Are Customers Ready for Artificial Intelligence to Be Used in Fashion?
Authors
Ashutosh Farela
Sri Satya Sai University of Technology and Medical Sciences Sehore, Bhopal (MP) (IN)
Dr. Sonal Singh
RKDF University Bhopal (MP) (IN)
Article Information
DOI: 10.51583/IJLTEMAS.2025.140500110
Subject Category: management
Volume/Issue: 14/5 | Page No: 1029-1040
Publication Timeline
Submitted: 2025-06-27
Published: 2025-06-26
Abstract
Abstract: Understanding how customers react to emerging technologies, particularly artificial intelligence (AI), is crucial for researchers and retailers alike, especially in light of the increased interest in fashion and digital advancements. Examining consumer perceptions and buying intentions about AI devices was the aim of the study. A conceptual model pertaining to consumers' attitudes and purchasing intentions toward an AI device—Echo Look—was developed and evaluated by modifying the technological acceptance model. In all, 626 participants (61% female) in the top 05 Indian cities between the ages of 20 and 60 took part in the study. The findings showed that customers' attitudes about AI were significantly influenced by perceived usefulness, perceived simplicity of use, and perceived performance risk. Purchase intention was positively impacted by good sentiments concerning technology. Implications for theory and practice are examined in light of these findings.
Keywords
Artificial intelligence, purchase intention, customer attitudes, fashion
Downloads
References
1. A white paper prepared for the Computing Community Consortium Committee of the Computing Research Association. Retrieved from https://arxiv.org/pdf/1707.04352.pdf [Google Scholar] [Crossref]
2. Adoption of sensory enabling technology for online apparel shopping. European Journal of Marketing, 43, 1101–1120. [Google Scholar] [Crossref]
3. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behaviour. Englewood Cliffs, NJ: Prentice Hall. [Google Scholar] [Crossref]
4. Amazon Fashion (Producer). (2017). Introducing Echo Look. Love your look. Every day. Retrieved from https://www.youtube.com/watch?v¼9X_fP4pPWPw&t¼33s [Google Scholar] [Crossref]
5. Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recom- mended two-step approach. Psychological Bulletin, 103, 411–423. [Google Scholar] [Crossref]
6. Applin, S. (2017). Amazon’s Echo Look: Harnessing the power of machine learning or subtle exploitation of human vulnerability? IEEE Consumer Electronics Magazine, 6, 125–127. [Google Scholar] [Crossref]
7. Arthur, R. (2018, January 15). Artificial intelligence empowers designers in IBM, Tommy Hilfiger and FIT collaboration. Forbes. [Google Scholar] [Crossref]
8. Baron, R. A., & Byrne, D. (1987). Social psychology: Understanding human interaction (5th ed.). Needham Heights, MA: Allyn & Bacon. [Google Scholar] [Crossref]
9. Batra, R., & Ahtola, O. T. (1991). Measuring the hedonic and utilitarian sources of consumer attitudes. Marketing Letters, 2, 159–170. [Google Scholar] [Crossref]
10. Belleau, B., Summers, T., Xu, Y., & Pinel, R. (2007). Theory of reasoned action: Purchase intention of young consumers. Clothing and Textiles Research Journal, 25, 244–257. [Google Scholar] [Crossref]
11. Brill, T. M. (2018). Siri, Alexa, and other digital assistants: A study of customer satisfaction with Artificial Intelligence applications (Doctoral dissertation). [Google Scholar] [Crossref]
12. Bues, M., Steiner, M., Stafflage, M., & Krafft, M. (2017). How mobile in-store advertising influences purchase intention: Value drivers and mediating effects from a consumer perspective. Psychology & Marketing, 34,157–174. [Google Scholar] [Crossref]
13. Cao, S. (2018, April 2). When Artificial Intelligence clashes with fashion, how will our future dresses look? Observer. Retrieved from. [Google Scholar] [Crossref]
14. Cho, H., & Fiorito, S. S. (2009). Acceptance of online customization for apparel shopping. International Journal of Retail & Distribution Management, 37, 389–407. [Google Scholar] [Crossref]
15. Computers in Human Behavior, 64, 383–392. [Google Scholar] [Crossref]
16. Curran, J., Meuter, M., & Surprenant, C. (2003). Intentions to use self-service technologies: A confluence of multiple attitudes. Journal of Service Research, 5, 209–224. [Google Scholar] [Crossref]
17. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technol- ogy. MIS Quarterly, 13, 319–340. [Google Scholar] [Crossref]
18. Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38, 475–487. [Google Scholar] [Crossref]
19. Davis, F. D., Bagozzi, R., & Warshaw, P. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35, 982–1003. [Google Scholar] [Crossref]
20. Davis, F. D., Bagozzi, R., & Warshaw, P. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22, 1111–1132. [Google Scholar] [Crossref]
21. Dodds, W., Monroe, K., & Grewal, D. (1991). Effects of price, brand, and store information on buyers’ product evaluations. Journal of Marketing Research, 28, 307–319. [Google Scholar] [Crossref]
22. Eadicicco, L. (2017, April 26). Amazon’s new Echo wants to be your personal style assistant. Time. Retrieved from http://time.com/4755982/amazon-echo-look-camera-release/ [Google Scholar] [Crossref]
23. Fairhurst, A. E., Good, L. K., & Gentry, J. W. (1989). Fashion involvement: An instrument validation proce- dure. Clothing and Textiles Research Journal, 7, 10–14. [Google Scholar] [Crossref]
24. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50. [Google Scholar] [Crossref]
25. Grewal, D., Gotlieb, J., & Marmorstein, H. (1994). The moderating effects of message framing and source credibility on the price-perceived risk relationship. Journal of Consumer Research, 21, 145–153. [Google Scholar] [Crossref]
26. Hager, G., Bryant, R., Horvitz, E., Mataric, M., & Honavar, V. (2017). Advances in artificial intelligence require progress across all of computer science. [Google Scholar] [Crossref]
27. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Prentice Hall. [Google Scholar] [Crossref]
28. Hwang, C., Chung, T. L., & Sanders, E. A. (2016). Attitudes and purchase intentions for smart clothing: Examining US consumers’ functional, expressive, and aesthetic needs for solar-powered clothing. Clothing and Textiles Research Journal, 34, 207–222. [Google Scholar] [Crossref]
29. International Journal of Retail & Distribution Management, 33, 148–160. [Google Scholar] [Crossref]
30. Iowa State University, Ames. Retrieved from https://lib.dr.iastate.edu/cgi/view content.cgi?article¼16721&context¼rtd [Google Scholar] [Crossref]
31. Journal of the Korean Society of Clothing and Textiles, 32, 1427–1437. [Google Scholar] [Crossref]
32. Journal of Fashion Marketing and Management: An International Journal, 10, 433–446. [Google Scholar] [Crossref]
33. Kim, H. (2008). The impact of body image self-discrepancy on body dissatisfaction, fashion involvement, concerns with fit and size of garments, and loyalty intentions in online apparel shopping (Retrospective theses and dissertations). [Google Scholar] [Crossref]
34. Kim, H.-Y., Lee, J. Y., Mun, J., & Johnson, K. (2017). Consumer adoption of smart in-store technology: Assessing the predictive value of attitude versus beliefs in the technology acceptance model. International Journal of Fashion Design, Technology and Education, 10, 26–36. [Google Scholar] [Crossref]
35. Kim, J., & Forsythe, S. (2007). Hedonic usage of product virtualization technologies in online apparel shop- ping. International Journal of Retail & Distribution Management, 35, 502–514. [Google Scholar] [Crossref]
36. Kim, J., & Forsythe, S. (2009). [Google Scholar] [Crossref]
37. Kim, M., & Cheeyong, K. (2015). Augmented reality fashion apparel simulation using a magic mirror.International Journal of Smart Home, 9, 169–178. [Google Scholar] [Crossref]
38. Kline, R. B. (2010). Principles and practice of structural equation modeling (3rd ed.). New York, NY: Guilford Press. [Google Scholar] [Crossref]
39. Lee, H.-H., & Chang, E. (2011). Consumer attitudes toward online mass customization: An application of extended technology acceptance model. Journal of Computer-Mediated Communication, 16, 171–200. [Google Scholar] [Crossref]
40. Lee, H.-H., & Moon, H. (2015). Perceived risk of online apparel mass customization: Scale development and validation. Clothing and Textiles Research Journal, 33, 115–128. [Google Scholar] [Crossref]
41. Lee, J.-H., & Im, J.-E. (2008). The effect of perceived justice on postcomplaint behavior in the internet open market—Focused on the moderating effect of fashion involvement. [Google Scholar] [Crossref]
42. Leong, L. W., Ibrahim, O., Dalvi-Esfahani, M., Shahbazi, H., & Nilashi, M. (2018). The moderating effect of experience on the intention to adopt mobile social network sites for pedagogical purposes: An extension of the technology acceptance model. Education and Information Technologies, 23,2477–2498. [Google Scholar] [Crossref]
43. Li, Y.-H., & Huang, J.-W. (2009). Applying theory of perceived risk and technology acceptance model in the online shopping channel. World Academy of Science, Engineering and Technology, 53, 919–925. [Google Scholar] [Crossref]
44. Lin, J. S. C., & Hsieh, P. L. (2006). The role of technology readiness in customers’ perception and adoption of self-service technologies. International Journal of Service Industry Management, 17, 497–517. [Google Scholar] [Crossref]
45. Lunney, A., Cunningham, N. R., & Eastin, M. S. (2016). Wearable fitness technology: A structural investiga- tion into acceptance and perceived fitness outcomes. Computers in Human Behavior, 65, 114–120. [Google Scholar] [Crossref]
46. MacCallum, R., Browne, M., & Sugawara, H. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1, 130. [Google Scholar] [Crossref]
47. Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2, 173–191. [Google Scholar] [Crossref]
48. McKinsey & Company. (2018). The state of fashion 2018. Retrieved from https://cdn.businessoffashion.com/reports/The_State_of_Fashion_2018_v2.pdf [Google Scholar] [Crossref]
49. Naderi, I. (2013). Beyond the fad: A critical review of consumer fashion involvement. International Journal of Consumer Studies, 37, 84–104. [Google Scholar] [Crossref]
50. O’Cass, A. (2004). Fashion clothing consumption: Antecedents and consequences of fashion clothing involve- ment. European Journal of Marketing, 38, 869–882. [Google Scholar] [Crossref]
51. Park, E. J., Kim, E. Y., & Forney, J. C. (2006). A structural model of fashion-oriented impulse buying behavior. [Google Scholar] [Crossref]
52. Park, J., & Stoel, L. (2005). Effect of brand familiarity, experience and information on online apparel purchase. [Google Scholar] [Crossref]
53. Retrieved from http://digitalcommons.udallas.edu/edt/1/ Browne, B. A., & Kaldenberg, D. O. (1997). Conceptualizing self-monitoring: Links to materialism and product involvement. Journal of Consumer Marketing, 14, 31–44. [Google Scholar] [Crossref]
54. Retrieved from https://www.forbes.com/sites/rachelarthur/2018/01/15/ai-ibm- tommy-hilfiger/#1e192fa478ac [Google Scholar] [Crossref]
55. Retrieved from https://www.pocket-lint.com/smart-home/news/amazon/140903-what-is-amazon-echo- look-and-how-does-it-work [Google Scholar] [Crossref]
56. Rosen, L. D., Whaling, K., Carrier, L. M., Cheever, N. A., & Rokkum, J. (2013). The media and technology usage and attitudes scale: An empirical investigation. Computers and Human Behavior, 29, 2501–2511. Sennaar, K. (2017). AI in fashion—Present and future applications. Emerj. Retrieved from https://www.tech emergence.com/ai-in-fashion-applications/ [Google Scholar] [Crossref]
57. Shim, S. I., Kwon, W. S., Chattaraman, V., & Gilbert, J. E. (2012). Virtual sales associates for mature consumers: Technical and social support in e-retail service interactions. Clothing and Textiles Research Journal, 30, 232–248. [Google Scholar] [Crossref]
58. Shim, S., Morris, N. J., & Morgan, G. A. (1989). Attitudes toward imported and domestic apparel among college students: The Fishbein model and external variables. Clothing and Textiles Research Journal, 7,8–18. [Google Scholar] [Crossref]
59. Shin, E., & Baytar, F. (2014). Apparel fit and size concerns and intentions to use virtual try-on: Impacts of body satisfaction and images of models’ bodies. Clothing and Textiles Research Journal, 32, 20–33. [Google Scholar] [Crossref]
60. Spreng, R. A., & Olshavsky, R. W. (1992). A desires-as-standard model of consumer satisfaction: Implications for measuring satisfaction. Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior, 5,45–54. [Google Scholar] [Crossref]
61. Tillman, M. (2018). What is Amazon Echo Look, how does it work, and when can you buy it? Pocket-lint. [Google Scholar] [Crossref]
62. Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27, 451–481. [Google Scholar] [Crossref]
63. Williams, M. D., Slade, E. L., & Dwivedi, Y. K. (2014). Consumers’ intentions to use e-readers. Journal of Computer Information Systems, 54, 66–76. [Google Scholar] [Crossref]
64. Wong, C., & Liu, C. (2018). PolyU and Alibaba join hands to promote integration of fashion and artificial intelligence. Retrieved from The Hong Kong Polytechnic University website: https://www.polyu.edu.hk/ web/en/media/media_releases/index_id_6513.html [Google Scholar] [Crossref]
65. Wu, L.-H., Wu, L.-C., & Chang, S.-C. (2016). Exploring consumers’ intention to accept smartwatch. [Google Scholar] [Crossref]
66. Yang, Y., & Wang, X. (2019). Modeling the intention to use machine translation for student translators: An extension of Technology Acceptance Model. Computers & Education, 133, 116–126. [Google Scholar] [Crossref]
67. Yanshu, S. U. N., & Guo, S. (2017). Predicting fashion involvement by media use, social comparison, and lifestyle: An interaction model. International Journal of Communication, 11, 4559–4582. ISSN: 1932-8036. [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- Wind Turbine Design for Low Wind Speed Applications: Advancing Renewable Energy Systems Through Wind Tunnel Experiments
- Fast Identification for Evidences in Crime Scene with Macroscopic Properties and Portable Techniques
- Evaluating the Impact of Hello Interval Timer on OSPF Performance for Real-Time Applications Using OPNET
- The Algorithmic Fortress: Ai-Powered Cybersecurity and Anti-Fraud in The Future of Fintech
- Accident Detection on Curved Roads Using Infrared Sensors in Hilly Regions A Case of Chadoora Tehsil, Badgam (J&K)