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Clothing and Textiles Research Journal

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• Description
• Aims and Scope
• Editorial Board
• Abstracting / Indexing
• Submission Guidelines

Clothing and Textiles Research Journal (CTRJ) aims to be the journal of choice among scholars studying clothing, textiles, and related topics across the discipline. The journal publishes impactful scholarship that shapes the discipline. As the official journal of International Textile and Apparel Association Inc, it is peer-reviewed and is published quarterly. CTRJ publishes articles in the following areas:

• Textile science
• Apparel science and technology
• Consumer behavior
• Social psychology
• History and culture
• Merchandising and retailing
• Textile and apparel industry
• Education and pedagogy

Clothing & Textiles Research Journal is the official publication of the International Textile & Apparel Association, Inc. (ITAA, www.itaaonline.org ). The ITAA is a professional, educational association composed of scholars, educators, and students in the textile, apparel, and merchandising disciplines in higher education. This journal is a member of the Committee on Publication Ethics (COPE) .

CTRJ invites high-quality manuscripts relevant to the CTRJ audience by demonstrating originality, strong theoretical/conceptual foundation, appropriate methods/approaches, significant results/outcomes, and valuable implications. Please refer to the following description for each track.

Clothing and Textiles Research Journal (CTRJ) aims to be the journal of choice among scholars studying clothing, textiles, and related topics across the discipline. The journal publishes impactful scholarship that shapes the discipline. As the official journal of International Textile and Apparel Association Inc (ITAA), it is peer-reviewed and is published quarterly. The CTRJ home page is found at https://journals.sagepub.com/home/ctr

• America: History and Life
• Clarivate Analytics: Current Contents - Physical, Chemical & Earth Sciences
• Elsevier: Engineering Village
• Family Scholar
• Journal Citation Reports/Social Sciences Edition
• Psychological Abstracts
• Social Sciences Citation Index (Web of Science)
• Textile Technology Index
• VINITI Abstracts Journal
• World Textile Abstracts

HOW TO SUBMIT A NEW MANUSCRIPT VIA THE CTRJ PORTAL

Create an account.

Log into the following web address: http://mc.manuscriptcentral.com/ctrj . Unless you have an account already, the first step will be to set up an author account for the contact author. Click on "Create Account: New Users Click Here" and follow the directions. Your log-in ID is your e-mail address. If you have accessed the system previously but do not know your password, click on "Forgot password" and the system will send you a temporary password to enter the system. Once in the system, you will be prompted to set up a permanent password of your own choosing.

Please be aware that as you set up your account, certain information is required and you will not be able to proceed with a manuscript submission until the required information is complete. One such requirement is the selection of key words which is intended to identify your areas of expertise; it is not at this point associated with a particular manuscript. Likewise, you are asked as an author to indicate if you have expertise in quantitative, qualitative or both types of research. Again, this is not particular to a given manuscript but to your general expertise. If you become published through CTRJ , this information may be used in considering you as a reviewer of manuscripts.

Prepare Your Manuscript for Online Submission

Before a paper is submitted, please note the information below and adjust your manuscript accordingly. Be sure to complete this process because the following guidelines are used to screen all manuscripts, and these guidelines must be met for the manuscript to be sent out for review.

Manuscript Preparation

Manuscripts should be prepared using the APA style manual (7th Edition, Publication Manual of the American Psychological Association ). All of the manuscript must:

• Be double-spaced (including references, footnotes, endnote, block quotes, tables and figures of the manuscript).
• Use Times New Roman, font size 12 for all of the text in the manuscript, headings, figures, and tables.
• Use left only justification.
• Indent at the beginning of each paragraph by one-half inch.
• Not add extra line spaces between paragraphs.
• Use 1 inch margins on all four sides.
• Not number the pages.
• Use continuous line numbering on the main manuscript pages.

Our review procedures follow the double anonymize practice and thus do not include your name on the abstract, manuscript, or have any self-identifying information within the manuscript. Your manuscript will not be sent out for review if there is any identifying information on the manuscript, abstract, tables, or figures.

The main manuscript document should include two major sections (in this order): Main Body and References. This will be uploaded as the "main document." Other sections such as tables and figures are uploaded separately from the main document.

Sections in a manuscript may include the following (in this order): (1) Title page, (2) Abstract and Keywords, (3) Text, (4) Notes, (5) References, (6) Tables, (7) Figures, (8) Appendices, and (9) Biography.

1. Title page. Upload as title page. Please include the following:

• Full article title
• Acknowledgments and credits
• Each author’s complete name and institutional affiliation(s), address, phone/fax, and email
• Grant numbers and/or funding information
• Corresponding author should be noted

2. Abstract and Keywords. Note: The abstract (150 words) is submitted separately from the main manuscript. Omit author(s)’s names in this process. The abstract will be submitted in the first step of submitting the manuscript; you will type the abstract in the box so labeled in ScholarOneManuscript. Be sure to save before moving to step two. Do not upload the abstract when you are uploading your manuscript.

The keywords are submitted in the second step of submitting the manuscript. You will select from a list of key words or input your own keywords. Be sure to save before moving forward to step three or going back to step one.

3. Text. Begin article text (main manuscript) on a new page headed by the full article title.

a. Headings and subheadings. Subheadings should indicate the organization of the content of the manuscript. Generally, three heading levels are sufficient to organize text. Level Format   1 Centered, Bold, Upper & Lowercase Text begins as a new paragraph.   2 Flush Left, Bold, Upper & Lowercase Text begins as a new paragraph.   3 Flush Left, Bold Italic, Upper & Lowercase Text begins as a new paragraph.   4        Indented, Bold, Upper & Lowercase, Ending with a Period. Text begins one space after the period of the heading.   5        Indented, Bold Italic, Upper & Lowercase, Ending with a Period.  Text begins one space after the period of the heading.   b. Citations. For each text citation there must be a corresponding reference in the reference list, and for each reference in the reference list there must be a corresponding text citation. Corresponding citations and references must have identical spelling and year. If you have three or more authors, ALL in-text citations are First Author et al. – e.g. (Brown et al., 2020). There is no longer a difference between first and subsequent citations. Each text citation must include at least two pieces of information, author(s) and year of publication. Following are some examples of text citations: (i) Unknown Author : To cite works that do not have an author, cite the source by its title in the signal phrase or use the first word or two in the citation parentheses. Example: The findings are based on the study of students learning to format research papers ("Using XXX," 2001). (ii) Authors with the Same Last Name: use first initials with the last names to prevent confusion. Example: (L. Hughes, 2001; P. Hughes, 1998) (iii) Two or More Works by the Same Author in the Same Year: For two sources by the same author in the same year, use lower-case letters (a, b, c, etc.) with the year to order the entries in the reference list. The lower-case letters should follow the year in the in-text citation. The lower case letters would also be used in the reference list. Example: Research by Freud (1981a) illustrated that… (iv) Personal Communication: For letters, e-mails, interviews, and other person-to-person communication, a personal communication citation should include the communicator's name, the fact that it was personal communication, and the date of the communication. Do not include personal communication in the reference list. Example: (E. Clark, personal communication, January 4, 2009). (v) Unknown Author and Unknown Date: For citations with no author or date, use the title in the signal phrase or the first word or two of the title in the citation parentheses and use the abbreviation "n.d." (for "no date"). Example: The study conducted by the research division discovered that students succeeded with tutoring ("Tutoring and APA," n.d.).

4. Notes. If explanatory notes are required for your manuscript, insert a number formatted in superscript following almost any punctuation mark. Footnote numbers should not follow dashes ( — ), and if they appear in a sentence in parentheses, the footnote number should be inserted within the parentheses. The Footnotes should be added at the end of the manuscript after the references. The word “Footnotes” should be centered at the top of the page.

5. References. Basic rules for the reference list:-

• The reference list should be arranged in alphabetical order according to the authors’ last names.
• If there is more than one work by the same author, order them according to their publication date – oldest to newest (therefore a 2008 publication would appear before a 2009 publication).
• When listing multiple authors of a source use “&” instead of “and.”
• Capitalize only the first word of the title and of the subtitle, if there is one, and any proper names (i.e. only those words that are normally capitalized).
• Italicize the title of the book, the title of the journal/serial, the volume number of the journal/serial, and the title of the web document.
• Every citation in the text must have the detailed reference in the Reference section.
• Every reference listed in the Reference section must be cited in text.
• Do not use “et al.” in the Reference list at the end; names of all authors of a publication should be listed there. However, for works with more than seven authors list the first six authors' names and then have three ellipses followed by the last author's name. Example: Zed, N., Alright, L., Volks, B., Clark, N., Times, R., Eagle, T., . . . Max, G. (2014).

Here are a few examples of commonly found references. For more examples please check the APA style manual (7 th Ed). Note: Format the references with a hanging indent, the first line of each reference is flush left and the subsequent lines are indented one-half inch.

Book with publisher

Airey, D. (2010).  Logo design love: A guide to creating iconic brand identities . New Riders.

Book with editors & edition

Collins, C., & Jackson, S. (Eds.). (2007). Sport in Aotearoa/New Zealand society. Thomson.

English, B. (2013). A cultural history of fashion in the 20th and 21st centuries: From catwalk to sidewalk (2nd ed.). Bloomsbury.

Book having author & publisher the same

MidCentral District Health Board. (2008). District annual plan 2008/09 . Author.

Chapter in an edited book

Dear, J., & Underwood, M. (2007). What is the role of exercise in the prevention of back pain? In D. MacAuley & T. Best (Eds.), Evidence-based sports medicine (2nd ed., pp. 257-280). Blackwell. https://doi.org/10.1002/9780470988732.ch2

• Periodicals:

Journal article with more than one author (print)

Gabbett, T., Jenkins, D., & Abernethy, B. (2010). Physical collisions and injury during professional rugby league skills training. Journal of Science and Medicine in Sport, 13 (6), 578-583. https://doi.org/10.1016/j.jsams.2010.03.007

Journal article – 7 or more authors

Crooks, C., Ameratunga, R., Brewerton, M., Torok, M., Buetow, S., Brothers, S., … Jorgensen, P. (2010). Adverse reactions to food in New Zealand children aged 0-5 years. New Zealand Medical Journal, 123 (1327). http://www.nzma.org.nz/journal/123-1327/4469/

• Internet Sources:

Internet – no author, no date

What is ecommerce? Launch and grow an online sales channel . (n.d.). https://sell.amazon.com/learn/what-is-ecommerce

Internet – Organization / Corporate author

National Council of Textile Organizations. (2022, May 11). State of the U.S. Textile Industry Address [Press release]. http://www.textilesinthenews.org/wp-content/uploads/2022/05/2022-State-of-the-Industry-Press-Release-FINAL-5.9.2022.pdf

• Examples of various types of information sources:

Act (statute/legislation)

Anti-Smuggling Act, 19 U.S.C. § 1701 (1935). https://www.loc.gov/item/uscode1958-004019005/

Liz and Ellory. (2011, January 19). The day of dread(s) [Web log post].  https://www.travelblog.org/Oceania/Australia/Victoria/Melbourne/St-Kilda/blog-669396.html

Brochure / pamphlet (no author)

Ageing well: How to be the best you can be [Brochure]. (2009). Ministry of Health.

Conference Paper

Williams, J., & Seary, K. (2010). Bridging the divide: Scaffolding the learning experiences of the mature age student. In J. Terrell (Ed.), Making the links: Learning, teaching and high quality student outcomes. Proceedings of the 9th Conference of the New Zealand Association of Bridging Educators , 104-116.

DVD / Video / Motion Picture (including Clickview & Youtube)

Gardiner, A., Curtis, C., & Michael, E. (Producers), & Waititi, T. (Director). (2010). Boy: Welcome to my interesting world [DVD]. Transmission.

Ng, A. (2011). Brush with history. Habitus , 13 , 83-87.

Newspaper article (no author)

Little blue penguins homeward bound. (2011, November 23). Manawatu Standard , p. 5

Podcast (audio or video)

April, C., & Cassidy, Z. (Hosts). (2018–present).  Dressed: The History of Fashion  [Audio podcast]. Dressed Media.  https://www.iheart.com/podcast/105-dressed-the-history-of-fas-29000690/

Software (including apps)

ZOZO, INC. (2014). WEAR – Fashion Lookbook (Version 6.32.0) [Mobile application software]. https://apps.apple.com/us/app/wear-fashion-lookbook/id725208930

Television programme

Flanagan, A., & Philipson, A. (Series producers & directors). (2011). 24 hours in A & E [TV series]. Channel 4.

Thesis (print)

Smith, T. L. (2008). Change, choice and difference: The case of RN to BN degree programmes for registered nurses [Unpublished master’s thesis]. Victoria University of Wellington.

Thesis (online)

Mann, D. L. (2010). Vision and expertise for interceptive actions in sport (Doctoral dissertation, The University of New South Wales). http://handle.unsw.edu.au/1959.4/44704  

Non-English reference book, title translated in English

Real Academia Espanola. (2001). Diccionario de la lenguaespanola [Dictionary of the Spanish Language] (22nd ed.). Author.

IMPORTANT NOTE: To encourage a faster production process of your article, you are requested to closely adhere to the points above for references. Otherwise, it will entail a long process of solving copyeditor’s queries and may directly affect the publication time of your article.

6. Tables. They should be structured properly and numbered consecutively in the order in which they appear in the text. Also, each table should be placed on a separate page. Each table must have a clear and concise title. When appropriate, use the title to explain an abbreviation parenthetically. Example: Comparison of Median Income of Adopted Children (AC) v. Foster Children (FC). Headings should be clear and brief. Follow APA style manual (7th edition) guidelines; do not include vertical lines in your table. For each table include a callout within the manuscript indicating the approximate location of the table (e.g., Place Table X about here."). Each table should be uploaded separately from the main manuscript.

7. Figures. They should be numbered consecutively in the order in which they appear in the text and must include figure captions. Also, each figure should be placed on a separate page. Figures will appear in the published article in the order in which they are numbered initially. The figure resolution should be 300dpi at the time of submission. For each figure include a callout within the manuscript indicating the approximate location of the figure (e.g., Place Figure X about here."). Each figure should be uploaded separately from the main manuscript.

IMPORTANT: PERMISSION - The author(s) are responsible for securing permission to reproduce all copyrighted figures or materials before they are published in CTRJ. A copy of the written permission must be included with the manuscript submission.

8. Appendices. They should be lettered to distinguish from numbered tables and figures. Include a descriptive title for each appendix (e.g., “Appendix A. Variable Names and Definitions”). Cross-check text for accuracy against appendices. If you include an appendix/appendices it/they will be counted as part of the 30 page maximum manuscript length.

9. Biography. A biographical sketch(es) (maximum 60 words) should be uploaded for the author(s) during step five, the "File Upload" process. Be sure to identify the biography document/file as "Author bio."

Uploaded manuscript length. Sage has allowed CTRJ a certain number of pages for each volume (year) so uploaded manuscripts must be no longer than 30 pages (main document with reference list, all tables and figures, and appendix/appendices if appropriate). When the submission is uploaded, each page in the main manuscript document will be counted as one page. Each table and each figure will be counted as one page. For example, a 26-page manuscript, plus two tables, plus two figures, equals a 30-page uploaded manuscript. If your manuscript exceeds this length it will not be reviewed.

Using AI in Manuscript Preparation

Sage has provided the guidance regarding the use of AI in authoring manuscripts submitted to the journal articles ( https://us.sagepub.com/en-us/nam/chatgpt-and-generative-ai-0 ).

Authors are required to:

• Clearly indicate the use of language models in the manuscript, including which model was used and for what purpose. Please use the methods or acknowledgments sections, as appropriate.
• Verify the accuracy, validity, and appropriateness of the content and any citations generated by language models and correct any errors or inconsistencies.
• Provide a list of sources used to generate content and citations, including those generated by language models. Double-check citations to ensure they are accurate and are properly referenced.
• Be conscious of the potential for plagiarism where the large language models (LLM) may have reproduced substantial text from other sources. Check the original sources to be sure you are not plagiarizing someone else’s work.
• Acknowledge the limitations of language models in the manuscript, including the potential for bias, errors, and gaps in knowledge.
• Please note that AI bots such as ChatGPT should not be listed as authors on your submission.

Uploading Your Manuscript

Once the manuscript is prepared as described above, you can upload it and submit it through your Author Center in the Manuscript Central CTRJ portal. You should be taken to a screen that gives the link to your Author Center when you log into your account or when you complete the set up of your account.

Enter your Author Center and click on "Submit a Manuscript." The system will ask you for information regarding the manuscript and its authors. The contact author will enter the co-author names e-mail address(es) and that will send a prompt e-mail to the co-author asking him/her to complete the account information. You will be prompted to indicate the manuscript type (this is used to assign the AE) and other details of the manuscript. As you complete the information for the manuscript, you will have a field that allows you to type your cover letter directly into the system or browse and attach one. The cover page with author information is automatically generated as you complete the information about the manuscript, so a separate cover page with author identification is no longer necessary.

After you have uploaded the various files for your manuscript, you will need to “View Proof” before the system will allow you to submit. The system will then compile the various files into a single pdf file for you to review. If there are any problems with the compiled file, you may remove it, make corrections to the component files, and “View Proof” again. When you are satisfied with the compiled pdf, you are ready to submit the manuscript.

You may work on your submission in multiple stages by saving but not submitting your work prior to logging out. When you return to work on a manuscript submission that is not complete, you will access the manuscript through the Author Center by clicking on “Unsubmitted Manuscripts.” Once the manuscript submission is complete, you will receive a system-generated e-mail letting you know that the manuscript submission was successful.

Submitting a Revision

To submit your revised manuscript, log into http://mc.manuscriptcentral.com/ctrj and enter your Author Center, where you will find your manuscript title listed under "Manuscripts with Decisions." Under "Actions," click on "Create a Revision." Your manuscript number will be appended to denote a revision.

When submitting your revised manuscript, we prefer that your response document be copied and pasted into the appropriate dialogue box. In your response to editors and reviewers DO NOT use bolding, different fonts, or different colored fonts to highlight your revised information. DO NOT use tables to indicate comments and responses in table format. All of these types of formatting are not preserved in Manuscript Central when you copy and paste them in the dialogue box. CAPS are preserved. In order to expedite the processing of the revised manuscript, please be as specific as possible in your response to the reviewer(s).

IMPORTANT: Your original files are available to you when you upload your revised manuscript. Please delete all files that are being replaced with revised files.

• Permissions

Authors are responsible for determining whether any material submitted is subject to copyright or ownership rights (e.g., quotations, illustrations, trade literature, data), and authors are responsible for obtaining permission to use such material when permission is required. Authors are also responsible for obtaining Institutional Review Board (IRB) approval prior to the initiation of the research if human subjects are to be used.

Review Process

Each manuscript is reviewed by at least four people: the editor, as associate editor, and two reviewers. The final recommendation is sent to the author(s). Outcomes about each of the decision categories is available here . Reviewers’ comments provide information and suggestions to authors that may be helpful in completing revisions. Authors are given a deadline for returning manuscripts at every stage in the publication process. Following manuscript acceptance and prior to publication, authors will receive galleys to check for errors.

Authors who would like to refine the use of English in their manuscripts might consider using the services of a professional English-language editing company. We highlight some of these companies at http://www.sagepub.com/journalgateway/engLang.htm . Please be aware that Sage has no affiliation with these companies and makes no endorsement of them. An author's use of these services in no way guarantees that his or her submission will ultimately be accepted. Any arrangement an author enters into will be exclusively between the author and the particular company, and any costs incurred are the sole responsibility of the author.

Note: CTRJ uses American-English language conventions.

If you or your funder wish your article to be freely available online to nonsubscribers immediately upon publication (gold open access), you can opt for it to be included in Sage Choice, subject to the payment of a publication fee. The manuscript submission and peer review procedure is unchanged. On acceptance of your article, you will be asked to let Sage know directly if you are choosing Sage Choice. To check journal eligibility and the publication fee, please visit Sage Choice . For more information on open access options and compliance at Sage, including self/author archiving deposits (green open access) visit Sage Publishing Policies on our Journal Author Gateway.

Supplemental Material

This journal is able to host additional materials online (e.g. datasets, podcasts, videos, images etc) alongside the full-text of the article. For more information please refer to our guidelines on submitting supplementary files .

Two-parts Research Manuscripts

If authors wish to submit two-part research manuscripts, they must submit both manuscripts at the same time with a clear distinction between the objectives of part one and part two. The two manuscripts may have the same goal but should have independent research objectives, and therefore, independent methods, results and contributions must be stated. It will be at the editors’ discretion if the manuscripts will be processed as two parts, need to be made into a single manuscript, or be rejected prior to double anonymized reviews. Two-part papers, if approved for further review, can both be assigned to the same reviewers throughout the double anonymized review processes.”

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Making Fashion Sustainable: Waste and Collective Responsibility

Debbie moorhouse.

1 Department of Fashion & Textiles, University of Huddersfield, Huddersfield, West Yorkshire, UK

Fashion is a growing industry, but the demand for cheap, fast fashion has a high environmental footprint. Some brands lead the way by innovating to reduce waste, improve recycling, and encourage upcycling. But if we are to make fashion more sustainable, consumers and industry must work together.

As the demand for apparel and shoes has increased worldwide, the fashion industry has experienced substantial growth. In the last 15 years, clothing production has doubled, accounting for 60% of all textile production. 1 One particular trend driving this increase is the emergence of fast fashion. The newest trends in celebrity culture and bespoke fashion shows rapidly become available from affordable retailers. In recent years, a designer’s fashion calendar can consist of up to five collections per year, and in the mass-produced market, new stock is being produced every 2 weeks. As with many commodities today, mass production and consumption are often accompanied by mass wastage, and fashion is no different.

In fashion, trends rapidly change, and a drive to buy the latest style can leave many items with a short lifespan and consigned to the waste bin. Given that 73% of clothing ends up in landfills and less than 1% is recycled into new clothing, there are significant costs with regard to not only irreplaceable resources but also the economy via landfilling clothing. At present, it is estimated that £140 million worth of clothing is sent to landfills in the UK each year. 2 Although a significant proportion of recycled fibers are downgraded into insulation materials, industrial wipes, and stuffing, they still constitute only 12% of total discarded material.

The world is increasingly worried about the environmental and social costs of fashion, particularly items that have short lifespans. Mass-produced fashion is often manufactured where labor is cheap, but working conditions can be poor. Sweatshops can even be found in countries with stricter regulations. The transport of products from places of manufacture to points of sale contributes to the textile industry’s rising carbon footprint; 1.2 billion metric tons of CO 2 were reportedly emitted in 2015. 1 Textile dyeing and finishing are thought to contribute to 20% of the world’s water pollution, 3 and microfiber emission during washing amounts to half a million metric tons of plastic pollution annually. 4 Fashion’s water footprint is particularly problematic. Water is used throughout clothing production, including in the growth of crops such as cotton and in the weaving, manufacturing, washing, and dyeing processes. The production of denim apparel alone uses over 5,000 L of water 5 for a single pair of jeans. When you add this to consumer overuse of water, chemicals, and energy in the laundry process and the ultimate discard to landfills or incineration, the environmental impact becomes extremely high.

As demand for fast fashion continues to grow, so too does the industry’s environmental footprint. Negative impacts are starkly evidenced throughout the entire supply chain—from the growth of raw materials to the disposal of scarcely used garments. As awareness of the darker side of fashion grows, so too does demand for change—not just from regulatory bodies and global action groups but also from individual consumers. People want ethical garments. Sustainability and style. But achieving this is complicated.

Demand for Sustainable Fashion

Historically, sustainable brands were sought by a smaller consumer base and were typically part of the stereotype “hippy” style. But in recent years, sustainable fashion has become more mainstream among both designers and consumers, and the aesthetic appeal has evolved to become more desirable to a wider audience. As a result, the consumer need not only buy into the ethics of the brand but also purchase a desirable, contemporary garment.

But the difficulty for the fashion industry lies in addressing all sustainability and ethical issues while remaining economically sustainable and future facing. Sustainable and ethical brands must take into account fairer wages, better working conditions, more sustainably produced materials, and a construction quality that is built for longevity, all of which ultimately increase the cost of the final product. The consumer often wrestles with many different considerations when making a purchase; some of these conflict with each other and can lead the consumer to prioritize the monetary cost.

Many buyers who place sustainability over fashion but cannot afford the higher cost of sustainable garments will often forsake the latest styles and trends to buy second hand. However, fashion and second-hand clothing need not be mutually exclusive, as can be seen by the growing trend of acquiring luxury vintage pieces. Vintage clothing is in direct contrast to the whole idea of “fast fashion” and is sought after as a way to express individuality with the added value of saving something precious from landfills. Where vintage might have once been purchased at an exclusive auction, now many online sources trade in vintage pieces. Celebrities, fashion influencers, and designers have all bought into this vintage trend, making it a very desirable pre-owned, pre-loved purchase. 6 In effect, the consumer mindset is changing such that vintage clothing (as a timeless, more considered purchase) is more desirable than new products because of its uniqueness, a virtue that stands against the standardization of mass-market production.

Making Fashion Circular

In an ideal system, the life cycle of a garment would be a series of circles such that the garment would continually move to the next life—redesigned, reinvented, and never discarded—eliminating the concept of waste. Although vintage is growing in popularity, this is only one component of a circular fashion industry, and the reality is that the linear system of “take, make, dispose,” with all its ethical and environmental problems, continues to persist.

Achieving sustainability in the production of garments represents a huge and complex challenge. It is often quoted that “more than 80% of the environmental impact of a product is determined at the design stage,” 7 meaning that designers are now being looked upon to solve the problem. But the responsibility should not solely lie with the designer; it should involve all stakeholders along the supply chain. Designers develop the concept, but the fashion industry also involves pattern cutters and garment technologists, as well as the manufacturers: both producers of textiles and factories where garment construction takes place. And finally, the consumer should not only dispose, reuse, or upcycle garments appropriately but also wash and care for the garment in a way that both is sustainable and ensures longevity of the item. These stakeholders must all work together to achieve a more sustainable supply chain.

The challenge of sustainability is particularly pertinent to denim, which, as already mentioned, is one of the more problematic fashion items. Traditionally an expression of individualism and freedom, denim jeans are produced globally at 1.7 billion pairs per year 8 through mass-market channels and mid-tier and premium designer levels, and this is set to rise. In the face of growing demand, some denim specialists are looking for ways to make their products more sustainable.

Reuse and recycling can play a role here, and designers and brands such as Levi Strauss & Co. and Mud Jeans are taking responsibility for the future life of their garments. They are offering take-back services, mending services, and possibilities for recycling to new fibers at end of life. Many brands have likewise embraced vintage fashion. Levi’s “Authorized Vintage” line, which includes upcycled, pre-worn vintage pieces, not only exemplifies conscious consumption but also makes this vintage trend more sought after by the consumer because of its iconic status. All material is sourced from the company’s own archive, and all redesigns “are a chance to relive our treasured history.” 9

Mud Jeans in particular is working toward a circular business model by taking a more considered, “seasonless” approach to their collections by instead focusing on longevity and pieces that transcend seasons. In addition, they offer a lease service where jeans can be returned for a different style and a return service at end of life for recycling into new fiber. The different elements that make up a garment, such as the base fabrics (denim in the case of Mud jeans) and fastenings, are limited so the company can avoid overstocking and reduce deadstock. 10 This model of keeping base materials to a minimum has been adopted by brands that don’t specialize in denim, such as Adidas’s production of a recyclable trainer made from virgin thermoplastic polyurethane. 11 The challenge with garments, as with footwear, is that they are made up of many different materials that are difficult to separate and sort for recycling. These business models have a long way to go to be truly circular, but some companies are paving the way forward, and their transparency is highly valuable to other companies that wish to follow suit.

Once a product is purchased, its future is in the hands of the consumer, and not all are aware of the recycling options available to them or that how they care for their garments can have environmental impacts. Companies are helping to inform them. In 2009, Levi Strauss & Co. introduced “Care Tag for Our Planet,” which gives straightforward washing instructions to save water and energy and guidance on how to donate the garment when it is no longer needed. Mud Jeans follows a similar process by highlighting the need to break the habit of regular unnecessary washing and even suggesting “air washing.” 10

At the same time, designers are moving away from the traditional seasonal production cycle and into a more seasonless calendar. In light of the coronavirus disease 2019 (COVID-19) pandemic, Gucci’s creative director, Alessandro Michele, has announced (May 2020) that the Italian brand will end the traditional five fashion shows per year and will “hold shows just twice a year instead to reduce waste.” 12 This is a brave decision because it goes against the practice whereby designers were pressured for decades to produce more collections per year, but the hope is that it will be quickly followed by more brands and designers.

Transparency

The discussion around sustainable fashion practices has led to a growing demand from consumers for transparency in the supply chain and life cycle of fashion garments. Consumers want to be informed. They are skeptical of media hype and “greenwashing” by fast-fashion companies wanting to make their brand appear responsible. They want to know the origin of the product and its environmental and social impact.

Some companies are responding by seeking a better understanding of the environmental impacts of their products. In 2015, denim specializer Levi Strauss & Co. extensively analyzed the garment life cycle to consider the environmental impact of a core set of products from its range. The areas highlighted for greatest water usage and negative environmental impact were textile production and consumer laundry care; the consumer phase alone consumed 37% of energy, 13 fiber and textile production accounted for 36% of energy usage, and the remaining 27% was spent on garment production, transport, logistics, and packaging. 14 This life-cycle analysis has led to innovation in waterless finishing processes that use 96% less water than traditional fabric finishing. 15 As noted previously, transparency here also inspires the wider industry to do likewise. Other companies have also introduced dyeing processes that need much less water, and much work is focused on improving textile recycling.

But this discussion does not just apply to production. Some high-street brands are using a “take back” scheme whereby customers are invited to bring back unwanted clothing either for a discount on future purchases or as a way to offload unwanted items of clothing. Not only might this encourage consumers to buy more without feeling guilty, but the ultimate destination of these returned garments can also be unclear. Without further transparency, a consumer cannot make fully informed decisions about the end-of-life fate of their garments.

Collective Responsibility

The buck should not be passed when it comes to sustainability; it is about collective responsibility. Professionals in the fashion industry often feel that it is in the hands of the consumer—they have the buying power, and their choices determine how the industry reacts. One train of thought is that the consumer needs to buy less and that the fashion retail industry can’t be asked to sell less. However, if a sustainable life cycle is to be achieved, stakeholders within the cycle must also be accountable, and there are growing demands for the fashion industry to be regulated.

With the global demand for new clothing, there is an urgent need to discover new materials and to find new markets for used clothing. At present, garments that last longer reduce production and processing impacts, and designers and brands can make efforts in the reuse and recycling of clothing. But environmental impact will remain high if large quantities of new clothing continue to be bought.

If we want a future sustainable fashion industry, both consumers and industry professionals must engage. Although greater transparency and sustainability are being pursued and certain brands are leading the way, the overconsumption of clothing is so established in society that it is difficult to say how this can be reversed or slowed. Moreover, millions of livelihoods depend on this constant cycle of fashion production. Methods in the recycling, upcycling, reuse, and remanufacture of apparel and textiles are short-term gains, and the real impact will come from creating new circular business models that account for the life cycle of a garment and design in the initial concept. If we want to maximize the value from each item of clothing, giving them second, third, and fourth lives is essential.

Acknowledgments

Thank you for support, in writing this Commentary, to Dr. Rina Arya, Professor of Visual Culture and Theory at the School of Art, Design, and Architecture of the University of Huddersfield, West Yorkshire, UK.

Declaration of Interests

The author is the co-founder of the International Society for Sustainable Fashion.

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The global environmental injustice of fast fashion

• Rachel Bick 1   na1 ,
• Erika Halsey 1   na1 &
• Christine C. Ekenga   ORCID: orcid.org/0000-0002-6209-4888 1  

Environmental Health volume  17 , Article number:  92 ( 2018 ) Cite this article

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Fast fashion, inexpensive and widely available of-the-moment garments, has changed the way people buy and dispose of clothing. By selling large quantities of clothing at cheap prices, fast fashion has emerged as a dominant business model, causing garment consumption to skyrocket. While this transition is sometimes heralded as the “democratization” of fashion in which the latest styles are available to all classes of consumers, the human and environmental health risks associated with inexpensive clothing are hidden throughout the lifecycle of each garment. From the growth of water-intensive cotton, to the release of untreated dyes into local water sources, to worker’s low wages and poor working conditions; the environmental and social costs involved in textile manufacturing are widespread.

In this paper, we posit that negative externalities at each step of the fast fashion supply chain have created a global environmental justice dilemma. While fast fashion offers consumers an opportunity to buy more clothes for less, those who work in or live near textile manufacturing facilities bear a disproportionate burden of environmental health hazards. Furthermore, increased consumption patterns have also created millions of tons of textile waste in landfills and unregulated settings. This is particularly applicable to low and middle-income countries (LMICs) as much of this waste ends up in second-hand clothing markets. These LMICs often lack the supports and resources necessary to develop and enforce environmental and occupational safeguards to protect human health. We discuss the role of industry, policymakers, consumers, and scientists in promoting sustainable production and ethical consumption in an equitable manner.

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Fast fashion is a term used to describe the readily available, inexpensively made fashion of today. The word “fast” describes how quickly retailers can move designs from the catwalk to stores, keeping pace with constant demand for more and different styles. With the rise of globalization and growth of a global economy, supply chains have become international, shifting the growth of fibers, the manufacturing of textiles, and the construction of garments to areas with cheaper labor. Increased consumption drives the production of inexpensive clothing, and prices are kept down by outsourcing production to low and middle-income countries (LMICs).

Globally, 80 billion pieces of new clothing are purchased each year, translating to $1.2 trillion annually for the global fashion industry. The majority of these products are assembled in China and Bangladesh while the United States consumes more clothing and textiles than any other nation in the world [ 1 ]. Approximately 85 % of the clothing Americans consume, nearly 3.8 billion pounds annually, is sent to landfills as solid waste, amounting to nearly 80 pounds per American per year [ 2 , 3 ].

The global health costs associated with the production of cheap clothing are substantial. While industrial disasters such as the 1911 Triangle Shirtwaist Factory fire have led to improved occupational protections and work standards in the United States, the same cannot be said for LMICs. The hazardous working conditions that attracted regulatory attention in the United States and European Union have not been eliminated, but merely shifted overseas. The social costs associated with the global textile and garment industry are significant as well. Defined as “all direct and indirect losses sustained by third persons or the general public as a result of unrestrained economic activities,” the social costs involved in the production of fast fashion include damages to the environment, human health, and human rights at each step along the production chain [ 4 ].

Fast fashion as a global environmental justice issue

Environmental justice is defined by the United States Environmental Protection Agency, as the “fair treatment and meaningful involvement of all people regardless of race, color national origin, or income, with respect to the development, implementation, and enforcement of environmental laws, regulations and policies” [ 5 ]. In the United States, this concept has primarily been used in the scientific literature and in practice to describe the disproportionate placement of superfund sites (hazardous waste sites) in or near communities of color. However, environmental justice, as it has been defined, is not limited to the United States and need not be constrained by geopolitical boundaries. The textile and garment industries, for example, shift the environmental and occupational burdens associated with mass production and disposal from high income countries to the under-resourced (e.g. low income, low-wage workers, women) communities in LMICs. Extending the environmental justice framework to encompass the disproportionate impact experienced by those who produce and dispose of our clothing is essential to understanding the magnitude of global injustice perpetuated through the consumption of cheap clothing. In the context of Sustainable Development Goal (SDG) 12 which calls for sustainable consumption and production as part of national and sectoral plans, sustainable business practices, consumer behavior, and the reduction and elimination of fast fashion should all be a target of global environmental justice advocates.

Environmental hazards during production

The first step in the global textile supply chain is textile production, the process by which both natural and synthetic fibers are made. Approximately 90 % of clothing sold in the United States is made with cotton or polyester, both associated with significant health impacts from the manufacturing and production processes [ 6 ]. Polyester, a synthetic textile, is derived from oil, while cotton requires large amounts of water and pesticides to grow. Textile dyeing results in additional hazards as untreated wastewater from dyes are often discharged into local water systems, releasing heavy metals and other toxicants that can adversely impact the health of animals in addition to nearby residents [ 6 ].

Occupational hazards during production

Garment assembly, the next step in the global textile supply chain, employs 40 million workers around the world [ 7 ]. LMICs produce 90% of the world’s clothing. Occupational and safety standards in these LMICs are often not enforced due to poor political infrastructure and organizational management [ 8 ]. The result is a myriad of occupational hazards, including respiratory hazards due to poor ventilation such as cotton dust and synthetic air particulates, and musculoskeletal hazards from repetitive motion tasks. The health hazards that prompted the creation of textile labor unions in the United States and the United Kingdom in the early 1900’s have now shifted to work settings in LMICs. In LMICs, reported health outcomes include debilitating and life-threatening conditions such as lung disease and cancer, damage to endocrine function, adverse reproductive and fetal outcomes, accidental injuries, overuse injuries and death [ 9 , 10 , 11 ]. Periodic reports of international disasters, such as the 2013 Rana Plaza factory collapse which killed 1134 Bangladeshi workers, are stark reminders of the health hazards faced by garment workers. These disasters, however, have not demonstrably changed safety standards for workers in LMICs [ 12 ].

Textile waste

While getting finished garments to consumers in the high-income countries is seen as the end of the line for the fashion industry, environmental injustices continue long after the garment is sold. The fast fashion model encourages consumers to view clothing as disposable. In fact, the average American throws away approximately 80 pounds of clothing and textiles annually, occupying nearly 5% of landfill space [ 3 ]. Clothing not sent directly to the landfill often ends up in the second-hand clothing trade. Approximately 500,000 tons of used clothing are exported abroad from the United States each year, the majority ending up in LMICs [ 8 ]. In 2015, the United States exported more than $700 million worth of used clothing [ 13 ]. Second-hand clothing not sold in the United States market is compressed into 1000-pound bales and exported overseas to be “graded” (sorted, categorized and re-baled) by low-wage workers in LMICs and sold in second-hand markets. Clothing not sold in markets becomes solid waste, clogging rivers, greenways, and parks, and creating the potential for additional environmental health hazards in LMICs lacking robust municipal waste systems.

Solutions, innovation, and social justice

Ensuring environmental justice at each stage in the global supply chain remains a challenge. Global environmental justice will be dependent upon innovations in textile development, corporate sustainability, trade policy, and consumer habits.

Sustainable fibers

The sustainability of a fiber refers to the practices and policies that reduce environmental pollution and minimize the exploitation of people or natural resources in meeting lifestyle needs. Across the board, natural cellulosic and protein fibers are thought to be better for the environment and for human health, but in some cases manufactured fibers are thought to be more sustainable. Fabrics such as Lyocell, made from the cellulose of bamboo, are made in a closed loop production cycle in which 99% of the chemicals used to develop fabric fibers are recycled. The use of sustainable fibers will be key in minimizing the environmental impact of textile production.

Corporate sustainability

Oversight and certification organizations such as Fair Trade America and the National Council of Textiles Organization offer evaluation and auditing tools for fair trade and production standards. While some companies do elect to get certified in one or more of these independent accrediting programs, others are engaged in the process of “greenwashing.” Capitalizing on the emotional appeal of eco-friendly and fair trade goods, companies market their products as “green” without adhering to any criteria [ 14 ]. To combat these practices, industry-wide adoption of internationally recognized certification criteria should be adopted to encourage eco-friendly practices that promote health and safety across the supply chain.

Trade policy

While fair trade companies can attempt to compete with fast fashion retailers, markets for fair trade and eco-friendly textile manufacturing remain small, and ethically and environmentally sound supply chains are difficult and expensive to audit. High income countries can promote occupational safety and environmental health through trade policy and regulations. Although occupational and environmental regulations are often only enforceable within a country’s borders, there are several ways in which policymakers can mitigate the global environmental health hazards associated with fast fashion. The United States, for example, could increase import taxes for garments and textiles or place caps on annual weight or quantities imported from LMICs. At the other end of the clothing lifecycle, some LMICs have begun to regulate the import of used clothing. The United Nations Council for African Renewal, for example, recently released a report citing that “Rwanda, Tanzania and Uganda are raising taxes on secondhand clothes imports and at the same time offering incentives to local manufacturers” [ 15 ].

The role of the consumer

Trade policies and regulations will be the most effective solutions in bringing about large-scale change to the fast fashion industry. However, consumers in high income countries have a role to play in supporting companies and practices that minimize their negative impact on humans and the environment. While certifications attempt to raise industry standards, consumers must be aware of greenwashing and be critical in assessing which companies actually ensure a high level of standards versus those that make broad, sweeping claims about their social and sustainable practices [ 14 ]. The fast fashion model thrives on the idea of more for less, but the age-old adage “less in more” must be adopted by consumers if environmental justice issues in the fashion industry are to be addressed. The United Nation’s SDG 12, “Ensure sustainable consumption and production patterns,” seeks to redress the injustices caused by unfettered materialism. Consumers in high income countries can do their part to promote global environmental justice by buying high-quality clothing that lasts longer, shopping at second-hand stores, repairing clothing they already own, and purchasing from retailers with transparent supply chains.

Conclusions

In the two decades since the fast fashion business model became the norm for big name fashion brands, increased demand for large amounts of inexpensive clothing has resulted in environmental and social degradation along each step of the supply chain. The environmental and human health consequences of fast fashion have largely been missing from the scientific literature, research, and discussions surrounding environmental justice. The breadth and depth of social and environmental abuses in fast fashion warrants its classification as an issue of global environmental justice.

Environmental health scientists play a key role in supporting evidence-based public health. Similar to historical cases of environmental injustice in the United States, the unequal distribution of environmental exposures disproportionally impact communities in LMICs. There is an emerging need for research that examines the adverse health outcomes associated with fast fashion at each stage of the supply chain and post-consumer process, particularly in LMICs. Advancing work in this area will inform the translation of research findings to public health policies and practices that lead to sustainable production and ethical consumption.

Abbreviations

Low and middle-income countries

Sustainable Development Goal

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Rachel Bick and Erika Halsey contributed equally to this work.

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Evaluation and trend of fashion design research: visualization analysis based on CiteSpace

• Yixin Zou   ORCID: orcid.org/0000-0002-1880-6382 1 ,
• Sarawuth Pintong 2 ,
• Tao Shen 3 &
• Ding-Bang Luh 1  

Fashion and Textiles volume  9 , Article number:  45 ( 2022 ) Cite this article

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Fashion or apparel refers to a topic discussed publicly as an indispensable discipline on a day-to-day basis, which has aroused rising attention from academic sessions over the past two decades. However, since the topic of fashion design covers knowledge in extensive ranges and considerable information, scholars have not fully grasped the research field of fashion design, and the research lacks directional guidance. To gain more insights into the existing research status and fronts in the fashion design field, this study conducts a quantitative literature analysis. The research of this study is conducted by employing CiteSpace technology to visualize and analyze 1388 articles regarding “fashion design” in the Web of Science (WOS) Core Collection. To be specific, the visualization and the analysis concentrate on the annual number of articles, author collaboration, institutional collaboration, literature citations, keywords clustering, and research trend evolution of the mentioned articles. As highlighted by this study, the effect of the US and the UK on academic research in fashion design is relatively stronger and extensive. Sustainable fashion refers to the research topic having aroused more attention since 2010, while new research topics over the past few years consist of “wearable fashion”, “transgender fashion” and “medical fashion”. The overall research trend of fashion design is developing as interdisciplinary cross research. This study systematically reviews the relevant literature, classifies the existing research status, research hotspots and frontier trends in the academic field of “fashion design”, and presents the knowledge map and information of literature for researchers in relevant fields.

Introduction

In academic research and writing, researchers should constantly search relevant literature to gain systematic insights into the subject area (e.g., the major research questions in the field, the seminal studies, the landmark studies, the most critical theories, methods and techniques, as well as the most serious current challenges). The process to answer the mentioned questions refers to an abstract process, which requires constant analysis, deduction and generalization. Any literature emerging over time may be critical, any research perspective may cause novel inspiration, and any detail can be the beginning of the subsequent research. However, when literature is being sorted and analyzed, if judgment only complies with personal experience, important literature will be inevitably missed, or the research direction will be lost in the research. For the process of conducting literature analysis, Hoover proposed that the quantitative methods of literature represent elements or features of literary texts numerically, applying effective, accurate and widely accepted mathematical methods to measure, classify and analyze literature quantitatively (Hoover, 2013 ). On this basis, literary data and information are more comprehensively processed. Prof. Chaomei Chen developed CiteSpace to collect, analyze, deliver and visualize literature information by creating images, diagrams or animations, thereby helping develop scientific knowledge maps and data mining of scientific literature. Knowledge visualization primarily aims to detect and monitor the existing state of research and research evolution in a knowledge field. Knowledge visualization has been exploited to explore trends in fields (e.g., medical, management science, biomedicine and biotechnology).

However, the international research situation in fashion design has not been analyzed by scholars thus far. Fashion, a category of discourse, has been arousing scholars’ attention since the late nineteenth century (Kim, 1998 ). In such an era, fashion is recognized by individuals of all classes and cultures, and it is publicly perceived. The field of fashion design is significantly correlated with people's lives (Boodro, 1990 ), and numerous nations and universities have long developed courses regarding fashion design or fashion. Besides, the development of fashion acts as a symbol of the soft power of the country. The discussion on fashion trend, fashion designer, fashion brands, artwork and other topics in the society turns out to be the hotspot discussed on a nearly day-to-day basis, and the discussion in the society even exceeds the academic research. However, the academic research of fashion design refers to a topic that cannot be ignored. The accumulation and achievements of academic research are manifested as precipitation of knowledge for developing the existing fashion field, while significantly guiding future generations. Studying the publishing situation and information in fashion design will help fashion practitioners or researchers classify their knowledge and provide them with novel inspiration or research and literature directions.

This study complies with the method of quantitative literature analysis, and CiteSpace software is adopted to analyze the literature in fashion design. Through searching web of science (WOS) Core Collection, 1388 articles regarding “fashion design” are retained. Co-citation, co-authoring and co-occurrence analysis refer to the major functions of CiteSpace. This study analyzes the articles regarding “fashion design”, and the focus is placed on the annual publication volume, author collaboration, institutional collaboration, national collaboration, literature citations, keyword co-occurrence, keyword clustering, and the research evolution, and visualized the literature and research as figures of these articles. The results here are presented as figures. This study provides the fronts knowledge, the current research status research, the hotspots and trends in fashion design research.

In this study, CiteSpace technology is adopted to analyze all collected literature data. CiteSpace, developed by Professor Chaomei Chen, an internationally renowned expert in information visualization at Drexel University, USA (Wang & Lu, 2020 ), refers to a Java application to visually analyze literature and co-citation networks (Chen, 2004 ). CiteSpace is capable of displaying burst detection, mediated centrality and heterogeneous networks regarding literate information. Visual analysis of the literature by using CiteSpace covers three functions, i.e., to identify the nature of specialized research frontiers, to label and cluster specialized research areas, as well as to identify the research trends and abrupt changes based on the data derived from the analysis. CiteSpace provides a valuable, timely, reproducible and flexible method to track the development of research trends and identify vital evidence (Chen et al., 2012 ).

To analyze the existing status of research and publications on the topic of “fashion design” in academia and different nations, the “Web of Science” (WOS) database is adopted as the data collection source here. Web of Science provides seamless access to existing and multidisciplinary information from approximately 8700 of the most extensively researched, prestigious and high-impact research journals worldwide, covering Science Citation Index (SCI) Social Science Citation Index (SSCI), as well as Arts and Humanities Citation Index (A&HCI) (Wouters 2006 ). Its vital feature is that it covers all article types, e.g., author information, institutional addresses, citations and References (Wouters 2006 ). Research trends and publications in specific industry areas can be effectively analyzed.

To be specific, the “Web of Science Core Collection” database is selected in Web of Science and the indexing range includes SCI, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI-S, BKCI-SSH, ESCI databases. This step aims to expand the search scope of journals and search a maximal amount of relevant literature. A “subject search” is adopted, covering the search title, the abstract, the author and the keywords. There have been other areas of research on clothing or textiles (e.g., textile engineering and other scientific research areas). However, in this study, to ensure that the topic of analysis is relevant, the subject search is conducted by entering “fashion design” or “Costume design” clothing design”, or “Apparel design”, and only academic research regarding fashion design is analyzed. To ensure the academic nature of the collected data, the search scope here is the “article” type. The time frame was chosen from 2000 to 2021 to analyze the publications on “fashion design” for past 21 years. After this operation, the results of the search were filtered two times. The search was conducted until September 23, 2021, and 1388 articles were retained on the whole.

All bibliographic information on the pages was exported into text format and subsequently analyzed with CiteSpace software. Retrieved publications were filtered and copies were removed in CiteSpace to ensure that the respective article is unique and unduplicated in the database. 1388 articles filtered down from 2000 to 2021 were analyzed in all time slices of 1 year, and most of the cited or TOP 50 of the respective item were selected from each slice.

Results and Discussion

Publications in the last 21 years.

The publication situation of WOS database with “fashion design” as the theme from 2000 to 2021 shown in Fig.  1 . On the whole, the number of articles published on the theme of “fashion design” is rising from 2000 to 2007, the number of articles published each year is almost identical, and the number of articles published in 2008–2009 is slightly increasing. The second wave of growth is in 2011, with an increase about 60% compared with the number of publications in 2010, and it has been rising year by year. 2017 is the peak year with a high volume of 171 publications. 2018 shows another decline, whereas over 100 publications remain. 2018, 2019 and 2020 show continuous growths again. As of September 2021, the number of publications in 2021 is 77. Although the number of articles declines in 2018, the overall number of articles over the past 2 decades is still rising. The reason for the low number of publications around year of 2000 is that fashion as a category of discourse has aroused the attention of scholars from the late nineteenth century (Kim, 1998 ). The year-on-year increase is explained as research on fashion is arousing rising attention from scholars. The significant increase in research papers regarding “fashion design” in 2016 and 2017 is that around 2016, and the fashion industry has been impacted by technological developments. Moreover, the way in which design and clothing made has incorporated considerable technological tools (e.g., 3D printing and wearable technology).

Total publications and sum of times cited from 2000 to 2021 according to the web of science. Data updated to September 2021

Author co-authorship analysis

The knowledge map of cited authors based on publication references can present information regarding influential research groups and potential collaborators (Liang, Li, Zhao, et al., 2017 ). The function of co-authorship analysis is employed in CiteSpace to detect influential research groups and potential collaborators. Citespace can calculate the most productive authors in related fields. Table 1 shows that the most productive authors, which related with fashion design theme.

The author with the maximal number of publications is Olga Gurova from Laurea University of Applied Sciences in Helsinki (Finland). Her research area has focused on consumer nationalism and patriotism, identity politics and fashion, critical approach to sustainability and wearable technology and the future. Sustainable design has been a hotspot over the past decade and continues to be discussed today, and wearable technology has been a research hotpot in recent years. Olga Gurova is in first place, thereby suggesting the attention given to the mentioned topics and studies in society and fashion area. The second ranked author is Marilyn Delong from University of Minnesota (The USA). The research area consists of Aesthetics, Sustainable apparel design, History and Material Culture, Fashion Trends, Cross-cultural Influence on Design, as well as Socio-psychological aspects of Clothing. The author with the identical number of 7 publications is Kirsi Niinimäki from Aalto University (Finland). Her research directions consist of sustainable fashion and textiles, so her focus has been on the connection between design, manufacturing systems, business models and consumption habits.

For Caroline Kipp, her research area includes modern and contemporary textile arts, decorative arts and craft, craftivism, jacquard weaving, French kashmere shawls, as well as color field painting. For Nick Rees-Roberts, his research area includes fashion film, culture and digital media. Veronica Manlow from Brooklyn College in the Koppelman School of Business (USA.) The research field consists of creative process of fashion design, organizational culture and leadership in corporate fashion brands. Kevin Almond has made a contribution to creative Pattern Cutting, Clothing/Fashion Dichotomies, Sculptural Thinking in Fashion, Fashion as Masquerade. Hazel Clark, and his research field covers fashion theory and history, fashion in China, fashion and everyday life, fashion politics and sustainment. As revealed from the organization of the authors' work institutions and nations in the table, most of the nations with the maximal frequency of publications originate from the US, thereby revealing that the US significantly supports fashion design research. In general, the research scope covers fashion design, culture, mass media, craft, marketing, humanities, technology and etc. Based on the statistics of authorship collaboration, this study indicates that scholars from the US and Finland take up the top positions in the authorship publication ranking.

Moreover, Fig.  2 shows the academic collaborations among authors, which are generated by selecting the unit of analysis, setting the appropriate thresholds. The distance between the nodes and the thickness of the links denote the level of cooperation among authors (Chen & Liu, 2020 ). The influential scholars and the most active authors have not yet developed a linear relationship with each other, and collaborative networks have been lacked. It is therefore revealed that the respective researcher forms his or her own establishment in his or her own field, whereas seldom forms collaborative relationships. Thus, this study argues that to improve the breadth and depth of the field of fashion design research, the cooperation and connections between authors should be strengthened (e.g., organizing international collaborative workshops, joint publications and academic conferences) to up-regulate the amount of knowledge output and create more possibilities for fashion research.

Author collaboration network

Institution co-authorship analysis

The number of articles issued by the respective institution and the partnership network are listed in Table 2 .

Figure  3 shows the collaborative relationships among research institutions, while the distance between nodes and the thickness of links represents the level of collaborative institutions. The size of the nodes represents the number of papers published by the institutions, while the distance between the nodes and the thickness of the links indicates the level of cooperation between the institutions.

Co-relationships between nations in “fashion design” research

As indicated from the Table 2 , most of the top ten publishing institutions are from the UK and the US, and two universities in the UK rank first and second with more than 20 publications, and most of the institutions are from the UK, thereby demonstrating that the UK's achievements and effect in the field of fashion are far more than other nations. According to the cooperation network between institutions, there are 5 main cooperation networks. First, London College of Fashion, achieving the most number of articles, and there are 8 institutions cooperating with London College of Fashion, among which the closest cooperation is with Parsons Paris Sch Art & Design in the US, whose more influential areas of articles are consumers behavior, unisex clothing, third gender, communicating sustainability, real installation, Italian fashion system, global market, local culture, knitwear and textile design, international scenario, conventional craft methodologies, innovative potential, as well as 3D software application.

The second network of partnerships concentrates on University Arts London as the central node, with frequent collaborators (e.g., Center St Martins Coll Art & Design, Loughborough University Technology, Hut Grp, Sothebys Inst Art, Project Mobile Ising Sexual Hlth, De Montfort University, University of Southampton, Royal Soc Arts, Royal Coll Art). The more influential areas of publication are: electric corset, future histories, clothing sustainability, south Asian youth culture, textile patterning technique, hybrid functional clothing, UK fashion upcycling businesses, rematerializing crafting understanding, fashion designers apprentice, design ethnography approach, developing apparel design guideline and so on.

The third collaborative network is formed by Aalto University, DongHua University, University Southern Denmark, and other institutions, with more influential publications below: haring clothe; fashion designer; Chinese ethnic minority; design recipe; clothing carbohydrate binge; training design researcher; fashion design; traditional handicraft, etc.

The fourth cooperation network consists of Hong Kong Polytechnic University, Ryerson University, Queensland University of Technology, Tsinghua University, York University, Art Comm China Fashion Associate, and other institutions. The influential publications areas are: Zhongshan suit; creative application; clothing design; Chinese male; medical moment; menswear design preference; cross-national study; aesthetic aspect; evaluative criteria; disease prevention.

The fifth network organized by University Minnesota Sch Design, Seoul Natl University, Cornell University, University Calif Davis, University North Carolina Greensboro, Colorado State University and others, with the influential research areas if sustainable apparel design practice, sustainable clothing, female users’ perspective, up-cycling design process; apparel design education; strategic ambiguity effective instructional tool, as well as apparel design.

It is noteworthy that: (1) although the University of Leeds has the maximal number of papers, it has not formed a collaborative network with the University of Leeds in the analysis of collaborative relationships; UK institutions have achieved prominent research results, but in the analysis of the number of author papers, and most of the authors with more papers originate from the US. In brief, British institutions, especially university institutions, generally achieve a high level of research, whereas there are fewer authors with a particularly high number of publications. (2) Asian culture covering South Asian youth culture and Chinese fashion culture appear 3 times in the research network as one of the important research areas that combine fashion and culture. (3) Moreover, the respective sub-network has exchanges and cooperation with universities or institutions from other nations, whereas the distance between the sub-networks is long. It indicates that the self-networks have not yet formed a unified network structure among each other, and are only active within their own groups. The issuing institutions that enter the top ten are nearly universities, which acts as the main power of academic articles punishment, and few other institutions (e.g., companies or social organizations). Accordingly, the cooperation between institutions should be boosted. It is necessary to exploit their strengths and advantages, expand the research field and research scope, and make more contributions to the research on “fashion design” topic.

Country co-authorship analysis

Table 3 lists the studies status on “fashion design” in different nations. The US has the maximal number of publications with 257 articles, followed by the UK with 226 articles.

Figure  4 shows the co-relationships between differently nations. The nodes in the Fig.  4 represent nations, and their sizes indicate the number of articles from different nations. The distance between the nodes and the thickness of the links represents the level of cooperation between nations. The purple rings of the purple nodes indicate high centrality, which means that the mentioned nodes are key points connecting different parts of the network. The thicker the purple ring, the higher the centrality of that node. With the U.S. as the centrality degree, it links France, Lebanon, Scotland, Nigeria, Italy, Ireland, South Korea, Thailand, England, the People’s Republic of China, Sweden, Turkey, Canada, Netherlands, Finland, Switzerland, Australia, 17 nations in total. With the U.S. as the centrality degree, it links France, Italy, Brazil, Scotland, Denmark, Thailand, South Africa, Turkey, Australia, Sweden, Wales, the People’s Republic of China, North Ireland, Sweden, Canada, Netherlands, Finland, Switzerland, Germany, Egypt, America, a total of 22 nations are linked. Although the previous information on the volume of publications by institutions shows that institutions in the UK nations are dominant. However, the total volume of publications compared, the US is higher than the UK, thereby demonstrating that some other institutions or organizations besides universities also contribute to the volume of publications. Furthermore, as revealed from the degree of crossover of the cooperation network in Fig.  5 , except for the UK and the US, there is but not close cooperation and connection between other nations, the nodes are far away, and the more prominent node centers are the US and the UK. This leads to the conclusion that nations should strengthen the intensity and density of cooperation and enhance their influence in fashion design research.

Linking relationships between nations

Cited references network among the literature

As indicated from the analysis of the previous network of authors, collaborating institutions and collaborating nations, the research results are more superior in the US and the UK. However, given the research statistics issued by WOW Travel in 2019, the top 10 influential nations in the field of fashion consist of the USA, the UK, Italy, France, Japan, Netherlands, Germany, Spain, the People' Republic of China, including Korea. This phenomenon is likely to be attributed to the different language systems of nations other than the UK and the US, and that some of the mentioned nations have their own search databases for articles. For this reason, the publication data are not retrieved. Other nations should actively publish in English or international academic journals to expand their effect on the international research field, not only in fashion trends or arts work creation.

Co-scholar study based on cited references

The literature can be termed a knowledge base, as well as a source of knowledge and ideas. A novel research cannot be outputted without the contribution of knowledge from previous authors, as well as the insights into and mastery of the literature. Co-scholar analysis builds a literature co-citation network by selecting several representative studies as the object of analysis. Vital references in a specific research area can be detected, and a knowledge graph of cited authors by complying with published references can present information regarding influential research field and knowledge. Table 4 shows the most distribution of the references in fashion design theme. Figure  5 is an analysis of the highly cited literature network. Describe and summarize the high-cited literature based on the information in the two charts.

The node density is 0.00313 for cited reference network, thereby illustrating that fewer links and co-citations among the literature. For the citation status of the respective literature, the analysis begins with the work of Fletcher Kate, appearing more frequently in the table, Fletcher Kate’s 2016 book “ Craft of use: post-growth fashion ” pertains to label 0 “fashion system”. The book explores “craft of use”, using ingenious ideas and practices to make garments/fabrics present as an alternative, dynamic, experiential framework for articulating and promoting sustainability in the fashion world (Fletcher, 2016 ). Fletcher, 2012 and Fletcher and Tham, 2014 pertain to the identical cluster 6. Fletcher Kate’s 2012 “ Fashion Sustainability ” counting 12 times, with ranking No.1 in Table 4 . The book’s contents about fashion sustainability in three main parts, i.e., fashion products, fashion system, as well as fashion design practice (Fletcher, 2012 ). According to the graph, Fletcher Kate's book exhibits a high frequency in the citation network and overall citation. The book from Fletcher ( 2008 ) talking about sustainable fashion and clothing, which has the second maximal citation, frequency of 9. The book is primarily concerned with sustainable fashion and sustainable design. Routledge handbook of sustainability and fashion, published in 2014. The major contents focus on sustainability, and fashion recognizes the complexity of aligning fashion with sustainability. It explores fashion and sustainability at the levels of products, processes and paradigms, while employing a truly multi-disciplinary approach to critically question and suggest creative responses to issues, i.e., Fashion in a post-growth society, Fashion, diversity and equity, Fashion, fluidity and balance across natural, social and economic systems, social sciences, arts and humanities interested in sustainability and fashion (Fletcher and Tham, 2014 ). Fletcher Kate made prominent contributions to fashion sustainable design and sustainable development.

The third most frequently cited book is Manzini’s ( 2015 ) book " Design, When Everybody Designs ", with eight citations. It presents Design and social Innovation, Collaborative organizations and encounters, Design ways and Design for novel cultures (Manzini, 2015 ). The ideas of social innovation design and sustainable design are presented.

The journal of “You are what you wear: How plus-size fashion figures in fat identity formation” from Lauren Downing Peters, takes up the third place in terms of frequency of citations. The research regarding fat identities are formed through the intimate practices of self-fashioning and via social channels (e.g., shopping and fashion blogging), thereby bridging the fields of fat studies and fashion studies. It also considers issues of performativity and is reflected as a situated bodily practice. Fashion design is combined with humanistic care (Peters, 2014 ).

The book Fashion and Culture: Cultural Studies, Fashion Studies, from SB Kaiser, 2012 be cited 7 times. The main topic is the integration of fashion, design and culture (Kaiser, 2012 ). Jenss ( 2016 ), Fashion Studies: Research Methods, Sites, and Practices , is cited 6 times. The book explores fashion in wide-ranging contexts by stressing material culture and ethnographic approaches in fashion studies. Ryan ( 2014 ), Garments of Paradise: Wearable Discourse in the Digital Age, research about the wearable fashion based on the new era (Ryan, 2014 ). Fashion design industry impressions of current sustainable practices , 2014, Noël Palomo-Lovinski, the article explores professional fashion designers' understanding and awareness of current sustainable design (Palomo-Lovinski & Hahn, 2014 ).

As revealed from the analysis of the co-cited literature, the literature and research areas arousing more attention in the fashion design area from 2009 to 2016 consist of fashion sustainable design and sustainable development, fashion humanities, fashion design strategies, wearable technology, fashion and culture, and Chinese fashion.

Co-occurrence analysis for the research frontier and trends

Hot research topics.

A research hotspot refers to a research issue or topic explored by a relatively large number of articles that are intrinsically linked within a certain period. The keywords are the authors' high distillation and summary of the core content of the article, reflecting the research value and direction of the article. Keywords achieving high frequency are generally exploited to identify the hot issues in a research field. The noun phrases extracted from the article can also represent the hotspot of research in a particular field to a certain extent. Clustering analysis of keywords is performed by CiteSpace software to generate keyword clustering knowledge graphs (Hu et al., 2019 ). The mentioned clusters reflect the last 21 years of topics in fashion design research (shown in Fig.  6 ).

Co-citation clusters about “fashion design” theme

The silhouette scores of the major cluster that were focused on in the review were sufficiently high. Analyzing the size of clusters by Silhouette and size, and the cluster labels could be defined by log-likelihood ratio (LLR) to explain the term contained in. The top 10 keywords in the respective cluster are summarized in the Table 5 .

As indicated from the analysis of the keywords in the respective cluster, the research content of the respective cluster overlaps with each other. However, international research in the “fashion design” field can be summarized as eight major research fields: “Skill/Tools/Technologies/Material with fashion innovation”, “Wearing class and Art”, “Sustainable fashion”, “fashion design/ fashion designer and arts work”,” Education”, “fashion industry and business”, “fashion with culture”, “Medical fashion”.

(1) Skill/ Technologies/Materials with fashion design innovation. The common label that appears are: Wearable new materialism; technological innovation, wearable technologies, future mode, digital exploration, digital design, technological innovation, smart material systems. Promoted by the rapid development of society and science and technology, interdisciplinary learning and research has also emerged in the fashion industry. Innovative design, fabric innovation or innovative display combined with high-tech, novel materials and virtual or digital industries turns out to be a novel topic of great interest in the fashion industry (Barati, Karana, & Hekkert, 2019 ; Burns, 2022 ; Bower & Sturman, 2015 ; Chuah, Rauschnabel, Krey, et al., 2016 ; Feng, 2020 ; Ferrara, 2019 ; Huang, Tang, Liu, et al., 2018 ; Juhlin, 2015 ; Rocamora, 2017 ; Smelik, 2018 ; Smelik et al., 2016 ; Ünay & Zehir, 2012 ).

(2) Wearing culture and Arts. The common labels consist of dressing strategies, transgender fashion, men fashion, human right, accessorizing bodyscape, popular art, applied art. It focuses on different types of people, human rights and humanistic concerns, including unisex fashion. The collection is designed and worn with a mix of different arts, cultures and trends, as well as regional dress cultures, such as Chinese. The collection is inclusive of fashion and highly integrated with art (Chance, Camilleri, Winstone, et al., 2016 ; Geczy & Karaminas, 2011 ; Hancock, Johnson-Woods, & Karaminas, 2013 ; Martin, 1999 ; Mocenco, Olaru, Popescu, et al., 2016 ; Nelson & Hwang, 2019 ; Sabine Linke, 2013 ; Tullio-Pow, Yaworski, & Kincaid, 2021 ; Vainshtein, 2012 ).

(3) Sustainable fashion. Including the labels of sustainability knowledge, sustainable fashion, sustainable practice, communicating sustainability, sustainability knowledge, sustainable consumption, etc. Sustainable development and sustainability are a hotspot of discussion in academia. Sustainable fashion, i.e., Eco-fashion, refers to part of a growing design philosophy and sustainable design trend aiming to create a sustainable system capable of supporting environmental, socially responsible and sociocultural aspects (Aakko & Koskennurmi-Sivonen, 2013 ; De Brito, Carbone, & Blanquart, 2008 ; Fletcher, 2013 ; Gordon & Hill, 2015 ; Gwilt, 2020 ; Henninger, Alevizou, & Oates, 2016 ; Lundblad & Davies, 2016 ; Mukendi, Davies, Glozer, & McDonagh, 2020 ; Niinimäki, 2013 ; Shen, 2014 ; Wang & Lu, 2020 ).

(4) Fashion design, fashion designer and arts work. As the fundamental topic in fashion design field, the labels consist of design strategies, young fashion designer, costume design, South Korean contemporary fashion design, China fashion design, etc. Is the research about the characteristics of fashion in different historical stages, region, area, culture and style study (Bugg, 2009 ; Chang & Lee, 2021 ; Creigh-Tyte, 2005 ; Kawamura, 2004 ; Kim & Farrell-Beck, 2005 ; Larner & Molloy, 2009 ; Ling et al., 2016 ; Millspaugh & Kent, 2016 ; Park, 1993 ; Sterlacci, 2019 ).

(5) Education. Responsible fashion business education, teaching system, fashion design course, interactive teaching, fashion entrepreneurship education, etc. Educational methods have constantly been a vital topic required to be discussed, and teaching methods and concepts have been reformed and innovated to respond to social and economic development, as well as to the constant innovation of knowledge, skills and cultural heritage (Armstrong & LeHew, 2013 ; Faerm, 2012 ; Fletcher, 2013 ; Lee & Sohn, 2011 ; Stensaker, 2007 ).

(6) Fashion industry and business. In modern commodity society, the concept of fashion is more than a way of life and an inner state of mind. People's pursuit of fashion will change the existing mode of life and behavior, thereby constantly creating new demands. Accordingly, the emergence of new products is promoted, as well as the development of novel industries. Fashion products are not only characterized by commercial products, but also help create a fashion industry chain and huge economic benefits for its high added value, easy dissemination and wide circulation (Guercini & Runfola, 2010 ; Pal & Gander, 2018 ; Pedersen, Gwozdz, & Hvass, 2018 ; Şen, 2008 ; Shamsuzzoha, Kankaanpaa, Carneiro, et al., 2013 ; Todeschini, Cortimiglia, Callegaro-de-Menezes, & Ghezzi, 2017 ).

(7) Fashion with culture. The labels include cultural heritage, traditional craft methodologies, new vision, cultural identity, cultural knowledge, etc. Understanding the effect of culture on the fashion industry and design creation gives insight into the style of fashion people want. For the identical reason, fashion impacts the way we live. Fashion is impacted by changes in culture (e.g., modernization, art, and even innovative technology). It is noteworthy that fashion is created by people living in different cultures and places. If one wants to understand fashion, one should be aware of the cultures of different places (e.g., traditional cultures, cultural heritage, new cultural contexts, and cross-cultural exchanges) (Fillin-Yeh, 2001 ; Jansen, 2014 ; Ko & Lee, 2011 ; Roche, 1996 ; Rocamora, 2017 ; Woodside & Ko, 2013 ; Zou and Joneurairatana, 2020a , b ).

(8) Medical fashion, the labels (e.g., mask making, world view, disease prevention, wearable development and fashioning masks). The medical area fashion is listed as a separate field because of the specificity and timing of this field. Since the outbreak of Covid-19 in 2020, the concern for health and disease worldwide has become an essential topic, and almost every research area has a connection with medical care, as impacted by such a general trend and environment, led to developments in the field of “medical care fashion” (e.g., the development of new materials, masks and protective fashion). In addition, due to the development of “human centred design” thinking, the current fashion industry not only pays attention to the creation of artistic works, but also pays more attention to humanistic care. The needs of special groups have also attracted the attention of the fashion industry, such as disabled people, etc. (Kim, et al., 2021 ; Koenig & Carnes, 1999 ; Li & Yim, 2021 ).

Keywords with the strongest citation bursts

Keywords with the strongest citation Bursts can be exploited to reflect the main research content of a research topic over time, and also to reflect the research trends in a certain time period. The tracking and identification of research trends can offer researchers information regarding the changes in research hotspots in the field of specialization, and can provide relevant inspiration and information for researchers in the field. Research frontiers are emerging theoretical trends and new topics that can be synthesized and judged in CiteSpace based on analysis of keywords with the strongest citation bursts (Li & Wang, 2018 ).

After running the CiteSpace software, 13 keywords with maximal citation bursts were obtained (shown in Fig.  7 ).

Top 13 keywords with the strongest citation bursts in “fashion design” area

In this study, the research scope is selected from 2000 to 2021, and the strongest citation bursts are concentrated after 2010. The mentioned consist of identity, culture, fashion trend, popular culture, design process, education, design practice, craft, etc. Moreover, the analysis of the strongest citation bursts complies with the following noteworthy points:

The topic of sustainable fashion has burst on the scene three times over the last decade, i.e., in 2015 for “sustainable design”, in 2015 for “sustainability”, as well as in 2018 for “sustainable fashion”. The evolution of sustainable fashion can be identified in the shift from “sustainable production” to “sustainable fashion” concepts. In the wake of the world's biggest ever garment industry disaster, the collapse of the Rana Plaza factory in Bangladesh, having caused death of over 1100 people (Rahman, 2014 ) the fashion movement by complying with the concept of “sustainability” is fading massively, which reveals an increased interest in sustainable fashion and ethical practices in the fashion industry (Westervelt, 2015 ). As sustainability turns out to be a “megatrend” (Mittelstaedt, Shultz, Kilbourne, et al., 2014 ), the fashion field has changed dramatically in accordance with the concept of “sustainable fashion” (e.g., sustainable design, fabrics, production and consumption) (Watson & Yan, 2013 ; Mora et al., 2014 ). Moreover, today sustainable fashion refers to a movement and process facilitating the transformation of fashion products and fashion systems towards greater ecological integrity and social justice. Sustainable fashion is not only concerned with fashion textiles or products, but concerned with the dependent social, cultural, ecological and financial systems correlated with people.

Research rends and frontier on fashion design

The identification and tracking of research frontiers present researchers with the latest developments in the disciplinary research evolution, predicts the trends in the research field, and identifies issues required to be explored more specifically. Research frontier topics are novel topics of interest in the field, indicating the social environment and research context. In brief, it can be referenced for relevant researchers in this field.

After CiteSpace is run, keyword timing profiles are generated by time segment based on Cluster co-occurrence analysis (shown in Fig.  8 ).

Time zone view in fashion design research

From the time zone view, the research in fashion design can fall into four phrases. The first phrase is that the research situation before 2004 did not form a cluster, thereby indicating that the research on fashion design was scattered before 2004. The second phrase is from 2004 to 2010, thereby revealing that the term of “international modernism” appeared twice. It can be explained by the frequent cross-cultural exchange activities between nations. The research emphasis shifts from fashion research in the traditional sense (e.g., apparel characteristics, designer and design styles) to cross-cultural and regionally fashion culture research (e.g., China, Europe, and the US). The third phase is from 2011 to 2017, more clusters appear in this time period, thereby demonstrating a higher volume of articles published. The research topics in fashion design show a diversity of clusters keywords and a wider range of research directions (e.g., culture, regional fashion, traditional apparel, humanities, education, design approaches and techniques). The fourth stage is from 2017 to the present, the keywords of clusters are more obvious, especially the label around 2017: “wearable technologies”. The mentioned keywords include wearable technology, wearable devices, fashion technology, smart wear, and technology socks. This novel technology is “skin electronics” or “fashion electronics”, which are intelligent electronic devices worn near or on the skin surface to detect, analyze and transmit information regarding the body information, body signals, vital signs or environmental data and others; in several cases, the information can be delivered to the wearer (Chuah, Rauschnabel, Krey, et al., 2016 ; Çiçek, 2015 ; Farrington, 2016 ). The second label is “Transgender Fashion”, unisex fashion, embodies the humanistic nature of fashion. Moreover, the label in 2021 is concerned with “Medical Moment”. With the global outbreak of Covid-19, how to against the virus is the daily topic be concerned by global. Protective clothing, mask has become a necessity in people's lives. Based on this context, the fashion industry has also been affected. The fashion industry think more about the care and needs of the human body, “Medical fashion” has become a popular topic of research. As indicated from the academic view, the research direction of fashion design is closer to the society hot trends and interdisciplinary research. Caring for people's physical, physiological and psychological aspects, fashion research tends to be more human centred design.

Conclusions

By analyzing the frontiers and trends of fashion design research, this study reveals that at the beginning of the research period, the topics of academic research were biased towards research in the humanities (e.g., fashion design, designers, culture, humanistic care, locality, as well as arts work). The direction of research over the past few years has been impacted by the overall global dynamics as well as technological and economic development, thereby demonstrating that the trend of interdisciplinary and cross-border cooperation has entered a stage of development in recent years. The data collection and analysis time of this article is at the end of 2021, but with the development of time and science and technology, such as Digital fashion, Virtual fashion, AI design, Inclusive design, etc. have also become hot topics at the moment. The researcher believe it will produce more academic research in fashion design in the future time.

On the whole, research on the topic of fashion design still has a considerable scope for research. Scholars, designers and practitioners in the fashion field still face huge task. Accordingly, the researcher proposed several suggestions for how to strengthen the process and results of academic research. From a horizontal perspective, (1) the international academic community and researchers should enhance the interact, discuss and conduct collaborative research with each other to provide sustainable vitality and motivation for the research; (2) transnational, cross-unit and cross-border academic exchange and cooperation should be enhanced to create more possibilities for academic research; (3) additional, multilingual journal platforms should be offered for fashion or art fields. From vertical perspective: Combining or contrasting history with modernity. For instance, using new technologies to redesign or study historical apparel, etc. By combining traditional culture with modern technology, the scope of the time-line of fashion design research can be extended.

This study uses quantitative literature analysis to convey information from the literature by creating images, diagrams and information description. The existing state of research in fashion design is reviewed, and provide the knowledge base, the existing state of research, as well as research hot-spots and publication trends in fashion design research. This study can provide existing literature, knowledge map, new inspirations, and research directions to fashion practitioners, researchers, and research institutions. Based on this paper, scholars can efficiently familiarize the field knowledge and facilitate strategic adjustments by relevant institutions.

Availability of data and materials

The datasets supporting the research process and conclusions of this article are included within the additional files. For databases and research results, which is available and has no restrictions to its use by academics or non-academics.

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The authors are thankful to the Design Science and Art Research Center from Guangdong University of Technology, for providing the research facilities and environment for this study.

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YZ: Postdoctor, School of Art and Design, Guangdong University of Technology, Guangzhou 510062, China.

SP: Lecturer and Ph.D., Ph.D in Culture-based Design Arts Program, Faculty of Decorative Arts, Silpakorn Univeristy, Bangkok 10170, Thailand.

TS: Postdoctor, College of Design and Innovation, Tongji University, Shanghai 200092, China.

D-BL: Professor and Ph.D., School of Art and Design, Guangdong University of Technology, Guangzhou 510062, China.

This research was received financial support from “Science and Technology Program of Guangdong Province: Overseas Famous Master Project” Guangdong province, China. The Project No. is 2020A1414010314.

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YXZ conceived the ideas, experimental design, data analization, interpretation of the results, and drafted the manuscript of the analysis. SP and ST gave technical guidance and provided continuous support to perform the experiment successfully,and gave the suggestions about the writing. DBL contributed to the interpretation of the results and revised the manuscript. All authors read and approved the final manuscript.

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Zou, Y., Pintong, S., Shen, T. et al. Evaluation and trend of fashion design research: visualization analysis based on CiteSpace. Fash Text 9 , 45 (2022). https://doi.org/10.1186/s40691-022-00316-6

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You can try without visiting: a comprehensive survey on virtually try-on outfits

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Since the last years and until now, technology has made fast progress for many industries, in particularly, garment industry which aims to follow consumer desires and demands. One of these demands is to fit clothes before purchasing them on-line. Therefore, many research works have been focused on how to develop an intelligent apparel industry to ensure the online shopping experience. Image-based virtual try-on is among the most potential approach of virtual fitting that tries on target clothes into customer’s image, therefore, it has received considerable research efforts in the recent years. However, there are several challenges involved in development of virtual try-on that make it difficult to achieve naturally looking virtual outfit such as shape, pose, occlusion, illumination cloth texture, logo and text etc. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of virtual try-on. This review first introduces virtual try-on and its challenges followed by its demand in fashion industry. We summarize state-of-the-art image based virtual try-on for both fashion detection and fashion synthesis as well as their respective advantages, drawbacks, and guidelines for selection of specific try-on model followed by its recent development and successful application. Finally, we conclude the paper with promising directions for future research.

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1 Introduction

In the last few years and especially during COVID-19 pandemic, online shopping for clothes has become a common practice among millions of people around the world. It shows a great progress and become a habitual activity for many consumers. This progress is conducted by the implementation of virtual try-on technology that enables the customer to visualize the produce on themselves and see how certain the products look on them before purchasing. In 2012, Converse was the first brand that used virtual iPhone try-on by allowing their clients to use phone cameras to see how shoes looked on them, and post photos on social media as well as make online purchases [ 92 ]. This technology applies very well to shoes, apparel, accessories, jewelry as well as make-up, where consumers long for a sense of “touch and feel” and they have total freedom regarding decision making, trying, and choosing products at their own pace, without feeling the pressure to make a purchase.

Approximately, 40% customers are willing to spend more if they can try the product through virtual reality [ 92 ], due to the fact that try-on experience makes it much easy to explore the many other options as well as customize or personalize the products according to their body shape. For this reason, online shopping for clothes has earned its place deservedly. Popular fashion brands including L’Oréal, Baume, Sephora, Adidas, Nike and Snap are opting try-on technology in order to improve the connectivity with customer and gain a competitive advantage in the market. With statistical proof, the global fashion apparel has exceeded 3 trillion US dollars, in currently year, and presents 2 % of the world’s Gross Domestic Product (GDP). In 2020, a revenue of 718 billion US dollars area attained in the fashion sector and an expectation to reach a growth of more than 8.4% for coming years [ 73 ].

During COVID19 pandemic lockdown, most of the business went into kind of a crisis mode and not only big brands, but also small retailers are thinking how they can survive [ 81 ]. Taking our time in shops will be difficult in a post-Covid-19 world as a result, online shopping is ingrained significantly in our daily as trade become more and more like shopping in person thanks to the efforts of businesses to add new features and services with the intent of providing their customers the same support and comfort that they would have during an in-person shopping experience. This goal has been achieved by using the computer technology to develop virtual try on applications that assist the fit of garment product to make consumers know how cloths look on themselves, how both the top and bottom matches together, and how the size of clothes fits to them.

Therefore, Online shopping would give more information and availability of all kinds of products to encourage fashion trailers to make the best investment by exploring new sales methods and optimizing the technological process of purchasing clothes like virtual fitting system. These solutions draw a new picture of online shopping experience and bring it to a high level of reality and comfort. One of these improvements is to allow consumers buying clothes after trying them like in real shops because the existing systems cannot provide the possibility for users to try-on various fashion items according to their desires. Thus, fashion brands need to better satisfy customer preferences and engage them with the personalized shopping experience to make more informed and confident purchase decisions. In addition, allowing consumers to virtually try on clothes will not only enhance their shopping experience, but also increase the fashion industries sales because these solutions can play an important role to reduce return rates and improve customer satisfaction.

Instead of using current graphics tools that fail to meet the increasing demands for personalized visual content manipulation, there are many proposed algorithms to address swapping clothes by using recent advances in computer vision tasks like fashion detection, fashion analysis or fashion synthesis. These solutions require considerable effort from researchers to perform the task of changing clothes with preserving details and identities. However, using current image editing technology e.g., Adobe Photoshop or Adobe illustrator cannot give a realistic result due to many challenges of changing clothing in 2D images, such as the deformation of the clothes, different poses, and different textures. Recent studies adopted deep-learning-based methods to encounter these problems and achieve more accurate results.

In the literature, a little number of fashion surveys are proposed [ 6 , 42 , 53 , 71 ]. Recently, a summary on intelligent clothing analysis was made by Liu et al. [ 42 ]. In addition, Song and Mei [ 71 ] presented on overview of fashion development with the emergence with multimedia. Then, a general survey designs the whole picture of intelligent fashion without taken a specific issue [ 6 ]. Another survey [ 53 ] is proposed to present AI applications in the fashion apparel industry, but it is based only on the structured task-based multi-label classification works. Next and due to the rapid development of computer vision, many tasks are appeared within intelligent fashion, hence, many related works must be updated. In this direction, this survey aims to conduct a comprehensive literature review of deep learning methods applied in the fashion industry by citing research works published in the last years and mentioning their relationship to the early studies. Our contribution consists in responding to the following research questions:

RQ1. What is the impact of adoption of Artificial Intelligence (AI) in the garment industry?

RQ2. How virtual try on system are developed?

RQ3. What are the common problems that need solving to ensure an intelligent fashion shopping?

In this paper, different sections are structured as follow: Section 2 outlines the research framework adopted to realize this research review. Section 3 is dedicated to virtual try-on applications, and divided into two parts, the first one presents the fashion detection tasks including fashion parsing, fashion synthesis, and landmark detection. The second one illustrates the works for fashion synthesis containing style transfer, pose transfer, and clothing simulation. Section 4 provides an overview of fashion benchmark datasets. Section 5 presents the performance of popular works on different tasks. Section 6 shows related applications and future directions. Finally, a conclusion is given in Section 7.

2 Research framework

In this study, a Systematic Literature Review (SLR) [ 29 ] is chosen to focus on research works related to virtual fitting system based on 2D images with deep learning methods and applied in the fashion industry. The SLR methodology adopted is shown in Fig.  1 . The review process commenced with collecting and preparing data from scientific databases. Subsequently, articles were selected in different phases according to our research framework, and we have selected more than 100 articles from both journals and conference.

Article Classification based on Research Questions

Articles in each tasks of the topic at hand such as fashion detection [ 10 , 13 , 14 , 28 , 30 , 31 , 32 , 34 , 35 , 37 , 38 , 39 , 40 , 41 , 43 , 44 , 52 , 55 , 56 , 57 , 64 , 76 , 77 , 78 , 79 , 83 , 85 , 93 , 94 , 95 , 102 , 103 ] and fashion synthesis [ 3 , 7 , 9 , 12 , 15 , 16 , 17 , 21 , 22 , 26 , 27 , 33 , 48 , 49 , 50 , 51 , 58 , 59 , 61 , 62 , 65 , 66 , 67 , 69 , 70 , 72 , 74 , 84 , 86 , 89 , 90 , 96 , 97 , 98 , 100 , 101 , 104 , 106 , 107 ], were retrieved from popular databases and engines such as Google scholar Footnote 1 and Research Gate Footnote 2 . Then, a screening process is used to select specific articles to address the research questions mentioned in previous section. Then, a categorization of research articles must be done according to the main steps used to develop image-based virtual fitting system with deep learning methods. After categorization, there is the process of information extraction and classification of the selected articles based on the key terms of research topic to address our research questions.

As shown in Fig.  1 that presented the article classification according to the research questions, RQ1 is focused on understanding the overall trend of AI in the Fashion industry. Hence, the focus of the screening process was limited to those articles discussing the implementation and execution of AI techniques to improve online shopping. RQ2 aimed at identifying the various stages on virtual fitting framework where the AI method was employed. RQ3 aims to understand the extent of online shopping problems which being a focus of research studies. These keys modules were considered during information extraction from research articles.

3 Fashion virtual try-on

In recent years, advanced machine learning approaches have been successfully applied to various fashion-based problems. The topics of fashion research in the literature of image-based garment transfer are summarized in Fig.  2 . One of the branches in fashion research is fashion detection, which aims to label each pixel in the scene (i.e., fashion parsing, landmark detection, and pose estimation), supported by fashion synthesis, which lead us a step closer to a fashion intelligent assistant.

Classification of based approaches for image-based virtual try-on System

3.1 Fashion detection

Fashion detection is an essential task for virtual try-on task, it consists of detecting the human body part to predict the region of clothing synthesis. To apply this task in virtual try-on systems, three aspects must be presented: Fashion parsing, Human Pose Estimation and Fashion landmark detection.

3.1.1 Fashion parsing

Fashion parsing or in other words human parsing with clothes classes, is a specific form of semantic segmentation. This task refers to generate pixel-level labels on the image which are based on the clothing items like hair, head, upper clothes, pants, etc. It is a very challenging problem since the number of garment types, the variation in configuration and appearance are enormous. In Fig.  3 , we present an example of fashion parsing results generated by the work of Ji et al. [ 28 ].

Examples of fashion parsing based on semantic segmentation [ 28 ]

In fashion domain, largest number of potential applications have been devoted to various tasks and particularly to human parsing [ 10 , 39 , 41 , 93 , 94 ]. At the beginning, Yamaguchi et al. [ 93 ] proposed a model by merging the fashion parsing and the human pose estimation. Then, they proposed clothes parsing with a retrieval-based approach [ 94 ] to resolve the constrained parsing problem. After that, a weak supervision approach for fashion parsing is presented by Liu et al. [ 41 ] who resort to label images with color-category labels instead of pixel-level. These works conduct results far from being perfect because between pose estimation and clothing parsing there is no consistent targets. Many restrictions are presented with these hand-crafted methods because they need to be developed carefully.

To deal with these issues, many methods based on Convolutional Neural Network (CNN) are proposed such as the deep human parsing-based work of Liang et al. [ 10 ] which resorts to an active template regression for semantic labeling. Then and with the aim to improve the generated results of their human parsing work, a Contextualized CNN (Co-CNN) [ 39 ] is designed to take the context of cross-layer, global image-level, and local super-pixel. In parallel, they proposed a deep human parsing with Active Template Regression (ATR) [ 39 ] to ensure the human parsing task by decomposing an image of person into semantic fashion and body regions. In 2018, Liao et al. [ 40 ] built a Matching CNN (M-CNN) network to solve the issues of parametric and non-parametric CNN-based methods. In the same year, Gong et al. [ 13 ] implemented an important self-supervised method under the name of Look Into Person (LIP) to eschew the necessity of labeling the human joints in model training (Fig.  4 ). With the intent to ameliorate their previous work [ 13 ], the same authors proposed a JPPNet network [ 102 ] to treat both the human parsing and human pose estimation task.

Annotation examples for LIP [ 13 ] with appearance variability and different views

Different from the previous mentioned works that only concentrated on single person parsing task, there are many others works [ 14 , 64 , 85 , 103 ] which focus on treating the scenario with multiple views of persons. Zhao et al. [ 103 ] designed a deep Nested Adversarial Network (NAN) to understand humans in crowed scenes. Gong et al. [ 14 ] proposed the first attempt to explore a detection-free Part Grouping Network (PGN) used for the semantic part segmentation for assigning each pixel as a human part and the instance-aware edge detection to group semantic parts into distinct person instances. With the aim to manage, simultaneously, single and multiple human parsing, Ruan et al. [ 64 ] developed a Context Embedding with Edge Perceiving (CE2P) framework. Recently, hierarchical graph is used for human parsing tasks to improve parsing performance such as the work of Wang et al. [ 85 ] that considered the human body as a hierarchy of multi-level semantic parts to capture the human parsing information.

3.1.2 Human pose estimation

Advanced in computer vision are realized by many tasks especially with deep learning-based approaches such as Human Pose Estimation (HPE) that is applied in many fields like fashion fitting to get specific postures from human body by joints’ localization. To overcome the challenges appeared with the task of HPE, many research efforts have been applied to the related fields. We present, in this section, recent research in HPE methods based on 2D images which are classified into two groups: single person pose estimation and multi-person pose estimation.

Single-person human pose estimation

Single-person Human Pose Estimation (HPE) is related to the task of localizing human skeletal keypoints from an image or video data. In the following Figure (Fig.  5 ), we present results of Single-person HPE obtained from the DeepPose [ 79 ] trained on Leeds Sports Pose (LSP) dataset [ 30 ]. According to the different structures of HPE task, methods based on CNN can take different aspects such as regression methods and detection methods.

Example of human pose estimation from DeepPose [ 79 ] on the LSP Dataset [ 30 ]

Regression-based methods produced joint coordinates by learning mapping directly from image [ 79 ]. The early deep learning-based network adopted by many researchers was AlexNet [ 31 ] due to its simple architecture. Toshev et al. [ 79 ] applied this network to learn joint coordinates from full images, and Li et al. [ 35 ] employed it as a multi-task framework to predict the joint coordinate from full image. However, Detection-based methods treat the body parts as detection targets based on two main representations: image patches and heatmaps of joint locations. The methods related to this category are intended to predict approximate locations of body parts [ 32 ] or joints [ 52 ].

Previous works attempt to adjust detected body parts into body models, but there are other recent works [ 57 , 76 , 77 , 78 ] which aim to encode human body structure information into networks. Tang et al. [ 77 ] proposed a hierarchical representation of body parts, then, they extended their work [ 76 ] to learn specific features of part group. Then, they committed to improve the network structure by proposing a densely connected U-nets and efficient usage of memory [ 78 ]. For Peng et al. [ 57 ], they exploited data augmentation to avoid the need of more data during training.

Multi-person human pose estimation

The second category of HPE methods is the multi-person HPE which aims to handle detection and localization tasks. It can be divided, according to its different level, into top-down methods and bottom-up methods. Top-down methods used bounding box and estimators of single-person pose to detect person from image and predict human poses. The bottom-up methods put into skeletons the prediction of 2D joints of persons in the image. Figure 6 shows examples of results from the work of Li et al. [ 38 ] that belongs to the bottom-up methods.

Example of multi-person HPE [ 38 ]

A combination of existing detection networks and single HPE networks used to implement the Top-down HPE methods [ 55 , 56 ] that achieved state-of-the-art performance in almost benchmark datasets while the processing speed is dependent to the number of detected people. For bottom-up HPE methods, the main components include body joint detection and joint candidate grouping. The two components are handled separately for most algorithms. The bottom-up methods-based works realized perfect performance expect some conditions like human occlusions or complex background.

3.1.3 Fashion landmarks detection

Fashion landmark detection is an important task in fashion analysis, it aims to predict clothes keypoints which are very essential for fashion images understanding by getting discriminative representation. The local regions of fashion landmarks give more significant variances since the clothes are more complicated than human body joints. Figure 7 shows results generated by the fashion landmark detection approach.

Example of results from Fashion Landmark Detection approach [ 37 ]. First row illustrates the results on DeepFashion-C [ 43 ], second row presents results on Fashion Landmark Dataset (FLD) dataset [ 44 ]

For the first time, Liu et al. [ 43 ] presented fashion landmark concept and, in parallel, they proposed a deep model called FashionNet [ 43 ] applied on predicted clothing landmarks. Then, they proposed a deep fashion alignment framework [ 44 ] based on CNN. This Framework is trained on different datasets and evaluated on two fashion applications, clothing attribute prediction and clothes retrieval. Another regression model proposed by Yan et al. [ 95 ] used to relax constraint of clothing bounding box due to its difficult application. A more recent work [ 83 ] mentioned that optimization on regression model is hard, so, they proposed to directly predict a confidence map of positional distributions for each landmark. Lee et al. [ 34 ] resorted to contextual knowledge to achieve perfect performance on landmark prediction.

3.2 Fashion synthesis

Fashion synthesis is the task for generating new style across images and being able to imagine what that person would look in a different clothing style by synthesizing a realistic-looking image. In the following, we review existing methods for addressing the problem of generating images of people in clothing by focusing on style transfer, pose transformation, and physical simulation.

3.2.1 Style transfer

In fashion synthesis task, style transfer is an important step that aims to transfer the style between images. It can be applied in various kinds of image especially facial image and garment image. CNN- based methods applied on this task exploit the feature extraction to obtain style information from image. Isola et al. [ 26 ] proposed the style transfer work, pix2pix, which is a general solution for style transfer. For specific goal, based on a texture patch, the work of Xian et al. [ 90 ] transferred the input image or sketch to the corresponding texture (Fig.  8 ).

Examples of image style transfer by TextureGAN [ 90 ]

Driven by increasing power of deep generative models, popular virtual try-on applications have appeared [ 12 , 16 , 27 , 50 , 62 , 84 , 98 ]. Han et al. [ 16 ] proposed a two-stage pipeline called VIrtual Try-On Network (VITON) to transfer desired in-shop clothing onto a consumer’s body by allowing the first stage to warp the input item to the desired deformation style and enabling the second stage to align the warped clothes to the consumer’s image. Many approaches following this pipeline have been proposed with more competitive performance such as CP-VTON [ 84 ] and CP-VTON+ [ 50 ], which adopt a thinplate spline (TPS) transformation learnable [ 9 ] based on Convolutional neural network architecture for geometric matching to align explicitly input clothing with body shape. All these works are powered by the use of TPS, thus, in the following Figure (Fig.  9 ) we present its application on VITON architecture [ 16 ].

Example of Warping a clothing image proposed by VITON [ 16 ]: Given the target clothing image and a clothing mask, the shape context matching is used to estimate the TPS transformation and generate a warped clothing image

However, results of these methods are limited in different cases (Fig.  10 ). One of the main causes resulting in such failed cases comes from warping stage which can be based on inaccurate clothing mask and warped target clothes image used to calculate TPS transformations, thus, its dependence on the shape context cannot be able to perform perfectly on the warping task, and this is the case on VITON [ 16 ]. Geometric matching module adopted in CP-VTON [ 84 ] utilizes grid points as control points for calculating TPS transformation to reduce image distortions in warped images, which can be seen Fig.  10 .

Results from the CP-VTON [ 84 ], CP-VTON+ [ 50 ] ACGPN [ 98 ] and CIT [ 62 ]

Then, a second-order difference constraint on Thin-Plate Spline (TPS) is proposed to produce geometric matching yet character retentive clothing images with the ACGPN network (Adaptive Content Generating and Preserving Network ) [ 98 ]. This method characterized by the existence of an additional semantic generation module used to generate a semantic alignment of spatial layout. It presents important results but with no consideration of the latent global long-range interactive correlation between the person representation and the in-shop clothing. Despite the perfect results generated with these methods [ 16 , 50 , 84 , 98 ], there are still a need to obtain more realistic image with no artifacts especially, when there are occlusions or large variations. For these reason a two-stage transformer pipeline is proposed under the name of Cloth Interactive Transformer (CIT) [ 62 ] to model the latent global relation in both stages (Fig.  10 ).

More recently, other works based on in-shop clothes items [ 12 , 27 ] are proposed to deal with this same problem with the difference that most of the above methods [ 16 , 50 , 62 , 84 , 98 ] were relied on human segmentation of different body parts to enable the learning procedure of virtual try-on. However, ensure the human parsing task with high performance manner required important training of the corresponding models, for the reason that the poor quality of segmentation guide to highly-unrealistic generated images. To reduce this issue due to the dependence to the masks as an inputs for the models, a Warping U-Net for a Virtual Try-On (WUTON) [ 27 ] is appeared as the first parser-free network without using of human segmentation for virtual try-on, as shown Fig.  11 . Then, another work called Parser Free Appearance Flow Network (PF-AFN) [ 12 ] is proposed in the same context, to produce highly photo-realistic try-on images without human parsing (Fig.  11 ).

Different architectures for warped Module: a based on segmentation mask from VITON [ 16 ], b without human segmentation from WUTON [ 27 ] and PF-AFN [ 12 ]

The previous works required in-shop clothing image for virtual try-on, but other existing models like FashionGAN [ 7 ] and M2E-TON [ 89 ] resolved this task basing on text description and model image by giving an input image and a sentence describing a different outfit. First, a GAN generates the segmentation map according to the description and then, another GAN ensures rendering of the output image by the segmentation map. Other works attempts to resolve the problem with arbitrary poses such as Fit-Me [ 21 ] which was the first work building virtual try-on dealing with this challenge. Then, FashionOn [ 22 ] applied the semantic segmentation to present more realistic results. Then, SwapNet [ 61 ] was one of the first works that expose the challenge of transferring all the clothing from one person’s image onto the pose of another target person by operating in image-space. This is done by generating a mutually exclusive segmentation mask of the desired clothing into the desired pose.

Another virtual try-on network called Vtnfp [ 100 ] proposed a similar strategy to synthesize photo-realistic images given the images of clothed person and target clothing item. Zheng et al. [ 106 ] presented an architecture to try-on clothing with arbitrary poses by using the body shape mask prediction for pose transformation. Based in the same design strategy, Han et al. [ 17 ] proposed ClothFlow which is an appearance-flow-based generative model allowing the transfer of different appearances and synthesize clothed persons for posed-guided person image generation and virtual try-on.

Recently, various works [ 48 , 51 , 66 , 67 , 74 , 96 ] address challenging problems of garment interchange between person’s pictures with preserving the identity in the source and target images by developing an image-based virtual try-on network. Feng et al. [ 74 ] resolve the problems of visual details and the missing of body parts by maintain the structural between the generated image and the reference image. Outfit-VITON [ 74 ] allows the visualization of a cohesive outfit from multiple images of clothed human models, while fitting the outfit to the body shape and pose of the query person. Sarkar et al. [ 66 , 67 ] achieve high-quality try-on results by aligning the given human images with a 3D mesh model via DensePose [ 79 ], estimating a UV texture map corresponding to the desired garments, and rendering this texture onto the desired pose (Fig.  12 ).

Garment transfer results generated by the work of Sarkar et al. [ 67 ]

In the current year, conditioning model is adopted by Dressing in Order (DiOr) [ 67 ] to support 2D pose transfer, virtual try-on, and several fashion editing tasks, and a Complementary Transferring Network (CT-Net) [ 96 ] is published to adaptively model different levels of geometric changes and transfer outfits between different people. Despite this diversity of these systems, the ability to preserve details or to present, correctly, the shape and the texture is still a challenging task.

3.2.2 Pose transformation

Pose transformation is a crucial task for fashion synthesis, it takes an input image of person and a target pose to generate images of this persons in different poses with the preserving of original identity (Fig.  13 ). To deal with this task, many works are proposed. Firstly, a pose guided person image generation PG2 [ 48 ] is presented with a two-stage adversarial network to achieve an early attempt on the challenging task of transferring a person to different poses by generating both poses and appearance simultaneously and using affine transform to keep textures in the generated results.

Examples of pose transformation results generated by PG2 work of Liqian Ma, et al. [ 48 ] from DeepFashion dataset [ 43 ] ( a ) and Market-1501 dataset [ 104 ] ( b )

The work of Siarohin et al. [ 70 ] used a deformable GAN to generate images of person according to a target pose which allowed the extraction of the articulated object pose by resorting to a keypoint detector. Guha et al. [ 3 ] address the problem of human pose synthesis with a modular generative neural network that synthesizes unseen poses by using four modules consisting of image segmentation, spatial transformation, foreground synthesis, and background synthesis. Si et al. [ 69 ] introduced a multi-stage pose-guided image synthesis framework which divided the network into three stages for pose transform in a novel 2D view, foreground synthesis, and background synthesis. Pumarola et al. [ 59 ] treat the limitation of data presented by the above research studies by borrowing the idea from [ 107 ] and leveraging cycle consistency.

Last year, the work of Song et al. [ 72 ] presented a solution for this limitation by proposing a novel approach which consisted of a decomposition of the hard mapping into semantic parsing transformation and appearance generation sub-tasks to improve the appearance performance. In addition, The generative model, Attribute-decomposed GAN (ADGAN) [ 49 ], produce realistic images with desired human attributes. The idea behind this work is to embed human attributes into the latent space as independent codes and then ensure the control of attributes via mixing and interpolation operations in explicit style representations.

3.2.3 Clothing simulation

For more improvement of fashion synthesis performance, the use of clothing simulation is essential. The works mentioned in the previous section are about the 2D domain where clothing deformation is not considered to generate realistic appearance. This important task presented many challenges like the need of creating more realistic results in real-time running with the treatment of more complex garments.

Computer graphics tools was the traditional way for realistic clothes generation models [ 15 , 58 , 97 ]. Yang et al. [ 97 ] proposed an approach to recover a 3D mesh of garment with 2D physical deformations by capturing the global shape and geometry of the clothing and extracting important details of cloth from a single-view image. The recovered clothing can be addressed to other human bodies in variety of poses for virtual fitting task. Guan et al. [ 15 ] aimed to dress people in a different variation and pose, and clothing types with an automatic process. Thus, they proposed DRAPE ( DRessing Any PErson ) model to simulate clothes deformation with varying shape and pose (Fig.  14 ). Then, ClothCap [ 58 ] is proposed as a multi-part 3D model to simulate clothing deformation of people in motion from 4D scans. This model ensures the virtual try-on task by capturing a clothed person in motion, extracting their clothing, and retargeting the clothing to new body shapes.

Example of clothing simulation results obtained with DRAPE model [ 15 ]

The simulation of the physical deformation has important role to ensure more performance for fashion synthesis due to the generation of dynamic details, clothing-body interactions, and the 3D information. Wang et al. [ 86 ] interested on this task and proposed a semi-automatic method to learn the intrinsic physical properties with different postures to generate garment animation which are shown in Fig.  15 . The proposed model encoded the main information of the clothing shape and learned to reconstruct garment shape with physical properties by considering the intrinsic garment and the body motion.

Examples of physical simulation from the work of Wang et al. [ 86 ]

To improve more realistic view to the garment on human body, Lahner et al. [ 33 ] proposed framework consisting of two modules. The first module aiming to recover shape deformations from 3D data of clothed persons in motion. The second module is a conditional Generative Adversarial Network (cGAN) that allowing to ensure realism and temporal consistency and lead the high-resolution details of clothing deformation sequences. Then, Santesteban et al. [ 65 ] proposed a two-level learning-based clothing animation method for virtual try-on simulation to ensure performance of the physical simulation with non-linear deformations of clothing. In addition, Yu et al. [ 101 ] proposed a physic-based simulation with performance capture called SimulCap . This model ensures tracking of people and clothing using a multi-layer surface. So, it combines the benefits of capture and physical simulation. The contribution of this work consisting of: (1) a multi-layer representation of garments and body including the undressed body surface and separate clothing meshes, (2) a physics-based performance capture procedure using body and cloth tracking for physical simulation and clothing-body interactions.

4 Benchmark datasets

Recent progress in virtual try-on systems have been driven by the building of fashion datasets, despite that, it is difficult to develop a universal dataset to evaluate the whole methods of virtual try-on because there are large variations in different tasks. Therefore, some researchers resort to create datasets to evaluate their proposed methods, this diversity makes the comparison on different algorithms very difficult. Datasets, also, bring more challenges and complexity through their expansion and improvement. This section discusses the popular publicly available datasets for virtual try-on tasks and their characteristics. Large number of benchmark datasets proposed to study fashion applications such as virtual try-on systems are summarized in Table 1 .

As summarized in Table 1 , for each task there are specific datasets with according setting. Market-1501 [ 104 ] and Deep-Fashion [ 43 ] are the most popular datasets for virtual try-on. FLD [ 44 ] is the most used dataset for fashion landmark detection. Several datasets were built to treat the fashion parsing task such as LIP dataset [ 13 ]. Datasets for physical simulation are different from other fashion tasks since the physical simulation is more related to computer graphics than computer vision. Dataset can be categorized into different types according to real data and created data especially when we are dealing with fashion physical simulation which interested on clothing-body interactions.

Despite the progress on 2D image-based fashion datasets like DeepFashion [ 43 ], DeepFashion2 [ 11 ] and FashionAI [ 109 ], the building of datasets basing on 3D clothing is almost rare or not sufficient for training like the digital wardrobe released by MG-Cloth [ 4 ]. Recently, Heming et al. [ 108 ] develop a comprehensive dataset named DeepFashion3D which is richly annotated and covers a much larger variations of garment styles.

5 Performance assessment

In image processing, measuring the perceptual assessments of generated results is an important step to validate research works. Therefore, there is an emerging demand for quantitative performance evaluation in image-based garment transfer, which is caused by the requirement to objectively judge the quality of virtual fitting systems to facilitate comparability of the various existing approaches and to measure their improvements.

5.1 Image quality assessment (IQA)

The measure of performance of computer vision tasks is ensured by image quality assessment methods which divided into objective or subjective methods. The last one is based on the perception of humans to evaluate the realistic appearance of generated images. With each year, the number of proposed IQA algorithms are progressively growing, by proposing new one or extending existing IQA algorithms. In this section, we present the most popular IQA algorithms used to evaluate tasks of image-based garment transfer.

5.2 IQA for fashion detection

For clothing fitting based on images, the fashion attributes must be first detected to predict the clothing style. Most works on clothing localization show validate results by using different metrics on different tasks such as landmark detection, pose estimation and human parsing.

5.2.1 Fashion parsing

In fashion Parsing, various metrics are used to evaluate proposed approaches on different datasets such as Fashionista [ 93 ] and LIP [ 13 ] and in terms of average Pixel Accuracy (aPA), mean Average Garment Recall (mAGR), Intersection over Union (IoU), mean accuracy, average precision, average recall, average F-1 score over pixels and foreground accuracy. Table 2 report some quantitative results measured by these metrics. Most of the parsing methods are evaluated on Fashionista dataset [ 93 ] in terms of accuracy, average precision, average recall and average F-1 score over pixels. In addition, There are objective comparisons for virtual try-on, in terms of inception score (IS) [ 82 ] or structural similarity (SSIM) [ 19 ].

IS is used to evaluate the synthesis quality of images quantitatively. SSIM is utilized to measure the similarity between input and output images ranging from zero (dissimilarity) to one (similarity). Further, SSIM is used also for pose transfer to compare the luminance, contrast, and structure information in images to evaluate many state-of-the-art methods. Table 3 shows evaluation metrics including SSIM, IS, masked version SSIM (mask-SSIM), masked version of IS (mask-IS) and Detection Score (DS) [ 70 ] applied on Market-1501 dataset [ 104 ] and DeepFashion dataset [ 43 ].

5.2.2 Human pose estimation

Research in HPE has made significant progress during the last years which conducted to the appearance of different work that needed to be evaluated with different metrics to measure the performance of human pose estimation models. The most known metrics in this field are Percentage of Correct Parts (PCP), Percentage of Correct Keypoints (PCK) and Average Precision (AP) which can applied in different datasets.

5.2.3 Fashion landmark detection

The most popular evaluation metrics in fashion detection are Normalized Error (NE) and Percentage of Detected Landmarks (PDL). NE is considered as the distance between predicted landmarks and ground-truth, while PDL is defined as the percentage of detected landmarks according to overlapping criterion. Typically, smaller values of NE or higher values of PDL indicate better results.

5.3 IQA for fashion synthesis

The image quality evaluation is essential for image generation methods to synthesize desired outputs. Recent image synthesis research commonly uses simple loss functions to measure the difference between the generated image and the ground truth, e.g., L1-norm loss, adversarial loss, and perceptual loss. Here, we will present related evaluation metrics to each tasks of fashion synthesis including style transfer, pose transfer and clothing simulation.

5.3.1 Style transfer and pose transfer

Image based garment transfer aims to transform a source person image to a target pose while retaining the appearance details. In this case two essential tasks are required to ensure this goal. That are, style transfer and pose transfer which are very challenging tasks especially in the case of human body occlusion, large pose transfer and complex textures and for measuring the quality of generated images common metrics are used. The evaluation for style transfer is generally based on subjective assessment by rating the results into certain degrees and the percentages of each degree are, then, calculated to evaluate quality of results.

5.3.2 Physical simulation

There are limited quantitative comparisons between physical simulation works. Most of them tend to calculate the qualitative results only within their work or show the vision comparison with related works. Figure 16 presents an example of these comparisons.

Evaluation of the work of Santesteban et al. [ 4 ] compared with DRAPE [ 65 ] and ClothCap [ 101 ]

As shown in this section, the fashion assessment is based on inception score or human preference score. However, inception score focuses more on the image quality, regardless of the aesthetic factors. Human preference score obtained from a small group can be easily influenced by the users’ personal preference or the environment. Thus, one of the challenging tasks in research domain is to build a novel fashion assessment metric that is objective and robust.

6 Application and future work

Automate the manual processes is a great achievement insured by technology advancements especially in the computer vision field. One of the largest industries that is influenced by technology advancement is Fashion Apparel. Due to computer vision powered tools, a great experience can be born for both retailers and consumers. In the following, we present the application of fashion technology uses in various areas and present the future works needed to realize the target benefits.

6.1 Application

Fashion is an ever-changing industry, where trends succeed one another, and companies must constantly rethink and adapt their products and strategies to maintain their position and assure customers’ preference. AI based research appears to be a promising avenue for the fashion industry and can be applied for various activities to enhance the working on this area and maximize the financial gains. Creating AI systems that can understand fashion in images, can create a next-level customer experience like online fashion shopping because apparel industry is basically about visual, thus, it can be dealing with computer vision to recognize images just as we do by making computers understand images.

Here is where the future research work will bring value and become useful for fashion business by making smart shopping. The application of computer vision is mainly done for fashion image analysis, object detection and image retrieval [ 40 , 43 ]. Many other researchers have represented their ideas for feature extraction and accurate attribute, for fashion related images [ 16 , 62 ]. Recently, many researchers tried to explore and provide solutions for different fashion tasks using the concepts of artificial intelligence. Several works contributed for fashion recommendation in [ 20 , 80 ], object detection and classification [ 37 , 43 , 44 , 83 , 95 ], Image Generation and Manipulation in [ 17 , 67 , 70 ]. Figure 17 illustrates an overview of the AI application in the field of Fashion.

Applications of AI techniques in fashion industry

6.2 Challenges

Going completely online brings a vast number of challenges for fashion retailers and gives an inspiration for new innovative digital products like virtual fitting systems to make the wholesale process completely digital. Published literature presented in this survey show the potential of AI techniques for providing important solutions to implement intelligent systems. Despite that, clothing companies do not widely use these advanced techniques due to various limitations related to these field. A virtual fitting would be a way to see the virtual effects, but it is still far from solved due to several challenges.

Image-based virtual try-on is among the most potential approach of virtual fitting that tries on a target clothes into a customer’s image and thus it has received considerable research efforts in the recent years, however, there are several challenges involved in development of virtual try-on that makes it difficult to achieve realistic outfit such as pose, occlusion, cloth texture, logo and text etc. In this section, we present the most important challenges which can be treated in the incoming studies in the field of adoption of AI techniques in clothing industry.

Try-on image generation

Creating realistic images and videos of persons by considering the pose, shape and appearance is a crucial challenge related to the application of computer vision in many fields like movie production, content creation, visual effects, and virtual reality, etc., In virtual try-on, the body shape and the desired pose of the person highly influence the final appearance of the target clothing item [ 21 , 22 , 61 , 89 , 100 , 106 ]. Thus, diverse questions must be asked to overcome many challenges: (1) How to deform the new clothing item and align it with the target person in a proper manner, and (2) How to generate the try-on image with preserving visual details of the clothing item, and maintaining the body parts of the person, during clothes interchange according to the person pose. Recently, diverse research works [ 51 , 66 , 67 , 74 , 96 ] take this challenge to respond to these questions and try to solve different issues related to the image generation but it seems that the necessity of obtaining photo-realistic images still persist, thus, the need to improve existing virtual try-on system.

Network efficiency

It is a very important factor to apply algorithms in real-life applications. Diversity data can improve the robustness of networks to handle complex scenes with irregular poses, occluded body limbs and crowded people. The main issue is related to system performance which is still far from human performance in real-world settings [ 33 , 65 , 86 , 101 ]. The demand for a more robust system consequently grows with it. Thus, it is crucial to pay attention to handling data bias and variations for performance improvements. Moreover, there is a definite need to perform the task in a light but timely fashion. It is also beneficial to consider how to optimize the model to achieve higher performance. Some existing methods used Transfer Learning and Data Augmentation [ 57 , 61 ], but we need to focus for more performant methods to achieve high quality results within efficient network.

Virtual try-on DATASETS

Datasets are very important for validating the new models. In particular, deep learning model needs large-scale data for training task. One of the early realistic and large-scale datasets in the fashion area is DeepFashion [ 43 ]. So, building new datasets would help quick progress in virtual try-ons and in some cases, there are a necessity to extend existing datasets by using different methods. 1) The GAN worked as a technique of data augmentation which helps in overcome the weakness of existing fashion datasets. 2) Synthetic technology can theoretically generate unlimited data while there is a domain gap between synthetic data and real data. 3) Cross-dataset supplementation to supplement 3D datasets with 2D datasets, can mitigate the problem of insufficient diversity of training data. 4) Transfer learning proves to be useful in this application. Therefore, how to create or extend a large-scale dataset constitutes a promising direction for both image-based dataset and video-based dataset.

Multi-modal virtual try-on

Depending only on the appearance features such as clothing that extracted from RGB images are not robust enough against environment variations as shown in the above methods [ 7 , 9 , 12 , 16 , 21 , 22 , 27 , 50 , 61 , 62 , 84 , 89 , 98 , 100 , 106 ]. Thus, authors should try to combine multiple modalities with complementary information for the final task to improve the accuracy. So, using deep learning on multimodal data is one of new directions in virtual try-on. Also, one of the challenges in the multimodal, needs to be considered in new studies, is developing a framework that handles missing features or modalities that occur by occlusions or pose variations. In the last year, some research works present their interest to this challenge [ 45 , 46 ].

Unsupervised /supervised fashion research

Most of current deep learning try-on systems depend on supervised learning [ 16 , 50 , 62 , 84 , 98 ] which train labeled data in the same environment. So, training annotation data in new and real-world environments will conduct to high annotation cost while the deep learning models need enormous data for training and labelling presents a tedious and time-consuming process. To overcome this problem and relieve the labelling burden, it is very useful to work with unsupervised models to extract discriminative features from unlabeled dataset instead of supervised or weakly supervised learning. In fact, current AI approaches require a lot of labeled data to achieve decent accuracy in their predictions. However, since labeling often requires expensive human labor and much time, AI techniques need to evolve toward Unsupervised Learning models that do not require labeled data to train the AI models. The use of this kind of learning begin with some works and become most in-demand in last year [ 59 , 72 , 75 , 100 ].

2D/3D virtual try-on

As mentioned in this survey, current methods such as [ 7 , 9 , 12 , 16 , 17 , 21 , 22 , 27 , 48 , 50 , 51 , 61 , 62 , 66 , 67 , 70 , 74 , 84 , 89 , 96 , 98 , 100 , 104 , 106 ] are still far from the built of an ideal virtual try-on system for many reasons related to the input data. Firstly, clothes deformation and occlusion make the garment rendering process very hard. Also, 3D human body modeling for arbitrary poses is still challenging [ 2 , 4 , 5 , 87 ]. Thus, new approaches should be proposed to capture detail of shape and clothing.

Fashion generation conditioned on text

Although the advancement on the development of intelligent fashion systems, the automatic synthesis of photo-realistic images from text is needed to obtain perfect results in the design process and to generate realistic images. This need is due to the diverse attributes of fashion images in color, pattern, style, etc. So, research works must focus on how handling complex conditions as well as data sources should be inspired. This challenge is treated with some studies for fashion intelligent system such as Semantic-Spatial Aware GAN [ 23 ] and Inspirational adversarial image generation [ 63 ].

6.3 Open issues and future directions

Technology has always played an important role in fashion industry and started a more profound and faster transformation that is changing the way in which customers shop and interact with products and brands. At the same time, companies are adopting these technologies to ensure a best shopping experience. Virtual try-on applications present the irreplaceable technology in fashion industry, it provides important benefits to the apparel industries and allows to try-on garment before purchasing, improves accuracy, and suggests well-fitted garment for body type. Throughout the pandemic, virtual try-on has offered a great service to e-consumers and brands unable to demo their products offline.

Virtual try-on solutions represent fit to body, as well as garment pattern design, style, colors to get the perfect results of clothing fitting because the main purpose of retailers is to prove virtual try-on matching with the real garments. Thus, the priority of researchers is to identify the key challenges and the critical success factors that determine the effectiveness of the implementations of digital technologies in the online garment industry to bridge the gap between physical and digital shopping and to attain the challenge of reaching the people wherever they are which has been needed during the pandemic, and will continue to be with the rise of e-commerce.

The implementation of virtual try-on application has the potential to provide a significant benefit to clothing e-retailers but their adoption in the clothing sector is still limited, and even the technological advances, the existing try-on applications are not completely developed yet and still not matured to obtain target results. Most of them are not realistic enough to feel comfortable when try-on a garment item because the structure of clothes is not coherent and done in an artificial manner. Therefore, there are still many unresolved challenges and gap between research and practical applications such as those mentioned in the previous section. This crucial challenges in adopting fashion technologies for fashion industry are appeared because real-world fashion is much more complex than in the experiments.

Following this objective, we present in this paper an interesting review of literature for the virtual try-on task, which can provide researchers with explicit research directions, facilitates their access to the related studies and improve the visibility of adopted methods. Thus, this literature review help to understand from existing works how we can implement an efficient virtual try-on system and how we can understand fashion image. However, people would show different views of themselves in the desired clothing product before making purchasing decision. Considering this objective, a virtual try-on system must be designed and developed, where given a person image, a desired pose, and a target clothing item, it can generate the try-on look of the person with the target appearances and desired poses. We illustrate this process in Fig.  18 .

Illustration of the idea of Virtual Try-On System

Towards this end, Most of the systems presented in this paper proceed as follow: they realized at first fashion detection to localize where in the image a fashion item appears or where the different body parts are localized. Then, they swap and interchange clothes between different images of persons and deal with the large variations on body poses and shapes via deep learning models. These studies show that there is significant progress has been made in this direction using learning-based image generation tools, such as GANs, and authorize various range of applications, such as human appearance interchange, virtual try-on, motion transfer, and novel appearances synthesis. However, because of the under constrained nature of these tasks, most existing methods have restriction in the visual quality on generated results and present observable artefacts such as blurring of small details, lose facial identity, unrealistic distortions of the body parts and garments as well as severe changes of the textures. The major procedures are not able to recover the texture details properly. Figure 19 show the result of the recent method of NHRR proposed by Sarkar et al. [ 67 ].

Limitation of generated results of the virtual try-on task presented by the work of Sarkar et al. [ 67 ]

Despite the important results given by the approaches discussed in this survey, and the power of measuring technologies developed with deep learning methods, several limitations persist like the lack of perfection and the incorrect fit on the human body. Therefore, future studies should focus at providing realistic presentations of different target appearances of the consumers and allow them to virtually choose and try-on preferred clothes, adjust size, style, and color of desired items by using the deep learning-based approaches.

7 Conclusion

The advancements made with AI technologies in fashion industry have not yet reach the goal of modeling the real-world problems which is still very limited and remain challenging, and this is because important hurdles exist at various levels. Thus, the implementation of the AI techniques into this task requires a careful consideration of the various practical features existing in the clothing industry to ensure optimal solutions. The different studies on intelligent fashion analysis surveyed in this paper are just the beginning of this wide research domain because up to now, enormous research efforts have been spent on these tasks and will continue to grow and expand due to the enormous profit potential in the ever-growing fashion industry. This future directions must bridge the gap between research and real industry demand by adding new features and services with the intent of providing customers the same support and comfort that they would have during an in-person shopping experience.

Data availability

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Consumer values, online purchase behaviour and the fashion industry: an emerging market context

PSU Research Review

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Article publication date: 21 September 2021

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This study examines consumer online purchase behaviour in the Nigerian fashion industry.

Design/methodology/approach

A cross-sectional study was conducted with a total useable sample size of 241 respondents contacted through on-site visitation. Descriptive and inferential statistics were used to test the influence of customer value on online purchase behaviour in the fashion industry.

Consumer values are categorised into terminal (happiness, love and satisfaction) and instrumental (time-saving, price-saving discount, service convenience and merchandise assortment) values. The findings show that both values have significant influence on online consumer purchase behaviour, while fashion consciousness moderates the relationship between consumer values and online purchase behaviour.

Practical implications

Online fashion retailers should focus on increasing the terminal and instrumental values of their products and making available goods that meet the needs of different generational cohorts in society.

Originality/value

Studies have examined various factors, for example, consumer values that are determinants of consumer online purchase in the fashion industry; however, there has been limited focus on the nature of fashion and online purchasing in emerging markets, particularly those in Sub-Saharan Africa.

• Customer values
• Online purchase behaviour
• Digital retailing
• Technology innovation

Adeola, O. , Moradeyo, A.A. , Muogboh, O. and Adisa, I. (2021), "Consumer values, online purchase behaviour and the fashion industry: an emerging market context", PSU Research Review , Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/PRR-04-2021-0019

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Copyright © 2021, Ogechi Adeola, Adenike Aderonke Moradeyo, Obinna Muogboh and Isaiah Adisa

Published in PSU Research Review . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Introduction

The fashion industry dates back to over a hundred thousand years, right from the availability and use of textiles by mankind ( Botti, 2019 ). The industry, over time, has added economic and material value to humanity, evolving with society, making it a very relevant aspect of human life and also a common area of research, particularly in this technology-driven world ( Bruce and Daly, 2006 ; Botti, 2019 ; Kilduff, 2005 ; Xue et al. , 2019 ). Globally, the fashion industry contributes about US$3000 bn, an estimated 2% of the world's gross domestic product (GDP) ( Botti, 2019 ). Today, technological revolution and the Internet have enabled the establishment of online fashion retail systems to displace aspects of the traditional store patronage ( Johnstone et al. , 2013 ; Kautish and Sharma, 2018 ; Pantano and Viassone, 2015 ).

The term “fashion” is a concept that is widely accepted by committees, class or groups of people and is directly affected by marketing factors, such as low predictability, high impulse purchase, short-life cycle and the high volatility of market demand ( Fernie and Sparks, 1998 ; Bhardwaj and Fairhurst, 2010 ).

Digital retailing in the fashion industry has gained prominence, providing ample opportunities for marketers to reach out to different generational cohorts (i.e. generations X, Y and Z) ( Pentecost and Andrews, 2010 ). Generational cohort is a theoretical approach to understanding the diverse group of individuals in a society. The term is used to describe individuals who share similar political, social, cultural and economic events during their childhood ( Fernández-Durán, 2016 ). The most widely used categorisation is Gen X, Y and Z ( Sima, 2016 ). Individuals who fall into these classifications are considered to share similar behaviour, perceptions of reality, values and consumption patterns, which must be understood from a marketing standpoint ( Fernández-Durán, 2016 ; Liang and Xu, 2018 ; Mahmoud et al. , 2021 ; Sima, 2016 ; Tan et al. , 2019 ). For example, individuals in Gen X (1965–1981) are regarded as digital immigrants while Gen Y (1982–1999) and Gen Z (2000–2012) are regarded as digital natives ( Mahmoud et al. , 2021 ). To contextualise the distribution of consumers in the fashion market, this classification must be well understood.

Retail digitisation has changed the process of shopping for consumers and the process of selling for organisations in the fashion industry by providing convenient and affordable services ( Hagberg et al. , 2016 ; Kautish and Sharma, 2018 ; Renko and Druzijanic, 2014 ). Consumers' desire to shop for clothing online has, however, been hindered by challenges of “fit” and “size” of cloths ( Miell et al. , 2018 ). There have been several studies (e.g. Loker et al. , 2004 , 2008 ; Song and Ashdown, 2012 ; Kim and LaBat, 2013 ; Beck and Crié, 2018 ) that focused on providing solutions to the challenges that can impede the benefits of online fashion retailing for businesses, shoppers, and generally hinder the growth of the industry.

These challenges have negatively influenced consumers' perception of online purchases in the fashion industry, especially with clothing purchase. Digital “fit” and “sizing” technologies have been introduced to address this challenge and give customers the needed satisfaction in their online fashion purchases in developed nations ( Miell et al. , 2018 ). Online purchase is gaining prominence in Nigeria ( Aminu, 2013 ; Usman and Kumar, 2020 ), but the rate and pace of online fashion (apparel) purchase have been low despite having a large population of Internet users ( Falode et al. , 2016 ). Falode et al. , investigated online and offline shopping motivation of apparel consumers in Ibadan, Nigeria and found that consumers prefer offline purchase of apparel to online. This is quite worrying as Nigeria has an active online population which offers fashion organisations enormous opportunities ( Falode et al. , 2016 ). Hence, understanding the factors that will engender the consumer's online purchase in the fashion industry is sacrosanct to the sustainability of the online fashion space in Nigeria.

Extant studies have attempted to provide predictive direction regarding what influences consumers' online purchases in the fashion industry. For example, Schmidt et al. (2015) posit that what consumers see and hear online, influences their buying behaviour. Pentecost and Andrews (2010) established that gender influences the rate of purchase and that females purchase more items in the fashion industry than their male counterparts. Pentecost and Andrews also found that Gen Y consumers have higher purchase frequency and impulse buying than other generational cohorts. Kautish and Sharma (2018) examined consumer values, fashion consciousness and behavioural intentions in the online fashion retail sector and found a significant relationship between consumer values, fashion consciousness and behavioural intention of the consumers in India. Their study was conducted to highlight the basic factors that influence consumer purchase and patronage of online retailing in the country's fashion industry. The authors identified three variables that determine the consumer's online behaviour: consumer values, fashion consciousness and behavioural intentions.

Generally, countries in Africa are known for their distinct socio-cultural values, which influence their fashion behaviour ( Aminu, 2013 ; Falode et al. , 2016 ). The role of socio-cultural values on consumer purchase behaviour has also been explored (see Agnihotri and Bhattacharya, 2019 ; Ansari, 2018 ; Craig and Douglas, 2006 ; Kacen and Lee, 2002 ; Koon et al. , 2020 ; Nwankwo et al. , 2014 ; Pepper et al. , 2009 ; Tendai and Crispen, 2009 ); however, there is a dearth of studies on consumer online purchase behaviour, in the fashion industry, with reference to sub-Saharan Africa. A key country in this region is Nigeria, known for its multi-ethnicity and large population. The country's median age is 18.4 years, which indicates the propensity of a technology-driven youthful population ( Varrella, 2020 ). With the challenge of “fit” and “size” and patronage of online fashion space in Nigeria ( Falode et al. , 2016 ; Ogbuji and Udom, 2018 ), this study assesses consumer purchase behaviour in online fashion retailing of an emerging market, particularly in a technology-driven society. Following Kautish and Sharma's (2018) study, we adopt the variables – values, fashion consciousness and behavioural intention to purchase – as predictors of online consumer purchase behaviour in the Nigerian fashion industry.

Theoretical framework

Theory of planned behaviour.

This paper adopts the theory of planned behaviour (TPB) by Azjen (1985 , 1991 ) to explain the purchase and patronage of online fashion retailing. Azjen (1991) asserts that an individual's behaviour is not spontaneous but rather is influenced and determined by various factors, such as intention, social norm and perceived control over certain phenomena. TPB is an extension of the theory of reasoned action (TRA) ( Azjen and Fishbein, 1980 ; George, 2004 ). The TRA proposed that intention is crucial in exhibiting certain behaviours, and it is measured by attitude and subjective norms ( Hagger, 2019 ). The theory focused on explaining behaviours within the individual's control, and the scope did not capture explanations on why individuals are not in total control of some of their behaviours, and this led to TPB. Azjen extended TRA with the propositions of the TPB and included the construct of perceived behavioural control to explain behaviours beyond the control of the individual ( Hagger, 2019 ).

According to George (2004) , the attitude towards a target behaviour and the subjective norms surrounding it determine intention. Several studies have applied the assumptions of TPB to purchase behaviour ( Arora and Sahney, 2018 ; Conner, 2020 ; George, 2004 ; Verma and Chandra, 2018 ) and also in studies on Internet purchasing behaviour (i.e. Battacherjee, 2000 ; George, 2002 , 2004 ; Jarvenpaa and Todd, 1997a , b ; Khalifa and Limayem, 2003 ; Limayem et al. , 2000 ; Pavlou, 2002 ; Song and Zahedi, 2001 ; Singh and Srivastava, 2019 ; Suh and Han, 2003 ; Tan and Teo, 2000 ; Verma and Chandra, 2018 ). The three antecedents of online-purchasing behaviour are measured and defined on the premise of TPB ( Ham et al. , 2015 ). These include attitude and intention (Do I want to do that?), subjective norms (Do others want me to do that?) and perceived control (Do I have the necessary ability to do that?).

Azjen (1991) proposes that intention is determined by an individual's attitude, subjective norms and perceived behavioural control. Attitude can either be positive or negative, and it is influenced by an individual's beliefs, which, in turn, inform the norms. Azjen (1991) adds that an individual's possession of resources and opportunities needed to engage in the behaviour would influence whether the individual will exhibit such behaviour. In other words, it is not sufficient to have intentions to purchase; individuals must also have the ability to purchase the product. For example, two individuals might have the same level of intention and belief in purchasing a particular product, but the one with the resources to purchase the product is more likely to make the purchase decision.

In the context of this study, behaviour is determined by intentions and beliefs (social norms) that align with the individual's values. Individuals will act in calculative ways, such that decisions are made based on the most favourable outcome. This paper hypothesises that consumers' values (terminal and instrumental values) and consumers' fashion consciousness are factors that determine their online purchases in the fashion industry. This implies that in an emerging market, despite the challenges of fit and size of apparels bought online ( Kaushik et al. , 2020 ), consumers' instrumental values, terminal values and fashion consciousness will stimulate purchase using the same medium. TPB is, therefore, adopted to explain and predict consumer online purchase behaviour in the fashion industry and in an emerging market; this is premised on the tenets of the theory that consumers' values (instrumental, terminal) and fashion consciousness will determine consumers' purchase in the online fashion industry.

Technology and the fashion industry

The retail business is experiencing continuous changes due to the dynamics in taste, innovation and consumer behaviour in the market ( Kennedy et al. , 2019 ; Suzuki and Park, 2018 ; Tendai and Crispen, 2009 ). The fashion industry, which is one of the oldest industries in the history of mankind, has been very dynamic, evolving according to the tastes, trends and needs of society. Xue et al. (2019) emphasise that retailers must understand how to use technology to facilitate consumer purchase behaviour in local and global markets of this era. Xue et al. (2019) project that proper investment in electronic retailing would enhance the business performance of retailers, sustain their competitive advantage and attract a larger population to the electronic market, if the purchase behaviours of consumers within the markets are understood. The fashion industry has evolved and imbibed the online retailing system to attract the attention of the majority in the market. As society is becoming more technology-driven, the fashion industry must position itself in line with this trend; however, some studies show that challenges emanating from online fashion commodities, like apparels, have negatively affected rather than boost retail sales ( Bonetti et al. , 2018 ; Hope-Allwood, 2016 ; Xue et al. , 2019 ).

Therefore, having a technology-driven retail strategy without understanding or paying attention to factors that influence consumer purchase behaviour will result in negative sales outcome, for consumers are driven by social and psychological factors in their purchase intention. Niemeier et al. (2013) as well as Xue et al. (2019) found hedonic factors, convenience (friendly-user interface and easy process) and entertainment as determinants of consumers' purchase of virtual products. Contributing to the array of knowledge on consumer purchase of virtual products, consumer values, fashion consciousness and behavioural intention are tested in this study.

Consumer values, fashion consciousness and behavioural intentions in the online fashion retail sector

Instrumental value influences consumer online purchase behaviour in emerging markets.

Terminal values influence consumer online purchase behaviours in an emerging market

Fashion consciousness influences consumer online purchase behaviour in the fashion industry

The relationship between consumer values (terminal and instrumental) and consumer online purchase behaviour is moderated by consumers' fashion consciousness

Research sample

We employed a cross-sectional design and surveyed 282 individuals through convenience sampling. The data collection method yielded a useable total of 241 survey reports through onsite visitation, representing a response rate of 88.5%, which is considered adequate. The remaining 41 survey reports were rejected due to incomplete information. The survey questionnaire contained close-ended questions and was administered to the respondents in August 2019. The study was conducted in an environment comprising both students and the working class, where a major public university in Lagos, Nigeria, is situated. The demographic characteristics of respondents are as follows: 52.3% of the respondents are students; 13.3% are unemployed; 2.90% are self-employed and the remaining 31.5% constitute other professions ( Table 1 ). Most of the respondents in the study fall within Generation Y (21–30 years, 45.6%; 31–40 years, 21%) and Z (Below 20, 28%) category. The descriptive statistics and correlation of the constructs are provided in Table 2 .

To ensure high content validity, all the measurement scales used for the consumer values, fashion consciousness and online consumer purchase behaviour were adopted from extant literature ( Kautish and Sharma, 2018 ). The survey asked respondents to indicate on a 7-point Likert scale, ranging from 1 = “strongly disagree” through to 7 = “strongly agree”, the extent to which each statement applied to them.

Control variables

We controlled for four variables in the analyses to account for other factors that were not captured in the research but could affect customer online purchase behaviour in Nigeria. These control variables include age of respondent, educational qualification, monthly income and online purchase frequency.

Scale validity and reliability

The Cronbach alpha reliability test ( α ), which shows internal consistency for each item that makes up a construct is as follows: consumer value has α value of 0.70; fashion consciousness has α value of 0.72 and consumer online purchase behaviour has α value of 0.80. These Cronbach alpha values are all above 0.7, which is the recommended minimum acceptable level ( Hair et al. , 1998 ). Confirmatory factor analyses (CFAs) of the adopted measures which confirm the discriminant validity are as follows: normed chi-square value ( χ 2  = 537.48; df = 129), the fit indices Non-Normed Fit Index (NNFI) = 0.70, Normed Fit Index (NFI) = 0.70, Goodness of Fit (GFI) = 0.80, Comparative Fit Index (CFI) = 0.74, p -value = 0.00000 and Root Mean Square Error (RMSEA) = 0.115. The CFA results confirmed the discriminant validity of the constructs. Table 2 shows the means, standard deviations and correlations of the variables. The ( χ 2 /df) value for the model is 4.2, which is within the acceptable range of 2–5 ( MacCallum et al. , 1999 ; Marsh et al. , 1988 , 1998 ; Kautish and Sharma, 2018 ).

Analysis and results

The following regression model was used to estimate the consumer online purchase behaviour influence of the two independent constructs: consumer value and fashion consciousness: Y i = β 0 + β 1 C V + β 2 F C + β 3 C V F C + e i

The subscript i denotes each respondent ( i  = 1,…, 241). Y is the dependent variable (Consumer online purchase behaviour). CV represents the vector for the variants, terminal and instrumental values, FC represents the vector for fashion consciousness, CVFC represents the vector for the moderating effects and e i is the error term. β 1 – β 3 represent the parameters of the coefficients. Figure 1 shows the research model.

Multiple regression analysis was carried out using the hierarchical method ( Cohen and Cohen, 1983 ). In this case, the independent variables were sequentially introduced, one after the other. The hierarchical regression analysis was carried out using six separate multiple regression analyses, as shown in Table 3 . In the first regression model, all the control variables were included. In the second regression model, consumer terminal value was regressed on the consumer online purchase behaviour and the control variables. In the third regression model, the instrumental value was regressed on the consumer online purchase behaviour and the control variables. In the fourth regression model, consumer values (terminal and instrumental values) were regressed on the consumer online purchase behaviour and the control variables. Finally, the interaction terms and consumer values (terminal and instrumental values) were regressed on the consumer online purchase behaviour and the control variables.

Overall, the four hypotheses are supported, as indicated in Table 3 . From model 1, none of the control variables is significant. From model 2, the results show that terminal value is significantly positively related to consumer online purchase behaviour ( β  = 0.633 at p  < 0.01), thus, supporting H1 ; all the control variables are not significant. From model 3, the results show that instrumental value is significantly positively related to consumer online purchase behaviour ( β  = 0.451 at p  < 0.01), thus supporting H2 ; all the control variables are not significant. From model 4, the results show that fashion consciousness is significantly positively related to consumer online purchase behaviour ( β  = 0.413 at p  < 0.01), thus supporting H3 ; almost all the control variables are not significant, except the age of respondents, which is significant ( β  = −0.169 at p  < 0.05). From model 5, the results show that terminal value is significantly positively related to consumer online purchase behaviour ( β  = 0.048 at p  < 0.01), thus also supporting H1 . Instrumental value is significantly positively related to consumer online purchase behaviour ( β  = 0.219 at p  < 0.01), thus also supporting H2 .

Fashion consciousness is significantly and positively related to consumer online purchase behaviour ( β  = 0.142 at p  > 0.05), thus also supporting H3 . All the control variables are found to be insignificant. From model 6, the results show that the interaction term (terminal value × instrumental value × fashion consciousness) is significantly positively related to consumer online behaviour, thus supporting H4 . Instrumental value is not significant, whereas terminal value is significantly related to consumer online purchase behaviour. Fashion consciousness is not significantly related to consumer online purchase behaviour.

All the control variables are found to be insignificant. From model 6, the interaction between consumer value and fashion consciousness accounted for significantly more variance than just consumer value and fashion consciousness alone; R 2 change = 0.008, p  < 0.01, indicating that there is potentially significant moderation between consumer value and fashion consciousness on consumer online purchase behaviour. The Durbin–Watson ranges from 1.6–1.9, which are approximately 2, and shows no evidence of autocorrelation ( Gujarati, 2003 ). The overall statistical measures, such as ( R 2 , R , F and p -value) indicate the adequacy of the model (see Table 3 ).

Discussions and implication

The role of consumer values in influencing online purchase has been documented in the literature ( Limayem et al. , 2000 ; Nwankwom et al. , 2014 ; Kautish and Sharma, 2018 ). However, very few studies have examined the role of technological innovation in influencing customer value towards online purchase, especially as related to the fashion industry. Kautish and Sharma (2018) examined consumer values, fashion consciousness and behavioural intentions in India's online fashion retail sector and suggested that similar studies should be conducted in emerging economies with diverse cultures. This study, thus, fills this gap by examining consumer values and purchase in the fashion industry through technological platforms in emerging markets, like Nigeria.

Consumer values were grouped into instrumental and terminal values to illustrate the practical implications of the study. The first hypothesis examined the influence of instrumental value on consumer online purchase behaviour in an emerging market, and the result shows that there is a positive significant relationship between instrumental values and online purchase of fashion apparels. This implies that purchasing apparel online saves consumers' time, cost of purchase, convenience, discount in services received and it offers varieties of goods to choose and buy. In other words, key factors that attract and influence the purchase of fashion items online using technological innovation are the convenience, low cost, discount and variety of commodities offered by online stores. This result supports the theoretical proposition by Azjen (1991) that behaviours of individuals are influenced by calculative permutations on the cost and benefits of their actions. Consequently, intentions become actions when it is perceived that the action has more benefit than cost. This finding also supports the observations of Kautish and Sharma (2018) that instrumental values to be derived by consumers in the purchase of a commodity online will influence their purchase decision.

The second hypothesis on the influence of terminal values and consumer online purchase behaviour in an emerging market reveals a significant and positive relationship between terminal value and consumer online purchase behaviour. This implies that happiness, love and satisfaction are consumers experience when they purchase fashion apparels online. In addition, customers perceive a sense of freedom and comfort when they successfully make online purchases. This also supports the submission of Allen et al. (2002) as well as Kautish and Sharma (2018) that terminal value reward from online purchase of a product influences consumer purchase. Online stores, hence, must ensure that their products provide ease of purchase and are low cost and also that the apparels reflect the desires of the customers, such that they provide comfort, satisfaction and happiness when worn.

The third hypothesis examines the influence of fashion consciousness on consumer online purchase behaviour in the fashion industry, and the result shows a significant and positive relationship between fashion consciousness and purchase behaviours. This implies that students, professionals, the employed and unemployed in emerging markets like Nigeria, support and are mindful of fashion trends; the result also showed that students are more interested in fashion trends than the unemployed and self-employed; this result might be associated with the fact that Gen Y and Z consumers are the most represented in this study. This result supports the observations of Babin and James (2010) , Fernandes (2013) , Kautish and Sharma (2018) that fashion consciousness influences the decision to purchase apparels and other related fashion items online. Kautish and Sharma's (2018) submitted that Gen Y consumers have a higher purchase frequency and impulse buying than other generational cohorts. However, this study extends knowledge from the work of Kautish and Sharma (2018) , which was focused on students to show that it is not only this category of individuals who are fashion-conscious but also professionals, the self-employed and even the unemployed in emerging markets, like Nigeria.

The fourth hypothesis tested the moderation of customer values (terminal and instrumental) and online purchase behaviour by fashion consciousness, and the result shows that fashion consciousness moderates the extent to which consumers' values influence their purchase behaviour. A society with a high rate of fashion-conscious individuals will purchase fashion apparels online more than a society with less fashion-conscious people. In addition, it shows that an individual's consciousness for fashion plays a primary role in the online purchase of fashion apparels and other fashionable items.

Additional findings in the study reveal that terminal value has a greater influence on online consumer purchase of fashion apparel. This is indicated by its higher coefficient score (0.633) compared to the scores for the instrumental value (0.451) and fashion consciousness (0.413) (see Table 3 ). This shows that happiness, love, satisfaction, a sense of freedom and comfort derived from online purchase of fashion apparels influence customers' behaviour more than instrumental values (ease of purchase, cost, convenience, discount and product varieties). Interestingly, these findings do not support the observations of Kautish and Sharma (2018) in India, which indicated that instrumental value has a greater influence on consumer purchase. The reverse is the case in this study, as the terminal value reflects the highest coefficient among the two constructs. Nigerians in the study were more interested in the terminal value obtained from the purchase of fashion apparels online, as opposed to customers in India, which might be due to their social and cultural differences.

Implication for practice

The findings from this study have both business and technology-use implications. First, organisations and businesses in the fashion industry must continue to implement innovative and technological ideas on how to provide customers with the values that appeal to them from the online purchase of apparels as this has proven to be a key factor influencing customers' purchase. Consumers in this study are influenced by the convenience and time efficiency of purchase, cost-effectiveness, discount and availability of varieties; hence, managers, business owners and app developers for the fashion market must ensure that their services take into consideration all of these factors for online purchase to be continually stimulated.

Additionally, managers and app developers must understand the kind of apparels that conform to consumers' satisfaction and design, such apparels to meet this need, as this is also paramount to stimulate purchases. Consumers in the Nigerian fashion market are conscious of apparels that give them comfort, a sense of love, happiness and are trendy; therefore, online fashion retailers must have in stock apparels that possess these characteristics. In addition, the targeted audience should not be students or the younger generation alone, as this study has shown that the larger Nigerian populace is fashion conscious. Business owners should have apparels that cut across generations X, Y and Z; they should ensure that there are various offerings to capture different population classifications in the market, thereby meeting all needs. Businesses can focus more on generation Y and Z as they are the most populous in emerging markets and are more used to digital innovations. In spite of this, generation X must still be captured in their product offerings and designs.

The focus should be on increasing terminal values (happiness, love and satisfaction, a feeling of freedom and comfort) of fashion apparels purchased. Instrumental values (ease of purchase, cost, convenience, discount and product varieties) values are important to the Nigerian market; however, there is a preference for clothes that satisfy more terminal values.

Limitations and direction for future research

The study covered consumer values, fashion consciousness and online purchase in the fashion industry in an emerging market – Nigeria. This study is limited to the online fashion (apparel) market and did not take into consideration other viable sectors. Hence, future studies can fill this gap. Other markets, for instance, electronics and automobiles, can be examined in future studies to extend the knowledge of online purchasing and the impact of technological innovations.

Through the lens of a cross-sectional methodology and quantitative techniques, convenience sampling was used to select respondents from a mixed population of students, working-class professionals, the self-employed and unemployed within a multi-cultural and industrial environment, Lagos.

Future studies can consider using random sampling techniques, triangulate their methods and expand the geographical coverage of the sample as non-attention to these factors can be considered a limitation of the study.

The model above represents the direct effects models ( Hypotheses 1 , 2 , 3 ) and the moderation model ( Hypothesis 4 )

Demographic characteristic of respondents

Descriptive statistics and correlations

Note(s): n  = 421; standardised regression coefficients are reported

* p  < 0.10; ** p  < 0.05; *** p  < 0.01

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Further reading

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