal component analysis: a review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065):20150202, 2016.
M., Khaniwale. Consumer Buying Behavior. In International Journal of Innovation and Scientific Research, 14(2):278–286, 2015.
I. Krak, O. Barmak, E. Manziuk, and A. Kulias. Data classification based on the feature’s reduction and piecewise linear separation. In International Conference on Intelligent Computing & Optimization, pages 282–289. Springer, 2019.
P. Kumar, S. Nandagopalan, and L. Swamy. Investigation of emotions on purchased item reviews using machine learning techniques. In Proceedings of the International Conference on Intelligent Computing & Optimization, pages 409–417. Springer, 2018.
R. Kumar. Consumer behaviour and role of consumer research in marketing. In Journal of Commerce and Trade, 12(1):65–76, 2017.
T. Lang. and M. Rettenmeier. Understanding consumer behavior with recurrent neural networks. In Proceedings of the Workshop on Machine Learning Methods for Recommender Systems, 2017.
S. Ng. Principal component analysis to reduce dimension on digital image. Procedia computer science, 111:113–119, 2017.
R. Rodriguez-Aguilar, J. A. M. Saucedo, and P. Vasant. Estimation of electricity prices in the Mexican market. In International Conference on Intelligent Computing & Optimization, pages 11–17. Springer, 2019.
L. Rokach and O. Maimon. Top-Down Induction of Decision Trees Classifiers—A Survey. In IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 35(4):476–487, 2005.
E. Siregar. Young attitudes towards f-commerce advertising. In Proceedings of the International Conference on Industrial Technology and Management (ICITM), pages 218–223, 2018.
I. Schulze-Horn, S. Hueren, P. Scheffler, H. Schiele. Artificial intelligence in purchasing: Facilitating mechanism design-based negotiations. Applied Artificial Intelligence. 2;34(8):618-42, 2020.
S. Gupta, S. Modgil, S. Bhattacharyya, I. Bose. Artificial intelligence for decision support systems in the field of operations research: review and future scope of research. Annals of Operations Research. pp 1:1-60, 2022.
F. Roger, L. U. William, Bruce Patton. Getting to Yes: Negotiating Agreement Without Giving In. Penguin Books, 2011.
Bizrate. “Facebook commerce survey”, 2023 [Online]. Available: https://www.bizrate.com/facebook-commerce-survey/#:~:text=Only%2022%25%20reported%20making%20a,features%20to%20aid%20the%20process.
M. Zabeen, H. Ara, and N. Sarwar. F-Commerce in Bangladesh: “Venit, Vidit, Vicit”. IOSR J. Hum. Soc. Sci, 17(5), 1-08, 2013.
G. Walther, E. Schmid, S. T. Spengler, Negotiation-based coordination in product recovery networks, International Journal of Production Economics, Volume 111, Issue 2, 2008, Pages 334-350, ISSN 0925-5273, https://doi.org/10.1016/j.ijpe.2006.12.069, 2008.
G. Zhimin, Y. Tong, Y. Rui, Channel coordination under Nash bargaining fairness concerns in differential games of goodwill accumulation, European Journal of Operational Research, Volume 285, Issue 3, pp 916-930, https://doi.org/10.1016/j.ejor.2020.02.028. (https://www.sciencedirect.com/science/article/pii/S0377221720301466), 2020.
S. Yoon. “Why diversity within your organization matters – Lessons from 11 entrepreneurs”, 2021 [Online]. Available: https://www.weforum.org/agenda/2021/11/why-diversity-within-your-organization-matters/.
C. M. Bishop, and M. N. Nasrabadi. Pattern recognition and machine learning. Vol. 4, no. 4. New York: springer, 2006.
Y. Jiang, X. Li, and H. Wang, “AI-driven personalization strategies in social commerce: User profiling, recommendation systems, and consumer engagement,” Journal of Retailing and Consumer Services, vol. 68, p. 103001, 2022.
M. Rizwan and S. Malik, “Trust modeling and risk perception in Facebook-based online purchasing: An empirical investigation,” Electronic Commerce Research and Applications, vol. 46, p. 101040, 2021.
A. Al-Rawi, “Community influence, peer validation, and consumer decision patterns in Facebook Commerce (F-Commerce),” International Journal of Information Management, vol. 69, p. 102597, 2023.