How Does Digital Technology Inspire Global Fashion Design Trends? Big Data Analysis on Design Elements
Source abstract
Abstract
Digital technology has changed every process of the fashion industry significantly. Using big data analysis methods such as text-mining, network, CONCOR, and content analyses, this study aims to understand the impact of digital technology trends from the fashion design perspective. The influence of digital technology on fashion design elements (e.g., color, print and graphic, textiles, and style and details) was evident through various keywords related to digital technology, humans, and nature, and the relationships between these keywords were confirmed. The analysis of the implicit meanings and directions of the derived keywords resulted in four clusters: (1) human- and nature-oriented design in the digital world as a new reality; (2) new textiles reflecting digital technology; (3) sustainable design technology; and (4) new utility fashion in the digital space. This study proposed a new design research methodology in which big data were incorporated and could be applied to educational curricula, allowing students to derive practical design elements through big data analysis and serving as a guide for planning and developing technology-inspired designs. Practically, it provided specific information on the direction of digital-technology-inspired fashion design trends, which could assist fashion designers and aspiring entrepreneurs in planning.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Fashion and Cultural Textiles · Consumer Perception and Purchasing Behavior · Digital Media and Visual Art
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Fashion and Textiles, Seoul National University, Seoul 08826, Republic of Korea
Department of Fashion Design & Merchandising, College of Social Science, Gachon University, Seongnam-Si, Gyeonggi-Do 13120, Republic of Korea
Funding
Gachon University research
References (51)
Show source-provided references
1. Team YouCam (2024, April 17). 2024 Fashion Tech Guide: Generative AI, AR Virtual Fitting, 3D Sampling, Omnichannel Strategy. Available online: https://www.perfectcorp.com/ko/business/blog/general/fashion-tech-the-complete-guide.
2. Mcdowell, M. (2024, April 17). Digital fashion is alive and well for Gen Z. Available online: https://www.voguebusiness.com/story/technology/digital-fashion-is-alive-and-well-for-gen-z.
3. Perspectives on fashion technology during the pandemic era: A mixed methods approach using text mining and content analysis10.5805/SFTI.2022.24.5.545 ↗
4. Big data and digital design models for fashion design
5. Digital 3D fashion designers: Cases of atacac and the fabricant10.1080/1362704X.2021.1981657 ↗
6. Digital 3D design as a tool for augmenting zero-waste fashion design practice10.1080/17543266.2020.1737248 ↗
7. Fashion intelligence in the metaverse: Promise and future prospects10.1007/s10462-024-10703-8 ↗
8. Digital fashion innovations for the real world and metaverse10.1080/17543266.2022.2071139 ↗
9. Gill, S., Januszkiewicz, M., and Ahmed, M. (2022). Digital fashion technology: A review of online fit and sizing. Digital Manuf. Technol. Sustain. Anthropol. Appar., 135–163.10.1016/B978-0-12-823969-8.00008-3 ↗
10. Evolution of trend perception in the fashion industry: Within a digital technology-driven landscape
11. Traditional vs. big-data fashion trend forecasting: An examination using WGSN and EDITED10.1080/17543266.2020.1732482 ↗
12. Rahman, O., Hu, D., and Fung, B.C.M. (2023). A systematic literature review of fashion, sustainability, and consumption using a mixed methods approach. Sustainability, 15.10.3390/su151612213 ↗
13. Understanding the evaluation of quality of experience for metaverse services utilizing text mining: A case study on Roblox
14. Analysis of meta fashion meaning structure using big data: Focusing on the keywords ‘metaverse’ + ‘fashion design’10.5805/SFTI.2023.25.5.549 ↗
15. Lee, S.-E., Ju, N., and Lee, K.-H. (2021). Visioning the future of smart fashion factories based on media big data analysis. Appl. Sci., 11.10.3390/app11167549 ↗
16. Lay, R. (2024, April 17). Digital Transformation—The Ultimate Challenge for the Fashion Industry. Available online: https://www2.deloitte.com/ch/en/pages/consumer-industrial-products/articles/ultimate-challenge-fashion-industry-digital-age.html.
17. Saravanan, D., and Nithyaprakash, V. (, January April). Fashion Trends and Its Impact on Society. Proceedings of the International Conference on Apparels and Fashion, Tamilnadu, India.
18. Rijmenam, M.V. (2024, April 17). Digital Fashion: The Next Frontier in Fashion. Available online: https://www.thedigitalspeaker.com/digital-fashion-next-frontier-fashion/.
19. Fashion Transformation 4.0: Beyond Digitalization & Marketing in Fashion Industry
20. Jung, Y.C. (2013). Big Data, Communication Books.
21. Schwab, K. (2016). The Fourth Industrial Revolution, MegaStudy. Song, K.J., Translator.
22. StartsUs Insights (2024, April 17). Top 8 Fashion Technology Trends & Innovations in 2023. Available online: https://www.startus-insights.com/innovators-guide/fashion-technology-trends/.
23. Silva, E.S., Hassani, H., Madsen, D.Ø., and Gee, L. (2019). Googling fashion: Forecasting fashion consumer behaviour using Google Trends. Soc. Sci., 8.10.3390/socsci8040111 ↗
24. Kandi, N.A. (2023). How big data analytics will transform the future of fashion retailing. Handbook of Big Data Research Methods, Edward Elgar.10.4337/9781800888555.00009 ↗
25. Perceptions and trends of digital fashion technology: A big data analysis10.5805/SFTI.2021.23.3.380 ↗
26. Amed, I., and Berg, A. (2024, April 09). The State of Fashion 2020: Coronavirus Update—It’s Time to Rewire the Fashion Industry. Available online: https://www.businessoffashion.com/reports/global-markets/the-state-of-fashion-2020-coronavirus-update-bof-mckinsey-report-release-download/.
27. Textile Journey (2024, April 09). The State of Fashion 2022: An Uneven Recovery and New Frontiers. Available online: https://www.textilejourney.com/post/the-state-of-fashion-in-2022-an-uneven-recovery-and-new-frontiers.
28. IBM (2024, April 09). What Is Text Mining?. Available online: https://www.ibm.com/kr-ko/topics/text-mining.
29. Text mining methods and techniques
30. Kim, E.J., and Kim, J.Y. (2023). Exploring the online news trends of the metaverse in South Korea: A data-mining-driven semantic network analysis. Sustainability, 15.10.3390/su152316279 ↗
31. Youn, C., and Jung, H.J. (2021). Semantic network analysis to explore the concept of sustainability in the apparel and textile industry. Sustainability, 13.10.3390/su13073813 ↗
32. Park, S.U., Ahn, H., Kim, D.K., and So, W.Y. (2020). Big data analysis of sports and physical activities among Korean adolescents. Int. J. Environ. Res. Public Health, 17.10.3390/ijerph17155577 ↗
33. Analyzing appearance management of Korean middle-aged men in media: A big data approach through newspaper articles
34. (2024, April 09). TF-IDF (Term Frequency-Inverse Document Frequency). Available online: https://textom.co.kr/analyze/29059/N.
35. Majeed, S., Uzair, M., Qamar, U., and Farooq, A. (2020, January 5–7). Social Network Analysis Visualization Tools: A Comparative Review. Proceedings of the 2020 IEEE 23rd International Multitopic Conference (INMIC), Bahawalpur, Pakistan.10.1109/INMIC50486.2020.9318162 ↗
36. A study of the consumer major perception of packaging using big data analysis: Focusing on text mining and semantic network analysis
37. Perception and trend differences between Korea, China, and the US on vegan fashion: Using big data analytics
38. Jung, S.H. (2015). Qualitative Methodology of Advertising Research, Communication Books.
39. Baidoo, A., and Maggioni, S. (2024, March 05). Womenswear Forecast A/W 22/23: Awestruck. Available online: https://www.wgsn.com/fashion/article/89995.
40. Ross, A., and The WGSN Womenswear Team (2024, March 05). Women’s & Young Women’s Forecast S/S 20: Designing Emotion. Available online: https://www.wgsn.com/fashion/article/79916.
41. Zagor, N. (2024, March 05). Menswear Forecast A/W 22/23: Awestruck. Available online: https://www.wgsn.com/fashion/article/90258.
42. Chow, F. (2024, March 05). Men’s Prints & Graphics Forecast A/W 23/24: NatureVerse. Available online: https://www.wgsn.com/fashion/article/93008.
43. Palmer, H. (2024, March 05). Men’s Textiles Forecast A/W 22/23: Awestruck. Available online: https://www.wgsn.com/fashion/article/89466.
44. Palmer, H. (2024, April 09). Men’s Textiles Forecast S/S 23: Full Spectrum. Available online: https://www.wgsn.com/fashion/article/91190.
45. Paget, N. (2024, March 05). Men’s Forecast S/S 24: SenseScapes. Available online: https://www.wgsn.com/fashion/article/62e7e16ad9d176103118e910.
46. Casey, C., and Bulmer, V. (2024, March 05). Women’s Knit & Jersey Forecast S/S 25: Digitopia. Available online: https://www.wgsn.com/fashion/article/647f202963a9e3884e568a9a.
47. (2024, March 05). 21SS Sports & Outdoor_II Inner Desire. Available online: https://www.fashionnet.or.kr/trend-forecast/5504/.
48. Varga, C., and the WGSN Active Team (2024, March 05). Active Style Trend Concepts A/W 20/21: Tech-tility. Available online: https://www.wgsn.com/fashion/article/81998.
49. Fenton, C. (2024, March 05). Active Style Trend Concepts S/S 21: GameScape. Available online: https://www.wgsn.com/fashion/article/83902.
50. Ketteniss, V. (2024, March 05). Men’s Style Trend Concepts S/S 21: GameScape. Available online: https://www.wgsn.com/fashion/article/84346.
51. Ross, A. (2024, March 05). Women’s Style Trend Concepts S/S 21: GameScape. Available online: https://www.wgsn.com/fashion/artile/84323.