A design analysis for eco-fashion style using sensory evaluation tools: Consumer perceptions of product appearance

Melissa Wagner, Antonela Curteza, Yan Hong, Yan Chen, Sebastien Thomassey and Xianyi Zeng

Journal of Retailing and Consumer Services, 2019, vol. 51, issue C, 253-262

Abstract: Eco-designed fashion products can have a distinctive style in terms of environment-friendly appearance. In this study, an experimental design analysis process is proposed to help fashion designers in assessing consumers’ perception of eco-style and ensure the success of sustainable product development. Our aim is to highlight the extent to which eco-fashion style exists in garment style. Since consumer perception towards ethical fashion is rather subjective, as human perception can be vague and full of uncertainty, we used sensory evaluation tools as well as the fuzzy logic method to process the data. We found that one can describe the garment style with eco-fashion descriptors based on the fuzzy logic analysis tool. The proposed experiment design process is applicable to the analysis of garment style regarding eco-fashion style and is able to distinguish the eco-products.

Keywords: Eco-fashion design; Consumer perception; Fuzzy logic (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0969698918311111
Full text for ScienceDirect subscribers only

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:eee:joreco:v:51:y:2019:i:c:p:253-262

DOI: 10.1016/j.jretconser.2019.06.005

Access Statistics for this article

Journal of Retailing and Consumer Services is currently edited by Harry Timmermans

More articles in Journal of Retailing and Consumer Services from Elsevier
Bibliographic data for series maintained by Catherine Liu ().