Fabric perceptions in digital contexts: exploring the correlation between certainty and accuracy

Abstract

As the fashion industry increasingly adopts digital platforms, fashion designers face challenges in digitally exploring and selecting fabrics. This transition underscores the complexities in digitally capturing fabrics. Extending our previous research, this study specifically examined the relationship between designers' certainty and their perceptual accuracy in digital fabric evaluations because uncertainty can diminish confidence in digital assessments, leading to an over-reliance on physical samples and slowing digital progress. The research employed a comparative analysis, engaging designers in both digital and physical fabric evaluations. The objectives of this study were: to evaluate the precision of digital fabric representations against physical assessments; to assess designers' psychological certainty and its correlation with assessment accuracy; and to identify fabric attributes that are most prone to misrepresentation digitally. The findings indicated that there is no direct correlation between evaluation accuracy and psychological certainty. Notably, for drapability, digital evaluations exhibited both high levels of certainty and accuracy, implying potential enhancements in digital representations of other textile attributes. The results of this study will contribute to the development of advanced digital fabric evaluation systems that can accurately replicate the physical fabric experience, thus ensuring a high level of certainty. This, in turn, will enhance the reliability and effectiveness of fabric selection on digital platforms.

Introduction

The emergence of digitization has brought about significant transformations across various industries, and the fashion industry is no exception, particularly when it comes to the digital representation of fabric attributes. Fabrics play a critical role in design decisions due to their intricate visual and tactile subtleties. However, digital mediums often fall short in delivering a true physical experience, essential for fashion designers, prompting the question: How can one accurately convey the texture and richness of a fabric without direct, physical interaction? The COVID-19 pandemic has only heightened the urgency for this inquiry, propelling the industry toward virtual showrooms and digital fabric libraries, where the effectiveness of digital representation becomes commercially imperative.

The shift toward virtual interactions, especially in selecting fabrics remotely, underscores the need for both precision and certainty in those choices. A designer's hesitance can undermine even the most accurate assessments, resulting in an excessive dependency on physical samples and a neglect of digital progress.

In prior research by Jang & Ha, (2023), we established guidelines for the digital presentation of fabrics, mainly validating these guidelines based on the certainty expressed by experts in their evaluations, with less emphasis on the actual accuracy of these assessments. Literature such as Bradfield et al., (2002) and Deffenbacher, (1980), however, indicates a frequent disparity between subjective confidence and objective accuracy. This disparity has led to situations where participants, despite their assurance in digital assessments, demonstrated a notable lack of precision. These findings, challenging our initial hypothesis that a designer's confidence might align with accuracy, point to the necessity for an in-depth exploration of this relationship.

Building on these insights, the study is guided by three principal objectives. Firstly, it aims to evaluate the precision of digital representations of fabric attributes by comparing them with physical assessments. Secondly, the study assesses the psychological certainty of designers in their digital evaluations and its relationship with the actual accuracy of these assessments. Lastly, it identifies specific attributes that are prone to misrepresentation in digital formats and investigates the causes of these discrepancies.

To address these research questions, we employed a mixed-methods approach, drawing on the robustness of quantitative data complemented by the richness of qualitative insights. Participants were involved in two experimental conditions: evaluating fabrics digitally via a computer screen and directly assessing the same fabric samples. By comparing scores from these two methods, we aim to measure the accuracy of digital evaluations. Additionally, participants were prompted to rate their level of certainty regarding their judgments. These results will enable us to understand the correlation between the accuracy of evaluations and the certainty expressed by participants. Following these evaluations, in-depth interviews were conducted to uncover factors that may contribute to any disparities observed.

The essence of this research lies in enhancing the understanding of the balance between psychological confidence and the accuracy of evaluations. A better understanding of the dynamics between certainty and accuracy will greatly benefit the field and may redefine approaches in future research. The ultimate goal is to enhance digital representation methods, ensuring that the visual and tactile qualities of fabrics are accurately portrayed with a high degree of certainty, thereby enabling fashion designers to reliably utilize digital tools in their professional settings.

Literature review

Certainty and accuracy in perception

Exploration of the relationship between certainty and accuracy in decision-making is well-established in psychological research but is rarely touched upon in fashion studies. However, this relationship is crucial in the fashion industry where perception significantly impacts decision-making. Generally, there is an assumption that a higher level of confidence in one’s sensory judgment leads to a more accurate perception of materials. However, studies have indicated that a stronger level of confidence does not necessarily lead to a more accurate perception of materials.

To investigate the relationship between certainty and accuracy, psychologists explored the concept of overconfidence. Scholars have studied overconfidence through various dimensions such as overestimation of one’s actual performance (Alpert & Raiffa, 1982; Ludwig & Nafziger, 2010; Moore & Cain, 2007), overplacement of one’s performance relative to others (Alicke & Govorun, 2020; Larrick et al., 2007), and overprecision in one’s beliefs (Klayman et al., 1999; Soll & Klayman, 2004).

Results are inconsistent depending on the subjects. Experimental evidence has shown that the presence of overconfidence is associated with the nature and complexity of tasks (Moore & Healy, 2008). In gerenal, in difficult tasks, individuals tend to overestimate their actual performance, while mistakenly perceiving themselves as worse than others. Conversely, in easier tasks, individuals often underestimate their actual performance, while erroneously believing they perform better than others. Soll & Klayman, (2004) have also made significant contributions in this area, identifying that individuals frequently exhibit overconfidence, leading to a discrepancy between actual accuracy and their confidence levels in decision-making. These findings are crucial, enhancing understanding of human psychology and decision-making behaviors, with implications across various sectors, including the fashion industry. Factors such as the nature and framing of questions contribute to overconfidence, highlighting the complexities in comprehending and interpreting certainty and accuracy in decision-making.

In addition to the concept of overconfidence, the literature has delved deep into methodological considerations. Deffenbacher, (1980), for instance, has emphasized the inherent challenges in understanding and statistically determining the relationship between certainty and accuracy, urging for more sophisticated approaches. Such methodological insights are pivotal, offering a lens through which the relationship between certainty and accuracy can be more robustly examined and understood.

Further expanding on this, research by Bradfield et al., (2002) has illustrated how external factors, such as feedback, can influence confidence. Their findings reveal that feedback can enhance individuals' confidence levels, even if it does not lead to improved decision accuracy, underlining the multifaceted nature of decision-making processes and the various factors that can influence them.

Fabric perception in digital representation

Fabric perception in digital representation is another significant area of exploration within the literature, especially concerning the fashion industry. In this domain, advancements have been made to visually convey the textile attributes of materials, reducing the necessity for physical touch (Xue et al., 2013). Such capabilities have transformed digital fabric representation, leveraging cognitive processing to enhance the consumer experience.

The role of interface design and information presentation is underscored through various researches, demonstrating their impact on user interactions and experiences within digital platforms. Chiang & Dholakia, (2003) and Hassanein & Head, (2007), for instance, have highlighted how these factors can profoundly influence consumer behavior, pointing towards the critical role of design and information in shaping user experiences and satisfaction.

Furthermore, the evolution of discussions surrounding information quality within digital representations marks a significant shift in focus towards more comprehensive and multi-dimensional considerations. Research has moved beyond traditional accuracy metrics, placing increased emphasis on aspects such as context relevance and reliability (Negash et al., 2003). Such developments reflect the broader considerations necessary for effective and meaningful digital representations, particularly within the dynamic and evolving landscapes of the fashion industry.

Methods

Research scope

This study was conducted utilizing a framework consistent with our prior investigation (Jang & Ha, 2023). An initial assessment was carried out to pinpoint fabric types that pose challenges in digital identification. To plan our study, we surveyed 28 womenswear designers who have been in the field for over three years in the beginning of this research. The questionnaire included questions about fabric types and textile attributes, and visuals were omitted to prevent potential biases. We aimed to gather instinctual answers rooted in their past experiences. While the survey results indicate challenges in perceiving textures of solid cotton, polyester, and silk, it is understood that these findings cannot be generalized to all fabrics due to the diversity of other physical parameters. However, given the vast array of textiles, the focus on fiber type serves as a preliminary step to narrow down the field of investigation and establish a starting point for further research, In addition, four textile attributes: thickness, stretchiness, luster, and drape, were recognized as the most elusive to discern virtually. Consequently, our research concentrated on these attributes for online presentations of cotton, polyester, and silk.

To preliminarily explore the potential impact of textile attributes for a follow-up study, three fabric variants were selected for each type of material. Each variant exhibited distinct levels of thickness, luster, stretchiness, and drapability, resulting in a total of nine fabrics. These fabrics were chosen through physical evaluation by three fashion and textile experts, all of whom hold doctoral degrees. They confirmed that each sample possesses different characteristics. Furthermore, the Korean Textile Inspection and Testing Institute (KOTITI) provided mechanical measurements of the basic attributes of these fabrics, which are presented in Table 1. It was reported that there is no standardized objective method available for measuring luster; therefore, luster measurements were not included in the table.

Table 1 Description of the sample fabrics

Participants

In the study, 24 female individuals aged between 20 and 40 were chosen as participants as shown in Table 2. Evidence suggests that females often exhibit superior finger sensitivity relative to males, and tactile sensitivity is generally higher among younger age groups than in older individuals (Musa et al., 2019). The participants included Fashion designers, as well as PhD students majoring in fashion design. All participants possess normal vision, with corrected visual acuity of 20/20 or better. The study secured ethical approval from the Institutional Review Board at Seoul National University, with the approval number being IRB No. 2307/001-002.

Table 2 Participants’ information

Procedure

The study procedure consisted of three sequential stages: Digital evaluation, Physical evaluation, and a post evaluation interview.

Digital evaluation

During the digital evaluation phase, participants were required to evaluate nine fabrics by viewing their representations on a screen. Their evaluations were documented using a 7-point Likert scale, which assessed both the properties of the fabric and participants' confidence in their evaluations. For reference, participants were provided with a sheet containing four sets of fabric swatches to guide their evaluations. For each attribute (thickness, luster, stretchiness, drapability), two contrasting fabric swatches were presented, indicating the extremes of the Likert scale (1 and 7).

In the study, digital photography and videography were utilized to capture the fabric samples, a method preferred over scanning to effectively represent the fabrics' quality from various angles. To ensure the production of high-quality visual data, all photos and videos were captured using an iPhone 15 Pro, set to a resolution of 48 megapixels. The decision to use an iPhone 15 Pro instead of a professional DSLR camera was influenced by findings from prior research (Jang & Ha, 2023), which emphasized that digital visual representations should be easily producible by anyone, including fabric retailers. This approach highlights the importance of accessibility and practicality in creating digital representations of fabrics, aiming for methods that can be widely adopted without necessitating expensive, specialized equipment. Additionally, the use of the iPhone was supported by its various accessibility features provided by Apple, which aid in fine-tuning the display and interaction experience on both iPhone and MacBook devices. This consideration was crucial to mitigate potential media shift between the physical samples and their digital representations. The camera and display monitors were calibrated to ensure color accuracy and consistency across all digital visual materials. This calibration process was conducted prior to the photography session and was regularly monitored, underlining the commitment to maintaining the integrity and reliability of the digital representations throughout the study. In the study, a precise calibration process was implemented for the MacBook Pro 15″ with Liquid Retina XDR display, strictly adhering to the guidelines set forth by Apple. This calibration was uniquely performed using Apple's QuickTime movie test patterns, effectively bypassing the need for traditional calibration instruments. The core of this process involved the use of an in-house spectroradiometer to assess the display's color fidelity. For consistency in the evaluation process, the display settings on the MacBook Pro were standardized. The white point was precisely set to D65 to mirror the natural daylight standard and conform to the P3 color space requirements. Luminance was optimized at 120 cd/m2, striking a perfect balance that ensures both sufficient brightness and comfort for the viewer's eyes. The gamma was set at a standard rate of 2.2, catering to the needs of web and desktop publishing, while the MacBook's native contrast ratio was utilized without modification. By reducing potential variability in the evaluation outcomes, the study achieved a high level of accuracy and reliability in its display calibration efforts.

The methodology for digital display followed guidelines established in our previous study (Jang & Ha, 2023). As recommended, six distinct photos and two videos were used for the digital presentation of each fabric as shown in Fig. 1: a Frontal Photo, Close-up Photo, Photo of Spiral-shaped Crease, Photo of Irregular Folds, Photo of Fabric on a Dress Form, Photo of Draping, Video of Fabric Moving with a Hand, and Video Using Hands to Stretch the Fabric.

Fig. 1

Example of the digital representations

The Frontal Photo, considered a fundamental visual representation in this field, was captured from a distance of 30 cm from the fabric. To better represent the fabric's texture, a Close-up Photo was taken from a distance ranging between 5 and 10 cm at a 45-degree angle. For the Photo of Spiral-shaped Crease and the Photo of Irregular Folds, the center of the fabric sample was grasped and rotated 360 degrees twice in a clockwise direction. To photograph natural irregular creases, fabric was spread on the floor, lifted about 30 cm high, then released. The Photo of Fabric on a Dress Form covered the form entirely to showcase the fabric's drape and transparency. The Draping Photo depicted a fabric draped in a basic dress shape, taken from a frontal view.

The Video of Fabric Moving with a Hand had the fabric placed on an outstretched palm, moving in various angles. This video, based on prior research, had a suggested duration of approximately 10 s. The second video, aimed at showing fabric stretchiness, was recorded against a grid paper background. This helped in visually assessing the fabric's stretch as it was pulled horizontally and then diagonally. Throughout this video, one hand remained stationary to offer viewers a clear perspective on the extent of stretch. The video also incorporated subtitles like 'not stretched', 'lightly pulled', and 'pulled maximum', based on our guidelines. This video typically lasted 15 s, with an allowable maximum of 20 s.

All photos and videos maintained uniformity in lighting and setting for consistency. In the process of creating visual representations for the digital evaluation phase, meticulous attention was given to the standardization of illumination conditions to maintain consistency and accuracy in fabric representation. The photography and videography sessions were conducted in a room with white painted walls and devoid of windows to eliminate natural light fluctuations. Prior to capturing the images, specific camera positions and fabric placements were marked and consistently used for all nine samples to maintain uniform shooting settings. The light sources employed were daylight-balanced LED lights with a color temperature of 5500 K, chosen for their ability to replicate natural daylight. This lighting minimizes color cast and preserves the true colors and textures of the fabrics. The lighting setup was strategically arranged in a three-point system, comprising key light, fill light, and back light. This arrangement ensured an even distribution of light across the fabric samples, effectively reducing shadows and highlights that could potentially alter the appearance of the fabrics.

For both the physical and digital evaluations, the background color was standardized to white to eliminate any influence of background hues on tactile perception. The physical evaluations were conducted with the fabric samples placed on a white table within a room with white walls, providing a consistent and neutral visual context. Similarly, for the digital evaluations, a laptop displaying the fabric images was positioned on the same white table, preserving the uniformity of the visual environment across both evaluation types.

The digital display included essential objective information such as fabric type, composition, and thinness. Participants navigated freely, revisiting previous fabrics for comparison and had the option to zoom into visuals without any time constraints. All actions during this phase were diligently observed and recorded.

Physical evaluation

In the physical evaluation phase, participants had direct tactile and visual interaction with the fabric samples. These evaluations employed the same evaluative criteria as the digital stage, using a 7-point Likert scale to assess fabric properties and measure participants' confidence levels. While the evaluations took place under different conditions reflective of each medium's nature, the core elements being evaluated—the fabrics themselves and the assessment parameters—were consistent, ensuring that the results from both phases could be directly compared. Altuna & Arslan, (2016) experimentally examined the effects of 5-point and 7-point Likert scales on data characteristics and respondent assessments. Ultimately, the 7-point scale exhibited the best suitability for the data.

Each fabric sample was intentionally labeled using randomly assigned numbers. Taking into account a study by Xiao, (2016) that emphasized the effect of color on tactile perception, only black fabrics were chosen for evaluation. These fabric samples were half a yard in size. After being meticulously ironed, they were laid out on a pristine table, allowing participants to make side-by-side comparisons as they assessed.

To aid their evaluations, participants were furnished with a reference sheet that showcased four sets of fabric swatches. For every attribute, two contrasting fabric swatches were displayed, representing the minimum and maximum ends of the Likert scale (1 and 7). Before physically engaging with the fabric samples, participants were instructed to clean their hands using wet tissues. The assessment was conducted in a controlled environment, maintaining a consistent room temperature between 25 and 26 °C. Daylight-balanced LED lighting with a 5500 K color temperature was utilized as the primary illumination source to simulate natural daylight within a windowless setting. The placement of LED lights, mounted strategically on the ceiling, was designed to ensure an even distribution of light throughout the evaluation area. Furthermore, to maintain uniformity across all evaluations, white masking tape was utilized to mark specific locations on both the table and the floor, indicating where participants should be seated, where materials should be placed, and the positioning of evaluation sheets. This technique facilitated a rapid verification process, ensuring the consistent arrangement of participants, materials, and evaluation tools in every session. The setup was imperative for maintaining consistent lighting conditions and bolstering the overall reliability of the study’s comparisons between digital and physical experiences. By keeping the lighting's type, color, position, and intensity constant, the study minimized the effects of lighting variability, thereby sharpening the focus on exploring the intrinsic differences between the compared experiences.

Participants were granted the freedom to handle and examine the fabrics without any restrictions on time. All participant interactions and reactions during this segment were carefully observed and recorded.

Post-evaluation interview

Upon concluding both the digital and physical evaluations, participants were given a short interlude. During this intermission, a preliminary comparison of the results from the two evaluations was undertaken, with particular emphasis on identifying areas showing notable disparities. Subsequently, participants engaged in a semi-structured interview typically ranging between 30 min to an hour in duration.

To begin, participants were handed their evaluation sheets and were guided to the highlighted sections that indicated significant variances in their assessments. They were then prompted to articulate the specific considerations they factored in during their evaluation processes. This was pivotal in gleaning insights into how designers recalibrate their assessment methodologies when faced with the ambiguities of evaluating fabrics via digital mediums.

Furthermore, participants were probed on the reasons they believed led to the discrepancies between evaluations derived from digital representations and those from hands-on physical assessments. Such a line of inquiry was aimed at understanding the potential biases when working with digital fabric representations.

Lastly, in an effort to bolster the reliability and clarity of digital fabric evaluations in the future, participants were asked about potential enhancements or tools that could aid in forming a more precise perception of fabrics.

Data analysis

In this research, a mixed-methods approach was employed for comprehensive exploration. For the quantitative segment, experimental results were assessed using IBM SPSS Statistics (Version 27). The Wilcoxon signed rank test, a non-parametric test, was utilized to determine potential differences in accuracy evaluations between screen assessments and actual evaluations. A comparison of certainty between the certainty of digital evaluation and physical evaluation when they evaluate fabrics was also analyzed using the same method.

There were no significant difficulties in the statistical analysis related to accuracy and certainty. However, challenges became particularly pronounced when investigating the relationship between certainty and accuracy statistically, as emphasized in prior literature (Deffenbacher, 1980). These complexities prompted a greater focus on qualitative methodologies.

In line with these challenges, it is important to note the limitations of this study. The statistical validation of psychological assessments did not account for inter-individual and intra-individual variability. This decision was made following a determination that conventional reliability measures would not be meaningful in this context. Further, to avoid potential learning effects, repetition of measurements was excluded, aligning with our goal to capture the immediate natural perceptions of designers.

To overcome the limitations and deepen understanding of the correlation, post-evaluation interviews were conducted. Qualitative methods are particularly adept at unveiling varied perspectives on a topic, identifying linked elements, and delineating the intricate connections between multifarious variables (Creswell, 2021).

For a deeper exploration of the participants' experiences, the qualitative data from interviews was subjected to Giorgi’s, (1970) phenomenological method. This approach first requires a holistic reading of the participants' descriptions to grasp the overall sense. Subsequently, meaningful units are discerned and these everyday expressions are transformed into generalized, more abstract terms to reveal the essence of the experience. Themes and patterns were then derived from these abstracted descriptions, providing a structured understanding of the participants' experiences.

To ensure the reliability and validity of the qualitative data analysis, an inter-coder agreement method was implemented. This process involved categorizing similar responses, coding these responses, and comparing the coding performed by three experts holding PhD in fashion. Adjustments were made until a consensus on the coding was reached among the students.

Despite historical suggestions in psychology research indicating the difficulty in obtaining statistically meaningful results concerning the correlation between accuracy and certainty, this research endeavored to explore this with the Pearson correlation coefficient (PCC). As intimated by past studies, drawing statistically significant outcomes on this front remained elusive. Nevertheless, the qualitative method illuminated certain significant insights.

By integrating findings from both quantitative and qualitative analyses, the research aspires to deliver a comprehensive response to the posed research questions. This combined analysis aims to elucidate the perceptions of fashion designers concerning fabric attributes, experienced both tangibly and digitally.

Results and discussion

Accuracy of digital evaluation

The result of physical assessment and digital assessment was compared to check the accuracy of digital evaluation. The quantitative data was analyzed using the Wilcoxon signed-rank test to compare evaluation scores for screen and actual evaluation from the same sample (n = 24). The significance level was assessed through the z-test, and a P-value lower than 0.05 was interpreted as indicating a statistically significant mean difference.

As a result, thickness was identified as the most accurate attribute. Only one sample, a silk fabric, showed low accuracy. The perceived thickness was significantly higher when viewed on screen (M = 4.67, SD = 0.56) compared to the actual physical material (M = 3.63, SD = 1.06; z = − 0.3566, P < 0.001). Table 3 presents these results.

Table 3 The comparison on thickness evaluation

In the evaluation of luster, a significant difference was observed in 8 out of 9 fabrics (p < 0.05). The results are detailed in Table 4. With the exception of the lightweight polyester chiffon, which is inherently sheer, the luster of the remaining fabric samples was not accurately captured on screen. Intriguingly, participants assigned higher scores in the digital evaluations across all fabric samples, suggesting that the luster appears more exaggerated on screen. These findings are detailed in Table 4.

Table 4 Comparison on luster evaluation

The evaluation of stretchiness was identified as the most challenging attribute, with significant differences found across all fabric samples. The results, as shown in Table 5, exhibited very low p-values across all samples, indicating statistically significant differences. Except for the two 100% cotton fabrics, which showed p ≤ 0.002, seven fabric samples displayed a substantial discrepancy between the digital evaluation and physical evaluation, with p < 0.0001. Participants assigned higher scores in the digital evaluations across all fabric samples, suggesting that the stretchiness appears more exaggerated on screen. The fact that there are remarkable gap in evaluation of stretchiness suggest that a need of new method to present stretchiness more accurately without showing them too strong.

Table 5 Comparison on stretchiness evaluation

The comparative evaluation of drapability in both digital and physical settings revealed dependency on the fabric type. The statistical results are displayed in Table 6. A lightweight cotton fabric, exhibiting subtle sheerness, manifested the lowest p-value (p < 0.001). Additionally, significant differences in the two evaluation tasks were observed in other fabrics, including a heavyweight cotton, lightweight polyester chiffon, and medium-weight silk.

Table 6 Comparison on drapability evaluation

Designers’ certainty in their perceptions

We considered that complete confidence may not always be present in physical evaluations. Therefore, we asked participants to rate their certainty during physical assessments, which helped establish criteria for analyzing confidence in digital evaluations.

Table 7 illustrates that participants had the least certainty when evaluating fabric thickness, compared to their confidence levels in physical evaluations (p < 0.05). Specifically, fabrics without sheerness resulted in lower certainty compared to assessments of sheer fabrics (Fabric 1, 4, 7).

Table 7 Certainty on thickness evaluation

In the evaluation of luster, no statically significant difference between the certainty in digital evaluation and physical evaluation except for the fabric 1 as shown in Table 8. This result suggests that the visual representations presented for digital evaluation was satisfying participants’ needs of information.

Table 8 Certainty on luster evaluation

Participants’ certainty on evaluation of stretchiness shows a statically significant difference in fabric 1, 2, 4, 5, 8 as shown in Table 9. These fabrics are all non-stretch fabrics (P < 0.05). There is no meaningful difference in certainty on Fabrics (3, 6, 7, 9) containing polyurethane, spandex, or Lycra (P > 0.05). This suggests that these fabrics are visually convincing the stretchiness of it compared to other fabrics (1, 2, 4, 5, 8) that does not have stretch component.

Table 9 Certainty on stretchiness evaluation

In the participants’ certainty in drapability, no statically meaningful difference was found (P > 0.05) as shown in Table 10. The fact that the certainty of digital and physical evaluation is very similar suggests that the visual representation of drapability was effective to ensure designer’s perception on the fabrics.

Table 10 Certainty on drapability evaluation

The correlation between accuracy and certainty

Thickness

A remarkable finding emerged in the evaluation of thickness. Most participants (n = 21) articulated that assessing thickness was the most challenging attribute when evaluated on screen, particularly with non-sheer fabrics. Despite this, the accuracy of these evaluations remained high, with the exception of assessments pertaining to medium-weight silk fabric (p < 0.05). It was a surprising outcome to observe that thickness, despite being the attribute evaluated with the lowest certainty, was among the most accurately assessed.

One participant, a fashion designer, shared reflections:

… Evaluating thickness was most challenging, except for the sheer fabrics. It was really hard to feel the non-transparent fabrics. Even though numbers for thickness was written on screen, getting a true sense of the fabric’s thickness was still a struggle. I wished to gauge the thickness as naturally as if I were touching it directly, but it just didn’t work out that way. It was surprising that my digital evaluation matches pretty close to the in-person evaluation. when I was just going by what I saw on the screen, I wasn’t that confident. (Participant 3)

The observation that 'thickness'—the attribute evaluated most accurately—also exhibited the lowest certainty underscores the notion that confidence does not invariably correlate with the accuracy.

When participants were asked to discuss the discrepancy between accuracy and certainty of the results, many found it challenging to articulate a reason, as the outcome was unexpected. Participants also expressed curiosity regarding the cause of these surprising results. However, a few were able to provide insightful perspectives:

… When the visuals got a bit confusing, that solid number of thicknesses written on screen kind of helped steer my evaluation a bit more accurately. ... Looking at the results now, it feels like the visuals didn’t fully convince me, but that written number did make a difference in my judgment. When it comes to evaluating fabrics, there’s something more reassuring about actually seeing or touching them. So, the written info is good to have and it helps in making a final decision on evaluation, but it doesn’t really boost my confidence while I’m in the middle of evaluating. (Participant 8)

… I’m not entirely sure why my evaluation of thickness was so accurate. It might be because the actual measurements of thickness were provided. I felt a bit confused, so I tried to relate the measured thickness with what I was seeing visually... trying to combine both pieces of information to make a decision. But honestly, it might have been more helpful if the weight of the fabric was provided along with the thickness. Just knowing the thickness alone didn’t really give me much confidence, but having the weight usually helps me get a better sense of the fabric. (Participant 15)

These insights unveil a crucial aspect of fabric evaluation. Designers typically rely on sensory experiences, such as sight and touch, to assess fabrics. Consequently, their confidence in evaluation seems to be more intertwined with these sensory experiences. While textual information, like numerical thickness, can enhance the accuracy of the evaluation, it may not necessarily bolster the evaluators' psychological certainty or confidence in their decision-making process.

This suggests an essential consideration for improving evaluative confidence: there should be a focus on the precise conveyance of sensory information. Both visual and textual information play roles in influencing accuracy, but certainty appears to be relatively more dependent on visual information. Enhancing the clarity and accuracy of visual representations could be instrumental in bolstering evaluators' confidence and certainty in their assessments.

Luster

In the evaluation of luster, participants generally exhibited high confidence in their digital assessments. However, this confidence did not always translate into the assessment accuracy. A notable number of participants (n = 6) found the digital evaluation of luster more straightforward and easier compared to the physical evaluation. However, it’s crucial to highlight that the ease of digital evaluation does not necessarily equate to enhanced accuracy. Significant differences were observed between the two evaluation methods for luster (p < 0.05), pointing towards a lower accuracy in the digital assessments.

Participants tended to assign higher scores in the digital evaluations across all fabric samples. This trend suggests that the luster or shininess of the fabrics appears somewhat exaggerated on screen compared to their actual physical appearance. Some participants mentioned this:

… Luster was the easiest attribute to evaluate. It appeared so vividly on the screen. The first video being particularly helpful. However, seeing the fabrics in person was a revelation—the luster was much less pronounced than it appeared digitally. The fabrics seemed shinier on screen. (Participant 21)

… The screen presentation seemed to exaggerate the shininess of the fabrics due to the lighting. In a real-world setting, without special lightings, the fabrics’ luster appears much more subdued and subtle. (Participant 4)

A participant, who had lower certainty for physical evaluations compared to digital ones, shared:

… I believe the actual luster lies somewhere between my digital and physical assessments. In person, I only had the chance to observe the fabrics under natural light, but I was curious to see how they would shine under more intense lighting. (Participant 13)

This suggests that a physical evaluation might not always accurately reflect a fabric’s true luster. Fabrics, particularly shiny ones, can appear differently depending on the lighting conditions, and their evaluation might vary based on these conditions. Fashion designers often consider how fabrics will appear under various lighting conditions during the design process. Another designer, specializing in womenswear for fashion shows, echoed this sentiment:

… When evaluating fabric luster, I typically manipulate the fabric, observing it under natural light and then, I also move around with the fabric to see how it reacts in different levels of lighting. A shiny silk fabric, for example, might appear even shinier under different lighting, similar to what was presented in the video. However, under this basic natural light, it is hard to see the possibilities of the fabric. (Participant 11)

These findings suggest that for subsequent research evaluating luster, unlike other attributes, a setting with various types of lighting that participants can control should be provided.

All participants mentioned that the visual representations, photos with creases and a video of moving fabric on hand, clearly show the luster so they did not need more information. Despite the low accuracy, the high confidence in the evaluation could be attributed to satisfaction with the provided visual representations. Generally, for visual attributes that are perceptible to the eye, there tends to be a higher level of certainty, even if they might appear exaggerated.

Stretchiness

Participants generally expressed confidence in digitally assessing the stretchiness of fabrics. However, it was observed that stretchiness was the attribute with the least accuracy in digital evaluation. Participants noted that some fabrics appeared to be extremely stretchy in the videos, which made them feel confident about their assessments. However, the actual physical experience often contradicted the digital perception, as the fabrics were found to be less stretchy in reality.

… It is obviously impossible to feel the real stretchiness of the fabric on screen, but the video was kind of helpful, and I was quite confident about the stretchiness of them. But when I touched the actual fabric in physical evaluation after the digital one, I was quite surprised that some of them were actually not that stretchy compared to what I felt on screen. (Participant 1)

This reflection indicates that while digital representations, such as videos, can aid in forming an initial perception, they cannot fully replicate the tactile experience of fabric stretchiness. Another participant shared,

… Since one hand was fixed in a position and you stretched the fabric with the other hand, I think it was intended to show how much it stretches out. But I think the stretched length doesn’t match the actual stretchiness. I think I have my own criteria to determine the level of stretchiness, and visuals can’t provide me to feel the same. (Participant 15)

This feedback suggests that personal criteria and previous experiences play a significant role in evaluating fabric stretchiness, and digital visuals alone may not suffice to provide an accurate understanding. A participant who showed low certainty in evaluating stretchiness stated,

… I know that I can’t decide the stretchiness of a fabric based on visual representations. This is something that cannot be replaced with another method. I just need to touch and feel with my own hands. (Participant 13)

The results show the limitation of digital visuals in conveying the textile attributes of stretchiness. Participants found the videos somewhat helpful but not sufficient to make accurate assessments. The physical touch and personal tactile experience are irreplaceable in evaluating the stretchiness of fabrics. The participants seem to rely on their personal criteria and previous physical experiences to make assessments, indicating the essential role of touch in evaluating fabric properties like stretchiness.

Drapability

The ability to assess fabric drapability digitally has made strides with the effectiveness. Some notable distinctions were found in evaluations of heavyweight cotton, lightweight polyester chiffon, and medium-weight silk. However, overall, many participants expressed confidence in assessing drapability through digital means, citing the visual aids as a significant contributor to their assurance.

On the other hand, some participants (n = 3) emphasized the irreplaceable nature of the visual and tactile experience.

… While visuals are informative, they can’t replace the hands-on experience. I often manipulate fabrics, creating pleats or gathers, to understand their drape. Although adding images of pleats might give more insight into a fabric's flexibility, it still can't capture the true essence of the fabric's behavior. (Participant 19)

This highlights the subjective nature of fabric assessment, suggesting that while some individuals lean heavily on visual cues, others value the physical experience, underscoring the importance of catering to diverse evaluation methods.

Overall, it was discerned that there is no direct correlation between evaluation accuracy and psychological certainty. High evaluation accuracy does not necessarily equate to high psychological certainty, and the other way around. While most participants anticipated that high psychological certainty would always lead to greater evaluation accuracy, the results did not support their assumption. The relationship between accuracy and certainty varied depending on the evaluation criteria and the specific characteristics of each material.

The analytical insights from Fig. 2 underscore a complex interplay between evaluative accuracy and psychological certainty across four key fabric attributes: thickness, luster, stretchiness, and drapability. This figure serves as a visual representation of the relationship between digital and physical evaluations and certainty with their P values.

Fig. 2

Comparison of digital vs. physical evaluations and certainty: P values

For 'thickness', Fig. 2 shows a stark contrast between high accuracy and low certainty. Despite the initial challenges participants faced when evaluating thickness digitally, their accuracy remained unexpectedly high. This divergence suggests that the provision of numerical thickness data may have anchored their assessments, allowing for precision despite a lack of confidence.

In assessing 'luster', the graph highlights a discrepancy where high certainty in digital evaluation did not parallel with accuracy. Participants perceived an exaggerated luster on screen, which was not consistent with physical evaluations.

'Stretchiness' presents that confidence in digital evaluation did not equate to accuracy, as indicated by the data points. Participants felt assured in their digital assessments due to the dynamic visual aids, but this certainty did not translate into the physical assessment accuracy, revealing the limitations of digital media in conveying the tactile sensation of fabric stretch.

Lastly, 'drapability' stands out as an attribute where both certainty and accuracy in digital evaluations were relatively high. This suggests that the visual representations provided were sufficient for participants to make accurate assessments of how fabrics would drape, aligning with the physical evaluations.

Conclusions

This study has illuminated the unique challenges faced by fashion designers when evaluating fabrics within digital environments, encompassing technical limitations, perceptual discrepancies, and user experience gaps. While digital fabric presentations offer convenience, they currently fall short of replicating the visual and tactile nuances designers depend on to judge fabric suitability, prompting a reevaluation of their effectiveness.

In our analysis, the effectiveness of digital presentation methods from our prior study (Jang & Ha, 2023) was assessed, particularly in terms of accuracy and certainty. It is promising that, despite challenges in conveying certain attributes like stretchiness, digital methods have succeeded in accurately representing drapability, bolstering designer confidence. This success demonstrates the potential of digital presentations to enhance the assessment of various fabric attributes comprehensively.

However, accuracy alone is not sufficient; fashion designers require a high degree of certainty in their judgments to fully embrace digital methods for professional fabric selection. For example, if there's doubt that the digital representation of a fabric's drape aligns with its physical feel, designers may hesitate to rely on such methods for their final choices. Ensuring precision and fostering user certainty are both critical to establishing a robust digital fashion design system.

The path forward involves interdisciplinary collaboration, drawing on computer science, material science, and sensory psychology, to translate the visual and tactile sensation of fabric handling into the digital realm. Technological innovations, particularly advanced haptic feedback devices, are poised to transform how designers interact with digital fabric simulations, creating experiences similar to direct physical handling. The adoption of such technologies could eventually remove the need for traditional sensory evaluations and propel fashion design into a fully digital domain.

For digital fabric evaluation systems to gain complete acceptance in the fashion industry, they must not only deliver precise outcomes but also cultivate trust. Systems should be intuitive, reducing the learning curve and fitting seamlessly into designers' existing workflows. Precision must be complemented by the perception of reliability. This study highlights the need for future advancements to prioritize both the technical precision of digital presentations and the enhancement of system intuitiveness and reliability. Such strides will empower designers to make confident, informed fabric choices.

In summarizing, this study provides valuable insights into the complexities of fabric perception in the digital versus physical realms, establishing the way for technological progress in fashion design. Continued research is vital for facilitating a seamless adoption of digital methods. As technology progresses to more precisely simulate the textures and feel of fabrics, digital evaluations with firm confidence in their accuracy are expected to become as reliable as traditional hands-on assessments.