• 한국의류산업학회지
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• 제21권5호
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• pp.597-605
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• 2019

This paper examined the garment formability and appearance of perspiration absorption, fast dry, and breathable fabrics. The mechanical properties and seam pucker properties of these fabrics were measured and regression analysis was conducted between fabric structural parameters and their mechanical and seam pucker properties. The superior total appearance value (TAV) of fast dry knitted fabrics for sports-wear was achieved in fabrics with high extensibility and bending rigidity; consequently, it increased with increasing stitch density and tightness factor. The formability of the fast dry knitted fabric also improved with an increasing stitch density and tightness factor. The seam pucker was influenced by bending rigidity and a good seam pucker was exhibited in the fast dry knitted fabrics with low stitch density and tightness factor. However, the formability (F) of the breathable fabric improved by increasing extensibility and bending rigidity that decreased with an increasing cover factor and the thickness of the breathable fabric. In addition, seam pucker deteriorated with an increasing cover factor and the thickness of the breathable fabric, which was similar to the results of the formability predicted in fabric mechanical properties. A superior seam pucker was achieved in fabrics with high extensibility and low bending rigidity.

• 한국의류학회지
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• 제46권5호
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• pp.868-889
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• 2022

Fashion companies are using a big data approach as a key strategic analysis to predict and forecast sales. This study investigated the effectiveness of the past sales, web search volume, information amount, brand promotion, and the advertising endorser on the sales forecasting model. The study conducted the autoregressive distributed lag (ARDL) time series model using the internal and external social big data of a national fashion brand. Results indicated that the brand's past sales, search volume, promotion, and amount of advertising endorser information amount significantly affected the sales forecast, whereas the brand's advertising endorser search volume and information amount did not significantly influence the sales forecast. Moreover, the brand's promotion had the highest correlation with sales forecasting. This study adds to information-searching behavior theory by measuring consumers' brand involvement. Last, this study provides digital marketers with implications for developing profitable marketing strategies on the basis of consumers' interest in the brand and advertising endorser.

• 한국의류학회지
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• 제47권3호
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• pp.492-510
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• 2023

The metaverse, an expansion of the real world, refers to a three-dimensional virtual world where daily life and economic activities can be conducted through avatars. This study examines the effect of avatar identification on metaverse immersion, vicarious pleasure, and fashion brand item sharing intention by subdividing avatar identification into similarity identification, wishful identification, and embodied presence. In addition, it investigates the difference in the influence relationship between avatar identification, immersion, and vicarious pleasure according to the degree of fashion involvement. The total of 319 participants were analyzed using SPSS 26.0 and AMOS 24.0. The results showed that similarity identification, wishful identification, and embodied presence had significant impacts on immersion and vicarious pleasure, influencing sharing intention. There was also a difference in the effect of avatar identification on consumer responses depending on fashion involvement. This study provides theoretical implications for experiential marketing and presents practical suggestions for developing metaverse marketing strategies.

• 패션정보와 기술
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• 제2권
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• pp.108-117
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• 2005

• 패션정보와 기술
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• 제9권
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• pp.56-65
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• 2012

• 패션정보와 기술
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• 제6권
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• pp.16-21
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• 2009

• 한국의류학회지
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• 제31권1호
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• pp.162-175
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• 2007

• 패션정보와 기술
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• 제5권
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• pp.2-9
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• 2008

• 한국의류학회지
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• 제45권5호
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• pp.881-891
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• 2021

This study proposes a simple drape measurement method for the 3D virtualization of garments. The proposed method uses angles or disks of different diameters to evaluate the drape properties easily. We divided 710 fabrics into ten groups based on the drape coefficient, of which 49.6% had drape coefficients of 30 or less. The drape properties were measured to classify the groups into smaller clusters using the angle formed when the center of the fabric was fixed. Accordingly, three clusters were formed for 60° and 100° angles. A method was devised using ten disks of different diameters to classify the remaining two clusters, except the cluster containing only the D10 group (D1-D5 and D5-D9). Three criteria-grade match, a sum of deviation, and standardization of deviation-were used for the classifications. The discriminative ability between groups was high for D1-D5 with disks with 24.0 and 25.5 cm diameters. Furthermore, a disk with a diameter of 16.5 cm was effective for D5-D9. The three-dimensional drape shapes were unique for the ten groups, which can be utilized as fundamental data for 3D virtualization.

• 한국의류학회지
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• 제45권6호
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• pp.978-985
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• 2021

This study aims to propose a prediction model for the drape coefficient using artificial neural networks and to analyze the nonlinear relationship between the drape properties and physical properties of fabrics. The study validates the significance of each factor affecting the fabric drape through multiple linear regression analysis with a sample size of 573. The analysis constructs a model with an adjusted R² of 77.6%. Seven main factors affect the drape coefficient: Grammage, extruded length values for warp and weft (m_warp, m_weft), coefficients of quadratic terms in the tensile-force quadratic graph in the warp, weft, and bias directions (c_warp, c_weft, c_bias), and force required for 1% tension in the warp direction (f_warp). Finally, an artificial neural network was created using seven selected factors. The performance was examined by increasing the number of hidden neurons, and the most suitable number of hidden neurons was found to be 8. The mean squared error was .052, and the correlation coefficient was .863, confirming a satisfactory model. The developed artificial neural network model can be used for engineering and high-quality clothing design. It is expected to provide essential data for clothing appearance, such as the fabric drape.