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The purpose of this study was to explore participants’ fit perceptions of customized garments and investigate the factors that affected the customized fit.

Because different patternmaking methods generate varied fit results, eight customized dresses were developed for four fit models aged 18–25 using two different made-to-measure (MTM) systems (Gerber AccuMark and Telestia Creator). The authors designed a questionnaire to assess the fit of the final garments on each of the four models. A total of 12 participants (four fit models and eight experts) attended a live evaluation meeting and completed the questionnaire. The quantitative and qualitative data of the participants’ fit perceptions were collected and analyzed.

The authors compared the fit outputs of the two MTM systems and determined the fit issues from the participants’ perceptions. The results showed that the participants evaluated the customized fit mainly according to the ease, seam placement and wrinkle occurrence. The majority of fit models and experts preferred Dress B created using Telestia Creator to Dress A developed using Gerber AccuMark. The participants’ fit perceptions also revealed that many factors, such as the MTM systems, participants’ fit preferences, models’ body characteristics, fabric and ease, impacted the fit results. In addition, the experts perceived more fit issues than the models.

The fit output comparison of the two MTM systems could be valuable for pattern makers to use the systems. The research results would also be useful for apparel companies to conduct a fit evaluation and improve their customization processes.

The design and testing of clothing for activewear requires complex assessments of the suitability of the clothing when the body is in motion. The purpose of this paper is to investigate full body 3D scanning of active body poses in order to develop “watertight” digital models and half-scale dress forms to facilitate design, pattern making and fit analyses. Issues around creating a size set of scans in order to facilitate fit testing of activewear across a size range were also explored.

Researchers experimented to discover effective methods for 3D body capture in the cycling position and reconstruction of the body in a reliable way. In total, 25 cyclists were scanned and size representatives were selected from these participants. Methods of creating half-scale forms were developed that make optimum use of modern materials and technologies. Half-scale dress forms were created in two active positions in a range of sizes for fit testing and design. A set of half-scale and full-scale bike shorts in two styles were manufactured and fit tested on the half-scale forms compared to fit testing on the scan participants to test validity of this method of assessing fit.

Issues in capturing and reconstructing areas occluded in the scanning process, and reconstructing the interface with the bicycle seat were addressed. Active digital forms were developed across the size range, from which both digital avatars and physical mannequins were developed for pattern development and fit testing. The production and use of precisely half-scaled tools for garment testing was achieved and validated by comparing fit test results in active positions on the half-scale forms and on participants who were scanned to create these forms.

Design modifications for active positions to date are based on linear measurements alone, which do not define the 3D body adequately. Despite much research using body scanners, only limited data exist on the body in active poses, and the concept of creating half-scale forms by scanning fit models throughout the size range in active body positions is a novel concept. The progress made in resolving material and process experiments in creating the actual half-scale forms, and testing their suitability for fit testing provides a basis for further research aimed at developing similar dress forms for other activewear garments.

The authors investigated the role of cultural (i.e. traditional and religious) values in predicting the relationships between the attitudes toward the environment and slow fashion and the purchase intention for sustainable apparel.

An online survey was conducted in Amazon Mechanical Turk with the millennials of the US (n = 317). The data were analyzed through structural equation modeling (SEM).

The connection with indigenous cultural values in terms of the (1) traditional values positively influenced attitudes toward the environment and slow fashion; (2) religious values positively influenced attitude toward slow fashion but did not influence attitude toward the environment. (3) Both attitudes towards environment and slow fashion positively influenced purchase intention for sustainable apparel. (4) The connection with cultural values did not influence purchase intention toward sustainable apparel directly; however, (5) attitude toward slow fashion mediated the relationship between connection with cultural values and purchase intention for sustainable apparel both in terms of traditional and religious values, and (6) attitude toward the environment mediated the relationship between connection with traditional values and purchase intention for sustainable apparel.

Instead of focusing only on pro-environmental messages, marketers should use culture-specific cues to evoke favorable attitudinal and behavioral responses toward sustainable apparel.

Advertisement cues of sustainable apparel imbuing the target market's cultural values could help in protecting culturally significant elements of nature in the long-term by evoking positive attitudes toward the environment and slow fashion and encouraging purchase intention for sustainable apparel in the short-term.

The authors indicated the importance of indigenous cultural values in shaping favorable attitudes toward the environment and slow fashion and purchase intention for sustainable apparel.

Beside the development of technology and accessibility, ease of use, ability to reach various products and compare many products at the same time make online shopping even more popular. Despite the great advantages provided by online shopping for either consumers or retailers, there are certain issues that must be solved to improve online shopping advantages. Finding right size is one of the biggest barriers against apparel online retailing. Since the use of apparels is directly related with fitting, choosing right size is becoming more critical for retailers and consumers. The purpose of this paper is to contribute to the solution of the problem.

For the study, the specific size measurements of male shirts (collar, shoulder, chest, waist, arm length in cm) from four different sizes (small, medium, large, x-large) and from eight different brands were collected and stored in a database. Totally, weight, height and body measurements (collar, shoulder, chest, waist and arm length in cm) of 80 male candidates, between the ages of 18 and 35, were measured individually. These data were then used for experiments.

Any product with known measurements can be compared with users’ body measurement based on fuzzy logic rule and the best-fitted size can be selected for users. Similarly, using the proposed web design, users are able to see desired products on users with similar body type.

In this study, a new mathematical method based on fuzzy relations for apparel size finder is proposed. Beside, this method can group users based on body measurements in order to find people with similar size.

Sizing system is a fundamental topic in garment fitting. The purpose of this study was to assess the fit of existing police uniforms (shirt, jacket, overcoat and trousers) and develop a sizing system for upper and lower body uniforms of Amhara policemen in Ethiopia.

In total, 35 body dimensions of 889 policemen were taken through a manual anthropometric survey following the procedures in ISO 8559:1989 after each subject was interviewed on issues related to garment fit. The anthropometric data were pre-processed, key body dimensions were identified by principal components analysis and body types were clustered by the agglomerative hierarchical clustering algorithm and verified by the XGBoost classifier in a Python programming environment. The developed size charts were validated statistically using aggregate loss and accommodation rate.

About 44% of the subjects encountered fit problems every time they own new readymade uniforms. Lengths and side seams of shirts, and lengths and waist girths of trousers are the most frequently altered garment sites. Analysis of the anthropometric measurements resulted in 13 and 15 sizes for the upper and lower bodies, respectively. Moreover, the comparison of the developed upper garment size chart with the existing size chart for a shirt showed a considerable difference. This indicates that inappropriate size charts create fit problems.

The study considers the analysis of fit problems and sizing system development in a less researched country. Moreover, the proposed data mining procedure and its application for size chart development is unique and workable.

A common pipeline of apparel design and simulation is adjusting 2D apparel patterns, putting them onto a virtual human model and performing 3D physically based simulation. However, manually adjusting 2D apparel patterns and performing simulations require repetitive adjustments and trials in order to achieve satisfactory results. To support future made-to-fit apparel design and manufacturing, efficient tools for fast custom design purposes are desired. The purpose of this paper is to propose a method to automatically adjust 2D apparel patterns and rapidly generate acustom apparel style for a given human model.

The authors first pre-define a set of constraints using feature points, feature lines and ease allowance for existing apparels and human models. The authors formulate the apparel fitting to a human model, as a process of optimization using these predefined constraints. Then, the authors iteratively solve the problem by minimizing the total fitting metric.

The authors observed that through reusing existing apparel styles, the process of designing apparels can be greatly simplified. The authors used a new fitting function to measure the geometric fitting of corresponding feature points/lines between apparels and a human model. Then, the optimized 2D patterns are automatically obtained by minimizing the matching function. The authors’ experiments show that the authors’ approach can increase the reusability of existing apparel styles and improve apparel design efficiency.

There are some limitations. First, in order to achieve interactive performance, the authors’ current 3D simulation does not detect collision within or between adjacent apparel surfaces. Second, the authors’ did not consider multiple layer apparels. It is non-trivial to define ease allowance between multiple layers.

The authors use a set of constraints such as ease allowance, feature points, feature lines, etc. for existing apparels and human models. The authors define a few new fitting functions using these pre-specified constraints. During physics-driven simulation, the authors iteratively minimize these fitting functions.

This study seeks to examine the effects product presentation types on consumer's perceived risk reduction about apparel attributes (i.e. silhouette, colour, texture, and fit), and the effect of 3D virtual models' body shapes on consumer's perceived risk reduction about apparel fit. Internet shopping trust and consumer's actual body size have been considered as moderating variables, respectively.

Three types of web sites were developed for the experiment. Participants were recruited by a survey firm, explored the web sites, and filled out online questionnaires.

3D virtual models significantly reduce perceived risk about the four apparel attributes more than 2D images. Especially, they are effective to reduce perceived risk about apparel fit when their bodies represent a consumer's actual body shape.

This study verifies the informative role of 3D virtual models in apparel shopping. The implications related to the usage of the technology and the suggestions for future research are discussed.

Virtual try-on apps (VTOs) hold great potential to transform online apparel shopping, yet their acceptance by consumers has been lukewarm. By drawing on two theories – Sirgy’s theory of the self and Sundar’s theory of interactive media effects (TIME) – the study tests a PLS model with two new constructs: Photo Satisfaction and Consumer Inspiration, and their impact on Adoption Intention.

This mixed methods study examines the effects from personalization and customization affordances in the latest generation of apparel VTOs. The proposed model was tested in a quasi-experimental within-subjects design, as 61 female participants completed a goal-directed and an exploratory apparel shopping task using two VTO apps available on the market.

Inspiration induced from the customization affordance in the exploratory task influenced Adoption Intention as strongly as Fit Confidence did. For both conditions, users’ satisfaction with their avatar picture had a stronger effect on Perceived Augmentation than the individual trait of Body Esteem. The study also demonstrates how TIME’s Sense of Agency component could help theorize positive consumer responses to mobile marketing technology, which in our study were induced via the app’s Mix and Match feature. Also demonstrated was that Perceived Augmentation could be influenced by body-image affective judgments made during the pre-task VTO avatar creation phase.

VTO’s customization feature “Mix and Match” appears vital for the adoption of such apps for exploratory m-shopping of apparel. Also important is the satisfaction with one's photograph during the avatar-creation phase. We further suggest that apparel retailers work with Augmented Reality developers to include dynamic avatar capability as well as social sharing affordances in VTOs.

60 billion USD worth of returns occur annually in the online apparel retail industry, mostly related to fitting problems, not to mention the larger carbon footprint from the increased transportation. A more wide-spread adoption of VTOs can not only help with these problems but also lighten the traffic in brick and mortar fitting rooms, a potential hazard at this time of pandemic.

This is the first study to examine Consumer Inspiration in the context of online apparel retailing and to demonstrate the importance of Photo Satisfaction during the avatar-creation phase in influencing the subsequent user experience in apparel VTOs.

The purpose of the current study is to explore how valenced fit reviews affect the consumer decision-making process during online apparel shopping.

A single factor (valence of fit review) within-subject experimental design was employed to examine how the valenced fit review (negative vs positive) affects the consumer online purchase decision process. A mock website was created to simulate the online shopping environment through four steps for developing a stimulus website for the main study. The data were analyzed using repeated multivariate analysis of variance and structural equation modeling.

A total of 418 female consumers completed an online self-administrated survey. Results showed that positive fit review was more compelling than negative fit review for female consumers when they like the apparel product. Two aspects of information credibility (review and site credibility) and confidence in purchase decision evoked by both fit reviews and overall product information were significant determinants of the consumer purchase decision process in increasing consumers’ future purchase intentions through attitude to the online retailer.

The current study was an attempt to fill the gap in knowledge regarding the crucial role of fit reviews in apparel product purchase decisions in an online context. This study confirmed the type of fit reviews that would be influential on female consumers’ online purchase decision-making process for apparel products when they liked the apparel product, supporting positive confirmation bias from the information processing point of view. This study contributed to the importance of the two concepts (i.e. credibility and confidence in the purchase decision) in online information processing and purchase decision-making process.

This study examines how consumer involvement with apparel influences perceptions of an apparel product (T‐shirt) presented in a print advertisement. Consumer involvement with apparel was examined in relation to three advertisement response concepts: attitude toward the advertisement, product attribute beliefs, and product attitude. Also as part of the study, three dimensions of apparel involvement were tested (fashion, comfort, and individuality). Finally, an advertising processing model that integrates apparel involvement with the three advertisement response concepts was tested. A convenience sample of students attending a midwestern university in the USA participated in data collection. Respondents were presented with a full‐page advertisement for a fictitious brand of apparel and answered items on the questionnaire. Findings confirmed that dimensions of apparel involvement shaped consumer attitudes. A combination of apparel involvement dimensions (fashion, individuality, and comfort) influenced consumer beliefs about product attributes in the advertisement. In terms of gender differences, the comfort variable showed to be a stronger component of apparel involvement for men and women tended to be more involved in fashion. Findings also supported relationships among advertisement response variables previously tested by scholars. Product attribute beliefs and ad attitude were significant in product attitude formation.