1. Introduction
To what extent will digital technology expand? Globally, digital technology has induced changes across industrial fields, with the fashion industry being no exception. Digital technology has significantly changed every process in the industry, from planning and producing fashion products to their sales [1]. Fashion technology, along with its innovative tools, is pivotal for transforming the industry from design to manufacturing to sales.
These digital technologies have changed global fashion design trends. Gen Z continues to spend time in digital spaces, purchasing and wearing digital fashion. The virtual-world platform Roblox has witnessed 165 billion avatar updates and the purchase of 1.65 billion digital fashion items. For users, styling avatars is considered more important than styling themselves [2]. Environments such as artificial intelligence (AI), three-dimensional (3D), metaverses, and non-fungible tokens (NFTs) are creating new trends in fashion design for consumers. Therefore, we highlight the need to combine new fashion design trends with digital technology and to explore this objectively through big data analysis. Recent studies in the fashion field have used big data to discover new value from reliable information for future predictions [3]. In addition, fashion design based on big data could clarify research and reflect objective data mining results in a manner that might otherwise be subjective, allowing for design focuses that align with consumer needs [4].
Previous research on digital technology and fashion has primarily focused on case studies of fashion designers or fashion companies, on qualitative research, on the creation or review of fashion design models using digital technology, on analyzing various cases and trends combining digital technology with fashion, and on the perception of changes in fashion industry trends [5,6,7,8,9,10]. In addition, previous studies using big data have centered on comparatively analyzing fashion trends, offering perspectives of fashion technology through text mining, analyzing service experience in the metaverse, the meaning and structure of meta fashion, and articles related to smart factories [3,4,11,12,13,14,15].
These studies have only analyzed various cases and trends related to the integration of fashion with digital technology, and studies that used big data are limited to comparative analyses of fashion trends or text mining from a fashion technology perspective. Such approaches have limitations in analyzing specific elements and changes in fashion design that are influenced by technological advancements.
Therefore, we propose a new approach, using big data to analyze the impact of fashion technology trends systematically, and specifically, on fashion design. This approach introduces an innovative research method in the field of fashion design by utilizing big data analysis to specify and clarify abstract design elements and characteristics, thereby providing guidance for creative and timely design development. Since there have been no previous instances of applying big data to design elements in the field of fashion design research, this represents an innovative research method. This study investigates these trends inspired by digital technology, analyzes specific design elements and characteristics, and explores their inherent meanings through text-mining analysis of big data. This study aimed to answer the following questions:
What keywords conceptualize global fashion trends inspired by digital technology?
What are the main design elements and characteristics of global fashion trends inspired by digital technology?
What are the future directions and implications of fashion trends and design features inspired by digital technology?
The significance of this study is that it represents the convergence of design and big data field studies and analyzes the ever-evolving digital technology fashion trends and fashion design fields using big data. Moreover, objectively analyzing the current state of fashion trends inspired by digital technology and the specific elements and characteristics of fashion design through text could guide the future direction of digital-technology-inspired fashion design trends. In addition, we anticipate that from an educational perspective, this study will benefit not only practitioners in the fashion industry but also educators and students in the field of fashion design.
2. Literature Review
2.1. Digital Technology Trend in Fashion
Fashion is one of the most challenging fields and is affected by global economic uncertainty, distinct trends, and industrial changes [16]. Fashion trends continuously emerge and fade, and society’s established values evolve according to beliefs and culture. Fashion is not merely an ambitious projection of good old values reinterpreted to serve some functions and agendas, but a fresh concept worthy of depicting society’s appreciation and evoking a rejuvenation that makes us more instinctual [17].
However, modern fashion trends have entered an era of leap. Sociocultural phenomena, such as the Fourth Industrial Revolution (4IR), the advancement of digital technologies, and the emergence of the COVID-19 pandemic, have brought diverse changes across the fashion industry. In particular, digital technologies such as 3D, AI, NFTs, and metaverses have transformed various aspects of the fashion industry, including production, development, marketing, and sales, establishing them as megatrends. Rijmenam [18] explained that digital fashion involves selling digital clothing and accessories using virtual avatars and models. Whereas traditional fashion requires physical materials such as yarn and fabric, digital fashion can be realized through data and codes. This implies that there are no limitations on what can be designed or created. Fashion is undergoing a significant digital transformation, with garments and apparel being presented and sold online, and fashion trends and styles being launched, discussed, and negotiated primarily online [19]. Sayem [8] argued that digitalizing the fashion industry aimed to streamline the design, production, and business of physical products in the real world and to achieve sustainability with the help of various digital tools. However, the emergence of the metaverse has opened new horizons for digital fashion. Generally, innovations in digital fashion have four main themes: digital design and e-prototyping, digital business and promotion, digital human and metaverse, and digital apparel and smart e-technology. Technologies in 3D modeling, VR, AR, and digital technology will be actively used in the fashion business as they facilitate realistic virtual fashion products and offer users new dimensions of experience within the metaverse space [14].
Kim et al. [10] analyzed how the mechanisms of trend actions and the form of the new fashion industry are changing in an environment based on digital technology. The configuration of a new fashion industry is analyzed within a digital-technology-driven environment to comprehend the corresponding shifts in trend mechanisms. Both fashion consumers and producers recognize that trend longevity is short and positive regarding the introduction of consumer participation systems and diversity expansion in trends. Consumers are hopeful, while producers are negative about the future of large-brand operations, and both are aware of the importance of sustainability, though they inadequately respond to it. Further, consumers and producers differ in their perception of the necessity of introducing new technologies.
Trends in the fusion of fashion and digital technology are inevitable, as they highlight the need for change among all members of the fashion industry. Therefore, it is necessary to examine the impact of digital technology on fashion trends from various perspectives. This study aimed to use big data analysis as a tool to objectively understand the impact of digital technology trends from the fashion design perspective.
2.2. Big Data Analysis in Fashion Trends
Big data refers to data, including numerical, textual, and visual data, generated in a digital environment and characterized by their vast scale, rapid generation cycles, and diversity [20]. As we enter the era of the 4IR, the importance of big data has grown. In his book “The Fourth Industrial Revolution”, Schwab [21] mentions the importance of quality improvement using digital technology and big data as a megatrend of the 4IR.
In addition, big data is being discussed extensively in the fashion industry. StartUs Insights [22], in their report “Top 8 Fashion Technology Trends & Innovations in 2023”, mentioned big data and analytics as key trends. Brands could leverage big data and analytics to create new collections of designs and plans. Furthermore, fashion brands and retailers could use data analysis to improve their communication with customers and enable precise personalization, thereby increasing sales and profitability. Traditionally, fashion trend prediction has been a human-based process on which designers heavily rely for artistic perspectives. However, with the advent of data science and the increased availability of consumer data inputs, the potential use of big data tools for predicting fashion trends has garnered increasing interest among academics and practitioners in the fashion industry [11]. The efficacy of big data analysis lies in its strategic use by fashion companies to predict trends and customize consumer experience, allowing customers to drive new product development [11,16].
Furthermore, the use of big data analysis in digital technology and fashion has diversified. Silva et al. [23] analyzed Google fashion trends using big data to explore predictions of consumer behavior in fashion. Kandi [24] investigated the application and benefits of big data analysis in fashion retail, describing it as an opportunity to create strategies for better decision making under uncertain conditions and environments. DuBreuil and Lu [11] comparatively analyzed trend predictions from WGSN and EDITED using big data analysis, and their findings suggested the overall feasibility and great potential of using big data tools to help fashion companies create new products. In addition, the findings illustrated the limitations of using big data tools for trend forecasting as a creative activity. Kim and Lee [14] conducted a meaning structure analysis and discussed the outlook for metafashion using big data text mining. Important terms such as 3D, apparel, platforms, NFT, education, AI, avatars, MCM, and metafashion emerged, and three clusters were derived: metafashion design and industry, metafashion design and education, and metafashion design platforms. Exploring perceptions and trends related to digital fashion technology that is expanding across the fashion industry through big data analysis, Song and Lim [25] found that the digital transformation of fashion has become a major issue in the fields of design, manufacturing, distributing, and marketing. They noted that consumer interest is drawn by investments and promotions by fashion groups with specialized skills and highlighted the growing interest of the education sector in digital fashion technology and the need for a shift in the educational paradigm.
Therefore, exploring the influence of digital technology reflected in fashion trends through big data analysis is an effective approach. Specifically, such an analysis helps concretely identify consumer needs and functions to predict the future. Previous studies primarily focused on the recognition of fashion trends, tendencies, and consumer behavior. Thus, through this research, we aim to analyze fashion design trends inspired by digital technology using text-mining methods to uncover hidden insights.
3. Methodology
3.1. Data Collection
The data collected for the study were taken from the global fashion trend analysis site WGSN (https://www.wgsn.com/en (accessed on 5 March 2024)) and the Korean trend analysis site FashionNet (https://www.fashionnet.or.kr (accessed on 5 March 2024)). WGSN is recognized as a global forecasting company that provides influential and reliable information on global trends. FashionNet is a representative trend-analysis institution that promptly delivers the latest trends to Korean consumers. The collection period was set from the 2020 spring/summer season to the 2025 fall/winter season, for a total of 12 seasons. This period was chosen because the global fashion industry faced a severe downturn owing to the COVID-19 pandemic in 2020, and technologies such as digital platforms, metaverses, and NFTs have been proposed as solutions to these challenges [26,27].
Excluding trends in footwear, accessories, lingerie, and jewelry, 446 cases related to forecast data in fashion design trends were initially collected, with 348 from WGSN and 98 cases from FashionNet. In this study, we were focused more on the specific fashion design elements and characteristics of clothing and garments inspired by the digital technology trend, so we excluded footwear, accessories, lingerie, and jewelry trends from our analysis. Next, only trends that prominently featured digital technology were selected, resulting in a final count of 106 and 36 cases from WGSN and FashionNet, respectively, with a total of 136 cases for analysis. WGSN materials were in the form of English PDF files, whereas FashionNet provided its materials through Korean web pages. Subsequently, the content of the data analysis was translated into English. Afterwards, all data were manually typed and saved as analyzable TXT UTF-8-encoded files for data preprocessing. Finally, based on the design elements indicated in the WGSN and FashionNet data, we categorized fashion design elements into color, print and graphic, textiles, and style and details.
3.2. Text Mining in Big Data Analytics
In big data analysis, data mining involves identifying patterns and extracting useful insights from large datasets [28]. Text mining is a variation of data mining that aims to discover interesting patterns within large databases and extract hidden information from unstructured and semi-structured data [29]. The text-mining process starts with the collection of documents from various resources, and tools for searching and preprocessing specific documents and verifying their formats and character sets are used. Text analysis involves semantic analysis to extract high-quality information from text [29]. Text mining deals with unstructured data that are irregular in shape and difficult to handle; therefore, it is closely related to natural language processing (NLP) methods, which enable computers to recognize and process human language [20]. Technologies are produced using NLP to teach computers how to analyze, understand, and generate texts. Technologies for the extraction, summarization, categorization, clustering, and visualization of information are used in the text-mining process [29]. Information extraction involves tokenization and stemming, whereas NLP includes summarization, part-of-speech (PoS) tagging, and text categorization [28], which are then used for visualization.
The analysis preprocessing tool used to perform text mining in this study was Textom SV version (https://textom.co.kr (accessed on 5 March 2024)), a big data analysis program developed by IMC Co., Ltd. This tool not only facilitates the collection of big data from various acquisition channels but also produces highly compatible data that can be applied to various statistical programs [30]. Several previous studies have attempted big data analysis using Textom [30,31,32,33]. For data preprocessing, morphological analysis was conducted with the analysis language set to English using Stanford-core NLP as the analyzer. The analyzed parts of speech were restricted to nouns, adjectives, and numerals. Subsequently, data cleansing was performed to standardize unnecessary words without meaning, eliminating notational redundancies of words with the same meaning, and correct spacing issues. Words such as S/S and A/W from the trend season notations were extracted as S, A, and W, and stop words were removed. Words extracted as nouns and adjectives with identical dictionary meanings but that are written differently, such as “Real” and “Reality”, were unified under the more frequently occurring word, in this case “Reality”. In addition, the hom*onym “Space”, which could mean both a physical space and the cosmos, was differentiated by refining the less frequently occurring cosmic “Space” to “Universe”. “Camo” and “Camouflage” were standardized to “Camouflage” as they were the abbreviation and original word, respectively, with the same meaning. Foreign words in the original data, such as “Ombré” where é was not extracted, were recorded as “Ombre”. Lastly, cases such as “Digital Lavender”, where spacing separates two words but trend data present them as a unique keyword, were consolidated into a meaningful combination and rendered as “Digitallavender”. However, the original words, not the cleansed versions, were used for specific content analyses and citations in the text.
3.3. Data Analysis in Text Mining
We conducted term frequency (TF), term frequency–inverse document frequency (TF-IDF), network, and convergence of iterated correlation (CONCOR) analyses using the final refined set of words. TF indicates how often a word appears within the analyzed data, with a higher frequency suggesting the word’s importance. TF-IDF assigns weights to each word in a document based on word frequency and inverse document frequency, implying that words with high TF-IDF values might contain the document’s key messages [34]. Therefore, comparing the results of TF and TF-IDF helps to clearly identify keywords within a document. We selected the top 30 words using TF, inspired by digital technology in fashion trends and representing the keywords of each design element and characteristic, and visualized them in tables and word cloud images.
Next, we performed a network analysis to understand the connections and relationships among the 30 words extracted from the design element analysis. Using Textom, we created a 1-mode symmetric matrix with 30 words, performed network analysis using NetDraw in UCINET version 6.785, and visualized the results. UCINET employs NetDraw for network visualization, which supports various layouts for visualization purposes, including isolated nodes, components, subgroups, and centrality measures [35]. The visualization displays nodes representing each keyword and the lines between them, where the size of a node indicates the frequency of the keyword, and the thickness of the lines connecting the nodes represents the strength of the links between the words.
In addition, we conducted a CONCOR analysis to identify digital technology fashion trends and design elements. CONCOR analysis is a structural equivalence analysis that aims to identify nodes in a network with structurally equivalent positions, representing the similarity between nodes [36]. Using the top 100 words from the TF, we formed a 1-mode symmetric matrix and conducted a CONCOR analysis based on the network results among these 100 words. Because CONCOR analysis operates on the correlation coefficients of words and analyzes them until the words converge into their respective clusters, designating an appropriate number of convergences is necessary [37]. Therefore, after testing various convergence counts through dendrograms, we set the cluster diagram depth to three, which was deemed the most suitable, and visualized the final results into four clusters.
Finally, we deeply analyzed the related content by revisiting the original trend report based on the words appearing in each cluster. In qualitative research, content analysis focuses on interpreting textual data obtained through a systematic classification process that codes for and confirms themes or patterns [38]. Therefore, we focused on interpreting and understanding the specific content and meaning of sentences based on the words in each cluster. Furthermore, we endeavored to enhance the reliability and validity of our content analysis using direct quotations. Figure 1 illustrates the research process.
4. Results
4.1. Main Keywords in Digital-Technology-Inspired Fashion Trends
Table 1 displays the top keywords used for global fashion trends inspired by digital technology. Keywords such as “Digital”, “World”, “New”, “Reality”, “Future”, and “Technology” exhibited the highest frequency. The characteristic of content was that it mainly revolved around the digital and virtual worlds. Further, the disappearance of gaps in time and space and the creation of a new reality and digital world were expressed with related keywords, such as “Space”, “Between”, and “Physical”. The word “Reality” showed a higher rank in TF-IDF than in TF, which suggested that its significance in the global fashion trend was inspired by digital technology, alongside “Digital”. Moreover, the term “Aesthetic” indicated the impact of digital technology on design.
In addition, keywords such as “Future”, “Technology”, and “Innovation” were used, and words, such as “Metaverse”, “AI”, “Roblox”, “3D”, and “Avatar” appeared. Notably, “Metaverse” and “Roblox” were becoming significant issues in the current fashion design industry, and avatar fashion in virtual spaces influenced the physical world. Higher TF-IDF rankings for “Metaverse”, “AI”, and “Avatar” than their TF rankings highlighted their importance.
Keywords such as “Game”, “Sports”, and “Online” were also used. Online games in virtual spaces were a significant element of digital fashion trends, as shown by the WGSN’s trend forecast title for the year 2021 S/S—“GameScape”. Inspirations stemming from online games and sports were reflected in the fashion trends.
Another important keyword was “Future”. The digital technology trend, filled with grand science and vision for a splendid future, had provided new inspiration through its integration with nature. Connected to this was the emergence of the keyword “Sustainable”. In addition, various elements inspiring digital technology emerged as keywords, such as “Fantasy”, “Universe”, “Past”, “Smart”, “Luxury”, and the futuristic, yet somber, “Dystopia”. Lastly, the keyword “Digitopia”, proposed by the WGSN as the trend forecast title for the 2025 S/S, indicated an intensifying influence of digital technology on fashion trends. The higher TF-IDF values compared to TF values for “Dystopia” and “Digitopia” proved this hypothesis. The keywords listed in Table 1 are visualized in the word cloud in Figure 2.
4.2. Key Design Elements and Characteristics in Digital-Technology-Inspired Fashion Trends
4.2.1. Color
In Table 2, the keywords for color elements indicate the coexistence of nature and the digital world. The most significant keyword was “Bright”. Dark tones, suggested by “Black” and “Dark”, also appeared at the top, supporting the presence of the word “Contrast”. The keyword “Nature” showed a higher TF-IDF rank than TF, indicating its importance within color elements. Thus, “Green”, inspired by forests and wetlands, frequently appeared. The keywords “Blue”, “Cool”, and “Aquatic”, inspired by the sea, ocean, and water, were also prominent. In addition, words such as “Pastel”, “Neutral”, “Calm”, “Midtone”, “Brown”, and “Grey” surfaced, reflecting the subtle natural colors of environments like deserts.
Furthermore, neologisms presented as color names helped us examine the fusion of digital technology with nature. Terms such as “Digitallavender”, “Electrickumquat”, and “Futuredusk” creatively combined digital elements with names from nature, emphasizing the surreal and artificial qualities of natural colors. The words “Crimson” and “Dopaminebright” had higher TF-IDF ranks than TF, indicating their relative importance. These “Vivid” colors were all inspired by digital, online, and virtual worlds, reflecting a bold, joyful, and lively mood. In addition, pink-toned colors such as ”Pink”, “Red”, “Crimson”, and “Pinkflash” prevailed, enlivening the digital technology fashion trend. Conversely, the word “Monochrome” also emerged, and futuristic colors like “Silver” and “Neon” were noted. The keywords listed in Table 2 are visualized in the word cloud shown in Figure 3.
Next, Figure 4 shows the associations among the 30 keywords. An analysis showed a high connection strength between “Bright”, “Dark”, “Black”, and “Blue”. This indicated that combinations of contrasting colors co-occurred. Accordingly, connections between “Black”, “Red”, and “Dark” suggested that contrasting combinations were evident. The keyword “Nature” had the highest connection strength with “Green” and showed relevance with “Contrast”. Relationships between “Dark”, “Midtone”, and “Dopaminebright” showed combinations of contrasting color elements. In addition, there was a connection between “Pastel” and “Dopaminebright”. Consequently, in color elements, a mixture of natural and artificial colors and bright and dark colors reflected a bright and vivid digital ambiance with a simultaneously dark and futuristic feel.
4.2.2. Print and Graphic
Print and graphic elements in Table 3, “Effect” appeared most frequently. However, “Floral” and “Nature” ranked highest in TF-IDF, indicating that floral elements were a key design inspiration in the digital-technology-inspired fashion trend. Overall, various “Motive” and “Technique” were employed to create diverse “Effect” in prints and graphics. In addition to flowers, keywords such as “Landscape”, “Animal”, “Ocean”, and “Water” highlight various inspirations from nature. “Skin” was used in combinations like animal skin and digital skin. These motifs employed various “Filter”, “Pixel”, “3D” effects, or “Technique” to produce artificial moods, such as “Ombre”, “Camouflage”, “Geometric”, “Tiedye”, and “Psychedelic”. Thus, nature’s motifs were not used as they were but were digitally distorted and reinterpreted to enhance digital mood.
Classic motifs like “Stripe” appeared with high TF-IDF, emphasizing a digital reinterpretation of stripes. “Game” also recorded high TF-IDF, indicating its importance as a keyword—notably, various digital images from games were inspirational. “Surreal” and “Abstract” reflect the overall mood in prints and graphics. Moreover, keywords such as “Virtual”, “Future”, “Fantasy”, and “Universe” showed designs inspired by future digital worlds, especially “Fantasy”, ”Universe”, and “AI”, which ranked higher in TF-IDF than in TF, suggesting their greater importance as actual print and graphic elements. The keywords listed are visualized in the word cloud shown in Figure 5.
Network analysis Figure 6 showed high connection strengths between “Effect” and “Technique”, “Filter”, “Texture”, “Floral”, and “Ombre”, highlighting the main effects of prints and graphics. In addition, “Nature”, ”Texture”, “Floral”, “Surreal”, “Filter”, and “Distorted” displayed interrelations, suggesting that prints and graphics were being newly reinterpreted rather than just reflecting nature as it was. Connections among “Surreal”, “Landscape”, “Photograph”, and “Distorted” suggested that landscape photographs were being transformed into surreal, distorted graphics. High relevance between “Game”, ”Fantasy”, ”Skin”, and “Virtual”, indicated that various graphics and design motifs were being created in the virtual world of games.
4.2.3. Textiles
For Table 4 on textiles, “Recycling” was the most prominent keyword. “Texture”, “Yarn”, and “Technical”, related to the creation and expression of textiles, also appeared frequently. Notably, “Recycling” and “Yarn” ranked high in the TF-IDF, indicating their importance as textile elements.
Along with recycling, “Certified” also emerged as a prominent keyword. Accompanying “Certified” were keywords like “GOTS” (Global Organic Textile Standard), “BCI” (Better Cotton Initiative), and “GRS” (Global Recycled Standard), all of which represent internationally certified textiles in terms of environmental protection and sustainability. Consequently, it is reasonable that the TF-IDF values for “Nature” and “Eco” were high.
Furthermore, “Yarn” was discussed alongside “Technical”, “Jacquard”, and “Ombre”. Different types of ”Yarn” were used to create surface effects like those seen in “Jacquard” and “Ombre”. The texture of the textile also varied with the characteristics of “Yarn”, manifesting in frequently appearing keywords such as “Soft”, “Shine”, “Comfort”, and “Smooth”. In particular, a high TF-IDF value for “Shine” highlighted it as an important surface effect in the digital-technology-inspired textile fashion trend. In addition, in the textile elements of digital-technology-inspired fashion trends, natural and synthetic materials such as “Cotton”, “Polyester”, “Jersey”, ”Silk”, and “Nylon” were used diversely. The keywords listed are visualized in the word cloud shown in Figure 7.
The Figure 8 results showed a high correlation between keywords such as “Recycling”, “Cotton”, “Polyester”, “Yarn”, and “Nylon”. This indicated a strong association between the use of recycled textiles and yarn in both natural and synthetic textiles. While the TF keyword analysis confirmed the use of materials like “Cotton”, “Polyester”, “Jersey”, “Silk”, and “Nylon”, the focus was not just on conventional textiles but on their use as recycled yarns or materials. Naturally, connections between “Recycling” and certifications like “GOTS”, “BCI”, and “GRS” were found. The strong link between “Recycling” and “Technical” showed the importance of continual material technology for recycling. The relationship between “Nature” and “Texture” was relevant as it was among “Yarn”, “Jacquard”, “Stripe”, and “Ombre”. This could be because yarns are used in textile production to create various patterns and techniques. In essence, in the textile elements of digital-technology-inspired fashion trends, the advancement of technology and development of textiles coexist with the trend of environmental consciousness.
4.2.4. Style and Details
For Table 5 on elements of style and details, keywords such as “Future”, “Technical”, and “Composition” were significant. However, the higher TF-IDF score for “Game” than for “Composition”, indicated that gaming played an essential role in style and details. Comfortable attire suitable for gaming and styles and worn in both virtual and real-world settings was important. Thus, keywords like “Soft”, “Comfort”, “Lightweight”, and “Stretch” also indicated the points of feel that consumers desire in fashion style and wearability, leading to the emergence of keywords like “Body” and “Protective” that focused on comfort and protection.
The keyword “Layered” referred to mixing and matching independent “Composition” items for styling, while “Cutout” details had a higher TF-IDF score than “Layered”, which showed that the former was a more crucial element. Styling mood and “Shape” saw the co-occurrence of contrasting keywords. “Simple”, “Minimal”, “Volume”, “Exaggerated”, “Oversize”, ”Glamorous”, and “Bold” represented opposing elements present in the digital technology fashion style and items.
In style items, classic “Tailored” and “Sportswear” keywords were evident. Furthermore, layered designs or styling were facilitated by modular elements. Thus, separate modular design elements were recombined with other items. Other detail elements included “Seam”, “Trimming”, and “Pocket”. The keywords listed are visualized in the word cloud shown in Figure 9.
In Figure 10, the keyword “Future” was strongly connected to “Minimal”, “Composition”, and “Cutout”. Associations existed between “Composition”, “Volume”, and “Exaggerated”, and between “Modular” and “Layered”, “Layered” and “Soft”, and “Layered” and “Seam”. The relevance of association between “Body” and “Protective”, and between “Technical” and “Sportswear” indicated a demand for protective functionality in sportswear.
4.3. Directions and Implications in Digital-Technology-Inspired Fashion Trends
4.3.1. Cluster 1: Human- and Nature-Oriented Design in the Digital World as a New Reality
In Cluster 1, shown in Figure 11, keywords such as “Digital”, “Future”, “World”, “New”, “Nature”, “Reality”, “Virtual”, “3D”, “AI”, “Metaverse”, “Surreal”, “Fantasy”, “Avatar”, “Game”, and “Emotion” were grouped together; therefore, the cluster was named “Human- and nature-oriented design in the digital world as a new reality”. The most important aspect of the digital-technology-inspired fashion trend was the blurred distinction between reality and the virtual world, heralding the arrival of the new digital age. At this time, using various technologies such as AI and 3D to turn reality into virtual and vice versa was no longer a difficult feat in fashion design.
“The metaverse will enable simultaneous existence across connected realms, where we will require a wardrobe that can coexist in both physical and digital worlds… Merging physical, digital, and virtual worlds to create their own universe, these consumers can exist as they are or take on a new persona through the use of 3D digital design, which they can layer on IRL or through the use of an avatar…” [39]
However, this does not merely signify the advancement of innovative technology. An important fact was that the essence of natural and human emotions was a central motif in the digital world. WGSN demanded of us to “Focus on designing for the human, rather than the ideal” [40]. Although it was a digital world where advanced technology was used, at the heart of the sought-after aesthetic lay the exploration of the essence of humanity and nature coexisting in this world, and importantly, the reinterpretation of these through futuristic digital technologies to create a new aesthetic.
“Technology has enabled us to shrink multiple worlds and identities into our screens, but has it also shrunk our humanity? Awestruck celebrates a recalibration towards tech that inspires wonder and warmth, and science that stirs emotion.” [41]
Consequently, digital-technology-inspired fashion design must not only incorporate technological advancements that meld with innovative science but also consider designs that evoke emotions associated with nature and humans. Within the cool and dark moods of the future and the digital realms, warm and emotion-filled designs could transform life into a new digital world.
4.3.2. Cluster 2: New Textiles Reflecting Digital Technology
In Cluster 2, labeled “New textiles reflecting digital technology”, keywords such as “Effect”, “Texture”, “Development”, “Surface”, “Ombre”, “Stripe”, “Classic”, “Abstract”, “Geometric”, “Camouflage”, “Vintage”, “Craft”, “Jacquard”, “Universe”, “Metallic”, “Sophisticated”, and “Shine” were grouped together. Advancements in digital technology for fashion design have fostered various techniques and technologies. It has connected the real and virtual worlds in many ways, including fabric surfaces, print, and graphic techniques. Designs inspired by nature and traditional and classic elements were presented using digital touch, highlighting abstract and geometric appearances.
“Take inspiration from natural wonders… Swap classic camo for swamp textures; replace tie dye with water ripples; refresh stripes with dusty textures; and evolve #GraphicSkins with cellular patterns.” [42]
In materials, developers were creating digital textures that led to the unveiling of new surfaces through light and reflection, producing technical textiles with a liquid sheen that seemed to flow between the physical world and a fluid reality reminiscent of “Liquid viscosity” [43]. The metaverse inspired transformative color woven with a digitally layered chroma color [44]. In addition, the Universe (space) was an important source of inspiration that could be reinterpreted using various digital techniques.
“consumer interest in space exploration… realisation of commercial space travel are key drivers in pushing Mars-like landscapes as a new aesthetic. This will be a commercial direction to update texture prints and perennial stripes. Reimagine rugged textures and rippling dunes as #OmbréStripes and organic #ZigZagStripes with dusty and speckled patterns.” [42]
Traditional classics, crafts, universals, and nature were developed using diverse digital techniques that provided consumers with opportunities to experience digital technology fashion in visual and tactile ways.
4.3.3. Cluster 3: Sustainable Design Technology
In Cluster 3, keywords like “Recycling”, “Functional”, “Sustainable”, “Eco”, “Certified”, “GOTS”, “BCI”, “GRS”, “Waste”, and “Protection” were grouped under the theme “Sustainable design technology”. Sustainability is a key aspect of the advancement of digital technology. The WGSN’s exhortation to “Work with nature for sustainable practices in tune with the planet” [45], emphasizes the use of advanced technologies to protect the Earth and the environment. The development of materials, post-processing, sustainable design, and disposal were integral to sustainability in every process of fashion design production and creation.
“A sustainable future is a better future. Sustainable practices aren’t just a buzzword, but a necessity. To preserve the future, it’s vital that responsible materials and methods are put into place. Look to #upcycling, #naturaldyes, #recycledmaterials and innovative alternatives to ensure a more enlightened future.” [41]
In the development of materials and dyeing processes, sustainable production methods were realized through the use of water, environmentally friendly dyes, recycling, and waste reuse. Technology and nature continue to coexist in fashion. In addition to recycling, digital technology offers ways to reduce waste during the design process. This includes digitalizing everything from design planning to production processes to minimize the waste of materials and fabrics.
“Utilise digital tools to reduce waste. Use new technology to work smarter and more sustainably by investing in computer-aided design programs… Seamless and whole-garment knitting technology improves efficiency and reduces yarn wastage.” [46]
While past technological developments have often led to the destruction and pollution of nature, current digital technology trends prioritize its coexistence and protection. This is actively reflected in fashion design elements, and advancements in digital technology are expected to offer more efficient and sustainable methods in the future.
4.3.4. Cluster 4: New Utility Fashion in the Digital Space
Cluster 4, which was named “New utility fashion in the digital space” grouped keywords such as “Sports”, “Sportswear”, “Smart”, “Minimal”, “Modular”, “Utility”, “Hyper”, “Dystopia”, and “Past”. In digital technology trends, the intersection of virtual and real-world sports has provided a significant source of inspiration and exerted considerable influence on the field of fashion design. In particular, as sports and games in virtual spaces have become trendy, sports fashion within the virtual world has gained attention, making the design of digital spaces increasingly important. It states the following:
“Modern individuals, engulfed in anxiety and depression, hold dystopian views about the future; yet, deep within, they dream of romance, they are creating new sports styles in the digital world. With dark colors as the base, materials that emit a mysterious glow in the dark are delicately suggested, expressing a sensibility that is both dark and romantic.” [47]
Moreover, as sports in virtual spaces are gaining attention, the market for new sportswear designed to stand out both on screen and in the real world is attracting attention [48]. While smart advanced technology significantly impacted sports design and functionality, it was characterized by a blend of inspiration from both the past and a retro mood in its design influences.
“As the distinction between physical and virtual becomes more blurred, we will see activewear focus on experimentation and escape, and reimagine retro styles through a digital lens.” [49]
Ultimately, such minimal and modular designs facilitated the combination of sportswear with other items, creating a new fashion style. Tailored garments were combined with sportswear to conceptualize set outfits [50], and hyper-digital uniforms were being produced that reflected the themes of games and sports, updating casual and utility styles [51]. Therefore, future fashion designs targeting sports should differ from previous sportswear designs, and attention should be paid to boundless combinations across digital and real spaces, times, and items.
5. Discussion
This study aimed to identify the main keywords of global fashion trends inspired by digital technology, analyze design elements and characteristics, and derive the specific attributes and implications of each cluster as fashion trends. The findings of this study were as follows.
The keywords that conceptualized digital-technology-inspired global fashion trends were “Digital”, “World”, “New”, “Reality”, “Future”, etc., highlighting the merging of the virtual digital world with the real world.
We identified the influence of digital technology through the main keywords derived from the design elements of color, print and graphic, textiles, and style and details. For color elements, top keywords included “Bright”, “Black”, “Dark”, “Pastel”, and “Green”, highlighting a mix of bright, vivid colors and darker, moody atmospheres with both naturally inspired and digitally artificial colors harmonizing. In print and graphic elements, “Effect”, “Nature”, “Floral”, and “Texture” appeared frequently, with motifs inspired by nature even as digital technology was extensively used in various ways. Textile elements were dominated by “Recycling”, “Texture”, “Yarn”, and “Technical”, emphasizing the importance of eco-friendliness and recycling, with a focus on the use of various technologies and development of environmentally friendly materials. Lastly, in style and details, the keywords “Future”, “Technical”, “Composition”, “Soft”, and “Game” were prominent, indicating a preference for sportswear styles and functional items that fit both digital and real worlds, characterized by minimal yet versatile layered combinations.
Finally, the results of the CONCOR analysis showed that it was typified into four clusters. Cluster 1: Human- and nature-oriented design in the digital world as a new reality; cluster 2: New textiles reflecting digital technology; cluster 3: Sustainable design technology; cluster 4: New utility fashion in the digital space.
Consequently, digital-technology-inspired fashion trends were pursuing coexistence based on emotions and respect for both humans and nature, alongside the advancement of technology, and this had a significant impact on the expression of fashion design elements. The theoretical, educational, and practical implications drawn from the study findings were as follows.
5.1. New Perspectives on Design Research Methodology
From an academic perspective, analyzing digital-technology-inspired fashion design trends using big data text mining was significant. Research on big data and text mining with a primary focus on recognizing trends and inclinations from a fashion technology perspective has been conducted in the fields of marketing and retailing. However, this study attempted to combine the aesthetics of beauty with big data by analyzing specific fashion design elements and meanings inspired by digital technology trends through text. Traditionally, design has relied heavily on human emotions and handcrafted elements, making it a field in which abstract and subjective meanings could be assigned and interpreted. Therefore, determining the criteria for analysis using objective standards was a difficult and complex task. This study presented a new design research methodology in which design was integrated with big data. By analyzing fashion design elements through text mining, we could derive the main keywords and understand their connections and meanings, which became objective analytical criteria. Exploring digital-technology-inspired fashion design elements through text-mining analysis could be considered a new academic endeavor. This approach allowed us to propose new methodological directions for future design research.
5.2. A Practical Guide to Fashion Design Education Inspired by Technological Innovation
This study provides important insights for educators and students in the digital technology era. While previous research has emphasized producers’ and consumers’ roles, students’ roles in embracing modern technological innovations are also crucial. As this research proposed new methodological directions using big data for design analysis, it could be applied to design education. Digital technology trends are a vital part of fashion design education. Therefore, big data could be applied to the field of fashion design, and an educational curriculum could be proposed for developing designs targeted at students. This study provides foundational data and guidelines for students to develop fashion designs based on technological inspiration. For example, the analysis of fashion design trends and elements inspired by digital technology through big data analysis revealed the importance of integrating humans and nature with technology, particularly in terms of environmental friendliness. It included values that we must preserve in the future, such as humaneness, respect for nature, and the rediscovery of tradition. By applying this to education, we can guide the direction of students’ design, their technological development, and the blending of various values with their design creativity. Therefore, the educational significance of this study lies in its ability to analyze fashion design trends and specific design elements inspired by digital technology, presenting directions for timely design development.
5.3. Insights and Directions for the Future Fashion Industry
This research has industrial significance as it explores fashion design trends inspired by currently prominent digital technologies and provides information that outlines future directions. Data from agencies that perform global fashion trends influence the world fashion market and lead trend setting. Objective analysis of these trends from a fashion design perspective inspired by digital technologies could offer design directions and serve as valuable information for brand and design identity. This is particularly beneficial for fashion designers and aspiring entrepreneurs in various aspects, such as launching new brands and planning seasons and designs. It could be used as information for entrepreneurs to start new brands, understand modern fashion trends, and offer specific design directions. Thus, this study holds industrial importance in precisely identifying fashion designs inspired by digital technology and suggests necessary design directions for the future of the fashion industry.
6. Conclusions
This study aimed to explore the design elements and characteristics of digital-technology-inspired fashion design trends using text mining in big data analysis, deriving types, and analyzing implicit meanings within these trends. Thus, we found that digital-technology-inspired fashion design trends empathized with and respected humans and nature, which significantly impacted fashion design elements. We proposed a new approach for designing research methodologies from an academic perspective. This methodology could be introduced in an educational curriculum to enable students to derive design elements through big data research. Furthermore, the study findings could provide information on the development of specific designs when students attempt designs inspired by digital technology. Finally, it could help fashion designers and entrepreneurs by providing specific directional information on digital-technology-inspired fashion design trends. Nevertheless, one limitation of this study was that it primarily analyzed the fashion design field, which relies heavily on visual elements, through text only. Therefore, we suggest that a future study combine various data-mining methods capable of analyzing text and image data for a more in-depth analysis of fashion design.
