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Öğe Predicting consumers' garment fit satisfactions by using machine learning(Walter De Gruyter Gmbh, 2024) Oosterom, Evrim Buyukaslan; Baytar, Fatma; Akdemir, Deniz; Kalaoglu, FatmaThe 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.Öğe Students' attitude towards distance teaching of studio-based and virtual reality-based fashion design courses(Taylor & Francis Ltd, 2023) Oosterom, Evrim BuyukaslanIn this study, studio-based and virtual reality-based courses were taught online to fashion design students during a semester. Students' satisfaction with distance learning in these two courses was measured by relating to their computer self-efficacy and spatial learning capability. The perceived usefulness of distance learning and students' online engagement were tested as the mediator and moderator, respectively. This research showed that spatial ability skills play a significant role in students' satisfaction with the distance learning of a studio-based course. In contrast, it is not significant for the virtual reality-based course.On the other hand, students' computer self-efficacy directly affects the perceived usefulness of distance learning of the virtual reality-based course and indirectly affects course satisfaction. However, this relationship does not exist for the studio-based course.