Abstract
Purpose
The study aimed to clarify differences in fabric hand perceptions among Japanese and Chinese participants and implement online shopping strategies that enable consumers to easily recognize fabric texture.
Design/methodology/approach
Forty (20 Japanese and 20 Chinese) participants knowledgeable about clothing and fabric were recruited. Participants evaluated fabric by sight and touch in a visuotactile experiment (VTE). The stimulus material comprised 39 fabric samples representing a broad range of fabric attributes (7 fibers, 5 weaving/knitting techniques and 3 yarn thicknesses and density). A Mann–Whitney U test and a factor analysis were conducted to determine differences in responses for the different fabric variables.
Findings
The fabric hand perceptions factors were similar between both groups. Japanese participants showed a stronger preference for fabrics that felt wet. Japanese participants’ fabric hand perceptions had a 3-factor structure, while Chinese participants had a 2-factor structure. Chinese participants regarded “crisp” as perceptually and linguistically equivalent to “stretchy.”
Originality/value
The study’s findings suggest that Chinese people have stronger preferences in fabrics than Japanese people do. Japanese people evaluate fabric hand in a more nuanced manner than Chinese individuals, including discerning different fabric attributes, such as fiber and yarn thickness and density. Thus, nationality may influence fabric hand perceptions more than fabric knowledge does. Specifically, in evaluating “crispness,” the results required further analysis because differences in nationality may have affected evaluations regarding perception and linguistic perspectives. The findings provide design guidelines for implementing online shopping strategies adapted to each participant group.
Keywords
Citation
Ishikawa, T., Tsunetou, J., Yanagida, Y., Yanaka, M., Mitsui, M., Sasaki, K. and Ayama, M. (2023), "Differences in fabric hand perceptions among Japanese and Chinese individuals", International Journal of Clothing Science and Technology, Vol. 35 No. 3, pp. 334-349. https://doi.org/10.1108/IJCST-04-2022-0050
Publisher
:Emerald Publishing Limited
Copyright © 2023, Tomoharu Ishikawa, Junki Tsunetou, Yoshiko Yanagida, Mutsumi Yanaka, Minoru Mitsui, Kazuya Sasaki and Miyoshi Ayama
License
Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
Introduction
The use of online shopping for clothing and fabrics is increasing, especially amid the COVID-19 pandemic (Aston et al., 2020; Koch et al., 2020). However, there is often a discrepancy between the texture of the cloth judged from a presented image vs that of the actual cloth. To address this issue, it is essential to clarify the mechanisms by which consumers judge fabric texture and thus employ images in online shopping that enable consumers to easily recognize fabric textures.
People’s evaluations of fabric hand are shaped by several factors, including the fabric attributes, such as its weave or the thickness of the yarn, the perceptual information (such as tactile or visual information) regarding these attributes and the person’s expertise in fabrics. There is a large body of literature on fabric hand. The pioneering studies in this area are those by Kawabata (1980) and Niwa (1990). Using standard fabric hand values provided by skilled technicians, these authors developed the “Kawabata Evaluation System for Fabrics” (KES-FB) to evaluate fabric hand based entirely on the mechanical properties of fabric. Subsequent studies examined how fabric hand evaluations are shaped by evaluator attributes, fabric attributes and perceptual modality and how these variables interact (Kobayashi, 1972; Nishimatsu and Sakai, 1987; Yashima et al., 2017). However, few studies have considered the influence of the evaluator’s expertise together with fabric and perceptual variables. A previous study (Tsunetou et al., 2018) attempted to address this issue by examining how fabric hand evaluations were shaped by the evaluator’s expertise, fabric attributes and perceptual modality. Participants were divided into experienced and less experienced groups and evaluated the fabric hand of varying attributes via two perceptual modalities: tactile and visuotactile. It was found that expertise level influenced fabric hand evaluations. Specifically, for both modalities, the hand evaluations of the experienced participants were likelier to result from an accurate analysis of the key fabric attributes and the tactile and visuotactile hand evaluations exhibited similarities. However, the study was limited in that the sample was solely Japanese.
Clothing industries aiming to globalize their production and sales may be interested in cross-cultural differences in fabric hand evaluations. For example, Stean et al. (1988) surveyed the summer-wear hand evaluations of judges in Japan, Australia, New Zealand, India, the US and China. According to a factor analysis, the Japanese and Chinese hand evaluations exhibited a factor structure that differed from the evaluations in other countries, and this was attributable to the mechanical properties of the fabrics. Moreover, Kim and Winakor (1996) examined the adjectives that American and South Korean consumers used to describe fabric hand. They reported that fabric perceptions were shaped by gender, culture and language, and that this was especially true for fabric preferences. Suehiro et al. (2014) examined how mechanical properties informed Japanese and Chinese participants’ perceptions of a fabric’s “shittori,” which is defined as a sensation similar to moisturized skin and semantically described as “baby’s skin.” Japanese participants perceived fabric as “shittori” if it used a knitting technique, featured a surface with frictional resistance and exhibited rigid shear, bending and low breathability. Contrastingly, Chinese participants perceived fabric as “shittori” if it featured gentle shear and bending. However, these studies focused on either a limited set of fabric attributes or a few evaluation parameters; for example, participants only evaluated the fabric regarding its intended use. Consequently, the effect of nationality on hand evaluations for fabrics of varying attributes remains unclear.
Method
The present study sought to determine cross-cultural differences in fabric hand perceptions. Twenty Japanese and 20 Chinese individuals, knowledgeable about clothing, were recruited. China was selected as the comparison nationality because the Chinese account for the largest proportion of the world’s population and have many connections to Japan. Participants undertook one visuotactile experiment (VTE). The participants’ responses were analyzed to determine intergroup differences regarding fabric attributes.
Experimental method
The fabric stimuli were stored for over 24 h in a standardized environment, with a temperature of 20°C and 65% humidity (Figure 1). Tests used a Judge II lighting booth (X-Rite, Grand Rapids). Each participant sat before the booth and evaluated the fabric therein. The booth was illuminated with a standard light source (D65). A total of 40 individuals participated: 20 Japanese (19 women, 1 man; aged 20–23 years) and 20 Chinese participants (19 women, 1 man; aged 21–30 years). Participants were students of Bunka Gakuen University and had formally studied fabrics and clothing. Informed written consent was obtained from all participants.
Figure 2 shows the experimental procedure. Participants washed their hands in preparation for the evaluation experiment, in which 39 types of fabrics were evaluated. One session was conducted with 13 types of fabric evaluation, and there were a total of three sessions, with 5-min breaks between them. The participants followed a standardized procedure when providing tactile evaluations of the fabric hand. This procedure mirrored Saito and Harada’s (1987) three-step process (Figure 3). Upon completing these three steps, the participants rated how the fabric felt to touch. Participants could feel the fabric again and reconsider their rating if necessary, because we aimed for fabric hand ratings to be as accurate as possible. The scoring system consisted of a set of fabric hand descriptors rated on a 5-point scale, ranging from 0 = disagree to 4 = completely agree (Inoue, 2002, Figure 4). Nine descriptors were taken from Ishikawa et al. (2015): thin, thick, flat, rustic, soft, crisp, wet, dry and stretchy. We also added two thermal descriptors, two descriptors assessing participants’ preference for the fabric and a visual descriptor: warm and cool, likeable and comfortable and shiny, respectively. The participants were provided sheets on which to state their ratings, with evaluation words randomly written for each subject. In addition, to control the meaning of each evaluation word, similar words examined in previous research (Ishikawa et al., 2015) were summarized in a table and explained. For Japanese participants, the text was provided entirely in Japanese. For Chinese participants, the content was provided in Japanese, accompanied by a Chinese translation. The Chinese translation was carefully conducted by a Chinese-native clothing expert to preserve the meaning in the Japanese clothing field as much as possible.
Fabrics
The study used a total of 39 fabric stimuli representing various combinations of three fabric attribute variables: fiber, weaving/knitting and yarn thickness and density. The fibers used were cotton, hemp, wool, silk, cupro, nylon, or polyester. The weaving/knitting variable was plain weave (PW), twill weave (T), satin weave (S), plain stitch (PS), or rib stitch (R). The yarn thickness and density were classified as thick (T; >500 dtex and 38.3 ± 19.34 yarns/inch), medium (M; 250–500 dtex and 56.6 ± 23.98 yarns/inch), or fine (F: <250 dtex and 94.2 ± 45.05 yarns/inch). To control the influence of color on visual impression, only beige fabric samples were used. The dimensions were also standardized at 20 cm2. Table 1 shows the mechanical properties of all 39 fabrics. These characteristics were measured using KES-FB Series (Katotech) [1].
Results
Evaluations results
Mann–Whitney U test
To compare the ratings between the Japanese and Chinese participants, a Mann–Whitney U test was conducted on the responses for the different fabric variables. Table 2 shows the results of the Mann–Whitney U test by fabric attribute. The results indicate significant intergroup differences in ratings for five of the descriptors (crisp, wet, cool, likeable and comfortable) regarding any or all fabric attributes. The significant intergroup differences for the descriptors are discussed below.
Wet
Figure 5 shows the Japanese and Chinese participants’ average and standard deviation of “wet” ratings for each fiber represented by the symbols and error bars, respectively. The Japanese ratings and deviations are on the X-axis and that of the Chinese are represented on the Y-axis. The data suggest that Japanese participants mostly rated the fabrics as wetter than the Chinese participants did. However, the Japanese participants’ standard deviation of evaluation for each fabric was larger than that of the Chinese participants. Only hemp fabric (indicated by a diamond in Figure 5) was rated as less wet by the Japanese participants.
Likeable
Figure 6 shows the intergroup responses for “likeable.” The graph is plotted identically to the format in Figure 5. Figure 6 indicates that the Chinese rated more fabrics as “likeable” than the Japanese participants did; however, both groups’ standard deviation of evaluation for each fabric material were large. Similarly, the Chinese participants deemed more fabrics with many fibers to be “comfortable” than did their Japanese counterparts. Accordingly, we examined whether the responses for “likeable” correlated with the responses for semantically similar descriptors. Table 3 shows the four comparative descriptors (soft, wet, comfortable and dry) that exhibited a strong correlation with “likeable” (the absolute value of the Pearson’s correlation coefficient was >0.6 in each case). “Likeable” was strongly positively correlated with “soft,” “wet,” and “comfortable,” and strongly negatively correlated with “dry,” regardless of nationality and sensory modality (Table 3).
Crisp
Figure 7 show the intergroup responses for “crisp.” The Japanese participants tended to perceive fabric as crisper if it was woven (plain, twill, or satin weave) than if it was stitched (plain or rib stitch). However, the Japanese participants’ standard deviation of evaluation for each fabric weaving technique was larger than that of the Chinese participants. The “crisp” ratings of Chinese participants were strongly positively correlated with their ratings for “stretchy.” Figure 8 depicts the Japanese and Chinese “crisp” and “stretchy” ratings for all 39 fabrics. The symbols represent the different weaving/knitting techniques. The figure shows correlations between each group’s responses for “crisp” and “stretchy;” specifically, it was negative among the Japanese group (R_J = −0.76) and strongly positive among the Chinese group (R_C = 0.97).
Factors determining fabric hand perceptions
To determine how fabric hand perceptions were influenced by nationality (Japanese/Chinese), a factor analysis was performed on the results for each fabric according to nationality. The factor scores (termed “fabric hand spaces”) were then analyzed. Specifically, we analyzed the nationality-related differences within a fabric hand space, derived from a combination (“VTE space”) of Japanese (“Japanese space”) and Chinese participants’ (“Chinese space”) ratings. Using the principal factor method, factors exhibiting an eigenvalue of >1 were extracted, and varimax rotation was applied. Tables 4–6 show the results for each analysis, with bold font indicating factor loadings of descriptors that were closely associated with specific factors. The Japanese space presented a 3-factor structure: moist/preference, thermal sensation and flexibility. The Chinese space presented a 2-factor structure: thermal sensation and flexibility/preference. The VTE space presented a 3-factor structure: flexibility/preference, thermal sensation and surface. These factors were named based on the descriptors with the largest loadings.
Fabric hand spaces
Figures 9 and 10 show the Japanese and Chinese VTE space results, respectively. Although the VTE space had a 3-factor structure, we displayed only the first two factors in Figures 9 and 10 for clarity. Both figures show the fiber-specific factor scores plotted against the factor axes. Different values for a given fiber attribute are linked by each line to indicate their distribution range, termed “fiber spaces.” The X-axis and Y-axis represent the Factor 1 and Factor 2 scores, respectively. The fabric symbols are as follows: ■ = cotton, ♦ = hemp, ▲ = wool, ● = silk, ∗ = cupro, × = nylon and + = polyester. Table 7 shows the factor scores for Factors 1 and 2 for each fabric, that is, the x- and y-axis coordinates for each fabric, respectively, in Figures 9 and 10. The portion of a factor axis occupied by a fiber space describes how much the fabric attributes influenced the ratings. That is, if a fiber space extends across large sections of the axes, it implies that the participants keenly discerned these attributes when evaluating a specific fabric. To investigate further, the fiber spaces for the Japanese and Chinese participants were compared for each of the three factors (“factor width”) in the VTE. Table 8 shows the results for VTE spaces. Differences in the overall area occupied by the fiber spaces were also examined. Table 8 displays the average results for factor width, factor width difference and area difference. Both factor width difference and area difference were determined by subtracting the average for the Chinese participants from that of the Japanese participants. Accordingly, a positive value describes differences in the factor width and fiber space for Japanese participants compared to Chinese participants. Conversely, a negative value describes such differences among Chinese participants compared to Japanese participants. The data in Figures 9 and 10 and Table 8 indicate that the factor width and area difference were greater among Japanese participants in all cases.
Discussion
Intergroup differences in strength of preference
The Chinese participants rated the fabrics as more likeable than the Japanese participants did (Figure 6), indicating that the participants’ nationality affected their preferences for the fabrics. This is consistent with Kim and Winakor (1996), who reported that American and South Korean individuals differed regarding their strength of preference for fabrics. Moreover, both Japanese and Chinese participants associated likeability ratings with qualities of “soft,” “wet,” and “comfortable,” and the opposite was true for “dry” (Table 3). Additionally, the associations of “likeable” with “wet” and “dry” were stronger among the Japanese participants. This may suggest that Chinese participants preferred soft fabrics, while Japanese participants favored both soft and moist fabrics. This is consistent with Suehiro et al. (2014), who reported that Japanese participants displayed a stronger preference for moist fabrics than Chinese participants did.
Intergroup differences in fabric hand spaces
The fabric hand space for Japanese participants (Table 4) presented a 3-factor structure, while that of Chinese participants (Table 5) presented a 2-factor structure. This is in line with Ishikawa et al. (2012), who analyzed Japanese and Chinese perceptions of high dynamic range (HDR) images and found that Japanese perceptions had a more complex factor structure. Notwithstanding the differences between the two studies, including the difference in stimuli, the findings suggest that Japanese people perceive stimulus material in a more precise and nuanced manner than Chinese people do. Moreover, our results indicated that the fibers’ factor width and area in VTE spaces was greater among the Japanese participants. Given that each fabric had different attributes (fiber, weaving/knitting and yarn’s thickness and density), the factor width and space area can be regarded as indicative of how keenly the participants differentiated between these attributes when evaluating the fabrics. Thus, Japanese responses exhibited greater factor width and space area, implying that Japanese participants perceived these differences more acutely.
Perceptual and linguistic differences with “crisp” and “stretchy”
This experiment was conducted under strict control, with the Chinese fabric texture evaluation words carefully translated by a native Chinese textile expert to preserve the meaning. The descriptors “crisp” and “stretchy” were negatively correlated among Japanese participants, but positively correlated among Chinese participants (see Figure 8). This implies that Japanese participants differentiated between crisp and stretchy textures, while Chinese participants perceived crispness similarly to stretchiness. This may be due to sensory and linguistic differences in evaluation. Regarding the sensory perspective, research indicates that the sensations generated by presentation stimuli are influenced by cultural aspects, such as growth environment and lifestyle (Ishii et al., 2009; Matsunaga et al., 2018; Rhode et al., 2016). Chinese participants have fewer opportunities and ways to distinguish between “crisp” and “stretchy” evaluations of fabrics than Japanese participants, and the sense of distinction was not cultivated. Therefore, “crisp” and “stretchy” were evaluated similarly. From the linguistic perspective, hari-no-aru (“crisp”) is a unique Japanese expression regarding fabric. It is related to the texture described in Kawabata (1980) and was defined as a representative word for expressing complex sensations (Ishikawa et al., 2015). Thus, it may have been evaluated as a synonym for “stretchy” because there was no direct translation in Chinese for hari-no-aru. The issue of semantic differences was highlighted by Kim and Winakor (1996), who asked American and South Korean participants to rate fabric stimuli using English and Hangul unipolar adjectives. Future research should address this issue. This finding represents an important discovery for designing subject-adaptive online shopping according to the relationship between the texture evaluation words of fabrics and their actual evaluations.
The influence of expertise compared with that of nationality
Tsunetou et al. (2018) examined how differences in sartorial knowledge affected fabric hand among Japanese individuals. They found that sartorially aware participants used their knowledge to discern differences in fabric attributes. Moreover, with the addition of visual information, the less knowledgeable participants struggled to discern the shear property as the visual information affected their tactile perceptions. Thus, differences in expertise may explain differences in fabric hand evaluations. The participants in our study were all students in clothing-related disciplines, but their nationality differed. Our results indicated that nationality explained numerous differences, including differences in fabric hand descriptors and the number of factors extracted in the factor analysis. Thus, the evidence suggests that differences in nationality affect perceived fabric hand more than differences in expertise do. The findings obtained in this paper are considered to provide design guidelines for realizing online shopping adapted to each subject group.
Conclusions
This study aimed to identify differences in the fabric hand perceptions between Japanese and Chinese participants. Fabric stimuli were tested on Japanese and Chinese participants in a visuotactile task. Significant intergroup differences in the ratings of “crisp,” “wet,” “cool,” “likeable” and “comfortable” were found. The ratings for these descriptors were significantly influenced by all three fabric attribute variables (fiber, weaving/knitting and yarn thickness and density). The results for “crisp” suggested that sensory or linguistic interpretations differed among the groups and required further analysis. The results for “wet” revealed that Japanese participants rated the wetness of fabrics higher than their Chinese counterparts did although the reverse was true for the hemp fabrics. The results for the likeability descriptors (“likeable” and “comfortable”) revealed that Japanese participants showed a stronger preference for moist fabrics. A factor analysis suggested that the Japanese fabric hand evaluations were more precise, nuanced and varied. This was indicated by the larger number of factors extracted, as well as the larger factor widths and fiber space areas for the Japanese cohort. Finally, we would like to implement online shopping that is adapted to more consumers.
Our study had three main limitations. First, all participants were in their 20s, which may result in finger sensitivity bias related to differences based on age and may have thus affected the fabric hand test results (Musa et al., 2019). Second, the results may have been affected by cross-cultural differences (both linguistic and perceptual) regarding the fabric hand descriptors. When individuals with different native languages select or substitute descriptors, they must strive for perceptual and linguistic equivalence. They must discuss ways to maximize both equivalences while considering multiple perspectives. Finally, the number of participants was small, affecting the generalizability of our results. Accordingly, future studies must focus on conducting these experiments using a larger sample size to verify the reliability of the results obtained in the present study.
Figures
Fabric samples and mechanical characteristics
| Fiber | Weaving knitting | Thickness of yarn | Tensile | Bending | Shear | Compression | Surface | Thickness | Weight | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| LT | WT | RT | G | 2HG | 2HG5 | B | 2HB | LC | WC | RC | MIU | MMD | SMD | TO | W | |||
| – | gf⋅cm/cm2 | % | gf/cm⋅degree | gf/cm | gf/cm | gf⋅cm2/cm | gf⋅cm/cm | – | gf⋅cm/cm2 | % | – | – | μm | mm | mg/m2 | |||
| Cotton | PW | F | 0.66 | 10.51 | 46.22 | 1.08 | 1.23 | 4.55 | 0.04 | 0.03 | 0.33 | 0.12 | 52.11 | 0.14 | 0.02 | 2.44 | 0.35 | 11.21 |
| PW | M | 0.71 | 10.54 | 47.72 | 2.29 | 2.77 | 7.60 | 0.09 | 0.08 | 0.34 | 0.12 | 50.52 | 0.13 | 0.04 | 4.70 | 0.47 | 17.23 | |
| PW | T | 0.62 | 9.46 | 48.10 | 2.25 | 2.44 | 7.25 | 0.20 | 0.12 | 0.33 | 0.17 | 45.85 | 0.16 | 0.02 | 6.26 | 0.66 | 25.65 | |
| T | F | 0.66 | 10.67 | 44.60 | 1.68 | 3.30 | 8.26 | 0.08 | 0.08 | 0.41 | 0.28 | 36.53 | 0.16 | 0.01 | 2.00 | 0.60 | 16.25 | |
| T | M | 0.63 | 10.11 | 46.13 | 0.86 | 1.04 | 3.47 | 0.11 | 0.07 | 0.31 | 0.16 | 47.27 | 0.15 | 0.01 | 1.80 | 0.57 | 19.50 | |
| T | T | 0.60 | 10.63 | 44.09 | 1.00 | 1.26 | 3.77 | 0.17 | 0.14 | 0.39 | 0.16 | 46.29 | 0.15 | 0.01 | 2.67 | 0.70 | 26.89 | |
| S | F | 0.62 | 9.36 | 43.32 | 0.51 | 0.84 | 2.14 | 0.06 | 0.03 | 0.31 | 0.13 | 46.11 | 0.14 | 0.01 | 1.40 | 0.41 | 12.00 | |
| S | M | 0.65 | 9.43 | 45.19 | 1.11 | 1.91 | 4.60 | 0.14 | 0.10 | 0.33 | 0.18 | 44.28 | 0.15 | 0.01 | 1.54 | 0.67 | 23.53 | |
| PS | F | 0.83 | 9.28 | 32.02 | 0.80 | 2.77 | 3.05 | 0.02 | 0.02 | 0.32 | 0.26 | 39.44 | 0.17 | 0.01 | 2.86 | 0.74 | 13.94 | |
| PS | M | 0.87 | 8.01 | 32.22 | 0.77 | 3.18 | 3.47 | 0.03 | 0.03 | 0.32 | 0.29 | 37.07 | 0.20 | 0.01 | 3.44 | 0.86 | 16.03 | |
| R | F | 0.84 | 15.39 | 37.14 | 0.61 | 2.02 | 2.10 | 0.03 | 0.03 | 0.32 | 0.28 | 41.71 | 0.19 | 0.02 | 7.44 | 0.87 | 15.28 | |
| R | M | 0.96 | 8.54 | 33.59 | 1.12 | 4.66 | 5.33 | 0.14 | 0.13 | 0.31 | 0.32 | 39.33 | 0.22 | 0.04 | 7.17 | 1.36 | 31.19 | |
| Hemp | PW | M | 0.59 | 13.16 | 29.46 | 0.26 | 0.16 | 0.53 | 0.16 | 0.09 | 0.28 | 0.18 | 37.74 | 0.16 | 0.03 | 7.81 | 0.57 | 15.84 |
| PW | T | 0.61 | 14.30 | 35.28 | 0.30 | 0.19 | 0.66 | 0.22 | 0.11 | 0.42 | 0.26 | 41.62 | 0.19 | 0.03 | 9.22 | 0.69 | 21.90 | |
| T | F | 0.49 | 13.31 | 32.94 | 0.22 | 0.11 | 0.23 | 0.11 | 0.04 | 0.34 | 0.20 | 41.50 | 0.21 | 0.02 | 4.02 | 0.59 | 13.80 | |
| Wool | PW | M | 0.59 | 12.22 | 65.99 | 0.53 | 0.31 | 1.13 | 0.06 | 0.02 | 0.33 | 0.08 | 72.25 | 0.17 | 0.04 | 5.99 | 0.39 | 16.06 |
| PW | T | 0.51 | 11.68 | 56.41 | 0.54 | 0.94 | 1.83 | 0.18 | 0.06 | 0.39 | 0.17 | 62.42 | 0.16 | 0.01 | 5.32 | 0.74 | 25.64 | |
| T | M | 0.69 | 12.70 | 62.43 | 0.55 | 0.50 | 1.31 | 0.11 | 0.03 | 0.35 | 0.11 | 63.55 | 0.16 | 0.01 | 2.42 | 0.61 | 21.70 | |
| T | T | 0.51 | 10.44 | 52.65 | 0.49 | 0.97 | 1.63 | 0.14 | 0.05 | 0.42 | 0.26 | 66.73 | 0.16 | 0.01 | 2.90 | 0.83 | 24.62 | |
| S | M | 0.68 | 10.01 | 61.65 | 0.71 | 0.94 | 2.06 | 0.15 | 0.06 | 0.40 | 0.17 | 68.90 | 0.16 | 0.01 | 2.67 | 0.79 | 25.86 | |
| PS | M | 0.94 | 8.75 | 50.67 | 1.03 | 2.46 | 3.10 | 0.12 | 0.08 | 0.39 | 0.29 | 68.74 | 0.16 | 0.01 | 3.39 | 1.14 | 29.04 | |
| PS | T | 0.85 | 12.65 | 38.84 | 0.56 | 2.22 | 2.34 | 0.21 | 0.18 | 0.43 | 0.93 | 56.73 | 0.28 | 0.02 | 7.34 | 2.33 | 27.67 | |
| R | M | 0.74 | 7.18 | 54.36 | 0.57 | 1.01 | 1.19 | 0.07 | 0.04 | 0.40 | 0.74 | 61.97 | 0.26 | 0.02 | 8.69 | 1.90 | 26.19 | |
| Silk | PW | F | 0.60 | 18.27 | 46.60 | 0.22 | 0.03 | 0.17 | 0.01 | 0.00 | 0.47 | 0.05 | 81.81 | 0.17 | 0.02 | 2.61 | 0.22 | 6.56 |
| T | F | 0.58 | 3.93 | 68.37 | 0.23 | 0.18 | 0.40 | 0.04 | 0.02 | 0.46 | 0.04 | 76.61 | 0.16 | 0.01 | 0.89 | 0.16 | 6.39 | |
| S | F | 0.58 | 9.56 | 53.50 | 0.20 | 0.04 | 0.17 | 0.02 | 0.01 | 0.49 | 0.04 | 76.79 | 0.19 | 0.01 | 1.46 | 0.21 | 7.07 | |
| Cupro | PW | F | 0.69 | 7.19 | 65.33 | 2.84 | 2.99 | 9.22 | 0.10 | 0.04 | 0.29 | 0.04 | 75.46 | 0.17 | 0.03 | 8.03 | 0.27 | 12.94 |
| T | F | 0.72 | 4.24 | 71.40 | 12.17 | 23.65 | 30.24 | 0.79 | 0.27 | 0.50 | 0.03 | 91.45 | 0.19 | 0.04 | 7.49 | 0.33 | 20.29 | |
| S | F | 0.73 | 4.17 | 67.35 | 6.89 | 7.99 | 18.70 | 0.36 | 0.17 | 0.49 | 0.03 | 78.03 | 0.22 | 0.01 | 1.40 | 0.33 | 18.41 | |
| Nylon | PW | F | 0.63 | 19.02 | 42.34 | 0.27 | 0.64 | 0.66 | 0.00 | 0.00 | 0.50 | 0.22 | 56.78 | 0.27 | 0.01 | 9.67 | 0.60 | 8.53 |
| PW | M | 0.59 | 6.60 | 70.62 | 0.31 | 0.25 | 0.79 | 0.03 | 0.01 | 0.39 | 0.05 | 62.11 | 0.23 | 0.02 | 3.81 | 0.21 | 6.42 | |
| T | F | 0.64 | 10.90 | 57.34 | 1.16 | 1.36 | 4.63 | 0.04 | 0.03 | 0.31 | 0.05 | 65.21 | 0.29 | 0.01 | 0.65 | 0.32 | 17.45 | |
| S | F | 0.67 | 13.29 | 58.33 | 0.80 | 1.26 | 3.27 | 0.22 | 0.10 | 0.55 | 0.09 | 57.11 | 0.28 | 0.01 | 2.44 | 0.56 | 20.10 | |
| Polyester | PW | F | 0.64 | 4.18 | 67.87 | 0.35 | 0.35 | 1.19 | 0.05 | 0.04 | 0.48 | 0.04 | 75.95 | 0.30 | 0.01 | 1.04 | 0.18 | 9.39 |
| T | F | 0.77 | 25.04 | 55.47 | 0.39 | 1.01 | 1.02 | 0.01 | 0.01 | 0.50 | 0.15 | 48.51 | 0.35 | 0.02 | 2.73 | 0.68 | 23.28 | |
| T | M | 0.87 | 6.39 | 49.45 | 0.91 | 2.11 | 2.30 | 0.01 | 0.01 | 0.54 | 0.15 | 53.65 | 0.28 | 0.01 | 5.87 | 0.64 | 13.66 | |
| S | F | 0.70 | 6.18 | 64.03 | 0.25 | 0.21 | 0.57 | 0.05 | 0.02 | 0.42 | 0.02 | 129.00 | 0.20 | 0.02 | 1.30 | 0.10 | 6.60 | |
| PS | F | 0.66 | 4.69 | 68.56 | 0.22 | 0.12 | 0.35 | 0.04 | 0.01 | 0.36 | 0.02 | 115.13 | 0.17 | 0.02 | 1.40 | 0.10 | 6.66 | |
| R | F | 0.66 | 3.72 | 65.95 | 0.28 | 0.16 | 0.63 | 0.11 | 0.04 | 0.51 | 0.02 | 97.29 | 0.19 | 0.01 | 0.78 | 0.15 | 10.56 | |
Results of Mann–Whitney U test
| Category | Fiber | Weaving/knitting | Thickness of yarn |
|---|---|---|---|
| Thin | – | – | – |
| Thick | – | – | – |
| Flat | – | – | – |
| Rustic | – | – | – |
| Soft | – | – | – |
| Crisp | – | * | * |
| Wet | ** | ** | * |
| Dry | – | – | – |
| Stretchy | – | – | – |
| Warm | – | – | – |
| Cool | * | – | – |
| Likeable | ** | ** | ** |
| Comfortable | ** | ** | ** |
| Glossy | – | – | – |
Note(s): ∗(p < 0.05) ∗∗(p < 0.01)
Correlations between “likeable” and other evaluation words
| Subjects | Experiments | Soft | Wet | Comfortable | Dry |
|---|---|---|---|---|---|
| Japanese | TE | 0.87 | 0.88 | 0.96 | −0.8 |
| VTE | 0.8 | 0.79 | 0.87 | −0.78 | |
| Chinese | TE | 0.82 | 0.68 | 0.92 | −0.6 |
| VTE | 0.84 | 0.67 | 0.91 | −0.53 |
Factor analysis result of Japanese space
| Evaluation words | Moist preference factor | Thermal sensation factor | Flexible factor |
|---|---|---|---|
| Wet | 0.870 | 0.186 | 0.392 |
| Dry | −0.858 | −0.027 | −0.364 |
| Rustic | −0.834 | −0.440 | 0.013 |
| Comfortable | 0.782 | 0.258 | 0.510 |
| Flat | 0.699 | 0.598 | −0.254 |
| Like | 0.685 | 0.050 | 0.598 |
| Cool | 0.065 | 0.892 | −0.102 |
| Thick | −0.299 | −0.880 | −0.170 |
| Thin | 0.354 | 0.875 | 0.270 |
| Warm | −0.062 | −0.868 | 0.465 |
| Crisp | −0.155 | −0.003 | −0.953 |
| Soft | 0.437 | 0.147 | 0.871 |
| Stretchy | 0.147 | −0.288 | 0.782 |
| Eigenvalue | 4.219 | 3.850 | 3.573 |
| Contribute ratio | 32.45% | 29.62% | 27.49% |
| Cumulative contribute ratio | 32.45% | 62.07% | 89.56% |
Factor analysis result of Chinese space
| Evaluation words | Thermal sensation factor | Flexible preference factor |
|---|---|---|
| Cool | 0.930 | 0.091 |
| Flat | 0.920 | 0.092 |
| Thin | 0.909 | 0.143 |
| Thick | −0.894 | −0.013 |
| Rustic | −0.886 | −0.224 |
| Warm | −0.876 | 0.396 |
| Dry | −0.842 | −0.349 |
| Wet | 0.788 | 0.461 |
| Stretchy | −0.129 | 0.869 |
| Comfortable | 0.432 | 0.824 |
| Crisp | −0.199 | 0.808 |
| Like | 0.324 | 0.804 |
| Soft | 0.480 | 0.802 |
| Eigenvalue | 6.797 | 3.957 |
| Contribute ratio | 52.29% | 30.44% |
| Cumulative contribute ratio | 52.29% | 82.73% |
Factor analysis result of VTE space
| Evaluation words | Flexible preference factor | Thermal sensation factor | Surface factor |
|---|---|---|---|
| Soft | 0.955 | 0.179 | 0.104 |
| Comfortable | 0.846 | 0.163 | 0.360 |
| Like | 0.784 | 0.060 | 0.228 |
| Stretchy | 0.775 | −0.191 | −0.275 |
| Crisp | −0.203 | −0.086 | −0.069 |
| Cool | 0.134 | 0.888 | 0.194 |
| Thick | −0.266 | −0.855 | −0.304 |
| Warm | 0.322 | −0.849 | −0.403 |
| Thin | 0.391 | 0.834 | 0.337 |
| Flat | 0.140 | 0.475 | 0.819 |
| Rustic | −0.330 | −0.354 | −0.779 |
| Glossy | −0.062 | 0.414 | 0.725 |
| Dry | −0.633 | −0.180 | −0.672 |
| Wet | 0.603 | 0.161 | 0.633 |
| Eigenvalue | 4.125 | 3.623 | 3.333 |
| Contribute ratio | 29.47% | 25.88% | 23.81% |
| Cumulative contribute ratio | 29.47% | 55.35% | 79.16% |
Factor scores for factor 1 and factor 2 for each fabric
| Fiber | Weaving knitting | Thickness of yarn | Japanese | Chinese | ||
|---|---|---|---|---|---|---|
| X:Flexible preference factor | Y:Thermal sensation factor | X:Flexible preference factor | Y:Thermal sensation factor | |||
| Cotton | PW | F | −0.30 | 1.32 | 0.11 | 0.87 |
| PW | M | −1.81 | 0.42 | −1.05 | 0.02 | |
| PW | T | −1.39 | −0.61 | −0.56 | −1.00 | |
| T | F | −1.17 | −0.10 | −0.78 | −0.28 | |
| T | M | −0.46 | −0.78 | −0.53 | −0.39 | |
| T | T | −0.78 | −1.09 | −0.29 | −0.94 | |
| S | F | −0.17 | 0.28 | −0.11 | −0.05 | |
| S | M | −0.40 | −2.01 | −0.06 | −1.76 | |
| PS | F | 1.16 | 0.20 | 1.15 | 0.14 | |
| PS | M | 1.25 | −0.42 | 1.25 | −0.05 | |
| R | F | 1.80 | −0.11 | 1.99 | −0.12 | |
| R | M | 0.73 | −1.83 | 1.07 | −1.74 | |
| Hemp | PW | M | −0.76 | 1.78 | 0.05 | 1.21 |
| PW | T | −0.50 | 0.85 | −0.32 | 0.10 | |
| T | F | −0.47 | 1.41 | 0.32 | 0.99 | |
| Wool | PW | M | −0.73 | 1.07 | −0.27 | 0.49 |
| PW | T | −0.48 | −1.30 | −0.11 | −0.80 | |
| T | M | −0.81 | −0.11 | −0.43 | −0.45 | |
| T | T | 0.84 | −1.76 | 0.40 | −1.38 | |
| S | M | −0.44 | −1.46 | −0.27 | −0.90 | |
| PS | M | 0.99 | −1.74 | 0.67 | −1.48 | |
| PS | T | −0.38 | −1.08 | 0.00 | −1.00 | |
| R | M | 1.35 | −1.82 | 1.25 | −1.36 | |
| Silk | PW | F | 0.81 | 1.30 | 0.79 | 1.58 |
| T | F | 0.13 | 0.88 | 0.28 | 0.43 | |
| S | F | 0.98 | 0.28 | 0.66 | 1.13 | |
| Cupro | PW | F | 0.43 | 1.31 | 0.60 | 1.29 |
| T | F | 0.86 | 1.08 | 0.72 | 1.14 | |
| S | F | 0.13 | 0.08 | 0.07 | 0.84 | |
| Nylon | PW | F | −1.67 | 0.66 | −1.33 | 0.37 |
| PW | M | −2.51 | 0.21 | −2.35 | 0.03 | |
| T | F | −1.96 | 0.05 | −1.71 | −0.06 | |
| S | F | 1.78 | 0.94 | 1.96 | 1.14 | |
| Polyester | PW | F | −0.22 | 1.85 | −0.28 | 1.57 |
| T | F | −0.44 | 0.08 | −0.21 | −0.35 | |
| T | M | −1.62 | 0.06 | −0.83 | −0.42 | |
| S | F | 0.02 | 0.06 | −0.09 | 0.61 | |
| PS | F | 1.28 | 0.36 | 1.21 | 0.39 | |
| R | F | 1.27 | −0.42 | 0.74 | 0.29 | |
Difference in width of each fiber space and area (VTE space)
| VTE space | |||||
|---|---|---|---|---|---|
| Name | Japanese | Chinese | Width difference | Area | |
| Difference | |||||
| First | Flexible | 2.107 | 1.838 | 0.268 | 0.822 |
| Factor | Preference | ||||
| Second | Thermal | 1.796 | 1.498 | 0.298 | |
| Factor | Sensation | ||||
| First | Flexible | 2.107 | 1.838 | 0.268 | 0.45 |
| Factor | Preference | ||||
| Third | Surface | 1.677 | 1.355 | 0.323 | |
| Factor | Factor | ||||
| Second | Thermal | 1.796 | 1.498 | 0.298 | 0.117 |
| Factor | Sensation | ||||
| Third | Surface | 1.677 | 1.355 | 0.323 | |
| Factor | Factor | ||||
Note
Available at: https://english.keskato.co.jp/
References
Aston, J., Vipond, O., Virgin, K. and Youssouf, O. (2020), “Retail e-commerce and COVID-19: how online shopping opened doors while many were closing”, Statcan COVID-19, Data to Insight for a Better Canada, Catalogue No. 45280001, Canada.
Inoue, H. (2002), “Quantitative study on qualitative words for descriptive scales in sensory analysis”, Japanese Journal of Sensory Evaluation, Vol. 6 No. 1, pp. 20-27, doi: 10.9763/jjsse.6.20.
Ishii, K., Tsukasaki, T. and Kitayama, S. (2009), “Culture and visual perception: does perceptual inference depend on culture?”, Japanese Psychological Research, Vol. 51 No. 2, pp. 103-109, doi: 10.1111/j.1468-5884.2009.00393.x.
Ishikawa, T., Guan, Y., Chen, Y.C., Oguro, H., Kasuga, M. and Ayama, M. (2012), “Effect of tone curve and size on Kansei evaluation of high dynamic range images – comparison of Japanese and Chinese observers”, Kansei Engineering International Journal, Vol. 11 No. 2, pp. 67-79, doi: 10.5057/kei.11.67.
Ishikawa, T., Nakamori, S., Sasaki, K., Miyatake, K. and Ayama, M. (2015), “Identification of common words for the evaluation of clothes’ appearance and tactile sensation in online shopping – an indicator for producing images that express clothes’ textures –”, International Journal of Affective Engineering: Special Issue on KEER 2014, Vol. 14 No. 3, pp. 143-149.
Kawabata, S. (1980), “The standardization and analysis of hand evaluation”, The Hand Evaluation and Standardization Committee, 2nd ed., The Textile Machinery Society of Japan, Osaka.
Kim, H. and Winakor, G. (1996), “Fabric hand as perceived by U.S. and Korean males and females”, Clothing and Textiles Research Journal, Vol. 14 No. 2, pp. 133-144, doi: 10.1177/0887302X9601400204.
Kobayashi, S. (1972), “Nuno no huuai imege pattern no bunseki”, Journal of the Japan Research Association for Textile End–Uses, Vol. 13 No. 1, pp. 4-11.
Koch, J., Frommeyer, B. and Schewe, G. (2020), “Online shopping motives during the COVID-19 pandemic—lessons from the crisis”, Sustainability, Vol. 12 No. 24, 10247, doi: 10.3390/su122410247.
Matsunaga, R., Yasuda, T., Johnson-Motoyama, M., Hartono, P., Yokosawa, K. and Abe, J. (2018), “A cross-cultural comparison of tonality perception in Japanese, Chinese, Vietnamese, Indonesian, and American listeners”, Psychomusicology: Music, Mind, and Brain, Vol. 28 No. 3, pp. 178-188, doi: 10.1037/pmu0000219.
Musa, A.B.H., Malengier, B., Vasile, S. and Van Langenhove, L. (2019), “A comprehensive approach for human hand evaluation of split or large set of fabrics”, Textile Research Journal, Vol. 89 Nos 19-20, pp. 4239-4252, doi: 10.1177/0040517519832834.
Nishimatsu, T. and Sakai, T. (1987), “Significance of the influence of the sense of sight on the hand evaluation of pile fabrics”, Sen’i Gakkaishi, Vol. 43 No. 4, pp. 211-217, doi: 10.2115/fiber.43.4_211.
Niwa, M. (1990), “Fabric hand and mechanical properties—objective evaluation of fabric hard and its application–”, Sen’i Gakkaishi, Vol. 46 No. 6, pp. 245-252.
Rhode, A.K., Voyer, B.G. and Gleibs, I.H. (2016), “Does language matter? Exploring Chinese–Korean differences in holistic perception”, Frontiers in Psychology, Vol. 7, p. 1508, doi: 10.3389/fpsyg.2016.01508.
Saito, M. and Harada, T. (1987), “Differences in texture judgment methods for various types of cloth”, Journal of Textile Engineering, Vol. 40 No. 6, pp. 239-245.
Stean, A.E., D’Arcy, R.L., Postle, R. and Mahar, T.J. (1988), “A statistical analysis of subjective and objective methods of evaluating fabric handle. Part 1: men’s summer suiting fabrics”, Sen’i Kikai Gakkaishi (Journal of Textile Machinery Society of Japan), Vol. 40 No. 8, pp. 75-80.
Suehiro, Y., Sakamoto, Y. and Sukigara, S. (2014), “Comparison of ‘Shittori’ characteristic between Japanese and Chinese for interlock knitted fabric of ultra-fine fibers with different stitch density”, Journal of Textile Engineering, Vol. 60 No. 2, pp. 35-40, doi: 10.4188/jte.60.35.
Tsunetou, J., Ishikawa, T., Yanaka, M., Yanagida, Y., Sasaki, K. and Ayama, M. (2018), “Texture evaluation of various cloth attributes – influence of the differences between the subjective knowledge and sensory modality”, Journal of Textile Engineering, Vol. 64 No. 5, pp. 117-126, doi: 10.4188/jte.64.117.
Yashima, K., Yamazaki, M., Miyatake, K., Kim, K. and Takatera, M. (2017), “Development of textile proposal system (TPS)-verification of validity for luster evaluation in textile retrieval system”, The 12th Japan Society of Kansei Engineering Spring Competition, 29 March–30, No. March, Osaka.
Acknowledgements
This work was supported by JSPS KAKENHI [Grant number: 18H03317].