Presenting a Model Based on the Use of Artificial Intelligence Algorithms in Marketing for Small and Medium-Sized Enterprises in the Food Industry
Keywords:
Artificial intelligence algorithms, Marketing, Small and medium, sized enterprises, Food industryAbstract
Objective: The purpose of this study was to develop a comprehensive model based on XGBoost and DistilBERT artificial intelligence algorithms to identify, model, and explain the factors influencing AI adoption in marketing activities of food-industry small and medium-sized enterprises (SMEs) and to analyze customer behavior patterns.
Methodology: This study employed a mixed-methods design grounded in an interpretivist philosophy and an inductive approach. In the qualitative phase, semi-structured interviews were conducted with 12 managers and experts from the food industry and analyzed using the Attride-Stirling thematic analysis method. The analysis yielded 25 organizing themes grouped into technological infrastructure, human resources and knowledge, financial and economic resources, organizational factors, data and analytics, market factors, legal and ethical considerations, and strategic factors. In the quantitative phase, a researcher-developed Likert-scale questionnaire derived from the qualitative findings was administered to 384 food-industry managers and specialists. Following confirmation of data normality and instrument reliability, XGBoost was applied to numerical variables, whereas DistilBERT was used for textual data analysis.
Findings: The inferential results identified managerial acceptance, marketing personalization, and financial resources as the most influential determinants of AI adoption. The XGBoost model demonstrated strong predictive performance, achieving 92% accuracy, R² = 0.85, and RMSE = 1.23 while effectively prioritizing predictor importance. Furthermore, the DistilBERT model achieved 91% accuracy and an F1-score of 0.87 in customer behavior analysis and prediction based on textual data, outperforming alternative approaches in low-error prediction cases.
Conclusion: The integration of XGBoost and DistilBERT provides a localized and practically applicable framework for AI-driven marketing in food-industry SMEs. The proposed model enhances marketing decision-making, supports customer-centered strategy development, reduces marketing costs, improves conversion rates, and strengthens competitive advantage. Accordingly, investment in data infrastructure, managerial training, and collaboration with AI-focused technology firms are recommended to facilitate successful implementation.
Downloads
References
Acatrinei, C., Apostol, I. G., Barbu, L. N., Chivu, R. G., & Orzan, M. C. (2025). Artificial Intelligence in Digital Marketing: Enhancing Consumer Engagement and Supporting Sustainable Behavior Through Social and Mobile Networks. Sustainability, 17, 6638. https://doi.org/10.3390/su17146638
Adel, H., Dahou, A., Mabrouk, A., Abd Elaziz, M., Kayed, M., El-Henawy, I. M., Alshathri, S., & Amin Ali, A. (2022). Improving Crisis Events Detection Using DistilBERT with Hunger Games Search Algorithm. Mathematics, 10, 447. https://doi.org/10.3390/math10030447
Aghababaei, A., Aghababaei, F., Pignitter, M., & Hadidi, M. (2025). Artificial Intelligence in Agro-Food Systems: From Farm to Fork. Foods, 14, 411. https://doi.org/10.3390/foods14030411
Bhagya Raj, G. V. S., & Dash, K. K. (2022). Comprehensive Study on Applications of Artificial Neural Network in Food Process Modeling. Critical Reviews in Food Science and Nutrition, 62, 2756-2783. https://doi.org/10.1080/10408398.2020.1858398
Ding, H., Tian, J., Yu, W., Wilson, D. I., Young, B. R., Cui, X., Xin, X., Wang, Z., & Li, W. (2023). The Application of Artificial Intelligence and Big Data in the Food Industry. Foods, 12, 4511. https://doi.org/10.3390/foods12244511
Esmaeily, R., Razavi, M. A., & Razavi, S. H. (2024). A Step Forward in Food Science, Technology and Industry Using Artificial Intelligence. Trends in Food Science & Technology, 143, 104286. https://doi.org/10.1016/j.tifs.2023.104286
Hokmabadi, H., Rezvani, S. M. H. S., & de Matos, C. A. (2024). Business Resilience for Small and Medium Enterprises and Startups by Digital Transformation and the Role of Marketing Capabilities-A Systematic Review. Systems, 12, 220. https://doi.org/10.3390/systems12060220
Jayan, H., Min, W., & Guo, Z. (2025). Applications of Artificial Intelligence in Food Industry. Foods, 14, 1241. https://doi.org/10.3390/foods14071241
Li, J., Lin, B., Wang, P., Chen, Y., Zeng, X., Liu, X., & Chen, R. (2024). A Hierarchical RF-XGBoost Model for Short-Cycle Agricultural Product Sales Forecasting. Foods, 13, 2936. https://doi.org/10.3390/foods13182936
Liu, H., Wang, Y., & Yan, Z. (2024). Artificial Intelligence and Food Processing Firms Productivity: Evidence from China. Sustainability, 16, 5928. https://doi.org/10.3390/su16145928
Magdas, D. A., Hategan, A. R., David, M., & Berghian-Grosan, C. (2025). The Journey of Artificial Intelligence in Food Authentication: From Label Attribute to Fraud Detection. Foods, 14, 1808. https://doi.org/10.3390/foods14101808
Romero Martínez, M., Carmona Ibáñez, P., & Martínez Vargas, J. (2025). Predicting Business Failure with the XGBoost Algorithm: The Role of Environmental Risk. Sustainability, 17, 4948. https://doi.org/10.3390/su17114948
Sgroi, F., Sciortino, C., Baviera-Puig, A., & Modica, F. (2025). Analyzing consumer trends in functional foods: A cluster analysis approach. Journal of Agriculture and Food Research, 15, 101041. https://doi.org/10.1016/j.jafr.2024.101041
Sharabati, A. A. A., Ali, A. A. A., Allahham, M. I., Hussein, A. A., Alheet, A. F., & Mohammad, A. S. (2024). The Impact of Digital Marketing on the Performance of SMEs: An Analytical Study in Light of Modern Digital Transformations. Sustainability, 16, 8667. https://doi.org/10.3390/su16198667
Taneja, A., Nair, G., Joshi, M., Sharma, S., Sharma, S., Jambrak, A. R., Roselló-Soto, E., Barba, F. J., Castagnini, J. M., Leksawasdi, N., & Phimolsiripol, Y. (2023). Artificial Intelligence: Implications for the Agri-Food Sector. Agronomy, 13, 1397. https://doi.org/10.3390/agronomy13051397
Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021). Artificial Intelligence in Marketing: Systematic Review and Future Research Direction. International Journal of Information Management Data Insights, 1, 100002. https://doi.org/10.1016/j.jjimei.2020.100002
Wang, Y., & Liu, F. (2025). Impact of artificial intelligence innovation on food company performance. International Review of Financial Analysis, 103(5), 1115-1145. https://doi.org/10.1016/j.irfa.2025.104219
Yin, B., Tan, G., Muhammad, R., Liu, J., & Bi, J. (2025). AI-Powered Innovations in Food Safety from Farm to Fork. Foods, 14, 1973. https://doi.org/10.3390/foods14111973
Ziakis, C., & Vlachopoulou, M. (2023). Artificial Intelligence in Digital Marketing: Insights from a Comprehensive Review. Information, 14, 664. https://doi.org/10.3390/info14120664
Downloads
Published
Submitted
Revised
Accepted
Issue
Section
License
Copyright (c) 2025 جعفر طاهرزاده (نویسنده); حسن واحدی (مترجم); سید حسین حسینی, مهدی صانعی (نویسنده)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
