SANAL PROVA TEKNOLOJİSİNİN ÇEVRİMİÇİ SATIN ALMA KARARINDAKİ ROLÜ

Öz

Günümüzde internet teknolojilerinin yoğun kullanımı ile birlikte e-ticaret kavramı, farklı ürün kategorileri için müşteri davranışlarını ve satın alma süreçlerini anlamaya odaklı bir kanal haline gelmiştir. Kullanıcıların çevrimiçi satış sitesi ziyaretlerini satın alma ile tamamlamaları için çeşitli yöntemler kullanılmakta ve güncel teknolojilerle, satın alma eğilimlerinin olumlu yönde etkilenmesine katkıda bulunulmaktadır. Bu çalışmada, bu teknolojilerden biri olan sanal prova teknolojisinin müşteri deneyimi açısından etkisi araştırılmıştır. Elde edilen sonuçlara göre bu teknolojiyi tekrar kullanma isteği belirlenmiş ve ayrıca sanal prova teknolojisinin kullanıcıların satın alma niyetini arttırdığı görülmüştür. Ayrıca sanal prova teknolojisine yönelik algılarına göre, farklı özelliklere sahip iki farklı kullanıcı grubu belirlenmiştir. Bu teknolojiyi sunan firmaların, belirlenen gruplarla tanıtım ve iletişimlerinde farklılaştırma yollarını araması önerilmektedir.

Anahtar Kelimeler

• Sanal Prova
• Dijital etkileşim
• E-ticaret
• Segmentasyon

THE ROLE OF VIRTUAL TRY-ON TECHNOLOGY IN ONLINE PURCHASING DECISION

Öz

Today, with the intensive use of internet technologies, the concept of e-commerce became a focused channel to understand the customer behavior and purchasing processes for different product categories. Several methods are used to enable users to conclude their surf with the purchase and up-to-date technologies are contributed to positively nudging their purchasing tendencies. In this study, the impact of one of these technologies, Virtual Try-On, in terms of the customer experience was investigated. According to the results, the desire to use this technology again has been determined and it has also been seen that VTO technology had increased users’ purchase intention. Plus, two different user groups with different characteristics were identified according to their perceptions towards VTO technology. It is recommended that firms offering this technology seek to differentiate ways in their promotion and communication with designated groups.

Anahtar Kelimeler

• Virtual Try-On
• Digital interaction
• E-commerce
• Segmentation

Kaynakça

1. Divivier, A., Trieb, R., Ebert, A., Hagen, H., Gross, C., Fuhrmann, A., Luckas, V., Encarnac J. L., Kirchd E., Rupp, M., Vieth, S., Kimmerle, S., Keckeisen, M., Wacker, M., Strasser, W., Sattler, M., Sarlette, R., & Klein, R. (2004). Virtual try-on: Topics in realistic, individualized dressing in virtual reality. Human-Solutions. Retrieved September 12, 2021, from http://www.human-solutions.com/virtualtryon/download/VTOBeitragVRAR2004.pdf/
2. Brooke, J. (1996). SUS-A quick and dirty usability scale. Usability evaluation in industry, 189(194), 4-7.
3. Chen, W., Wang, H., Li, Y., Su, H., Wang, Z., Tu, C. and Chen, B. (2016, October). Synthesizing training images for boosting human 3d pose estimation. In 2016 Fourth International Conference on 3D Vision (3DV) (pp. 479-488). IEEE.
4. D, R., Joseph, G. (2020). Modelling Consumer Behaviour during Pandemics: A Coonceptual Model, International Journal of Management, 11(11), 816-822.
5. Guan, P., Reiss, L., Hirshberg, D. A., Weiss, A., and Black, M. J. (2012). Drape: Dressing any person. ACM Transactions on Graphics (TOG), 31(4), 1-10.
6. Han, X., Wu, Z., Wu, Z., Yu, R., and Davis, L. S. (2018). Viton: An image-based virtual try-on network. In Proceedings of the IEEE conference on computer vision and pattern recognition, (pp. 7543-7552).
7. Hauswiesner, S., Straka, M., and Reitmayr, G. (2013). Virtual try-on through image-based rendering. IEEE transactions on visualization and computer graphics, 19(9), 1552-1565.
8. Jetchev, N., and Bergmann, U. (2017). The conditional analogy gan: Swapping fashion articles on people images. In Proceedings of the IEEE International Conference on Computer Vision Workshops (pp. 2287-2292).

9. Nysveen, Herbjørn, Per E. Pederson and Helge Thorbjørnsen (2005). Intentions to Use Mobile Services: Antecedents and Cross-Service Comparisons, JAMS, 33(3), 330-346.
10. Oberlo (2021). Top online shopping categories, Accessed on 16.09.2021. Access address: https://www.oberlo.com/statistics/top-online-shopping-categories
11. Pons-Moll, G., Pujades, S., Hu, S., &and Black, M. J. (2017). ClothCap: Seamless 4D clothing capture and retargeting. ACM Transactions on Graphics (TOG), 36(4), 1-15.
12. Protopsaltou, D., Luible, C., Arevalo, M., and Magnenat-Thalmann, N. (2002). A body and garment creation method for an Internet based virtual fitting room. In Advances in modelling, animation and rendering (pp. 105-122). Springer, London.
13. Raj, A., Sangkloy, P., Chang, H., Hays, J., Ceylan, D.,and Lu, J. (2018, September). Swapnet: Image based garment transfer. In European Conference on Computer Vision (pp. 679-695). Springer, Cham.
14. Sekine, M., Sugita, K., Perbet, F., Stenger, B., and Nishiyama, M. (2014, October). Virtual fitting by single-shot body shape estimation. In Int. Conf. on 3D Body Scanning Technologies (pp. 406-413). Citeseer.
15. Ministry of Commerce (2021). E-commerce information platform official statistics, Accessed: 16.09.2021, https://www.eticaret.gov.tr/istatistikler
16. Wang, B., Zheng, H., Liang, X., Chen, Y., Lin, L., & Yang, M. (2018). Toward characteristic-preserving image-based virtual try-on network. In Proceedings of the European Conference on Computer Vision (ECCV). (pp. 589-604).
17. Yang, S., Ambert, T., Pan, Z., Wang, K., Yu, L., Berg, T., & Lin, M. C. (2016). Detailed garment recovery from a single-view image. arXiv preprint arXiv:1608.01250.