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PurposeThis paper aims to identify the relationship between users' perception image, clothing design features and users' preference and propose a clothing design scheme based on users perception image and users' preference.Design/methodology/approachIn this paper, men's suit is composed into multiple design features under the design elements. Using the orthogonal experiment method, 16 schemes of the representative suit are designed. Through perception evaluation experiment, users' perception images and preference degree of the samples are obtained. By partial least squares (PLS) analysis method, the models between users' perception image, suit design features and users' preference are built.FindingsThe interrelationship between the three is identified by establishing PLS models between users' perception image, suit design features and users' preference. According to the coefficients of the models, the optimization schemes of men's suits considering users' perception image and preference are proposed. Verification results show that the optimization schemes are significantly better than other schemes.Originality/valueThe results of this paper can be used for consumer demand-oriented clothing design and provide references and methods for converting consumer's perceived needs into clothing design features.

PurposeThis paper examines the problem of lack of historical data and inadequate consideration of factors influencing demand in the forecasting of demand for fast fashion clothing and proposes an improved Bass model for the forecasting of such a demand and the demand for new clothing products.Design/methodology/approachFrom the perspective of how to solve the lack of data and improve the precision of the clothing demand forecast, this paper studies the measurement of clothing similarity and the addition of demand impact factors. Using the fuzzy clustering–rough set method, the degree of resemblance of clothing is determined, which provides a basis for the scientific utilisation of historical data of similar clothing to forecast the demand for new clothing. Besides, combining the influence of consumer preferences and seasonality on demand forecasting, an improved Bass model for a fast fashion clothing demand forecast is proposed. Finally, with a forecasting example of demand for clothing, this study also tests the validity of the method.FindingsThe objective measurement method of clothing similarity in this paper solves the problem of the difficult forecasting of demand for fast fashion clothing due to a lack of sales data at the preliminary stage of the clothing launch. The improved Bass model combines, comprehensively, consumer preferences and seasonality and enhances the forecast precision of demand for fast fashion clothing.Originality/valueThe paper puts forward a scientific, quantitative method for the forecasting of new clothing products using historical sales data of similar clothing, thus solving the problem of lack of sales data of the fashion.

Purpose Except the physiological comfort, the design aesthetic of plaid shirts is the decisive factor affecting the purchase decision making of consumers. However, as there is subjectivity and indeterminacy in evaluation of the aesthetic, current research in the literature has little information about the appearance design of checks. The purpose of this paper is to extract the check design features, which influence consumer evaluation, and determine the preferences of men of different ages for the check designs, and the preference differences between them. Design/methodology/approach Through cluster and preference experiments, the perception and evaluation of the subjects were gained. By cluster analysis, typical classifications of check designs were fixed. With Multidimensional Scaling (MDS) algorithm, users’ perceptual space was revealed as to the plaid samples. In the PREFMAP analysis, results of the ideal vectors of men of different ages were drawn. Findings The study results revealed four typical classifications of check designs, identified three design dimensions that had effects on users’ evaluation, and built the relationship between design features of checks and consumer evaluation. Besides, in the study, the differences in preference for check designs between men of different ages were also explored. It was found that young men liked modern check designs, but disliked “small and complex” check patterns. Middle aged men preferred dark and cool colors, with dislike for “youthful vigor style.” While they cared more about check colors, young men attached more importance to the design style. Originality/value Currently, there is little information in the literature about the evaluation of pattern design. In this study, the use of MDS and PREFMAP analysis reveals visualized complex statistical results. The visualization approach is also applicable to design and positioning of other apparel, and can help designers better understand and position design styles and effects, given their consumer groups and design samples.

Purpose In the process of designing new clothes, designers should identify specific user groups’ preferences and attitudes toward certain types of design, ascertain the design elements that make clothes popular in the market, and combine these elements to devise the best clothing design scheme. The purpose of this paper is to discover which design elements influence dress purchases and how age affects consumers’ choices in regard to these elements. Design/methodology/approach This study uses conjoint analysis in dress design to provide an effective method for designers to identify consumers’ preferences. First, the important attributes and attribute levels of dress design were determined. Next, the experimental samples for the attitude measurement chart were generated by orthogonal design. Finally, the data of 318 samples were analyzed by conjoint analysis to determine consumers’ preferences. Findings The results revealed that the “silhouette” attribute is the most important decision criterion for dress purchase, followed by the “dress length” attribute. In contrast, the “waistline height” attribute is perceived as least important. The study also identified the dress design features’ preferences of consumers of different ages. According to the results of the analysis, user groups’ preferences and acceptability regarding different design features were revealed, and the favorite dress design portfolio for age-specific consumers was obtained. Originality/value Currently, there is little information in the literature about consumers’ preferences regarding dress design. In this study, the use of conjoint analysis reveals and visualizes complex statistical results. This research approach is also applicable to the design and decision-making processes used for other apparel, and it can help designers better incorporate different users’ needs into clothing design.

Purpose Today clothing has become the largest category in online shopping in China, and even in Asia-Pacific. The satisfaction degree of apparel online shopping can be improved by effective personalized recommendation. The purpose of this paper is to propose a personalized recommendation model and algorithm based on Kansei engineering, traditional filtering algorithm and the knowledge relating to apparel. Design/methodology/approach Users’ perceptual image and the design elements of apparel based on Kansei engineering are discussed to build the mapping relation between the design elements and user ratings employing verbal protocol, semantic differential and partial least squares. The implicit knowledge and emotional needs pertaining to users are accessed using analytic hierarchy process. A personalized recommendation model for apparel online shopping is established and the algorithm for the personalized recommendation process is proposed. To present the personalized recommendation model, men’s plaid shirts are taken as the example, and the recommendations of apparel for online shopping were implemented and ranked in the context of differing users’ emotional needs. A comparison between the traditional model and this model is made to verify the effectiveness. Findings The recommendation model is capable of analyzing data and information effectively, and providing fast, personalized apparel recommendation services in accordance with users’ emotional needs. The experimental results suggest that the model is effective. Originality/value Similar researches of recommendation mainly focus on the field of computer science, the basic idea of which is using users’ history accessing records or the preferences of other similar users for determination of users’ preferences. Since the attributes of apparel products are not factored in the approach referred above, the issue of personalized recommendation cannot be solved in a really effective way. Combining Kansei engineering and recommendation algorithm, a framework for apparel product recommendation is presented and it is a new way for improvement of recommendations for apparel products on shopping sites.