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@article{Forgács_Lukács_Csiszárik-Kocsir_Horváth_2024, title={Towards the Investigation of Online Shopping Behaviours Using a Fuzzy Inference System}, volume={7}, url={https://dmame-journal.org/index.php/dmame/article/view/1059}, DOI={10.31181/dmame7220241059}, abstractNote={<p>Online shopping has experienced substantial growth over the past decade, and this trend is expected to persist. The convenience it offers consumers serves as a driving force behind this expansion. Online retailers stand to benefit from a comprehensive understanding of consumer behavior and online shopping habits, as it enables them to formulate more effective marketing strategies and tailor their communications to the preferences of online shoppers. This paper aimed to develop a bespoke questionnaire leveraging data from a EuroStat report in 2021. As novel methodology a Sugeno- type predictive fuzzy model was constructed using these data, empowering businesses to make more precise predictions regarding the requirements and behaviors of distinct consumer groups. The study examined the following areas of consumers: online shoppers belonging to the X, Y, and Z generations; living in small towns, towns, or in the capital; and studying, working, or both. In addition, the likelihood of spending money online was determined regarding the following product categories: Bills, utilities; (2) Food, shopping; (3) Entertainment; (4) Wellness, beauty; (5) Electronic items; (6) Fashion; (7) Home, decoration and (8) Other goods. The results of this survey, combined with the fuzzy model developed, serve as valuable resources for online retailers seeking to enhance their marketing strategies and gain a deeper understanding of customer preferences. The conclusions highlight patterns and preferences among different age groups and locations, providing valuable insights for online retailers to enhance their marketing strategies when identifying main target groups for specific products. Additionally, the research offers a more comprehensive understanding of demographic attributes associated with these age cohorts than EuroStat data.</p>}, number={2}, journal={Decision Making: Applications in Management and Engineering}, author={Forgács, Anett and Lukács, Judit and Csiszárik-Kocsir, Ágnes and Horváth, Richárd}, year={2024}, month={Apr.}, pages={337–354} }