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Article

Leveraging 4D Golf Apparel Wear Simulation in Online Shopping: A Promising Approach to Minimizing the Carbon Footprint

by
Doyeon Kong
1,
Yoo-Kyoung Seock
2,
Steve Marschner
3 and
Heeju Terry Park
1,*
1
Department of Human Centered Design, Cornell University, Ithaca, NY 14853, USA
2
Department of Textiles, Merchandising and Interiors, University of Georgia, Athens, GA 30602, USA
3
Department of Computer Science, Cornell University, Ithaca, NY 14853, USA
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(14), 11444; https://doi.org/10.3390/su151411444
Submission received: 30 March 2023 / Revised: 16 July 2023 / Accepted: 21 July 2023 / Published: 24 July 2023
(This article belongs to the Special Issue Sustainable Materials and Management in Fashion Industry)
Browse Figures
Versions Notes

Abstract

:
As fashion e-commerce grows, the online return rates are running higher than ever before. Online customers buy the same product in multiple sizes or colors with the intention of returning what is not necessary as they are unable to have a tactile experience during their purchase. In terms of sustainability, returns have a huge negative impact on the environment, causing waste sent to landfills and carbon emissions. In the United States alone, over 15 million metric tons of carbon dioxide are annually emitted from transporting returned inventory. This study explored an innovative way to help reduce online returns due to fit and sizing issues using four-dimensional (4D) golf apparel wear simulation. The study observed how online customers reacted to an apparel wear simulation where they could see the body–clothing interactions, such as dynamic changes in the drape of a garment and cloth deformations caused by different body movements, with a focus on golf apparel. Female customers (n = 13) with experience playing golf and purchasing golf apparel online participated in randomized experiments where three different e-commerce demo websites embedded with simulations were shown. In-depth interviews were followed to collect qualitative data, and surveying was used to quantitatively assess the perceived usefulness of 4D golf apparel wear simulations. The findings of the study indicated that the wear simulation has the potential to help customers find the correct fit and size when shopping online. By exploring the idea of providing a more accurate representation of how apparel fits and interacts with the body, this study sheds light on the promising approach of leveraging 4D golf apparel wear simulations in online shopping to enhance sustainable fashion and potentially contribute to reducing the carbon footprint by minimizing returns.

1. Introduction

Quick, easy, and free-of-charge return policies that fashion companies offer have lowered the burden of returning online purchases. However, the cost of convenience is substantial in terms of the environmental impacts [1,2,3]. Unlike electronic devices that can be refurbished and resold, reselling even slightly damaged clothes is not easy and produces waste and carbon emissions in the process of destroying unwanted clothes [4]. The amount of clothing and footwear waste produced in the United States in 2018 was around 13 million tons, and about 70 percent was sent to landfills while only 13 percent was recycled [5]. In addition, the return of a single item leads to repeat delivery and generates approximately 180g of CO2 emissions each time [6]. In the United States alone, over 15 million metric tons of carbon dioxide are emitted from transporting returned items, which is equivalent to the amount of waste produced by 5 million people every year [7,8]. Considering the increase in the volume of carbon emissions due to unnecessary purchases and subsequent returns, reducing online returns is crucial, although the significance of managing the environmental impact has been overlooked [9].
Product returns happen across all retail industries, and yet it is more complex for apparel. Clothing sizes are inconsistent among brands, and size inconsistencies exist even within a single brand [10,11]. As online customers are unable to physically touch an apparel item during their purchasing process [12], it is hard to find the correct fit and size when shopping online. Moreover, customers’ perceptions of fit and size can be affected by various factors, such as body proportions and body satisfaction, which are subjectively determined [13,14]. For this reason, the fit is one of the primary reasons for returning apparel items in online shopping [15,16]. The Body Labs’ 2016 retail survey revealed that over $60 billion worth of apparel and footwear were returned globally due to incorrect sizing or fit problems [17]. Online customers often order the same item in multiple sizes or colors with the intention of keeping the one that fits the best and returning the others [18]. Narvar’s 2017 survey reported that 40% of customers had bracketed at least some of their online purchases, especially for apparel and home items, and this increased to 62% in 2020 [19,20]. Given the growth of e-commerce markets, the environmental impact of return shipping and reverse logistics is not trivial.
A pre-sales practice—an activity that occurs before a customer makes a purchase decision—involves gathering information about a product, and this kind of activity to lower fit uncertainty is reflected in the customer’s online shopping behavior [16,21]. Providing precise information about items on retailers’ websites is one of the most common pre-sales practices that online retailers utilize, which is crucial to reducing returns [22,23]. To mitigate product fit uncertainty and assist online customers in buying fit-dependent products, retailers can provide sufficient fit information by using digital technologies. In particular, digital product fitting can potentially help reduce product fit uncertainty in the pre-purchase process [24,25].
To reduce online returns due to fit and size uncertainties, fashion brands and companies have lately adopted new technologies, such as sizing recommendation systems [26,27,28,29]. However, there is a hassle behind the systems that require customers to take a fit quiz and enter their size measurements, which are prone to errors. Virtual fitting rooms and try-on services have also taken the stage in fashion retailing [30,31,32]. Immersive technology is defined as a technology that expands a physical reality to a virtual world and can be one way of displaying clothes [33]. The adoption of such technology enables online customers to try on fashion items virtually. Despite the advantage of image-based virtual try-on, however, there are limitations in that it lacks a sense-perceptual experience [34]. Although it allows customers to see how certain items look on them, it is still hard to judge a good fit or choose the right size using those augmented static images. For this reason, this approach has transitioned to only certain marketplaces, such as the beauty and sunglasses industries [35,36].
Alternatively, garment simulation is another solution for tackling fit and size issues in online shopping. Creating simulations to dress an animated avatar allows virtual fitting from a different angle, representing a wearer’s body size and proportions. The simulation of clothing involves the process of digitizing the physical and mechanical properties of fabric, and it has been intensively studied within the context of computer graphics [37,38]. Thus, limited research has been conducted to assist designers with virtual prototyping in the purview of apparel design, and it is not accessible to customers in the market yet [36,39,40]. Nevertheless, it has great significance since it is capable of showing clothed avatars in different poses and sizes with dynamic cloth deformations, which allows customers to see how clothes fit the digital avatar in detail and helps them find the right size. The availability of interactivity in the online shopping environment can play an even bigger role in sportswear, since the fit, mobility, and dynamic interactions between clothes and human bodies are the primary factors both in customer satisfaction and in the purchase decision process [41]. However, despite its potential to help reduce e-commerce returns due to fit and size problems, no research has explored how apparel wear simulation will be accepted by end users or online customers.
In this study, golf apparel was selected to test if apparel wear simulations would positively impact customers’ fit and size choices for the following reasons:
(1)
Golf is a lateral movement activity, and it requires a wide range of upper body motions, including arm swings, bending, and so on. Thus, human body–clothing interactions are essential in golf apparel, and apparel wear simulations can effectively display the details of an item in the online shopping environment.
(2)
Customers have relatively higher expectations for the styles, comfort, and fit of golf apparel because of the high price tags. Apparel wear simulation can likely be applied to various garment categories to scrutinize how it can effectively be customized to visualize golf apparel items.
(3)
The golf apparel market has shown steady growth and it is expected to reach $1.5 billion by 2027 [42]. Along with a great interest in outdoor activities since the pandemic, the golf apparel market reached all-time highs, and this trend is expected to continue [43,44]. In particular, the women’s golf apparel market appears to be a unique segment within the industry, with notable growth in the golfing population [45].
This study primarily aimed to help online customers find clothes that fit them right without the need to physically touch or wear them and to reduce the hassle of returning items by exploring a more accurate way of representing how apparel fits and interacts with the body. To that end, the study examined how online customers reacted to an apparel wear simulation focusing on golf apparel and assessed its perceived usefulness. It also investigated the potential benefits and implications of the apparel wear simulation and technical points for further development. By doing so, the study sought to explore innovative and future-proof ways to reduce online returns due to poor fit and incorrect sizing, and ultimately to mitigate the environmental impact of return shipping.

2. Emerging Technologies to Reduce Fit- and Size-Related Returns

In an attempt to reduce product returns, fashion companies have stepped up their sustainability efforts using emerging technologies such as artificial intelligence and virtual and augmented reality [28,46,47]. At the basic level, many fashion e-commerce websites offer size guides and product measurements. This information can help online customers understand general measures of an item, although it is hard to expect that customers would know precise size measurements. Some businesses take more technology-oriented actions; they provide quiz-based size recommendation solutions, asking customers to answer a few questions regarding their body measurements and fit preferences. For instance, ASOS offers a size recommendation tool, Fit Assistant, and provides information about what other similar customers have bought while allowing customers to take fit quizzes and enter personal information [29]. Taking one step further, there are also AI-powered size recommendation services. Virtusize [48] is a fit and size service that helps customers find the best size options based on customer shopping history and sizing information with other brands. True Fit is another example, which is an AI-driven size advisor based on customers’ fit preferences and sizing data. True Fit claims to have reduced returns from bracketing by about 50 percent with single-brand retailers [49,50]. In addition, 3DLOOK is a photo-based virtual fit and size recommendation solution, enabling customers to take photos using their smartphones and suggest size options based on the extracted measurement data from the photos [51].
With advancements in technology and structured, high-quality customer data accumulated over time, the use of big data has allowed for more consistent and reliable results in such systems [52]. SizeFlags is a neural network model that can tackle the problem of ordering an item in multiple sizes due to poor fit. It introduced a probabilistic Bayesian model based on annotated large-scale feedback data from customers along with computer vision algorithms [53]. Similarly, image- or video-based size recommender engines have also been studied [54,55,56]. Some size recommendation systems have also been proposed based on customers’ past purchase data, without asking for body measurements. A recent study explored an attention-based model that combats multi-user accounts and cross-category issues to provide more accurate and better size recommendations [39,57]. If more data are accumulated, fashion brands can more precisely understand their customer preferences, and fashion designers will be able to have a data-driven approach to product development, which would be beneficial to all stakeholders. It will be possible for online customers to get more reliable information about the fit and sizing and to choose their sizes without over-ordering items. Still, fit and size are assessed in the subjective realm, and personal preferences may be different depending on each customer. While such systems could propose better fit and size recommendations for online customers, apparel wear simulation would potentially be able to play an increasing role in helping them evaluate the fit and size of an apparel item before purchasing it in a more interactive way by using a clothed avatar. In spite of the huge achievements of data-enabled methods, they have not yet been thoroughly explored within the context of fashion retail. Only a limited number of sources revealed that such technologies have been employed in the industry. Therefore, this research was conducted to observe how online customers react to the 4D golf apparel wear simulation. Further, the study explored the potential for the adoption of this new technology in the market, with the aim of looking into possibilities for reducing returns and achieving sustainable fashion.

3. Method

3.1. Research Design

A mixed-methods research design was applied to this study. First of all, qualitative methods were adopted to observe how online customers react to golf apparel wear simulation and to explore the participant behaviors in depth. A qualitative research approach can be utilized to get detailed information about individuals’ perceptions and insights into a specific phenomenon [58]. In addition, this study included semi-structured interviews to investigate the reaction of online customers to 4D golf apparel wear simulation and to explore its potential adoption in the golf apparel market. On top of this, surveys were added to quantify the data collected during the interviews and to measure the participants’ perception of the usefulness of 4D golf apparel wear simulation.

3.1.1. Semi-Structured Interviews

Semi-structured interviewing is a flexible interview method for small-scale research [59]. In this study, the researcher set up a general structure by preparing pre-set questionnaires, and the detailed set interview format was left to be worked out depending on the progression of questions. The interviews were conducted from May 2022 to July 2022 with women aged between 24 and 55 who had purchased golf apparel during the year before. They were audio-recorded to reduce distractions for both the researcher and the research participants, transcribed verbatim, and analyzed using two transcription services, Otter.ai and CLOVA Note. The order of the interview questions was arranged differently, depending on the conversation flow (Table 1).

3.1.2. Surveys

The interviews were followed by surveys consisting of two components: a pre-survey and a post-survey. The pre-survey was designed to gather the participants’ demographic data, including their age, race, golfing level, and preferences for buying golf apparel online. It contained 16 questions about their age, race, household income, education, online shopping experience, etc. The post-survey consisted of questions about information quality and fit and size satisfaction. The questions were structured using a 5-point Likert scale ranging from −2 (representing “very unsatisfied”) to 2 (representing “very satisfied”) to collectively evaluate the participants’ perceptions of the usefulness of 4D golf apparel wear simulation.

3.2. Stimuli

A preference survey on basic golf polo shirts was used to select a sample garment for the simulations. As no research was conducted on how apparel wear simulation is useful for finding items with the right fit and size in the online shopping setting, this study focused on a narrow target audience. Given the fact that the female population in the golf apparel market has grown and female customers are more sensitive to fit and size compared to their male counterparts, the study was conducted on the female population. For the preference survey, the respondents were asked to choose their color preference among four different options: black, white, green, and red. Thirteen out of 15 respondents preferred the white color, and it appeared that the color choice had to do with the short-sleeve design that can be worn in summer. Based on the survey results, the white polo shirt was chosen as a sample garment, as shown in Figure 1. The sample garment was then modeled using different types of cloth simulation programs.
In this study, three demo websites that provide information about the sample garment were used as stimuli: demo A, demo B, and demo C. Demo A was designed to reproduce a typical fashion e-commerce website. It presented information about the product size, color, price, a size chart, model images, and close-up images (Figure 2). Demo B gave details about the size, color, price, a size chart, model images, and data-driven model-generated simulations (Figure 3). The simulations were outputs created by a pre-trained open-source neural network model that predicts cloth deformations from input body shapes, poses, and garment styles [60]. As the model was trained on a t-shirt subset of the given datasets, it rendered cloth deformations corresponding only to the t-shirt category. However, it featured various body motions that can be customized for use in a specific apparel category. For this reason, it was adopted in order to see if a wide range of body movements would help online customers find the size that fits them best. The t-shirt simulations were embedded in the demo website in front and back views (Figure 4) to enable customers to have clothing that fits correctly on a human body in various poses. The information displayed in demo C contained the size, color, price, a size chart, model images, and fashion design software-generated simulations (Figure 5) (Version: CLO 6.2). For the simulations, the patterns for the sample garment were manually traced on a pattern paper and then they were digitized using Digitizer Pattern Design Software (PDS) 3D (Version: O/21.1). The patterns were imported to a 3D fashion design software (Figure 6) after being digitized. In this study, CLO3D was employed as it is widely used by fashion brands and is representative of virtual garment visualization software in the industry. In addition, a golf swing motion was added to an avatar, imported from Mixmo, a library for full-body character animations, allowing online customers to better understand how golf apparel will look on a human body in a golf-specific pose. The avatars were based on each participant’s body measurements (bust, waist, hip circumference measurements, and height), which were collected in advance. Demo C also presented the simulations in a 360-degree spin format using an image processing tool, Sirv. The simulations were tailored to each participant’s measurements; thus, the participants were able to see avatars reflecting their sizes in the sample garment in three different sizes (S, M, and L). The fashion design software-created simulations demonstrated the t-shirt and the avatar in a golf swing motion well, allowing the participants to interact with the interface.

3.3. Data Collection

Fourteen participants took part in this study. One participant was removed from the sample due to incomplete survey responses. The study sample was thirteen female participants with ages ranging from 24 to 55 years (M = 41.6, SD = 9.8) and self-reporting as Asian (100%). The participants (over 18 years old) were required to have experience playing golf and buying golf apparel online more than twice in the past year to take part in this study and were recruited through snowball sampling and direct recruitment. The participant demographic data are summarized in Table 2. Most of the sample population (61.5%) were in their 40s, with 23.1% in their 20s and 7.7% in their 30s and 50s, respectively. Regarding education, most participants (92.3%) had a bachelor’s degree, followed by 7.7% who had a post-graduate degree. The sample was relatively balanced in the community-type distribution, with 53.8% living in big cities and 46.2% in small campus towns. There was a distribution of various household income ranges with 38.5% in the range of $50,000–$100,000 and $100,000–$200,000 respectively, followed by 15.3% who earned more than 200,000 and 7.7% in the range of $25,000–$50,000.
The participants were asked to complete a brief paper survey designed to understand what factors impact their online golf apparel buying behaviors and to collect demographic information. Semi-structured one-on-one interviews were followed to gather qualitative feedback and responses to open-ended questions. After the interviews, the researcher showed three mockup websites and gave the participants enough time to browse each website to observe their behaviors and interaction patterns. The participants were randomly exposed to one of the three demo interfaces, and then they were assigned one of the remaining two interfaces and the rest in order. The order of demonstrations was controlled and statistically balanced [61]. The participants were asked to choose their sizes based on the given information obtained from the demo websites. Then they tried on a t-shirt of their chosen size and moved around freely to measure the overall fit of the garment and physical comfort. Finally, they were asked to fill out a post-survey to assess how each demonstration was of help in choosing the size that best fit them. They also ranked their preferences for the demo sites in order after their try-on.

3.4. Data Analysis

Thematic analysis was employed to identify and analyze repeated patterns of meaning from the qualitative data, including open-ended survey questions, transcribed interviews, and the researcher’s notes. The analysis included a six-phase approach: (1) reading and re-reading through all qualitative data, (2) adding codes to highlighted statements, (3) organizing codes into themes, (4) reviewing potential themes, (5) naming and defining themes, and (6) producing a report of final themes [62]. In this process, the researcher confirmed unfamiliar terms with the participants to ensure the suitability of the method for data analysis.

4. Results and Discussion

This study throws new light on how online customers react to 4D golf apparel wear simulation in choosing the right fit and size by analyzing the perceived usefulness of three different golf apparel shopping interfaces. The research findings are ultimately expected to contribute to reducing e-commerce returns associated with fit and sizing issues.

4.1. Background Characteristics

The majority of study participants reported being neutral or dissatisfied, and there were no participants who reported being very satisfied with their online golf apparel shopping experience. The satisfaction level was higher among the participants who reported living in a small city (66.7%) than that of those living in big cities (37.5%). For those living in a small campus town, there were few options to buy golf apparel other than online shopping since the communities did not have many retail stores. Therefore, the participants living in a small town were inclined to rely on online shopping. In addition, they appeared to be reluctant to return items and instead used various shopping strategies to avoid online returns. They even gave up their purchasing if there was not enough product information in certain circumstances. More importantly, a few participants responded that they had bracketed their online purchases because of fit and sizing issues. In this regard, a better online shopping interface for those people seems to be an alternative to current online shopping platforms.
The pre-survey results also indicated that more than 70% of the participants self-identified as the early majority among the adopter categories defined by Everett Rogers [63]. They are a sizable segment of the population consisting of people who embrace innovation right after the innovators who are the first group to adapt to change. In this study, this group was primarily neutral or unsatisfied with their online golf apparel shopping experience. They are not the fastest group to react to innovation and need a little time to pass before adopting new ideas and technology. However, another fact that cannot be overlooked is that they make up a significant proportion of the entire population and are ahead of the average population. In this sense, it is expected that 4D golf apparel wear simulation technology may be readily accepted by the market if early users show positive reactions toward the golf apparel wear simulation and start adopting it. The participants’ background information is summarized in Table 3.

4.2. Participants’ Perceived Usefulness of Golf Apparel Wear Simulation

In total, ten participants (76.9%) preferred the interfaces where simulations were available and only three (23.1%) chose the interface with static images as their favorite option. The most favored choice was demo C (53.8%), followed by demo A (23.1%) and demo B (23.1%). The participants’ preference rankings on the demo websites are outlined in the order in which they appear as letters of the alphabet (A, B, and C) in Table 4. Those who favored demo B noted that the simulation of a clothed avatar in various poses made up for the lack of fit and size information by displaying the interaction between the sample garment and the avatar’s body, in comparison with demo A. They also preferred various motions the interface provided and found it helpful to look at the overall fit and cloth deformations of the sample garment through the simulation. However, a few participants reported poor simulation visualization issues with demo B, including those who preferred demo A. Despite the benefits of garment simulation, demo B was mentioned the most for being the least favorable interface due to the low-resolution images used for the simulation. Demo C was well-received in terms of interactivity, which played a prominent part in this interface, since the simulations reflected each participant’s body measurements, including their bust, waist, hip circumferences, and height. Therefore, the participants were able to view their respective t-shirt simulations, commenting that they could get a better sense of how each size would look on them. Additionally, the clothed avatar in a golf swing pose was helpful in choosing the size, since it showed the shape of the draped garment and the flexibility of the fabrics, which changed depending on the body movements.
Along with the qualitative feedback, 5-point Likert scale data were analyzed using descriptive statistics to describe the mean and standard deviation, as shown in Table 5. All interfaces were tested as stimuli, and the participants’ t-shirt try-on was referred to as demo D for the sake of convenience. Overall, the perceived usefulness of demo C was the closest equivalent to demo D, the actual try-on. The findings also revealed that demo B had relatively lower scores overall except for the length of the shirt. Given the high standard variations, however, the satisfaction levels with demo B were more spread out compared to the others, implying that the responses varied with no middle ground. This was also supported by the fact that demo B had at least one lowest mark across all body areas despite positive feedback from multiple participants.
In this study, two different golf apparel wear simulation approaches were examined to identify the pros and cons of each simulation: the data-driven model-generated simulation and the fashion design software-created simulation. The first method featured numerous body motions trained on motion capture datasets, such as walking and bending. Therefore, it is possible for fashion retailers to build on this data-driven system and to create countless body motions and clothed avatars in various body sizes tailored to their business needs. A short video can be an effective way of displaying clothes online [64], although a series of procedures for producing such videos could further increase the carbon footprint [65]. The data-driven approach may add to the initial development cost but it has the benefits of being easily deployable and cost-effective in the longer term. The second simulation was created using a digital fashion design program [66] adopted by prominent fashion brands such as Adidas, Boss, Patagonia, and Mango. If the apparel wear simulation is developed with other fashion design software programs, then it could be further customized with detailed avatar and cloth features. By doing this, it is expected to help consumers relate more to the fit of the avatars based on their real sizes, not to a fashion model’s body size, which is highly likely to be different from those of the general public.

4.3. Strengths and Areas of Improvement for Better Product Presentations

To investigate the strengths of golf apparel wear simulation and identify areas for further improvement, the researcher found four themes by exploring patterns of meaning from qualitative collected data. The themes were named as ‘interactivity’, ‘motions’, ‘realism’, and ‘fabric’.

4.3.1. Interactivity

The participants provided positive feedback on the interactive interface where they could view the clothed avatars based on their body size. Some of the participants stated:
“I usually go with XS but picked S because the avatar in S looked great (…) I really like the simulation based on my size and want to buy this shirt.”
(Participant #13)
“I liked the idea that the avatar had my body size so that I could imagine how this shirt will look on me. But the avatar seemed to make me look fat.”
(Participant #12)
Unlike other consumer goods, clothing is considered a high-involvement product [67], which is highly affected by individual customer imagery. The findings of the study also suggest that active consumer involvement is needed in the stage of selecting and evaluating items before purchases are made. Providing an interactive shopping interface where online customers evaluate the garment fit and size based on their inputs is expected to overcome the weak points of the current shopping platforms that contain fit and size uncertainties. Because the ideal body sizes of commercial fashion models are typically very thin and slim, they are significantly far from reality; clothed avatars based on customer body dimensions can help customers assess how the garment fits the avatar, so they can select their size more objectively. Moreover, the interactive shopping interface where customers add user inputs could provide more reliable simulations and a hedonic experience [22], which may contribute to an increase in site traffic for online retailers [68]. This finding is consistent with previous studies that identified a positive correlation between interactive product presentations and the frequency of visits to web pages [69,70].

4.3.2. Motions

Understanding the dynamics behind different body motions was a crucial factor that affected the participants’ size choices. As golf involves various upper body movements, the participants expected more room in the sleeves and the shirt hem for better mobility. The results of the study indicated that the participants found the simulation interfaces useful in judging the overall fit and size of the garment as they displayed how the sample garment changed according to body movements in detail. More specifically, the golf-stance-specific motion was more favored than various random body movements created by the data-driven simulation model. One of the participants mentioned:
“I love the idea of showing various motions so that consumers can see how the shirt will fit on the model. More golf-related movements would be more helpful, such as an avatar’s appearance from the back in a golf swing or an avatar squatting on the ground.”
(Participant #4)
Therefore, it should be noted that the wear simulation needs to be presented with one or two poses that are directly related to the end use of the apparel items to describe the mobility and the garment fit by body area well. There were also a few comments that the speed of the simulations created by this model was fast. The participants said:
“The movements look distracting, and even some motions are not related to golf stances.”
(Participant #2)
“The motions were not helpful, rather they were distracting.”
(Participant #13)
The speed of the 4D simulation in golf motions needs to be considered with caution so as not to distract customers from looking at the garment interacting with the body in motion to evaluate the product fit and size in online shopping. To sum up, a new shopping interface adopting golf apparel wear simulation should consider one or two golf-stance-specific motions, including a golf swing and a putting stroke, from the perspective of motions.

4.3.3. Realism

The realistic simulation was another key theme discovered in the participants’ collective experience, which was associated with fabric colors and textures. The two different simulations employed in this study tended to smooth out the surface of the fabric immoderately and presented the fabric features and structures in an idealized way. The realistic simulations were heavily related to the participants’ dissatisfaction. The participants who preferred static images over simulations implied that the simulations lacked authenticity. Additionally, they remarked that the simulations may be helpful if they provided a more realistic portrayal of the fabric properties, such as the texture and tensile strength, for better visualization of the virtual garment. The participants said:
“The surface of the shirt was not well presented in the simulations.”
(Participant #1)
“I was more comfortable understanding this product using the product photos. The simulation wasn’t life-like, and it was hard to imagine the fabric of the shirt.”
(Participant #2)
“How much the fabric stretches is important in golf apparel, so I usually get this information using close-up images or customer reviews. The interface provided fabric information in text form, but I didn’t think the fabric of the simulation was the same as the shirt.”
(Participant #12)
This finding was aligned with research that shows that there is a gap between the digital simulation of fabrics and garments using fashion design 3D software [71]. The 3D garment simulation was prone to creating smoother surfaces and unrealistic renderings, often making it hard to identify defects in the product. More importantly, non-realistic garment properties, including the color, texture, and drape, can make online customers find the garment simulation unreliable. Therefore, the physical properties of the garment fabrics need to be investigated carefully for more realistic visualization, and online retailers need to consider multiple factors that can affect how customers perceive the quality of a garment simulation.

4.3.4. Fabric

The fabric was another important concern regarding the simulations, as it affected how participants evaluated the quality and fit of the garment. A few participants noted that the absence of fabric details hindered their ability to determine whether the shirt would be a good fit for them. For instance:
“Fabric thickness is important for me to judge the overall fit of the golf t-shirt because I don’t want the shirt to highlight my body shape or where I gain weight. If the fabric weight is too light, I am trying to avoid it. I couldn’t get enough fabric information just from the simulation, so I preferred demo A the most because I could get the real fabric info.”
(Participant #2)
“For me, the quality of clothes depends on materials, especially golf apparel. I could imagine the fabric from the photos, but I couldn’t tell the real texture of the shirt from the simulation (demo B).”
(Participant #12)
A fabric or cloth can be made differently depending on the fabric widths and weave structures [46]. Therefore, it is very unlikely that customers will be able to imagine the garment fabric from the text information, even though e-commerce portals provide detailed information about apparel items. The existing shopping platforms provide fiber content details but barely describe the fabric thickness or other relevant information that is directly related to the fabric structure, weight, texture, breathability, or moisture management properties. For this reason, the research participants appeared to zoom in on the product images to closely look at the fabric of the sample garment during the interviews. To render a more realistic simulation, a large database of information on the physical properties of fabrics available in the sportswear market needs to be developed. If a larger database could be imported to fabric libraries in a 4D garment wear simulation system in the future, it could also improve the quality of the fabric simulation in terms of the texture, drape, and color.

5. Conclusions

The fashion industry is the second most polluting industry in the world, making up about 10% of carbon dioxide emissions globally [72,73,74]. Although sourcing sustainable and recycled materials is an essential part of pursuing greener fashion, it requires a long-term commitment from multiple entities [75]. Reducing additional fuel consumption and emissions stemming from online returns could be another way to enhance the sustainability of fashion, and yet the significance of managing its environmental impact has been overlooked [9]. While the study did not directly investigate the impact of 4D golf apparel wear simulation on reducing the carbon footprint, it did discuss the implications and potential benefits of integrating it into online shopping, which can contribute to a more sustainable e-commerce model, and as a result potentially lead to a reduction in the carbon footprint.
The study shed light on the promising approach of leveraging 4D golf apparel wear simulation to mitigate fit- and size-related uncertainties in online shopping and decrease the environmental impact of returns in the fashion industry. The main contributions of this paper are two-fold. First, the study investigated the potential for the adoption of golf apparel wear simulation to help online customers find items with the right fit and size with more confidence. Because golf apparel wear simulation has not been neither thoroughly studied in academia nor applied to industry, the results of this study may serve as preliminary data to understand how online customers perceive apparel wear simulation in choosing their sizes. Additionally, the study provided a blueprint for online retail businesses to optimize the current presentation of sportswear products and ultimately for improved returns management. Given the characteristics of the two different methods of simulation, retailers may take a holistic approach to develop a new online shopping experience that taps into apparel wear simulation.

6. Limitations and Future Research

The study has several limitations. The sample population was limited to a specific gender and ethnic group that participated in this study. This may limit the generalizability of the findings to a wider population. Using a mixed-method approach, however, this study lays the groundwork for future research with diverse populations. Given that male customers account for a large portion of the market, future research with male customers is expected to provide additional meaningful information. Additionally, customers have different body shapes and body ratios, and their perceptions of body size among ethnic groups can be different [76,77]. Therefore, future studies with a wider population, such as customers with different ethnicities and characteristics of online shopping, will be also useful to improve the validity of apparel wear simulation. In addition, future research can delve deeper into quantifying the actual environmental impact and carbon footprint reductions resulting from the adoption of such simulations in online shopping.
Further studies could explore the application of 4D golf apparel wear simulation to other sportswear where the body–clothing interaction is considered essential. For instance, swimwear and compression garments are supposed to feel snug and yet fit tightly, so accurate and proper sizing is very important. In this regard, further research on wear simulation for other sportswear types may add a new perspective on wear simulation and its potential applications.

Author Contributions

D.K. conducted the research, analyzed the data and results, and wrote the manuscript as the first author. H.T.P. provided comprehensive guidance on the research direction and manuscript writing. Y.-K.S. and S.M. gave overall guidance on the research design and support in refining the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Ethical approval for this study was obtained from Cornell University’s Institutional Review Board (IRB0143333).

Informed Consent Statement

All participants gave their informed consent for inclusion before they participated in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions. This manuscript was developed based on the first author’s master’s thesis.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Bertram, R.F.; Chi, T. A study of companies’ business responses to fashion e-commerce’s environmental impact. Int. J. Fash. Des. Technol. Educ. 2018, 11, 254–264. [Google Scholar] [CrossRef] [Green Version]
  2. Choi, T.M.; Guo, S. Responsive supply in fashion mass customisation systems with consumer returns. Int. J. Prod. Res. 2018, 56, 3409–3422. [Google Scholar] [CrossRef]
  3. Fashion on Climate—McKinsey & Company. Available online: https://www.mckinsey.com/~/media/mckinsey/industries/retail/our%20insights/fashion%20on%20climate/fashion-on-climate-full-report.pdf (accessed on 19 January 2023).
  4. Schiffer, J. The Unsustainable Cost of Free Returns. Vogue Business. Available online: https://www.voguebusiness.com/consumers/returns-rising-costs-retail-environmental (accessed on 30 January 2023).
  5. US EPA. Plastics: Material-Specific Data. Available online: https://www.epa.gov/facts-and-figures-about-materials-waste-and-recycling/plastics-material-specific-data (accessed on 2 January 2023).
  6. Khusainova, G. There is No Such Thing as a Free Return. Forbes. Available online: https://www.forbes.com/sites/gulnazkhusainova/2019/03/28/there-is-no-such-thing-as-a-free-return/?sh=8b42cbe7135d (accessed on 2 January 2023).
  7. Optoro. Optoro Impact Report; Optoro: Washington, DC, USA, 2018; Available online: https://www.optoro.com/2018-impact-report/ (accessed on 19 June 2022).
  8. Velazquez, R.; Chankov, S.M. Environmental impact of last mile deliveries and returns in fashion E-commerce: A cross-case analysis of six retailers. In Proceedings of the 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Macao, China, 15–18 December 2019; pp. 1099–1103. [Google Scholar]
  9. Bozzi, C.; Neves, M.; Mont’Alvão, C. Fashion E-Tail and the Impact of Returns: Mapping Processes and the Consumer Journey towards More Sustainable Practices. Sustainability 2022, 14, 5328. [Google Scholar] [CrossRef]
  10. Workman, J.E. Body measurement specifications for fit models as a factor in clothing size variation. Cloth. Text. Res. J. 1991, 10, 31–36. [Google Scholar] [CrossRef]
  11. Kinley, T.R. Fit and shopping preferences by clothing benefits sought. J. Fash. Mark. Manag. Int. J. 2010, 14, 397–411. [Google Scholar] [CrossRef]
  12. Citrin, A.V.; Stem, D.E., Jr.; Spangenberg, E.R.; Clark, M.J. Consumer need for tactile input: An internet retailing challenge. J. Bus. Res. 2003, 56, 915–922. [Google Scholar] [CrossRef]
  13. Kim, H.; Damhorst, M.L. The relationship of body-related self-discrepancy to body dissatisfaction, apparel involvement, concerns with fit and size of garments, and purchase intentions in online apparel shopping. Cloth. Text. Res. J. 2010, 28, 239–254. [Google Scholar] [CrossRef]
  14. Moon, H.; Lee, H.H. Consumers’ preference fit and ability to express preferences in the use of online mass customization. J. Res. Interact. Mark. 2014, 8, 124–143. [Google Scholar] [CrossRef]
  15. Su, X. Consumer returns policies and supply chain performance. Manuf. Serv. Oper. Manag. 2009, 11, 595–612. [Google Scholar] [CrossRef] [Green Version]
  16. Hong, Y.; Pavlou, P.A. Product fit uncertainty in online markets: Nature, effects, and antecedents. Inf. Syst. Res. 2014, 25, 328–344. [Google Scholar] [CrossRef]
  17. Cilley, J. Apparel & Footwear Retail Survey Report; Body Labs Inc.: New York, NY, USA, 2016. [Google Scholar]
  18. Nageswaran, L.; Cho, S.H.; Scheller-Wolf, A. Consumer return policies in omnichannel operations. Manag. Sci. 2020, 66, 5558–5575. [Google Scholar] [CrossRef]
  19. Narvar. Making Returns a Competitive Advantage. Available online: https://see.narvar.com/rs/249-TEC-877/images/Narvar_Consumer_Survey_Returns_June2017.pdf (accessed on 2 January 2023).
  20. Narvar. State of Returns: New Expectations: What Today’s Shoppers Expect. Available online: https://corp.narvar.com/state-of-returns-new-expectations (accessed on 2 January 2023).
  21. Gustafsson, E.; Jonsson, P.; Holmström, J. Reducing retail supply chain costs of product returns using digital product fitting. Int. J. Phys. Distrib. Logist. Manag. 2021, 51, 877–896. [Google Scholar] [CrossRef]
  22. Kim, H.; Niehm, L.S. The impact of website quality on information quality, value, and loyalty intentions in apparel retailing. J. Interact. Mark. 2009, 23, 221–233. [Google Scholar] [CrossRef]
  23. De Leeuw, S.; Minguela-Rata, B.; Sabet, E.; Boter, J.; Sigurðardóttir, R. Trade-offs in managing commercial consumer returns for online apparel retail. Int. J. Oper. Prod. Manag. 2016, 36, 710–731. [Google Scholar] [CrossRef]
  24. Gustafsson, E.; Jonsson, P.; Holmström, J. Digital product fitting in retail supply chains: Maturity levels and potential outcomes. Supply Chain Manag. Int. J. 2019, 24, 574–589. [Google Scholar] [CrossRef]
  25. Matt, C.; Hess, T. Product fit uncertainty and its effects on vendor choice: An experimental study. Electron. Mark. 2016, 26, 83–93. [Google Scholar] [CrossRef]
  26. Cheng, W.H.; Song, S.; Chen, C.Y.; Hidayati, S.C.; Liu, J. Fashion meets computer vision: A survey. ACM Comput. Surv. (CSUR) 2021, 54, 1–41. [Google Scholar] [CrossRef]
  27. Peng, F.; Al-Sayegh, M. Personalised size recommendation for online fashion. In Proceedings of the 6th International Conference on Mass Customization and Personalization in Central Europe, Novi Sad, Serbia, 23–26 September 2014. [Google Scholar]
  28. Magnenat-Thalmann, N.; Volino, P. From early draping to haute couture models: 20 years of research. Vis. Comput. 2005, 21, 506–519. [Google Scholar] [CrossRef] [Green Version]
  29. ASOS Customer Care. “What Is Fit Assistant?” ASOS Fit Assistant. Available online: https://www.asos.com/us/customer-care/product-stock/what-is-fit-assistant (accessed on 2 January 2023).
  30. Li, R.; Zou, K.; Xu, X.; Li, Y.; Li, Z. Research of interactive 3D virtual fitting room on web environment. In Proceedings of the 2011 Fourth International Symposium on Computational Intelligence and Design, Hangzhou, China, 28–30 October 2011; Volume 1, pp. 32–35. [Google Scholar]
  31. Pachoulakis, I.; Kapetanakis, K. Augmented reality platforms for virtual fitting rooms. Int. J. Multimed. Its Appl. 2012, 4, 35. [Google Scholar] [CrossRef]
  32. Pereira, F.; Silva, C.; Alves, M. Virtual fitting room augmented reality techniques for e-commerce. In Proceedings of the ENTERprise Information Systems: International Conference, CENTERIS 2011, Vilamoura, Portugal, 5–7 October 2011; Proceedings, Part II. Springer: Berlin/Heidelberg, Germany, 2011; pp. 62–71. [Google Scholar]
  33. Suh, A.; Prophet, J. The state of immersive technology research: A literature analysis. Comput. Hum. Behav. 2018, 86, 77–90. [Google Scholar] [CrossRef]
  34. de França, A.C.P.; Soares, M.M. Review of virtual reality technology: An ergonomic approach and current challenges. In Proceedings of the International Conference on Applied Human Factors and Ergonomics, Los Angeles, CA, USA, 17–21 July 2017; Springer: Berlin/Heidelberg, Germany, 2017; pp. 52–61. [Google Scholar]
  35. McAuley, J.; Targett, C.; Shi, Q.; Van Den Hengel, A. Image-based recommendations on styles and substitutes. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, Santiago, Chile, 9–13 August 2015; ACM: New York, NY, USA, 2015; pp. 43–52. [Google Scholar]
  36. Wang, Y.; Ko, E.; Wang, H. Augmented reality (AR) app use in the beauty product industry and consumer purchase intention. Asia Pac. J. Mark. Logist. 2021, 34, 110–131. [Google Scholar] [CrossRef]
  37. Li, J.; Daviet, G.; Narain, R.; Bertails-Descoubes, F.; Overby, M.; Brown, G.E.; Boissieux, L. An implicit frictional contact solver for adaptive cloth simulation. ACM Trans. Graph. (TOG) 2018, 37, 1–15. [Google Scholar] [CrossRef] [Green Version]
  38. Narain, R.; Samii, A.; O’brien, J.F. Adaptive anisotropic remeshing for cloth simulation. ACM Trans. Graph. (TOG) 2012, 31, 1–10. [Google Scholar] [CrossRef] [Green Version]
  39. Mohammed Abdulla, G.; Singh, S.; Borar, S. Shop your right size: A system for recommending sizes for fashion products. In Proceedings of the 2019 World Wide Web Conference, San Francisco, CA, USA, 13–17 May 2019; pp. 327–334. [Google Scholar]
  40. Spahiu, T.; Shehi, E.; Piperi, E. Advanced CAD/CAM systems for garment design and simulation. In Proceedings of the 6th International Conference of Textile, Istanbul, Turkey, 15–17 October 2014; pp. 1–6. [Google Scholar]
  41. Bae, S.; Miller, J. Consumer Decision-Making Styles for Sport Apparel: Gender Comparisons between College Consumers. ICHPER-SD J. Res. 2009, 4, 40–45. [Google Scholar]
  42. Golf Clothing Market Size & Competition Analysis to 2027. Available online: https://www.kbvresearch.com/golf-clothing-market (accessed on 14 January 2022).
  43. Han, K.H. Effects of Golf Wear Consumers’ Post-COVID-19 Changes of Clothing Benefits Sought on Selection Criteria and Post-Purchase Satisfaction. Int. J. Costume Fash. 2021, 21, 31–48. [Google Scholar] [CrossRef]
  44. Golf Industry Facts. National Golf Foundation—Golf Industry Facts. Available online: https://www.ngf.org/golf-industry-research (accessed on 8 July 2022).
  45. Stachura, M. The Numbers are Official: Golf’s Surge in Popularity in 2020 Was Even Better than Predicted. Golf Digest. Available online: https://www.golfdigest.com/story/national-golf-foundation-reports-numbers-for-2020-were-record-se (accessed on 26 July 2022).
  46. Gokarneshan, N.; Alagirusamy, R. Weaving of 3D fabrics: A critical appreciation of the developments. Text. Prog. 2009, 41, 1–58. [Google Scholar] [CrossRef]
  47. Luce, L. Artificial Intelligence for Fashion How AI is Revolutionizing the Fashion Industry; Apress: New York, NY, USA, 2019. [Google Scholar]
  48. Virtusize. Available online: https://www.virtusize.com/virtusize (accessed on 29 January 2023).
  49. Pookulangara, S.; Parr, J.; Kinley, T.; Josiam, B.M. Online sizing: Examining True Fit® technology using adapted TAM model. Int. J. Fash. Des. Technol. Educ. 2021, 14, 348–357. [Google Scholar] [CrossRef]
  50. Salerno-Garthwaite, A. Bracketing: Fashion’s Hidden Returns Problem. Vogue Business. Available online: https://www.voguebusiness.com/consumers/bracketing-fashions-hidden-returns-problem (accessed on 29 January 2023).
  51. Virtual Clothing Try On: The Future Of Fashion? Available online: https://3dlook.me/content-hub/virtual-clothing-try-on (accessed on 29 January 2023).
  52. Buhl, H.U.; Röglinger, M.; Moser, F.; Heidemann, J. Big data: A fashionable topic with (out) sustainable relevance for research and practice? Bus. Inf. Syst. Eng. 2013, 5, 65–69. [Google Scholar] [CrossRef] [Green Version]
  53. Nestler, A.; Karessli, N.; Hajjar, K.; Weffer, R.; Shirvany, R. Sizeflags: Reducing size and fit related returns in fashion e-commerce. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, New York, NY, USA, 14–18 August 2021; pp. 3432–3440. [Google Scholar]
  54. Kim, D.; Ryu, W.; Lee, S.; Lim, B.; Lee, S. A Unity3D-based mobile fashion coordination system. Int. J. Adv. Media Commun. 2016, 6, 86–92. [Google Scholar] [CrossRef]
  55. Sheikh, A.S.; Guigourès, R.; Koriagin, E.; Ho, Y.K.; Shirvany, R.; Vollgraf, R.; Bergmann, U. A deep learning system for predicting size and fit in fashion e-commerce. In Proceedings of the 13th ACM Conference on Recommender Systems, New York, NY, USA, 16–20 September 2019; pp. 110–118. [Google Scholar]
  56. Hsiao, W.L.; Grauman, K. ViBE: Dressing for diverse body shapes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 13–19 June 2020; pp. 11059–11069. [Google Scholar]
  57. Hajjar, K.; Lasserre, J.; Zhao, A.; Shirvany, R. Attention gets you the right size and fit in fashion. In Recommender Systems in Fashion and Retail; Springer International Publishing: Berlin/Heidelberg, Germany, 2021; pp. 77–98. [Google Scholar]
  58. Creswell, J.W. A Concise Introduction to Mixed Methods Research; SAGE Publications: New York, NY, USA, 2014. [Google Scholar]
  59. Drever, E. Using Semi-Structured Interviews in Small-Scale Research. In A Teacher’s Guide; Scottish Council for Research in Education: Edinburgh, UK, 1995. [Google Scholar]
  60. Patel, C.; Liao, Z.; Pons-Moll, G. Tailornet: Predicting clothing in 3d as a function of human pose, shape and garment style. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 13–19 June 2020; pp. 7365–7375. [Google Scholar]
  61. Suresh, K.P. An overview of randomization techniques: An unbiased assessment of outcome in clinical research. J. Hum. Reprod. Sci. 2011, 4, 8. [Google Scholar] [CrossRef]
  62. Clarke, V.; Braun, V.; Hayfield, N. Thematic analysis. Qual. Psychol. A Pract. Guide Res. Methods 2015, 3, 222–248. [Google Scholar]
  63. Rogers, E.M. Diffusion of Innovations: Modifications of a Model for Telecommunications. Die Diffusion von Innovationen in der Telekommunikation; Springer: Berlin/Heidelberg, Germany, 1995; pp. 25–38. [Google Scholar]
  64. Ma, R.; Shao, B.; Chen, J.; Dai, D. The impacts of online clothes short video display on consumers’ perceived quality. Information 2020, 11, 87. [Google Scholar] [CrossRef] [Green Version]
  65. Vladimirov, I.; Nikolova, D.; Terneva, Z. Overview of Methods for 3D Reconstruction of Human Models with Applications in Fashion E-Commerce. In Proceedings of the 2021 56th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST), Sozopol, Bulgaria, 16–18 June 2021; pp. 19–22. [Google Scholar]
  66. Wang, Y.X.; Liu, Z.D. Virtual clothing display platform based on CLO3D and evaluation of fit. J. Fiber Bioeng. Inform. 2020, 13, 37–49. [Google Scholar]
  67. Solomon, M.R. The missing link: Surrogate consumers in the marketing chain. J. Mark. 1986, 50, 208–218. [Google Scholar] [CrossRef]
  68. Dholakia, R.R.; Zhao, M.; Dholakia, N.; Fortin, D.R. Interactivity and Revisits to Websites: A Theoretical Framework; Research Institute for Telecommunications and Information Marketing: Kingston, RI, USA, 2002. [Google Scholar]
  69. Fiore, A.M.; Kim, J.; Lee, H.H. Effect of image interactivity technology on consumer responses toward the online retailer. J. Interact. Mark. 2005, 19, 38–53. [Google Scholar] [CrossRef]
  70. Hood, K.M.; Shanahan, K.J.; Hopkins, C.D.; Lindsey, K.K. The influence of interactivity on visit and purchase frequency: The moderating role of website informational features. J. Internet Commer. 2015, 14, 294–315. [Google Scholar] [CrossRef]
  71. Lee, E.; Park, H. 3D Virtual fit simulation technology: Strengths and areas of improvement for increased industry adoption. International Journal of Fashion Design. Technol. Educ. 2017, 10, 59–70. [Google Scholar]
  72. Dottle, R.; Gu, J. The Real Environmental Impact of the Fashion Industry. Available online: https://www.bloomberg.com/graphics/2022-fashion-industry-environmental-impact (accessed on 9 December 2022).
  73. Environmental Impact of the Global Apparel and footwear Industries Study. Full Report and Methodological Considerations. Quantis 2018. Available online: https://quantis.com/wp-content/uploads/2018/03/measuringfashion_globalimpactstudy_full-report_quantis_cwf_2018a.pdf (accessed on 29 January 2023).
  74. Kant, R. Textile dyeing industry an environmental hazard. Nat. Sci. 2011, 4, 17027. [Google Scholar] [CrossRef] [Green Version]
  75. Reiley, K.; DeLong, M. A consumer vision for sustainable fashion practice. Fash. Pract. 2011, 3, 63–83. [Google Scholar] [CrossRef]
  76. Cachelin, F.M.; Rebeck, R.M.; Chung, G.H.; Pelayo, E. Does ethnicity influence body-size preference? A comparison of body image and body size. Obes. Res. 2002, 10, 158–166. [Google Scholar] [CrossRef]
  77. Makhanya, B.P.; de Klerk, H.M.; Adamski, K.; Mastamet-Mason, A. Ethnicity, body shape differences and female consumers’ apparel fit problems. Int. J. Consum. Stud. 2014, 38, 183–191. [Google Scholar] [CrossRef] [Green Version]
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Figure 1. The sample garment fitted to a dress form from (a) front, (b) back, and (c) side views.
Figure 1. The sample garment fitted to a dress form from (a) front, (b) back, and (c) side views.
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Figure 2. Demo A with details of static model images.
Figure 2. Demo A with details of static model images.
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Figure 3. Demo B with details of data–driven model–generated simulations.
Figure 3. Demo B with details of data–driven model–generated simulations.
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Figure 4. Front and back views of data–driven model–generated simulations.
Figure 4. Front and back views of data–driven model–generated simulations.
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Figure 5. Demo C with details of fashion design software–created simulations.
Figure 5. Demo C with details of fashion design software–created simulations.
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Figure 6. Golf polo shirt simulation processes, including (a) pattern creation, (b) pattern digitization, (c) pattern importing, (d) pattern editing, and (e) rendering using Clo3D.
Figure 6. Golf polo shirt simulation processes, including (a) pattern creation, (b) pattern digitization, (c) pattern importing, (d) pattern editing, and (e) rendering using Clo3D.
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Table
Table 1. Overview of semi-structured interview design and items.
Table 1. Overview of semi-structured interview design and items.
ThemeItems
Online shoppingBrand loyalty; quality; price; fit and size; product description; return
GolfGolf experience and skill level
Internet and new technologyNew technology (virtual try-on) experience; familiarity with digital avatars
4D golf apparel wear simulationExpected benefits; applications in fashion; alternatives
Open-ended questionOnline shopping experience
Table
Table 2. Research participants’ demographic data.
Table 2. Research participants’ demographic data.
N%
Age group20s323.1
30s17.7
40s861.5
50s17.7
EthnicityAsian/Pacific Island13100
EducationBachelor’s degree1292.3
Post-graduate degree17.7
Community typeSmall city/town646.2
Big city753.8
Household income$25,000–$50,00017.7
$50,000–$100,000538.5
$100,000–$200,000538.5
More than $200,000215.3
Total 13100
Table
Table 3. Participants’ background information.
Table 3. Participants’ background information.
ParticipantAge GroupGolf Practice FrequencyPrimary
Consideration		Shopping SatisfactionAdopter Type
#140s1–3 rounds per weekSize/fitSatisfiedLate majority
#240sOccasionallySize/fitNeutralEarly majority
#340s1–3 rounds per week Design/styleUnsatisfiedLaggards
#440s1–3 rounds per weekBrand name/imageNeutralEarly majority
#520s1–4 rounds per monthQualityNeutralEarly majority
#640s1–3 rounds per weekPriceSatisfiedEarly majority
#740s1–3 rounds per weekQualitySatisfiedLate majority
#840sA few times a yearDesign/styleSatisfiedEarly majority
#930sRarelyBrand name/imageSatisfiedEarly majority
#1040s1–3 rounds per weekDesign/styleSatisfiedEarly majority
#1120s1–4 rounds per monthDesign/styleNeutralEarly majority
#1220sOccasionallyQualityUnsatisfiedEarly majority
#1350s1–3 rounds per week Size/fitNeutralEarly adopters
Table
Table 4. Participants’ preference rankings related to demo websites.
Table 4. Participants’ preference rankings related to demo websites.
Participant1st2nd3rd
#1ACB
#2ACB
#3ACB
#4BCA
#5BCA
#6BAC
#7CBA
#8CBA
#9CAB
#10CAB
#11CAB
#12CAB
#13CAB
Table
Table 5. Overall participants’ perceived usefulness of interfaces in the selection of the right size.
Table 5. Overall participants’ perceived usefulness of interfaces in the selection of the right size.
Demo ADemo BDemo CDemo D *
Mean (S.D.)Mean (S.D.)Mean (S.D.)Mean (S.D.)
Collar0.57 (1.02)0.29 (1.14)0.57 (1.02)1.36 (0.63)
Shoulder0.43 (0.85)0.36 (1.08)0.57 (1.09)1.43 (0.65)
Armhole0.64 (0.93)0.14 (1.17)0.79 (0.80)1.36 (0.84)
Sleeve0.71 (0.91)0.71 (1.27)1.00 (0.78)1.57 (0.51)
Length0.50 (1.16)0.29 (1.44)0.86 (1.17)1.00 (1.18)
Fit0.43 (0.85)0.07 (1.21)0.57 (1.02)1.36 (0.63)
Size0.57 (0.76)0.14 (1.29)0.57 (0.94)1.36 (0.63)
* Participants’ t-shirt try-on was referred to as demo D for the sake of convenience. The response format was a 5-point Likert scale ranging from −2 (very dissatisfied) to 2 (very satisfied). The highest score(s) by analysis item was highlighted across the demo interfaces.
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Kong, D.; Seock, Y.-K.; Marschner, S.; Park, H.T. Leveraging 4D Golf Apparel Wear Simulation in Online Shopping: A Promising Approach to Minimizing the Carbon Footprint. Sustainability 2023, 15, 11444. https://doi.org/10.3390/su151411444

AMA Style

Kong D, Seock Y-K, Marschner S, Park HT. Leveraging 4D Golf Apparel Wear Simulation in Online Shopping: A Promising Approach to Minimizing the Carbon Footprint. Sustainability. 2023; 15(14):11444. https://doi.org/10.3390/su151411444

Chicago/Turabian Style

Kong, Doyeon, Yoo-Kyoung Seock, Steve Marschner, and Heeju Terry Park. 2023. "Leveraging 4D Golf Apparel Wear Simulation in Online Shopping: A Promising Approach to Minimizing the Carbon Footprint" Sustainability 15, no. 14: 11444. https://doi.org/10.3390/su151411444

APA Style

Kong, D., Seock, Y. -K., Marschner, S., & Park, H. T. (2023). Leveraging 4D Golf Apparel Wear Simulation in Online Shopping: A Promising Approach to Minimizing the Carbon Footprint. Sustainability, 15(14), 11444. https://doi.org/10.3390/su151411444

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