Innovation Pathways of AIGC-Empowered Fashion Design from the Perspective of New Quality Productivity

Yufei Wan (), Zhe Li () and Xinping Song ()
Additional contact information
Yufei Wan: Beijing Institute of Fashion Technology, School of Art and Design
Zhe Li: Beijing Institute of Fashion Technology, School of Art and Design
Xinping Song: Capital United Think Tank Federation

A chapter in Proceedings of the 2025 7th International Conference on Economic Management and Cultural Industry (ICEMCI 2025), 2025, pp 196-205 from Springer

Abstract: Abstract This study focuses on exploring how Artificial Intelligence Generated Content (AIGC) empowers production tools, labor, and design objects in the new quality productivity of fashion enterprises, while analyzing its mechanisms and implementation pathways in fashion design innovation. Through literature review and case studies, the research reveals how AIGC reconstructs new quality productivity in fashion, forming a framework of new quality fashion design tools – new quality fashion designers – new quality fashion consumers. The findings indicate: (1) New quality fashion design tools reconstruct traditional design processes through intelligent, collaborative, and ecological transformation; (2) New quality fashion designers break conventional thinking patterns, achieving deep integration of design thinking and management logic, thereby reshaping value creation paradigms; (3) New quality fashion consumers and marketing establish a demand-integrated value co-creation ecosystem, driving bidirectional empowerment and symbiotic evolution between commercial value and user needs.

Keywords: Generative artificial intelligence (AIGC); New quality productive forces; Innovation in fashion design; High-quality development (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-888-2_20

Ordering information: This item can be ordered from
http://www.springer.com/9789464638882

DOI: 10.2991/978-94-6463-888-2_20

Access Statistics for this chapter

More chapters in Advances in Economics, Business and Management Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().