Yazar "Kalaoglu, Fatma" seçeneğine göre listele

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    Exploring the factors influencing consumers' virtual garment fit satisfactions
    (Emerald Group Publishing Ltd, 2020) Buyukaslan, Evrim; Baytar, Fatma; Kalaoglu, Fatma
    Purpose Virtual garment fit will be an important determinant for the online purchase decision of consumers in the near future. Therefore, the purpose of this study was to develop a conceptual model to explore the factors that might impact consumers' virtual garment fit satisfactions (VFS). Design/methodology/approach Virtual body satisfaction (VBS), acceptance of the virtual try-on technology and virtual fabric properties were examined as factors that would potentially impact consumers' VFS. Forty-five women, from 18 to 35 years old, were recruited for the study. Participants were scanned by using a 3D body scanner and their scans were used for virtual try-on. Seven circular skirts with different fabric properties were created by using a commercial 3D simulation software. Participants evaluated the fit of these virtual skirts on their own virtual bodies. Participants' VFSs and their correlations with VBSs, acceptance of virtual try-on technology and virtual fabric properties were analyzed by Pearson's correlation test. Findings Participants' VBSs at hips were correlated fairly good with their VFSs (r = 0.50,N= 180,p< 0.01) and their acceptance of virtual try-on technology was weakly correlated to VFSs (r = 0.24,N= 180,p< 0.01). However, no significant correlation was found between virtual fabric properties and participants' VFSs. Research limitations/implications This study did not examine the ideal beauty notion, which may affect consumers' expectations about how the garments should fit on them. Another limitation was the use of a single skirt design as a stimulus. Originality/value Studies that explore virtual garment fit often measure the garment ease or the virtual fabric tension and ignore consumer perspective, which is essential for online purchase decision. This study is unique as it prioritizes consumers' perspectives.
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    Predicting consumers' garment fit satisfactions by using machine learning
    (Walter De Gruyter Gmbh, 2024) Oosterom, Evrim Buyukaslan; Baytar, Fatma; Akdemir, Deniz; Kalaoglu, Fatma
    The objectives of this study were to apply alternative machine learning (ML) algorithms to predict consumers' garment fit satisfactions (real fit satisfaction [RFS]) and compare the efficiencies of these algorithms to predict RFS. Skirts made from different fabrics were used as test garments. Mechanical properties of the skirts' fabrics were assigned as predictor variables to estimate RFS. Study participants' virtual body models were created by using 3D body scanner and used for virtual fitting. Each participant physically tried on the skirts and evaluated the fit. Participants also viewed the skirt simulations on their avatars and evaluated the virtual fit, which represented participants' virtual fit satisfactions (VFS). Random Forest (RF), support vector machine (SVM), and conditional tree (CT) algorithms were used to learn from the data to predict participants' RFSs. The mean correlations between the predicted and observed RFS values in the validation sets were 0.74 (RF), 0.70 (SVM-linear kernel), 0.72 (SVM-radial kernel), and 0.55 (CT). According to the variable importance analysis, VFS had the highest importance among 35 predictor variables. ML is used mostly for sales forecasting and manufacturing purposes in the fashion industry. However, garment fit, which restrains consumers from shopping online, did not get enough attention in ML studies. Besides, the ML algorithms used in fashion and apparel studies are often genetic algorithms and neural networks; therefore, there is a need to test other algorithm types. In this study, we offered alternative ML algorithms (i.e., RF, SVM, and CT) to predict consumers' garment fit satisfactions.