虛擬試穿技術，或稱VTO，是線上服飾購物體驗中，最新和最雄心勃勃的解決方案之一。儘管目前學界對VTO的功能和技術方面有所研究，但對隱私方面卻沒有深入鑽研與討論。我們發現，對於「隱私對採用VTO技術的影響程度」的理解，以及「數據收集、個性化和隱私之間的平衡」的理解，兩者間存在著差距。在我們的研究中，我們使用隱私計算理論（Privacy Calculus Theory，PCT）來顯示各種VTO實現對隱私關注的影響，以及利益和隱私之間的平衡在VTO使用意圖中發揮的作用。我們使用基於場景的實驗，採用2x2x2的全因子設計，將參與者隨機分配到八個不同的場景組合中，並向他們展示我們每個構念的兩個場景之一。場景有助於我們理解數據資料量和個性化對隱私關注和感知推薦準確性的影響。我們用控制數據的形式作為調節變項，看它是否會改變由數據資料量和VTO環境的個性化引起的隱私顧慮。最後，我們研究了隱私顧慮和感知到的推薦準確性對VTO使用意圖的影響。我們使用偏最小平方法（partial least squares，PLS）和結構方程式模型（structural equation modeling，SEM）來分析我們的數據資料，以確定我們研究架構之間的路徑強度和效果。在我們的分析中，有五個假設得到支持。較高的數據資料量會導致更大的隱私顧慮，而對數據的控制則成功地降低了這些顧慮。有趣的是，與低數據資料量組相比，高數據資料量組受數據控制中介的影響更大。我們還證實，在考慮使用VTO技術的意圖時，隱私顧慮是一個重要因素。本文證明了在隱私顧慮和虛擬實境技術提供的技術優勢之間取得平衡的重要性。意即，在當前具有高度隱私意識的用戶的後GDPR環境中，需要更加關注創造考慮隱私問題的VTO體驗。

Virtual Try-On technology, or VTO, presents one of the newest and most ambitious solutions in online apparel shopping experiences. Even though there is research into the functionality and technical aspects of VTO technologies, privacy aspects are not deeply considered. We identify a gap in the understanding of the effects of privacy on VTO technology adoption and the balance between data collection, personalization, and privacy. In our research, we use privacy-calculus theory to show the effects of various VTO implementations on privacy concerns, as well as the role balance between benefit and privacy plays in VTO use intentions. Using scenario-based experiments with 2x2x2 full factorial design, we assign participants randomly into eight different scenario combinations and present them with one of two scenarios for each of our constructs. Scenarios help us to understand the effects of the amount of data and personalization on privacy concerns and perceived recommendation accuracy. We use a moderator in the form of control over data to see whether it changes the privacy concerns caused by the amount of data and personalization of a VTO environment. Lastly, we investigate the effects of privacy concerns and perceived recommendation accuracy on VTO use intentions. We analyze our data using partial least squares (PLS) with structural equation modeling (SEM) to determine path strengths and effects between our constructs. In our analysis five hypotheses are supported. A higher amount of data results in greater privacy concerns with control over data successfully lowering these concerns. Interestingly, the high amount of data group is affected more by the control over data mediator as opposed to the low amount of data group. We also show that privacy concerns are an important factor when considering the use intentions of VTO technologies. This paper demonstrates the importance of a balance between privacy concerns and the technological advantages VTO offers. It shows a need for more focus on creating VTO experiences that consider privacy concerns in the current post-GDPR environment with highly privacy-conscious users.

3DLOOK - YourFit. (n.d.). https://3dlook.me/yourfit/
Ader, J., Adhi, P., Chai, J., Singer, M., Touse, S., & Yankelevich, H. (2021, May 25). Returning to order: Improving returns management for apparel companies. McKinsey. https://www.mckinsey.com/industries/retail/our-insights/returning-to-order-improving-returns-management-for-apparel-companies
Alamsyah, N., & Zhu, Y.-Q. (2022). We shall endure: Exploring the impact of government information quality and partisanship on citizens’ well-being during the COVID-19 pandemic. Government Information Quarterly, 39(1), 101646. https://doi.org/https://doi.org/10.1016/j.giq.2021.101646
Apeagyei, P. R., & Otieno, R. (2007). Usability of pattern customising technology in the achievement and testing of fit for mass customisation. Journal of Fashion Marketing and Management: An International Journal, 11(3), 349–365. https://doi.org/10.1108/13612020710763100
Athey, S., Catalini, C., & Tucker, C. E. (2017). The Digital Privacy Paradox: Small Money, Small Costs, Small Talk. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2916489
Awad, N. F., & Krishnan, M. S. (2006). The Personalization Privacy Paradox: An Empirical Evaluation of Information Transparency and the Willingness to Be Profiled Online for Personalization. MIS Quarterly, 30(1), 13. https://doi.org/10.2307/25148715
Bacchi, U. (2019, December 4). Privacy concerns pushing people to change online behavior, poll shows. Reuters. https://www.reuters.com/article/us-global-tech-privacy-idUSKBN1Y803D
Bauer, P. C., Gerdon, F., Keusch, F., Kreuter, F., & Vannette, D. (2022). Did the GDPR increase trust in data collectors? Evidence from observational and experimental data. Information, Communication & Society, 25(14), 2101–2121. https://doi.org/10.1080/1369118X.2021.1927138
Baum, C., Brown, P., Gerstell, E., & Peng, A. (2020, March 26). Perspectives for North America’s fashion industry in a time of crisis. McKinsey. https://www.mckinsey.com/industries/retail/our-insights/perspectives-for-north-americas-fashion-industry-in-a-time-of-crisis
Beck, M., & Crié, D. (2018). I virtually try it … I want it ! Virtual Fitting Room: A tool to increase on-line and off-line exploratory behavior, patronage and purchase intentions. Journal of Retailing and Consumer Services, 40, 279–286. https://doi.org/10.1016/j.jretconser.2016.08.006
Bélanger, & Crossler. (2011). Privacy in the Digital Age: A Review of Information Privacy Research in Information Systems. MIS Quarterly, 35(4), 1017. https://doi.org/10.2307/41409971
Benbasat, I., & Wang, W. (2005). Trust In and Adoption of Online Recommendation Agents. Journal of the Association for Information Systems, 6(3), 72–101. https://doi.org/10.17705/1jais.00065
Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 25(3), 351. https://doi.org/10.2307/3250921
Big Data: What is Big Data? (n.d.). Retrieved December 17, 2022, from https://www.oracle.com/big-data/what-is-big-data/
Bornschein, R., Schmidt, L., & Maier, E. (2020). The Effect of Consumers’ Perceived Power and Risk in Digital Information Privacy: The Example of Cookie Notices. Journal of Public Policy & Marketing, 39(2), 135–154. https://doi.org/10.1177/0743915620902143
Boyle, M., & Greenberg, S. (2005). The language of privacy. ACM Transactions on Computer-Human Interaction, 12(2), 328–370. https://doi.org/10.1145/1067860.1067868
Brandimarte, L., Acquisti, A., & Loewenstein, G. (2013). Misplaced Confidences. Social Psychological and Personality Science, 4(3), 340–347. https://doi.org/10.1177/1948550612455931
Bretschneider, T., Koop, U., Schreiner, V., Wenck, H., & Jaspers, S. (2009). Validation of the body scanner as a measuring tool for a rapid quantification of body shape. Skin Research and Technology, 15(3), 364–369. https://doi.org/10.1111/j.1600-0846.2009.00374.x
Çadırcı, T. O., & Köse, Ş. G. (2018). Augmented Reality as a Tool to Enhance the Experiential Value of Online Shopping. In Virtual and Augmented Reality (pp. 850–874). IGI Global. https://doi.org/10.4018/978-1-5225-5469-1.ch041
Chellappa, R. K., & Sin, R. G. (2005). Personalization versus Privacy: An Empirical Examination of the Online Consumer’s Dilemma. Information Technology and Management, 6(2–3), 181–202. https://doi.org/10.1007/s10799-005-5879-y
Choi, J., Lee, H. J., & Kim, Y. C. (2011). The Influence of Social Presence on Customer Intention to Reuse Online Recommender Systems: The Roles of Personalization and Product Type. International Journal of Electronic Commerce, 16(1), 129–154. https://doi.org/10.2753/JEC1086-4415160105
Cortina, J. M. (1993). What is coefficient alpha? An examination of theory and applications. Journal of Applied Psychology, 78(1), 98–104. https://doi.org/10.1037/0021-9010.78.1.98
Culnan, M. J., & Armstrong, P. K. (1999). Information Privacy Concerns, Procedural Fairness, and Impersonal Trust: An Empirical Investigation. Organization Science, 10(1), 104–115. https://doi.org/10.1287/orsc.10.1.104
Daanen, H. A. M., & Psikuta, A. (2018). 3D body scanning. In Automation in Garment Manufacturing (pp. 237–252). Elsevier. https://doi.org/10.1016/B978-0-08-101211-6.00010-0
Daanen, H. A. M., & ter Haar, F. B. (2013). 3D whole body scanners revisited. Displays, 34(4), 270–275. https://doi.org/10.1016/j.displa.2013.08.011
D’Apuzzo, N. (2007). 3D body scanning technology for fashion and apparel industry (J.-A. Beraldin, F. Remondino, & M. R. Shortis, Eds.; p. 64910O). https://doi.org/10.1117/12.703785
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008
Delamore, P., & Sweeney, D. (2010). Everything in 3D: Developing the Fashion Digital Studio. Proceedings of the 1st International Conference on 3D Body Scanning Technologies, Lugano, Switzerland, 19-20 October 2010, 383–394. https://doi.org/10.15221/10.383
Dennis, S. (2017). Many unhappy returns: E-commerce’s Achilles heel. Forbes. https://www.forbes.com/sites/stevendennis/2017/08/09/many-unhappy-returns-e-commerces-achilles-heel/?sh=3add73c44bf2
Dinev, T., & Hart, P. (2006). An Extended Privacy Calculus Model for E-Commerce Transactions. Information Systems Research, 17(1), 61–80. https://doi.org/10.1287/isre.1060.0080
Dinev, T., Xu, H., Smith, J. H., & Hart, P. (2013). Information privacy and correlates: an empirical attempt to bridge and distinguish privacy-related concepts. European Journal of Information Systems, 22(3), 295–316. https://doi.org/10.1057/ejis.2012.23
Dodge, M., & Kitchin, R. (2005). Codes of Life: Identification Codes and the Machine-Readable World. Environment and Planning D: Society and Space, 23(6), 851–881. https://doi.org/10.1068/d378t
Doherty, N. F., & Ellis‐Chadwick, F. (2009). Exploring the drivers, scope and perceived success of e‐commerce strategies in the UK retail sector. European Journal of Marketing, 43(9/10), 1246–1262. https://doi.org/10.1108/03090560910976474
Dopson, E. (2021, August 25). The Plague of Ecommerce Return Rates and How to Maintain Profitability. Shopify. https://www.shopify.com/enterprise/ecommerce-returns
Dotzel, T., Shankar, V., & Berry, L. (2013). Service Innovativeness and Firm Value. Journal of Marketing Research, 50, 259–276. https://doi.org/10.1509/jmr.10.0426
Eide, S. A., Poljac, A.-M., & Sandnes, F. E. (2021). Image Search Versus Text Search Revisited: A Simple Experiment Using a Kahoot Quiz (pp. 186–196). https://doi.org/10.1007/978-3-030-78092-0_12
EU 2016/679 General Data Protection Regulation. (2016). THE EUROPEAN PARLIAMENT. https://eur-lex.europa.eu/eli/reg/2016/679/oj
Evanschitzky, H., Iyer, G., Hesse, J., & Ahlert, D. (2004). E-satisfaction: a re-examination. Journal of Retailing, 80(3), 239–247. https://doi.org/10.1016/j.jretai.2004.08.002
Fan, H., & Poole, M. S. (2006). What Is Personalization? Perspectives on the Design and Implementation of Personalization in Information Systems. Journal of Organizational Computing and Electronic Commerce, 16(3–4), 179–202. https://doi.org/10.1080/10919392.2006.9681199
Fehrenbach, D., & Herrando, C. (2021). The effect of customer-perceived value when paying for a product with personal data: A real-life experimental study. Journal of Business Research, 137, 222–232. https://doi.org/10.1016/j.jbusres.2021.08.029
Fiore, A. M., Kim, J., & Lee, H.-H. (2005). Effect of image interactivity technology on consumer responses toward the online retailer. Journal of Interactive Marketing, 19(3), 38–53. https://doi.org/10.1002/dir.20042
FISH, I. (2019, June 17). Asos launches virtual catwalk. Drapers. https://www.drapersonline.com/news/asos-launches-virtual-catwalk
Fit Analytics - Fit Finder. (n.d.). https://www.fitanalytics.com/fit-finder
Foysal, K. H., Chang, H.-J. (Julie), Bruess, F., & Chong, J.-W. (2021). Body Size Measurement Using a Smartphone. Electronics, 10(11), 1338. https://doi.org/10.3390/electronics10111338
Gallino, S., & Moreno, A. (2018). The Value of Fit Information in Online Retail: Evidence from a Randomized Field Experiment. Manufacturing & Service Operations Management, 20(4), 767–787. https://doi.org/10.1287/msom.2017.0686
Gouthier, M. H. J., Nennstiel, C., Kern, N., & Wendel, L. (2022). The more the better? Data disclosure between the conflicting priorities of privacy concerns, information sensitivity and personalization in e-commerce. Journal of Business Research, 148, 174–189. https://doi.org/10.1016/j.jbusres.2022.04.034
Ha, S., & Stoel, L. (2012). Online apparel retailing: roles of e‐shopping quality and experiential e‐shopping motives. Journal of Service Management, 23(2), 197–215. https://doi.org/10.1108/09564231211226114
Hilken, T., de Ruyter, K., Chylinski, M., Mahr, D., & Keeling, D. I. (2017). Augmenting the eye of the beholder: exploring the strategic potential of augmented reality to enhance online service experiences. Journal of the Academy of Marketing Science, 45(6), 884–905. https://doi.org/10.1007/s11747-017-0541-x
Incandela, D. (2022, February 2). Walmart Launches Zeekit Virtual Fitting Room Technology. Walmart. https://corporate.walmart.com/newsroom/2022/03/02/walmart-launches-zeekit-virtual-fitting-room-technology
Janakiraman, N., Syrdal, H. A., & Freling, R. (2016). The Effect of Return Policy Leniency on Consumer Purchase and Return Decisions: A Meta-analytic Review. Journal of Retailing, 92(2), 226–235. https://doi.org/10.1016/j.jretai.2015.11.002
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415
Joseph F. Hair, Jr., G. Tomas M. Hult, Christian M. Ringle, & Marko Sarstedt. (2016). A primer on partial least squares structural equation modeling (PLS-SEM) (Second Edition). SAGE Publications, Inc.
Kartsounis, G. A., Magnenat-Thalmann, N., & Rodrian, H.-C. (2003). E-TAILOR: Integration of 3D Scanners, CAD and Virtual-Try-on Technologies for Online Retailing of Made-to-Measure Garments. In E-Business Applications (pp. 137–152). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-55792-7_10
Kaushik, V., Kumar, A., Gupta, H., & Dixit, G. (2022). Modelling and prioritizing the factors for online apparel return using BWM approach. Electronic Commerce Research, 22(3), 843–873. https://doi.org/10.1007/s10660-020-09406-3
Kim, J., & Forsythe, S. (2008). Adoption of Virtual Try-on technology for online apparel shopping. Journal of Interactive Marketing, 22(2), 45–59. https://doi.org/10.1002/dir.20113
Kim, S., & Martinez, B. (2013). Fashion consumer groups and online shopping at private sale sites. International Journal of Consumer Studies, 37(4), 367–372. https://doi.org/10.1111/j.1470-6431.2012.01102.x
Kitchin, R., & McArdle, G. (2016). What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets. Big Data & Society, 3(1), 205395171663113. https://doi.org/10.1177/2053951716631130
Kobsa, A. (2002). Personalized hypermedia and international privacy. Communications of the ACM, 45(5), 64–67. https://doi.org/10.1145/506218.506249
Kokolakis, S. (2017). Privacy attitudes and privacy behaviour: A review of current research on the privacy paradox phenomenon. Computers & Security, 64, 122–134. https://doi.org/10.1016/j.cose.2015.07.002
Kouchi, M., Mochimaru, M., Bradtmiller, B., Daanen, H., Li, P., Nacher, B., & Nam, Y. (2012). A protocol for evaluating the accuracy of 3D body scanners. Work, 41, 4010–4017. https://doi.org/10.3233/WOR-2012-0064-4010
Lammerding, L., Hilken, T., Mahr, D., & Heller, J. (2021). Too Real for Comfort: Measuring Consumers’ Augmented Reality Information Privacy Concerns (pp. 95–108). https://doi.org/10.1007/978-3-030-68086-2_8
Leavitt, N. (2006). Recommendation Technology: Will It Boost E-Commerce? Computer, 39(5), 13–16. https://doi.org/10.1109/MC.2006.176
Lee, C. H., & Cranage, D. A. (2011). Personalisation–privacy paradox: The effects of personalisation and privacy assurance on customer responses to travel Web sites. Tourism Management, 32(5), 987–994. https://doi.org/10.1016/j.tourman.2010.08.011
Lee, J.-M., & Rha, J.-Y. (2016). Personalization–privacy paradox and consumer conflict with the use of location-based mobile commerce. Computers in Human Behavior, 63, 453–462. https://doi.org/10.1016/j.chb.2016.05.056
Logg, J. M., Minson, J. A., & Moore, D. A. (2019). Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes, 151, 90–103. https://doi.org/10.1016/j.obhdp.2018.12.005
Loureiro, S. M. C., Correia, C., & Guerreiro, J. (2021). The Role of Mental Imagery as Driver to Purchase Intentions in a Virtual Supermarket (pp. 17–28). https://doi.org/10.1007/978-3-030-68086-2_2
Maier, J. F., Eckert, C. M., & John Clarkson, P. (2017). Model granularity in engineering design – concepts and framework. Design Science, 3, e1. https://doi.org/10.1017/dsj.2016.16
Margulis, S. T. (2003). Privacy as a Social Issue and Behavioral Concept. Journal of Social Issues, 59(2), 243–261. https://doi.org/10.1111/1540-4560.00063
Mcknight, D. H., Carter, M., Thatcher, J. B., & Clay, P. F. (2011). Trust in a specific technology. ACM Transactions on Management Information Systems, 2(2), 1–25. https://doi.org/10.1145/1985347.1985353
Mehmood, A., Natgunanathan, I., Xiang, Y., Hua, G., & Guo, S. (2016). Protection of Big Data Privacy. IEEE Access, 4, 1821–1834. https://doi.org/10.1109/ACCESS.2016.2558446
Merle, A., Senecal, S., & St-Onge, A. (2012). Whether and How Virtual Try-On Influences Consumer Responses to an Apparel Web Site. International Journal of Electronic Commerce, 16(3), 41–64. https://doi.org/10.2753/JEC1086-4415160302
Milne, G. R., & Rohm, A. J. (2000). Consumer Privacy and Name Removal across Direct Marketing Channels: Exploring Opt-In and Opt-Out Alternatives. Journal of Public Policy & Marketing, 19(2), 238–249. https://doi.org/10.1509/jppm.19.2.238.17136
Miyazaki, A. D., & Fernandez, A. (2000). Internet Privacy and Security: An Examination of Online Retailer Disclosures. Journal of Public Policy & Marketing, 19(1), 54–61. https://doi.org/10.1509/jppm.19.1.54.16942
Nelson, R. R., Todd, P. A., & Wixom, B. H. (2005). Antecedents of Information and System Quality: An Empirical Examination Within the Context of Data Warehousing. Journal of Management Information Systems, 21(4), 199–235. https://doi.org/10.1080/07421222.2005.11045823
NeXR Technologies. (n.d.). https://www.nexr-technologies.com/scanning-systems/
NeXR Virtual Fitting H&M. (n.d.). https://www.nexr-technologies.com/virtual-fitting-hm-success-case/
Nyamekye, M. B., Agbemabiese, G. C., & Amoako, G. K. (2022). Factors Leading to Increased Online Engagement (pp. 139–159). https://doi.org/10.4018/978-1-7998-8294-7.ch007
O’Brien, H. L. (2010). The influence of hedonic and utilitarian motivations on user engagement: The case of online shopping experiences. Interacting with Computers, 22(5), 344–352. https://doi.org/10.1016/j.intcom.2010.04.001
Ohm, P. (2009). Broken Promises of Privacy: Responding to the Surprising Failure of Anonymization. U of Colorado Law Legal Studies Research Paper No. 9-12, 57(2010), 1701–1777.
Okonkwo, U. (2009). Sustaining the luxury brand on the Internet. Journal of Brand Management, 16(5–6), 302–310. https://doi.org/10.1057/bm.2009.2
Park, J. E., & Yoon, S.-J. (2017). Antecedents of consumer animosity and the role of product involvement on purchase intentions. American Journal of Business, 32(1), 42–57. https://doi.org/10.1108/AJB-08-2016-0028
Peng, F., Sweeney, D., & Delamore, P. (2012). Digital Innovation in Fashion -How to “Capture” the User Experience in 3D Body Scanning. International Journal of Industrial Engineering and Management, 3, 233–240.
Petroc, T. (2022). Volume of data/information created, captured, copied, and consumed worldwide from 2010 to 2020, with forecasts from 2021 to 2025. https://www.statista.com/statistics/871513/worldwide-data-created/
Plotkina, D., & Saurel, H. (2019). Me or just like me? The role of virtual try-on and physical appearance in apparel M-retailing. Journal of Retailing and Consumer Services, 51, 362–377. https://doi.org/10.1016/j.jretconser.2019.07.002
Poushneh, A. (2018). Augmented reality in retail: A trade-off between user’s control of access to personal information and augmentation quality. Journal of Retailing and Consumer Services, 41, 169–176. https://doi.org/10.1016/j.jretconser.2017.12.010
Rhahla, M., Allegue, S., & Abdellatif, T. (2021). Guidelines for GDPR compliance in Big Data systems. Journal of Information Security and Applications, 61, 102896. https://doi.org/10.1016/j.jisa.2021.102896
Ross, F. (2010). Leveraging Niche Fashion Markets through Mass Customization, Co-design, Style Advice, and New Technology: A Study of Gay Aesthetics and Website Design. Fashion Practice, 2(2), 175–197. https://doi.org/10.2752/175693810X12774625387431
Ross, F. (2012). A Study of How Small and Medium-sized Enterprise Tailors Utilize e-Commerce, Social Media, and New 3D Technological Practices. Fashion Practice, 4(2), 197–219. https://doi.org/10.2752/175693812X13403765252343
Ross, F. (2016). Fashion-Technology and Change in Product Development and Consumption for the High-End Menswear Sector: A Study Utilizing a 3D-4C’s Process Model (pp. 251–280). https://doi.org/10.4018/978-1-5225-1677-4.ch014
Schluep, R. B. (2017). Fashion Technology and the Development of New Business Models (pp. 1–37). https://doi.org/10.4018/978-1-5225-1865-5.ch001
Schuetzler, R. M., Grimes, G. M., & Scott Giboney, J. (2020). The impact of chatbot conversational skill on engagement and perceived humanness. Journal of Management Information Systems, 37(3), 875–900. https://doi.org/10.1080/07421222.2020.1790204
Sekhavat, Y. A. (2017). Privacy Preserving Cloth Try-On Using Mobile Augmented Reality. IEEE Transactions on Multimedia, 19(5), 1041–1049. https://doi.org/10.1109/TMM.2016.2639380
Sheehan, K. B., & Hoy, M. G. (1999). Flaming, Complaining, Abstaining: How Online Users Respond to Privacy Concerns. Journal of Advertising, 28(3), 37–51. https://doi.org/10.1080/00913367.1999.10673588
Sheng, H., Nah, F., & Siau, K. (2008). An Experimental Study on Ubiquitous commerce Adoption: Impact of Personalization and Privacy Concerns. Journal of the Association for Information Systems, 9(6), 344–376. https://doi.org/10.17705/1jais.00161
Smink, A. R., van Reijmersdal, E. A., van Noort, G., & Neijens, P. C. (2020). Shopping in augmented reality: The effects of spatial presence, personalization and intrusiveness on app and brand responses. Journal of Business Research, 118, 474–485. https://doi.org/10.1016/j.jbusres.2020.07.018
Smith, H. J., Dinev, T., & Xu, H. (2011). Information Privacy Research: An Interdisciplinary Review. MIS Quarterly, 35(4), 989. https://doi.org/10.2307/41409970
Sneader, K., & Singhal, S. (2021). The next normal arrives: Trends that will define 2021—and beyond. https://euamth.com/Uploads/images/2021/05/10/1620654778_news2_file.pdf
Solove, D. J. (2006). A Taxonomy of Privacy. University of Pennsylvania Law Review, 154(3), 477. https://doi.org/10.2307/40041279
Spiekermann, S., Acquisti, A., Böhme, R., & Hui, K.-L. (2015). The challenges of personal data markets and privacy. Electronic Markets, 25(2), 161–167. https://doi.org/10.1007/s12525-015-0191-0
Sundar, S. S., & Marathe, S. S. (2010). Personalization versus Customization: The Importance of Agency, Privacy, and Power Usage. Human Communication Research, 36(3), 298–322. https://doi.org/10.1111/j.1468-2958.2010.01377.x
Tam, K. Y., & Ho, S. Y. (2006). Understanding the Impact of Web Personalization on User Information Processing and Decision Outcomes. MIS Quarterly, 30(4), 865. https://doi.org/10.2307/25148757
True Fit Solutions. (n.d.). https://www.truefit.com/solutions
Tucker C. (2019). Privacy, Algorithms, and Artificial Intelligence. In The Economics of Artificial Intelligence (pp. 423–438). University of Chicago Press. https://doi.org/10.7208/chicago/9780226613475.003.0017
Tucker, C. E. (2013). Social Networks, Personalized Advertising, and Privacy Controls. Journal of Marketing Research, 50(5), 546–562. https://doi.org/10.1177/002224371305000501
Vecchi, A., Peng, F., & Al-Sayegh, M. (2014). ASSESSING THE APPLICABILITYOF A SIZING FRAMEWORK INTO ONLINE FASHION RETAIL. International Journal of Advanced Information Science and Technology.
Vecchi, A., Peng, F., & Al-Sayegh, M. (2017). Size Recommendations in Online Fashion Retail (pp. 348–362). https://doi.org/10.4018/978-1-5225-1865-5.ch015
VITRONIC. (n.d.). https://www.vitronic.com/en-us/3d-bodyscan
White, T. B., Zahay, D. L., Thorbjørnsen, H., & Shavitt, S. (2008). Getting too personal: Reactance to highly personalized email solicitations. Marketing Letters, 19(1), 39–50. https://doi.org/10.1007/s11002-007-9027-9
Wu, Z., & Weaver, A. C. (2012). Privacy-Preserving Trust Establishment with Web Service Enhancements. In Innovations, Standards and Practices of Web Services (pp. 54–73). IGI Global. https://doi.org/10.4018/978-1-61350-104-7.ch003
Xu, H., Luo, X. (Robert), Carroll, J. M., & Rosson, M. B. (2011). The personalization privacy paradox: An exploratory study of decision making process for location-aware marketing. Decision Support Systems, 51(1), 42–52. https://doi.org/10.1016/j.dss.2010.11.017
Xue, L., Parker, C. J., & Hart, C. A. (2021). How to Design Effective AR Retail Apps (pp. 3–16). https://doi.org/10.1007/978-3-030-68086-2_1
Yoo, J., & Kim, M. (2014). The effects of online product presentation on consumer responses: A mental imagery perspective. Journal of Business Research, 67(11), 2464–2472. https://doi.org/10.1016/j.jbusres.2014.03.006
Zeekit. (2020). https://web.archive.org/web/20201126221705/https://zeekit.me
Zhu, Y.-Q., & Chang, J.-H. (2016). The key role of relevance in personalized advertisement: Examining its impact on perceptions of privacy invasion, self-awareness, and continuous use intentions. Computers in Human Behavior, 65, 442–447. https://doi.org/10.1016/j.chb.2016.08.048