Academy of Marketing Studies Journal (Print ISSN: 1095-6298; Online ISSN: 1528-2678)

Research Article: 2022 Vol: 26 Issue: 2

Hermeneutic Approach In Understanding The Role Of Artificial Intelligence: Fashion & Apparel Industry/ Luxury Brand Retailing

Anand Hindolia, the University of Mumbai

Jyoti Arya, Indian Institute of Technology, Roorkee

Citation Information: Hindolia, A., & Arya, J. (2022). Hermeneutic approach in understanding the role of artificial intelligence: fashion & apparel industry/ luxury brand retailing. Academy of Marketing Studies Journal, 26(2), 1-14.


This paper explores technology of Artificial Intelligence (AI) and its application with a special focus on luxury brand retailing in fashion & apparel (F&A) industry. The massive impact of AI is witnessed in changing the F&A industry in the past years. Though, the past studies in this sector are not clustered and not much of comprehensive research is done to explore the AI in the field of F&A. The researchers have adopted qualitative approach in the form of reviewing literature. The paper will try providing insight information about application of AI in F&A industry and luxury fashion product retailing. This paper will also help the marketing personnel handling retailing to understand how luxury retailing will witness new era of retailing with the help of artificial intelligence.


Artificial Intelligence, Fashion & Apparel, Luxury Brand, Retailing, Retail Brand Marketing.


One out of three (food, apparel and shelter) basic need of humankind is apparel; it has been required and has been part of civilized society since ages. As homosapiens grew with cognitive skills, technology and societal needs, the apparel and textile also changed from not just a need to a need of recognition and trend, thus fashion. Fashion industry is a very silent contributor to the economies of the world. With the contribution of “38% to the Asia Pacific, 26% to Europe and 22% to North America” in the economy, one of the biggest contributors is F&A Lenzing (2019). As per the article of Business of Fashion, (2019), revenue of Fashion & Apparel is expected to grow its sales in the Asia Pacific by 7.5% and in Europe by 5.5% Sweeny (2015).

Artificial Intelligence has evolved as a vital edge in industrial modernization (Hager et al., 2017), it is also being used as an application of new type in the F&A industry (Wong & Liu, 2018; Amazon Fashion, 2017). As Artificial Intelligence is able to carry vital improvements in the cost, speed and flexibility in the fashion supply chain, it is need of the hour to use AI in rebuilding, merchandising, designing and marketing activities of fashion products (McKinsey & Company, 2018). AI also plays a major role in assisting designers with recommending products on the past purchase and buyer’s preferences for size, price, order history, general style, and activities on social platforms like fashion pictures uploaded on Pinterest (Cao, 2018). In continuation, fashion marketers/ retailers (such as Nike, Nordstrom Macy’s, Farfetch) are using Artificial Intelligence in, virtual assistants, chatbots and product navigators to grow and congregate consumers’ off-line and online experiences (Sennaar, 2017; McKinsey & Company, 2018). By examining and capturing insights from millions of videos and images using AI computer vision, AI has helped fashion gurus in collaborating trending key patterns, colors and styles, which may bring down theentire lead time and enlarge designers’creative discovery (Arthur, 2018).

With the perpetually shifting technology in today’s world, if business houses does not consider the up-gradation, it will face the obsolescence or rough competition in the operating/ or future market. It is very important for any organization to adopt the new technology and move ahead. Artificial intelligence is the buzz of the town now a days as everyone is talking about AI and its applications to business. The ultimate objective of any profit centric organization is to increase its profit through generating revenue or through cutting down their cost. Retailing especially in the luxury segment demand for more customised products and service, AI can be instrumental in providing a more tailored made offering to the customers.

According to Solomon (2018), AI will be having its breakthrough moment as majority of retailers will adopt it to strengthen several shares of the retail and e-commerce experience.AI is not just for the flicks any longer. Clients are now demanding better, faster and tailored experiences. Retailing has requirement to meet this demand and Artificial Intelligence can help. Luxury is very personal and relative in nature as it is an established fact that, to make a product luxury it must remain exclusive, elitist, rare and distinctive. Luxury creates an aura of extra ordinary, the unachievable or dream for majority who are not included in the target market segment. It is very interesting to understand this semantic, moving and imaginary galaxy of perception of luxury brand buyers. Image and personality needs all the elements in the correct balance that comprise the communication of luxury brands.

A change in socio-economic factors have resulted in addition of consumers who selectively buys new and better fashion and apparel products as well as services and not buy the other products to pay for their premium purchases and in the process drives ideas and enlargement in old sluggish markets. New Luxury consumers seek goods which help in engaging their emotions as most successful goods and services engage emotions in more than one space. New Luxury products are very unique as they bring easy to get elite experiences, which the base of marketing the luxury brands.

In the view of Pan (2019), the luxury sector has archeologically been an H2H (Human to Human) business in the field of marketing and luxury – as a whole - is one of the sluggish sectors to adopt the technology. The only and most important technology that is by now present but momentously misread is AI and this has undoubtedly started to give momentous recompenses to those who take the time to know its application.

The primarily reiterations of AI in luxury that were frequently used were chatbots. Chatbots are used for providing more support to Luxury brands customer service strategy. With the help of Artificial Intelligence chatbots have started to manage some parts of customer service experience in a very effective and efficient way. With the onset of technology and digital platform, a transformation is taking place in the luxury retail industry particularly with Gen Z and Millennials who prefer to start with online channels for discovering brands and represent a major chunk of spending on luxury goods.

Luxury brands have realized the new requirement of their customers of getting perfect in in-store experience which can be reproduced online. This has resulted in a radical transformation of elite luxury brands to join hands with connective technology like AI.

Tommy Hilfiger in 2016 was the first brands to launch a chat-bot at New York Fashion Week to market their products. The bot interacted with clients to benefit the buyer and recommend merchandises from its newest lines based on answers from pre-programmed inquiries, serving to produce a tailored buyer experience and attract targeted peoples to their website.

Just after, Burberry introduced their illustration of a chatbot initially offering buyers a behind the scenes picture of the brand and how clients could shop the trendiest products, but over a period of time. Burberry pulled out the chat-bot services from customer service and offered a product browsing, to store location, via online talk with a Burberry consultant.

Review of Literature

As per (eatlovesavor) an international luxury lifestyle magazine, by 2025, approximately 20% of all elite brand businesses are most likely to market/ transact on e-retailing. This business will quicken in the upcoming decades, as new buyer in evolving business come online. Luxury products consumers demand a continuous and tailored buying experience. More specifically, this is majorly the case with buyers who are young and Generation-Z buyer category, which usually kick start their discovery of brands online. This is the same category of new generation, prosperous clients who are lashing 85% of the international elite sales enlargement and now characterize in spread of 30% of all elite brands purchase. To serve to purchaser’s altering tastes, brands of luxury products are enhancing their visibility online and trying some or other level of Artificial Intelligence adaptation into their online retail platforms. As per Gartner, he states that more than 85% of interactions with customers will be done by AI by 2020.

Reavie (2018)  in his post on forbes magazine said that “With AI, each customer persona is a continually evolving profile, updating as interactions, context, behavior and location changes, empowering marketers to target and tailor promotions that reflect customers and their evolution.” A study done (Infosys), says “86% of customers state personalization plays a role in their decision”.

In a research by McKinsey, “when brands get personalization right, marketing spend can deliver five to eight times the ROI and lift sales by 10% or more”. Todorov (2017) in his post on forbes magazine depicted that IBM envisages that by 2020 up to 85% of all b2c communication will be done with the help of AI. For brands that are in the business of high detailed products and to surpassing buyer expectations, AI produces prodigious potential for creating a better buyer experience.

Beauloye (2019), Facebook Messenger- Louis Vuitton Digital Associate chat-bot influences AI expertise to give clients a more “cultured, tailored, graphic and relaxed online spending experience for each consumer” via Facebook, where LV amounts additional 23 million followers. Louis Vuitton Digital Assistant chatbot using Artificial Intelligence technology to spring clients a more “sophisticated, personalised, visual and conversational online shopping experience for each client”.

Daniel in his interview to Pan (2019), said “that brands that are considerably more open today, with some players like Gucci, Dior, Chanel, and even Rolls-Royce shifting an enormous focus to digital marketing”. Unconventional facts AI, querying and machine learning want quite an amount of skill than many in-house members possess. Some firms in the biosphere actually have good command over AI to market their products, and only some can syndicate AI with luxury approaches as Équité is doing.

Objective of The Study

The study's objectives are separated into two sections. The first is to conduct an in-depth examination of current AI trends in the fashion sector. The second step is to comprehend AI's role in luxury clothes retailing/ marketing.

The authors wanted to explore the AI technology and its application to luxury retailing in F&A industry, the research will attempt to answer questions like

Q1. What influence does AI have on the fashion and apparel industry?

Q2. Where is AI used in luxury fashion retailing/ marketing?

Research Methodology

This study includes a semi-systematic literature evaluation concentrating on AI and application of AI in the F&A business to address the objectives of the study; it is practically not possible to review all the related literature (Snyder, 2019). An in depth review of literature is done, literature published related to luxury brands and AI are considered for review. An unorthodox method i.e. a mix of literature has been adopted by the author; literature consists of Fashion/ technology journals, magazines, blogs, fashion brand websites, published interviews of fashion gurus and industry experts. The information is collected and analyzed by authors using Semi-systematic review technique with validation from industry experts. Also, Interaction with working professionals in fashion and luxury products industry has been taken to further analyze and conclude the paper. Research Model: The model used for the conducting study is as given below Figure 1.

Figure 1 The Model Used for the Conducting Study

Literature Retrieval

Here the author discusses the steps taken to retrieve the literature from various databases like Scopus, Google scholar and search engine like Google. Scopus and Google scholar are two data bases which index most of the research work and conference proceedings (AlRyalat et al., 2018) (Martín-Martín et al., 2018). In addition to this, many trade and business magazines and websites of fashion luxury brands has been referred to get to information on AI and its application in fashion industry and fashion marketing.

To find the literature, the search strings which include synonyms related to Artificial Intelligence, Fashion & Apparel and luxury retailing industry has been used. The final search strings are given in the Table 1 given below.

Table 1 Artificial Intelligence, Fashion & Apparel
Artificial intelligence Luxury Retailing/Marketing/Fashion and Apparel
Machine Learning Fashion Industry
Experts system Luxury Retailing/ Marketing
Intelligence system Luxury Fashion
Data Driven Textile Industry
AI Luxury Apparel Retailing/ Marketing
Data mining Apparel Industry

Selection of Literature

Here the author screens literature and selects the relevant literature to answer the research questions, the criteria for inclusion and exclusion are given in Table 2 and Table 3 respectively.

Table 2


Inclusion and Exclusion
Criteria Reason for selection
No time constraint To understand the overview of AI in the Luxury retailing and F&A domain to answer Q1&2
Scopus and WoS Databases which includes Journal Articles and Conference Proceedings  These databases cover the majority of scholarly journals and conference proceedings; to ensure that all articles related to our 2 Qs are received
All studies in the Fashion & Apparel industry that was using Artificial intelligence techniques To identify all phases in the Fashion & Apparel luxury retailing where AI has been implemented and executed (to answer Q2)
Table 3 Table for Exclusion
Criteria Reason for Exclusion
Grey literature To maintain the scientific reliability of the literature review
Language Non English literatures because of author’s limitations
Industry Industry that are not from F&A

Exclusion Criteria

Extracted documents: Here the author extracts the articles from different sources, Table 4 given below provides the details.

Table 4


Indexing Sources
Scopus Google Scholar Websites/
        Expert’s Interviews
Document Type Numbers Document Type Numbers Document Type Numbers
Journal Papers 142 Journal Papers 147 Online Magazine 7
Conference Article 139 Conference Article 120 Research firm document 3
Books and Book chapters 6 Books and Book chapters 3 Interviews 6
Review 3 Review 1  Review 3
Total 290 Total 271 Total 19

Information extraction: In this section, from the extracted articles, information is extracted in 5 phases; Figure 2 explains each step in detail.

Figure 2 Information Extraction

Article classification: This section attempts to explains the fashion and apparel industry with the help of verification of industry experts. On discussion with the industry experts the articles were classified into categories/ areas of AI and how they are used in F&A industry.

The AI in F&A has been classifed as given in the below Figure 3.

Figure 3 Artificial Inteligence

The AI use in F&A Luxury retailing has been classified as below Figure 4.

Figure 4 Findings and Analysis

Findings And Analysis.

The authors chose 124 publications based on the study framework, which included journal papers, conference proceedings, expert reviews, and interviews. The ontology set in the previous section was used to classify the articles. This section presents the results of author’s analysis in terms of article dispersion, which addresses the two research questions. Table 5 corresponds to Q1 by giving the general application of AI in the Fashion & Apparel business, whereas Table 6 corresponds to Q2 by showing the special application in the luxury retailing.

Table 5 AI Area
AI Area Method technique used Process in fashion industry Author

Machine Learning

Future decision,Supervised learning, Unsupervisived learning

Cutting/Dyeing/ Finishing/ Printing/ Finished Garments Inspection/ Retailing/ Sewing / Spinning

Bishop(2006), Stuart & Norvig (2009), Mohri, Rostamizadeh, & Talwalkar (2012),  Kumar & Poonkuzhali (2018), Hsiao, Lee, Chen, & Yen (2017), Brahmadeep & Thomassey (216), Ghosh, Guha, Bhar, & Das (2011), Pavlinic &Geršak (2004), Zhao et al (2018), Liu, Wang, Kamalha, Li, & Zeng (2017), A. Tehrani and Ahrens (2017), Li, Wang and Wang (2017), Hamad, Thomassey and Bruniaux (2017), Liu, Zeng, Bruniaux, Wang, Kamalha and Tao (2017), S. Darwish (2013), Tsai and Hsu (2013), Liang, Liang, and Wang (2013), Viktor, Pena, and Paquet (2011)


Intelligence decisions, Mathematical models with conventional data retrieval method, Optimisised decision making in apparel supply chain

Production/ Operations/ Supply chain/ Line balancing

Keen (1980),  Sprague (1980), Tu & Yeung (1997),  Wong & Leung (2008), Aksoy and Öztürk (2016), Z.-Hu, Wei, and Yu (2015), Kumar and Murali Bhaskaran (2015), Lee, Choy, Law, and Ho (2014), Nakandala, Samaranayake, and Lau (2013), Rabenasolo and Zeng (2012), Aksoy, Ozturk, and Sucky (2012)

Expert system

Decision without human intervention, Reasoning approach "If then" rule, Knowledge based decisions, Fashion retailing

Expert decision/ Environment concerns/ Production/ Retailing/ Sourcing

Jackson (1999), Leondes (2002), Stuart & Norvig (2009), Stella & Chuks (2004),  Metaxiotis (2004),  Wong, Zeng, & Au (2009), Istrat and Lalic (2017), Santiago, Laureano-Cruces, Antuñano-Barranco, Domínguez-Pérez, and Sarmiento-Bustos (2015),  Lee, Ho, Choy, and Pang (2014), Shahrabi, Hadavandi, and Salehi Esfandarani (2013), Lee, Choy, Ho, Chin, Law, and  Tse (2013), Yu, Hui, and Choi (2012)


Intelligent searching, Simple exhaustive search, heuristic search approach, genetic programming in apparel production, fitting services

Production optimization/ Order processing/ Lead time management

Stuart & Norvig (2009), Luger & Stubblefield (1993), Poole, Mackworth, & Goebel (1998), Nilsson (1998), Tecuci (2012), Holland (1975), Holland et al (1992),  Guruprasad & Behera (2009),  Lu, Xiang, Wu, & Gu (2015), Hui, Chan, Yeung, & Ng (2007), Martino, Iannone, Fera, Miranda, and Riemma (2017), . Lee, Choy, Ho, and Lam (2016), Martino, Yuce, Iannone, and Packianather (2016), Caro and Gallien (2010),

Image Recognition and Vision

High level interpretation of image or videos, exploring high dimensional data, creating supervised and un supervised model, video tracking, object recognition, motion estimation, inspection and process control, content based image retrieval system, augmented reality in the fashion and apparel industry with virtual try-on

Artificial shopping assistance/ Customer suggestion/ Intelligent recommendation/ Virtual trial

Ballard & Brown (1982), Sonka, Hlavac, & Boyle (2008), Jahne (2000),  Forsyth & Ponce (2003), Morris (2004), Steger, Ulrich & Wiedemann (2018)Kuo, Lee, & Shih (2017), Yuan, Khan, Farbiz, Yao, Niswar (2013), Cushen & Nixon (2011), Shin, Kim, and Kim (2010), Yuen, Wong, Qian, Chan, and  Fung (2009), Wong, Yuen, Fan, Chan, and Fung (2009)Yuen, Wong, Qian, Fan, Chan, and Fung (2009)


Table 6 Technology Area
Technology Area of use Author
Chat-bots/ AI smart Assistants/ E-service agents Shop-floor interaction, Smart retail assistance, Online customer service assistance, online chats assistance

Forbes, 2017a, 2017b, 2017c),Pan (2019),Tommy Hilfiger (2016),Beauloye (2019),Bolton et al (2013),Lee, et al (2017), Zhang et al  (2017),Choi et al (2016), Woodside & Ko (2013), Mimoun et al (2017), Zhang et al. (2017), Godey et al. (2016), Crosby & Johnson (2002), Gautam &Sharma (2017), Darke et al. (2016), Zhang & Dholakia (2018), Kim & Ko (2010), Holzwarth et al. (2006), Arthur (2017)

Virtual trial/ Augmented reality/ Virtual product experience Virtual dressing rooms, enlargement or magnification, 3-Drotation, mix and match, and personalized 3-Dvirtual mode Wagner (2007), Kim & Forsythe’s (2007), Dabholkar & Bagozzi (2002), Citrin et al. (2003), Childers et al. (2001),  Menon &Kahn (2002), Heijden (2000), Schlosser (2003), Kim, J., & Forsythe, S. (2008), Merle (2012), Ethan Kent (2015), Hauswiesner (2013), Rohinesh (2015), Simon (2016), Ghosh (2013), Lee, Kim, & Fiore (2010), Verhoef, Neslin,&Vrooman, (2007), Kim & Forsythe, (2008); Lee, Kim, & Fiore, (2010), Jiang &Benbasat,(2004–2005),  Li et al. (2002)
Personalization/ Customization Customized suggestions, suggested products on the basis of past purchase, customer body type, lifestyle, customer choice. Brady(2018), Jain, Rakesh, Nabi, and Chaturvedi(2018), Abraham, Van, Archacki, JGonzález and Fanfarillo (2019)), Shang, Wang, Xiong, Nyberg, Yuan, Liu, Guo, and Bao (2018), Luxury Institute (2020), Delloite (2017), OGUNDIMU (2018), Shankar (2018), Berthiaume(2021), Adam (2019), ODDONE (2019), Delloite (2019)

Discussions and Conclusion

On analysing the reviewed literature, it is found that AI techniques and methods like ML (Future decision, Supervised learning, Un-supervised learning) is being applied in processes like Cutting, Dyeing, Finishing, Printing, Finished Garments Inspection etc. AI is not only easing the process but also helping in intelligent decision making with the help of DSS (Mathematical models with conventional data retrieval method, Optimized decision). Decision without human intervention, Reasoning approach "If then" rule, Knowledge based decisions are being done by the Expert system. One of the concern in F&A is optimization of process this is eased by AI using Intelligent searching, Simple exhaustive search, heuristic search approach, genetic programming in apparel production.

In corresponds to application of AI in luxury retailing, the authors witnessed that techniques like Chat-bots, AI smart Assistants, AI based advertising to targeted customers and E-service agents are providing human less intelligent shopping assistance to buyers and ease of marketing of luxury brands to the targeted segments. Whereas Virtual trial, Augmented reality, Virtual product experience helps in giving customer a timesaving, safe and contact less experience online and in the retail store. The most important concern for luxury brands is to make their services and products personalized and customized which is possible using AI with the help of customized suggestions, suggested products on the basis of past purchase, customer body type, lifestyle, customer choice etc.

The authors now are able to conclude that there exists a strong influence of AI on F&A industry in all the areas like merchandising, product planning, line balancing, decision making and stock optimization etc. Luxury retailers are the innovators/ trends setters in the area of AI application by adopting marketing tools and techniques to retail their products. The existence of AI in luxury product retailing/ marketing is majorly witnessed in U.S. and European market with a trend starting in the Middle East. Brands like Louis Vuitton, Tommy Hilfiger, Burberry, Gucci, Channel, are already using this technology of artificial intelligence and research of Mckinsey, Euro monitor and Forrester supports that, by 2025, 20% of the total Luxury sales across world will be online, with increase in online luxury sale, Artificial Intelligence will gel well with online sales to provide a more delighted experience to buyers. The customer finds it very interesting and “for them” i.e., customised as per their need. The objective of providing customer delight by any luxury brand can be achieved by using this technology of AI.


Adam, C. (2019) How Luxury Brands use Personalization?.

Aksoy, A., & Öztürk, N. (2016). Design of an Intelligent Decision Support System for Global Outsourcing Decisions in the Apparel Industry. The Journal of The Textile Institute, 107(10), 1322-1335.

Indexed at,Google Scholar,CrossRef

Aksoy, A., Ozturk, N., & Sucky, E. (2012). A Decision Support System for Demand Forecasting in the Clothing Industry. International Journal of Clothing Science and Technology, 24(4), 221-236.

Indexed at,Google Scholar,CrossRef

AlRyalat, S.A.S., Malkawi, L.W., & Momani, S.M. (2019). Comparing Bibliometric Analysis using PubMed, Scopus, and Web of Science Databases. JoVE (Journal of Visualized Experiments), (152), e58494.

Indexed at,Google Scholar,CrossRef

Arthur, R. (2017). Louis Vuitton Becomes Latest Luxury Brand To Launch A Chatbot.

Google Scholar

Ballard, D.H., & Brown, C.M. (1982). Computer vision. Image, 17, 2-2.

Beauloye, F.E. (2019). How Personalisation and Artificial Intelligence Are Transforming Luxury Retail Online.

Berthiaume, D. (2021). LVMH Fashions Smart Personalization Strategy with Google Cloud.

Bishop, C.M., & Nasrabadi, N.M. (2006). Pattern Recognition and Machine Learning. 4(4), 738.

Brady, S. (2018). Think Tank: The Luxury of Personalization.

Childers, T.L., Carr, C.L., Peck, J., & Carson, S. (2001). Hedonic and Utilitarian Motivations for Online Retail Shopping Behavior. Journal of retailing, 77(4), 511-535.

Indexed at,Google Scholar,CrossRef

Citrin, A.V., Stem Jr, D.E., Spangenberg, E.R., & Clark, M.J. (2003). Consumer need for Tactile Input: An Internet Retailing Challenge. Journal of Business research, 56(11), 915-922.

Indexed at,Google Scholar,CrossRef

Cushen, M.S., & Nixon, G.A.(2011) “Markerless Real-time Garment Retexturing from Monocular 3d Reconstruction.” in 2011. IEEE International Conference on Signal and Image Processing Applications (ICSIPA), 88-93.

Indexed at,Google Scholar,CrossRef

Dabholkar, P.A., & Bagozzi, R.P. (2002). An Attitudinal Model of Technology-Based Self-Service: Moderating Effects of Consumer Traits and Situational Factors. Journal of the academy of marketing science, 30(3), 184-201.

Indexed at,Google Scholar

Daniel Langer, C.O. (2019). What AI Can Do for Luxury Brands: Interview.

Darwish, S.M. (2013). Soft Computing Applied to the Build of Textile Defects Inspection System. IET Computer Vision, 7(5), 373-381.

Indexed at,Google Scholar

Delloite. (2017). Disruptions in Retail through Digital Tranformation.

Delloite. (2019). Global Powers of Luxury Goods 2019.

EatLoveSavor. (n.d.). Will Artificial Intelligence Enhance Online Luxury Retail?.

Ethan, K. (2015). Virtual Fitting Rooms Allow Online Shoppers to try before they Buy. Horizon, The EU Research and Innovation Magazine.

Forsyth, D., & Ponce, J. (2011). Computer Vision: A Modern Approach. 792. Prentice hall.

Ghosh, A., Guha, T., Bhar, R.B., & Das, S. (2011). Pattern Classification of Fabric Defects Using Support Vector Machines. International Journal of Clothing Science and Technology. 23(2), 142-151.

Indexed at,Google Scholar,CrossRef

Guruprasad R., & Behera, B.K., (2009). Genetic Algorithms and its Application to Textiles, Text. Asia, 40(4-5), 35-38.

Indexed at

Hamad, M., Thomassey, S., & Bruniaux, P. (2017). A New Sizing System Based on 3D Shape Descriptor for Morphology Clustering. Computers & Industrial Engineering, 113, 683-692.

Indexed at,Google Scholar

Hauswiesner, S., Straka, M., & Reitmayr, G. (2013). Virtual Try-on through Image-based Rendering. IEEE transactions on visualization and computer graphics, 19(9), 1552-1565.

Indexed at,Google Scholar,CrossRef

Holland, J.H., Langton, C., Wilson, S.W., Varela, F.J., Bourgine, P., Koza, J.R., & Book, A.B. (1992). Genetic Programming Complex Adaptive Systems Genetic Programming On the Programming of Computers by Means of Natural Selection. Massachusetts Institute of Technology.

Google Scholar

Holland, J.H. (1992). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT press.

Indexed at,Google Scholar

Hsiao, S.W., Lee, C.H., Chen, R.Q., & Yen, C.H. (2017). An Intelligent System for Fashion Color Prediction Based on Fuzzy C-Means and Gray Theory. Color Research & Application, 42(2), 273-285.

Indexed at,Google Scholar,CrossRef

Hui, P.C., Chan, K.C., Yeung, K.W., & Ng, F.S. (2007). Application of Artificial Neural Networks to the Prediction of Sewing Performance of Fabrics. International Journal of Clothing Science and Technology, 19(5), 291-318.

Indexed at,Google Scholar,CrossRef

Hu, Z.H., Wei, C., & Yu, X.K. (2015). Apparel Distribution with Uncertain Try-on Time by Evolutionary Algorithm. International Journal of Clothing Science and Technology, 27(1), 75-90.

Indexed at,Google Scholar,CrossRef

Infosys. (n.d.). Rethinking Retail, Insight from Consumer and Retailer into an Omni Channel Shopping Experience.

Institute, L. (2020). Luxury Institute: Edge AI Is The Future Of Personalized Luxury.

Istrat, V., & Lalic, N. (2017). Association Rules as a Decision Making Model in the Textile Industry. Fibres & Textiles in Eastern Europe, 25(4), 8-14.

Indexed at,Google Scholar,CrossRef

Jackson, P. (1999). Introduction to Expert Systems, Third edition. Harlow, England; Reading, Mass. Addison-Wesley.

Jahne, B. (2000) Computer Vision and Applications: a guide for students and practitioners. Elsevier.

Indexed at,Google Scholar

Jain, G., Rakesh, S., Nabi, M.K., & Chaturvedi, K.R. (2018). Hyper-Personalization–Fashion Sustainability through Digital Clienteling. Research Journal of Textile and Apparel.

Indexed at,Google Scholar,CrossRef

Jiang, Z., & Benbasat, I. (2004). Virtual Product Experience: Effects of Visual and Functional Control of Products on Perceived Diagnosticity and Flow in Electronic Shopping. Journal of Management Information Systems, 21(3), 111-147.

Indexed at,Google Scholar,CrossRef

Keen, P.G. (1980). “Decision Support Systems: A Research Perspective.” Decis. Support Syst. Issues Challenges Proc. An In Task Force Meet, 23-44.

Indexed at,Google Scholar

Kim, J., & Forsythe, S. (2007). Hedonic Usage of Product Virtualization Technologies in Online Apparel Shopping. International Journal of Retail & Distribution Management.

Indexed at,Google Scholar,CrossRef

Kim, J., & Forsythe, S. (2008). Adoption of Virtual Try-on Technology For Online Apparel Shopping. Journal of Interactive Marketing, 22(2), 45-59.

Indexed at,Google Scholar,CrossRef

Kumar, S.V., & Poonkuzhali, S. (2018). Improvising the Sales of Garments by Forecasting Market Trends Using Data Mining Techniques. Int J Pure Appl Math, 119(7), 797-805.

Google Scholar

Kuo, C.F.J., Lee, C.L., & Shih, C.Y. (2017). Image Database of Printed Fabric with Repeating Dot Patterns Part (I)–Image ArchivingTextile Research Journal87(17), 2089-2105.

Indexed at,Google Scholar,CrossRef

Lee, C.K.H., Choy, K.L., Ho, G.T., Chin, K.S., Law, K.M., & Tse, Y.K. (2013). A Hybrid OLAP-Association Rule Mining Based Quality Management System For Extracting Defect Patterns In The Garment Industry. Expert Systems with Applications, 40(7), 2435-2446.

Indexed at,Google Scholar,CrossRef

Lee, C.K.H., Ho, G.T.S., Choy, K.L., & Pang, G.K.H. (2014). A RFID-Based Recursive Process Mining System for Quality Assurance in the Garment Industry. International journal of production research, 52(14), 4216-4238.

Indexed at,Google Scholar,CrossRef

Lee, C.K.H., Choy, K.L., Ho, G.T., & Lam, C.H. (2016). A Slippery Genetic Algorithm-Based Process Mining System for Achieving Better Quality Assurance in the Garment Industry. Expert systems with applications, 46, 236-248.

Indexed at,Google Scholar,CrossRef

Lee, H.H., Kim, J., & Fiore, A.M. (2010). Affective and Cognitive Online Shopping Experience: Effects of Image Interactivity Technology and Experimenting With Appearance. Clothing and textiles research Journal, 28(2), 140-154.

Indexed at,Google Scholar,CrossRef

Leondes, C.T. (2001). Expert Systems: The Technology of Knowledge Management and Decision Making for the 21st Century. Elsevier.

Indexed at

Li, B.R., Wang, Y., & Wang, K.S. (2017). A Novel Method for the Evaluation of Fashion Product Design Based On Data Mining. Advances in Manufacturing, 5(4), 370-376.

Indexed at,Google Scholar,CrossRef

Li, H., Daugherty, T., & Biocca, F. (2002). Impact of 3-D Advertising on Product Knowledge, Brand Attitude, and Purchase Intention: The Mediating Role of Presence. Journal of advertising, 31(3), 43-57.

Indexed at

Liang, J.F., Liang, J., & Wang, J.P. (2013). Empirical Study on B/C Apparel Consumption Behavior based on Data Mining Technology. Journal of Donghua University (English Edition), 30(6), 530-536.

Indexed at,Google Scholar

Liu, K., Wang, J., Kamalha, E., Li, V., & Zeng, X. (2017). Construction of a Prediction Model for Body Dimensions used in Garment Pattern making based on Anthropometric Data Learning. The Journal of The Textile Institute, 108(12), 2107-2114.

Indexed at,Google Scholar,CrossRef

Liu, K., Zeng, X., Bruniaux, P., Wang, J., Kamalha, E., & Tao, X. (2017). Fit Evaluation of Virtual Garment Try-on by Learning from Digital Pressure Data. Knowledge-Based Systems, 133, 174-182.

Indexed at,Google Scholar,CrossRef

Liu, Y., & Geng, Z.F. (2003). Three-Dimensional Garment Computer Aided Intelligent Design. Journal of industrial textiles, 33(1), 43-54.

Indexed at,Google Scholar,CrossRef

Lu, Z.J., Xiang, Q., Wu, Y.M., & Gu, J. (2015). Application of Support Vector Machine and Genetic Algorithm Optimization for Quality Prediction within Complex Industrial Process. In 2015 IEEE 13th International Conference on Industrial Informatics (INDIN), 98-103. IEEE.

Indexed at,Google Scholar,CrossRef

Luger, G.F. (2005). Artificial Intelligence: Structures and Strategies for Complex Problem Solving. Pearson education.

Indexed at

Martín-Martín, A., Orduna-Malea, E., Thelwall, M., & López-Cózar, E. D. (2018). Google Scholar, Web of Science, and Scopus: A Systematic Comparison of Citations in 252 Subject Categories. Journal of informetrics, 12(4), 1160-1177.

Indexed at,Google Scholar,CrossRef

Martino, G., Iannnone, R., Fera, M., Miranda, S., & Riemma, S. (2017). Fashion Retailing: A Framework for Supply Chain Optimization. Uncertain Supply Chain Management, 5(3), 243-272.

Indexed at,Google Scholar

Martino, G., Yuce, B., Iannone, R., & Packianather, M.S. (2016). Optimisation of the Replenishment Problem in the Fashion Retail Industry Using Tabu-Bees Algorithm. IFAC-PapersOnLine, 49(12), 1685-1690.

Indexed at,Google Scholar,CrossRef

Menon, S., & Kahn, B. (2002). Cross-Category Effects of Induced Arousal and Pleasure on the Internet Shopping Experience. Journal of retailing, 78(1), 31-40.

Indexed at,Google Scholar,CrossRef

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.

Indexed at,Google Scholar,CrossRef

Metaxiotis, K. (2004). RECOT: An Expert System for the Reduction of Environmental Cost in the Textile Industry. Information management & computer security, 12(3), 218-227.

Indexed at,Google Scholar,CrossRef

Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018). Foundations of machine learning. MIT press.

Morris, T. (2004). Computer Vision and Image Processing. Palgrave Macmillan Ltd.

Indexed at

Nakandala, D., Samaranayake, P., & Lau, H.C. (2013). A Fuzzy-Based Decision Support Model for Monitoring On-Time Delivery Performance: A Textile Industry Case Study. European journal of operational research, 225(3), 507-517.

Indexed at,Google Scholar,CrossRef

Nilsson, N.J.(1998). Artificial Intelligence : a New Synthesis, Morgan Kaufmann Publishers.

Pan, Y. (2019). What AI Can Do for Luxury Brands: Interview.

Pavlinic, D.Z., & Geršak, J. (2004). Design of the System for Prediction of Fabric Behavior in Garment Manufacturing Processe. International Journal of Clothing Science and Technology, 16(1-2), 252-261.

Google Scholar,CrossRef

Poole, D., Mackworth, A., & Goebel, R. (1998). Computational Intelligence.

Rabenasolo, B., & Zeng, X. (2012). A Risk-based Multi-criteria Decision Support System for Sustainable Development in the Textile Supply Chain. In Handbook on Decision Making, 33, 151-170.

Indexed at,Google Scholar

Reavie, V. (2018). Three Ways Artificial Intelligence Can Enhance Your Personalization Strategy.

Rohinesh, M. (2016) TrialKart App Lets You Try On Clothes Via A Virtual Dressing Room.

Santiago-Santiago, K., Laureano-Cruces, A. L., Antuñano-Barranco, J. M. A., Domínguez-Pérez, O., & Sarmiento-Bustos, E. (2015). An Expert System to Improve the Functioning of the Clothing Industry: A Development Methodology. International Journal of Clothing Science and Technology, 27(1), 99-128.

Indexed at,Google Scholar

Sennaar, K. (2019). AI in Fashion – Present and Future Applications.

Senthil Kumar, A., & Murali Bhaskaran, V. (2015). Multidirectional Product Decision Support System for User Activation Assessment in Textile Industry using Collaborative Filtering Methods. Int J Appl Eng Res, 10(20), 17205-17209.

Indexed at,   CrossRef

Shahrabi, J., Hadavandi, E., & Esfandarani, M. S. (2013). Developing a Hybrid Intelligent Model for Constructing a Size Recommendation Expert System in Textile Industries. International Journal of Clothing Science and Technology, 25(5), 338-349.

Indexed at,Google Scholar,CrossRef

Shang, X., Wang, F.Y., Xiong, G., Nyberg, T.R., Yuan, Y., Liu, S., & Bao, S. (2018). Social Manufacturing for High-end Apparel Customization. IEEE/CAA Journal of Automatica Sinica, 5(2), 489-500.

Indexed at,Google Scholar,CrossRef

Shao, J., Zhao, Z., Wang, J., & Song, P. (2010). Production Management and Decision System Oriented to the Textile Enterprise based on Multi-Agent. In 2010 International Conference on Computer Design and Applications, 4, 4-45. IEEE.

Indexed at,Google Scholar,CrossRef

Shin, Y., Kim, Y., & Kim, E.Y. (2010). Automatic Textile Image Annotation by Predicting Emotional Concepts from Visual Features. Image and Vision Computing, 28(3), 526-537.

Indexed at,Google Scholar,CrossRef

Snyder, H. (2019). Literature Review as a Research Methodology: An Overview and Guidelines. Journal of business research, 104, 333-339.

Indexed at,Google Scholar,CrossRef

Solomon, M. (2018). How TUMI Transformed Its Customer Service To Be As Bulletproof As Its Luggage.

Sonka, M.,  Hlavac, V., & Boyle, R. (2008). Image Processing, Analysis, and Machine Vision. Thompson Learning.

Indexed at,Google Scholar

Steger, C., Ulrich, M., & Wiedemann, C. (2018). Machine Vision Algorithms and Applications. Wiley, 2nd Edition ISBN: 978-3-527-41365-2.

Indexed at

Stella, A., & Chuks, N. (2004). School of Science Education. Expert Syst a Catal Educ Dev Niger.

Stuart J.R., & Norvig, P. (2009).  Artificial Intelligence: A Modern Approach, Prentice Hall.

Indexed at

Suyi, L., & Leduo, Z. (2008). Textile Pattern Generation Technique Based on Quasi-Regular Pattern Theory and their Transform. In 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, 2, 264-266. IEEE.

Indexed at,Google Scholar,CrossRef

Tecuci, G. (2012). Artificial intelligence. Wiley Interdiscip Rev Comput Stat, 4(2), 168-180.

Tehrani, A.F., & Ahrens, D. (2016). Supervised Regression Clustering: A Case Study for Fashion Products. International Journal of Business Analytics (IJBAN), 3(4), 21-40.

Indexed at,Google Scholar

Thomassey, S. (2016). ead Publishing. Intelligent Demand Forecasting Systems for Fast Fashion. In Information Systems for the Fashion and Apparel Industry. 145-161. Woodh

Google Scholar,CrossRef

Todorov, I. (2017). AI Is No Longer A Diamond In The Rough For Small Businesses.

Tsai, C.Y., & Hsu, C.H. (2013). Developing Standard Elderly Aged Female Size Charts Based on Anthropometric Data to Improve Manufacturing Using Artificial Neural Network-Based Data Mining. Theoretical Issues in Ergonomics Science, 14(3), 258-272.

Indexed at,Google Scholar,CrossRef

Tu, Y., & Yeung, E. H. (1997). Integrated Maintenance Management System in A Textile Company. The International Journal of Advanced Manufacturing Technology, 13(6), 453-461.

Indexed at,Google Scholar

Verhoef, P.C., Neslin, S.A., & Vroomen, B. (2007). Multichannel Customer Management: Understanding the Research-Shopper Phenomenon. International journal of research in marketing, 24(2), 129-148.

Indexed at,Google Scholar,CrossRef

Viktor, H.L., Peña, I., & Paquet, E. (2012). Who are our Clients: Consumer Segmentation through Explorative Data Mining. International Journal of Data Mining, Modelling and Management, 4(3), 286-308.

Wagner, D. (2007). Handheld Augmented Reality.

Indexed at,Google Scholar

Wong, W.K., Yuen, C.W.M., Fan, D.D., Chan, L.K., & Fung, E.H.K. (2009). Stitching Defect Detection and Classification using Wavelet Transform and BP Neural Network. Expert Systems with Applications, 36(2), 3845-3856.

Indexed at,Google Scholar,CrossRef

Wong, W.K., & Leung, S.Y.S. (2008). Genetic Optimization of Fabric Utilization in Apparel Manufacturing. International Journal of Production Economics, 114(1), 376-387.

Indexed at,Google Scholar,CrossRef

Wong, W.K., Leung, S.Y.S., Guo, Z.X., Zeng, X.H., & Mok, P.Y. (2012). Intelligent Product Cross-Selling System with Radio Frequency Identification Technology for Retailing. International Journal of Production Economics, 135(1), 308-319.

Indexed at,Google Scholar,CrossRef

Wong, W.K., Zeng, X.H., & Au, W.M.R. (2009). A Decision Support Tool for Apparel Coordination through Integrating the Knowledge-Based Attribute Evaluation Expert System and the T–S Fuzzy Neural Network. Expert Systems with Applications, 36(2), 2377-2390.

Indexed at,   Google Scholar,CrossRef

Yu, Y., Hui, C. L., & Choi, T. M. (2012). An Empirical Study of Intelligent Expert Systems on Forecasting of Fashion Color Trend. Expert Systems with Applications, 39(4), 4383-4389.

Indexed at,Google Scholar,CrossRef

Yuan, M., Khan, I.R., Farbiz, F., Yao, S., Niswar, A., & Foo, M.H. (2013). A mixed Reality Virtual Clothes Try-on System. IEEE Transactions on Multimedia, 15(8), 1958-1968.

Indexed at,Google Scholar,CrossRef

Yuen, C.W.M., Wong, W.K., Qian, S.Q., Chan, L.K., & Fung, E.H.K. (2009). A Hybrid Model using Genetic Algorithm and Neural Network for Classifying Garment Defects. Expert Systems with Applications, 36(2), 2037-2047.

Indexed at,Google Scholar,CrossRef

Yuen, C.W.M., Wong, W.K., Qian, S.Q., Fan, D.D., Chan, L.K., & Fung, E.H.K. (2009). Fabric Stitching Inspection using Segmented Window Technique and BP Neural Network. Textile Research Journal, 79(1), 24-35.

Indexed at,Google Scholar,CrossRef

Received: 03-Jan-2022, Manuscript No. AMSJ-22-11145; Editor assigned: 05-Jan-2022, PreQC No. AMSJ-22-11145(PQ); Reviewed: 19-Jan-2022, QC No. AMSJ-22-11145; Revised: 21-Jan-2022, Manuscript No. AMSJ-22-11145(R); Published: 24-Jan-2022

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