Perceptual imagery of soft sofa fabrics based on visual-tactile evaluation

In the contemporary era of quality and personalization, this article explores how soft sofa fabrics enhance users’ emotional experience and convey perceptual images. Users’ visual-tactile perception data on 10 common soft sofa fabrics were gathered by questionnaire surveys, utilizing the Kansei engineering approach, and the visual-tactile evaluation theory. With SPSS software, the data were processed and examined in-depth using a variety of techniques, including cluster analysis and factor analysis. The experiment screened fabric samples and emotional vocabularies via the KJ method and expert evaluation, and questionnaires were designed and implemented based on the semantic differential method and Likert scale. Fabrics were categorized into four groups based on cluster analysis, which are suitable for users pursuing different home environments. The two primary factors that comprise the fundamental aspects of the perceptual image of soft sofa fabrics were found to be the texture, quality experience factor, and the typical emotional reaction factor, which were extracted by factor analysis. Both theory and practice were considered, enriching the theoretical framework of emotional imagery and user emotion research while offering valuable practical guidance for the design, production, and marketing of soft sofas.

Authors

Institutions

Publication Details

Journal
BioResources
Published
2024-09-19
DOI
https://doi.org/10.15376/biores.19.4.8427-8442
Citations
1
Primary Topic
Textile materials and evaluations
Type
article
Field-Weighted Citation Impact
0.17
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Perceptual imagery of soft sofa fabrics based on visual-tactile evaluation

Ziyao Tu, Wei Wang
1 citations
BioResources
Textile materials and evaluations
0.17
article

Perceptual imagery of soft sofa fabrics based on visual-tactile evaluation

Ziyao Tu, Wei Wang
article en
1 citations

Abstract

In the contemporary era of quality and personalization, this article explores how soft sofa fabrics enhance users’ emotional experience and convey perceptual images. Users’ visual-tactile perception data on 10 common soft sofa fabrics were gathered by questionnaire surveys, utilizing the Kansei engineering approach, and the visual-tactile evaluation theory. With SPSS software, the data were processed and examined in-depth using a variety of techniques, including cluster analysis and factor analysis. The experiment screened fabric samples and emotional vocabularies via the KJ method and expert evaluation, and questionnaires were designed and implemented based on the semantic differential method and Likert scale. Fabrics were categorized into four groups based on cluster analysis, which are suitable for users pursuing different home environments. The two primary factors that comprise the fundamental aspects of the perceptual image of soft sofa fabrics were found to be the texture, quality experience factor, and the typical emotional reaction factor, which were extracted by factor analysis. Both theory and practice were considered, enriching the theoretical framework of emotional imagery and user emotion research while offering valuable practical guidance for the design, production, and marketing of soft sofas.

BioResourcesVol. 19(4)
Nanjing Forestry University (CN)
Openalex Percentile: Top 58%
Textile materials and evaluations
0.17
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Perceptual imagery of soft sofa fabrics based on visual-tactile evaluation — Ziyao Tu, Wei Wang · BioResources (2024) | TGRS Research Map | TGRS