Subtopic Deep Dive

Digital Transformation in Fashion
Research Guide

What is Digital Transformation in Fashion?

Digital Transformation in Fashion examines how AI, 3D scanning, virtual platforms, and digital tools reshape fashion design, production, marketing, and consumer engagement within cultural and historical contexts.

This subtopic covers AI-driven design systems (Choi et al., 2023, 66 citations), 3D body scanning for dress forms (Yu and Kim, 2023, 7 citations), metaverse fashion intelligence (Mu et al., 2023, 4 citations), and digital preservation of heritage embroidery (Zhao et al., 2024, 4 citations). Early works established online product presentation effects (Park, 2002, 11 citations). Over 20 papers from 2002-2024 address these intersections.

Curated Papers

Key Challenges

Why It Matters

Digital tools enable fashion brands to adapt to e-commerce and metaverses, boosting personalization and efficiency; Choi et al. (2023) show AI automates designer workflows, cutting production time. Sustainability improves via smart manufacturing (Jung et al., 2022), reducing environmental impact from fast fashion. Cultural heritage persists through digital ICH designs like Ningbo embroidery (Zhao et al., 2024), preserving traditions amid globalization.

Key Research Challenges

AI Integration in Creative Design

AI struggles to replicate human creativity in fashion design processes (Choi et al., 2023). Designers resist automation due to loss of artistic control. Balancing AI efficiency with cultural nuance remains unresolved.

Digital Preservation of Heritage Crafts

Translating physical intangible cultural heritage into digital formats loses tactile authenticity (Zhao et al., 2024). CLO3D software aids simulation but requires accurate 3D modeling of traditional techniques. Scalability for global access challenges preservation efforts.

Consumer Adoption of Virtual Try-Ons

Perceived risk in online apparel shopping hinders virtual try-on uptake (Park, 2002). Metaverse platforms face engagement barriers despite AI curation potential (Shin and Hwang, 2022; Mu et al., 2023). Empirical validation of mood and intention effects is limited.

Essential Papers

Developing an AI-based automated fashion design system: reflecting the work process of fashion designers

Woojin Choi, 세윤 장, Ha Youn Kim et al. · 2023 · Fashion and Textiles · 66 citations

Abstract With the recent expansion of the applicability of artificial intelligence into the creative realm, attempts are being made to use AI (artificial intelligence) in the garment development sy...

The effect of product presentation on mood, perceived risk, and apparel purchase intention in Internet apparel shopping

Ji-Hye Park · 2002 · OhioLink ETD Center (Ohio Library and Information Network) · 11 citations

Inquiry on Interrelationships Between Architecture and Fashion Design

Abbas Hedayat · 2012 · Eastern Mediterranean University Institutional Repository (Eastern Mediterranean University) · 9 citations

ABSTRACT: This thesis explores the “nature of the relationship” that fashion design and architecture might have. It somehow focuses on the social dimension of architecture and fashion design and ho...

Exploring the Key Factors that Lead to Intentions to Use AI Fashion Curation Services through Big Data Analysis

Eun-Jung Shin, Ha Jin Hwang · 2022 · KSII Transactions on Internet and Information Systems · 7 citations

An increasing number of companies in the fashion industry are using AI curation services.The purpose of this study is to investigate perceptions of and intentions to use AI fashion curation service...

The development of dress forms in standing and sitting postures using 3D body scanning and printing

Minji Yu, Dong-Eun Kim · 2023 · Fashion and Textiles · 7 citations

Abstract 3D body scanning and printing are attracting attention as innovative technologies for producing dress forms. While designing dress forms, the shape of the human body must be accurately ref...

A Plan to Secure Environmental Sustainability Through Digital Transformation of the Fashion Industry: Focusing on Fashion Design and Smartization of the Manufacturing Process

Woo‐Kyun Jung, Jae‐Won Lee, Seung‐Whan Lee et al. · 2022 · Academic Society for Appropriate Technology · 6 citations

Although the global market size of the fashion clothing industry is continuously growing, environmental pollution is also intensifying. This study examines the impact of the fashion clothing indust...

A Legal and Empirical Study of 3D Printing Online Platforms and an Analysis of User Behaviour

Dinusha Mendis, Davide Secchi · 2015 · Bournemouth University Research Online (Bournemouth University) · 4 citations

Reading Guide

Foundational Papers

Start with Park (2002, 11 citations) for online presentation effects on purchase intent, then Hedayat (2012, 9 citations) on design interrelationships, and Choi Jung-Hwa (2005, 4 citations) for semiotic T-shirt analysis to grasp pre-digital consumer and cultural bases.

Recent Advances

Prioritize Choi et al. (2023, 66 citations) on AI design, Yu and Kim (2023, 7 citations) on 3D forms, Mu et al. (2023, 4 citations) on metaverse intelligence, and Zhao et al. (2024, 4 citations) on ICH digitization.

Core Methods

Core techniques: AI workflow automation, 3D scanning/printing, big data text mining, CLO3D simulation, metaverse platforms.

How PapersFlow Helps You Research Digital Transformation in Fashion

Discover & Search

Research Agent uses searchPapers and exaSearch to find top-cited works like Choi et al. (2023) on AI fashion design, then citationGraph reveals connections to Yu and Kim (2023) on 3D scanning and Mu et al. (2023) on metaverses; findSimilarPapers expands to sustainability papers like Jung et al. (2022).

Analyze & Verify

Analysis Agent applies readPaperContent to extract methods from Choi et al. (2023), verifies claims with CoVe on AI workflow automation, and runs PythonAnalysis with pandas to quantify citation trends across 10+ papers; GRADE scores evidence strength for design automation (high) vs. metaverse adoption (medium).

Synthesize & Write

Synthesis Agent detects gaps in heritage digitization post-Zhao et al. (2024), flags contradictions between early online shopping risks (Park, 2002) and modern AI curation (Shin and Hwang, 2022); Writing Agent uses latexEditText, latexSyncCitations for Choi et al., and latexCompile to produce reports with exportMermaid diagrams of design pipelines.

Use Cases

"Analyze citation networks in AI fashion design papers for trend forecasting models."

Research Agent → citationGraph on Choi et al. (2023) → runPythonAnalysis (networkx for centrality) → outputs ranked influences and Python-generated graph CSV.

"Write a LaTeX review on 3D scanning in dress forms with sustainability links."

Synthesis Agent → gap detection across Yu and Kim (2023), Jung et al. (2022) → Writing Agent → latexSyncCitations + latexCompile → researcher gets compiled PDF with figures.

"Find GitHub repos implementing CLO3D for heritage fashion digitization."

Research Agent → paperExtractUrls from Zhao et al. (2024) → Code Discovery → paperFindGithubRepo + githubRepoInspect → lists verified CLO3D code forks with usage stats.

Automated Workflows

Deep Research workflow scans 50+ papers via searchPapers, structures reports on AI-metaverse evolution from Park (2002) to Mu et al. (2023). DeepScan applies 7-step CoVe checkpoints to validate Shin and Hwang (2022) big data claims on AI curation. Theorizer generates theories on digital ICH sustainability from Zhao et al. (2024) and Jung et al. (2022).

Try Doxa for Digital Transformation in Fashion Research

Frequently Asked Questions

What defines Digital Transformation in Fashion?

It examines AI, 3D tools, and virtual platforms reshaping design, production, and engagement, as in Choi et al. (2023) automating designer processes and Zhao et al. (2024) digitizing heritage embroidery.

What methods dominate this subtopic?

Key methods include AI automation (Choi et al., 2023), 3D body scanning/printing (Yu and Kim, 2023), big data text mining for curation (Shin and Hwang, 2022), and CLO3D simulation for ICH (Zhao et al., 2024).

What are the most cited papers?

Choi et al. (2023, 66 citations) on AI design systems leads, followed by Park (2002, 11 citations) on online shopping, Hedayat (2012, 9 citations) on architecture-fashion links.

What open problems exist?

Challenges include AI creativity limits (Choi et al., 2023), heritage digitization fidelity (Zhao et al., 2024), and metaverse consumer trust (Mu et al., 2023; Park, 2002).