Predicting Smart Tablet Preferences in Turkish E-Commerce Platforms Using Artificial Neural Networks and Machine Learning Techniques

Basit Öğe Kaydı
dc.contributor.authorBardak, Selahattin
dc.date.accessioned2026-04-25T14:20:27Z
dc.date.available2026-04-25T14:20:27Z
dc.date.issued2026
dc.departmentSinop Üniversitesi
dc.description.abstractThis study aims to predict Turkish consumer preferences for smart tablets on e-commerce platforms, focusing on consumer behavior in a developing country context. Key product attributes-such as processor speed, screen size, internal storage capacity, display resolution, RAM, processor core count, and battery capacity-were collected from major e-commerce websites in Turkey. Data analysis indicated that consumers predominantly prefer tablets with processor speeds between 1-3 GHz, internal storage capacities of 32-64 GB, 2-3 GB of RAM, screen sizes of 7-11 inches, and battery capacities between 5001-8000 mAh. To predict the most preferred tablet configurations, Artificial Neural Networks (ANNs), Deep Neural Networks (DNNs), and Random Forest (RF) models were developed and evaluated. Among these, the ANN model achieved the highest prediction accuracy, particularly regarding RAM preferences. The findings contribute to the growing body of research on consumer behavior modeling in emerging markets and may assist manufacturers and marketers in shaping strategic decisions related to product development and online retail strategies.
dc.identifier.doi10.3390/app16020832
dc.identifier.issn2076-3417
dc.identifier.issue2
dc.identifier.scopus2-s2.0-105028736981
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/app16020832
dc.identifier.urihttps://hdl.handle.net/11486/8579
dc.identifier.volume16
dc.identifier.wosWOS:001670067000001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorBardak, Selahattin
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofApplied Sciences-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260420
dc.subjectsmart tablets
dc.subjecte-commerce
dc.subjectmachine learning
dc.subjectconsumer behavior
dc.subjectforecasting models
dc.subjectemerging markets
dc.titlePredicting Smart Tablet Preferences in Turkish E-Commerce Platforms Using Artificial Neural Networks and Machine Learning Techniques
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu