• 한국HCI학회:학술대회논문집
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• 2008.02b
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• pp.7-12
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• 2008

This study aims to extract the factors which constitute use tendency on digital product, and to devide korean generation. We surveyed to digital product users who are 15 through 49 years old, executed factor analysis, cluster analysis, and so on, The results of factor analysis, we selected 11 factors, named as interest, entertainment, simplicity, anxiety of mal-operation, practicality, personality, consistency, multi-functionality, learning style of how-to-use, responsibility of disorder, and lending degree. The results of cluster analysis, we classified subjects into following three types: positive acceptor, passive follower, and conservative indifference. And, we executed frequency analysis iteratively. We devide korean digital product users to 4 generations -1727G, 2834G, 3538G, 3949G- by means of synthesizing the results. It can be said that generation is strong factor effect to use tendency, but the other factors like sex, occupation, school career, income become weak factor or not matters. Finally, we considered the reason, 3538G takes a crossing point role through comparison the generation division with cohort research on product use experience.

• Journal of Information Technology Applications and Management
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• v.30 no.2
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• pp.1-18
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• 2023

Recently, as technology development has accelerated and product life cycles have been shortened, it is necessary to derive key product features from customers in the R&D planning and evaluation stage. More companies want differentiated competitiveness by providing consumer-tailored products based on big data and artificial intelligence technology. To achieve this, the need to correctly grasp the required quality, which is a requirement of consumers, is increasing. However, the existing methods are centered on suppliers or domain experts, so there is a gap from the actual perspective of consumers. In other words, product attributes were defined by suppliers or field experts, but this may not consider consumers' actual perspective. Accordingly, the demand for deriving the product's main attributes through reviews containing consumers' perspectives has recently increased. Therefore, we propose a review data analysis-based required quality methodology containing customer requirements. Specifically, a pre-training language model with a good understanding of Korean reviews was established, consumer intent was correctly identified, and key contents were extracted from the review through a combination of KeyBERT and topic modeling to derive the required quality for each product. RevBERT, a Korean review domain-specific pre-training language model, was established through further pre-training. By comparing the existing pre-training language model KcBERT, we confirmed that RevBERT had a deeper understanding of customer reviews. In addition, all processes other than that of selecting the required quality were linked to the automation process, resulting in the automation of deriving the required quality based on data.

• Journal of the Korean Society of Safety
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• v.18 no.3
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• pp.141-148
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• 2003

Since the introduction of The Product Liability Law, effective since July 2002, more and more companies and end-users have been giving their attention to the safety of products. A number of existing risk analysis techniques are being implemented to manufacturing sites. However, they have certain restrictions such as incurring different techniques that are to be implemented at each stage of the product development. This is due to their domain of the analysis differing from one to another. Moreover, the results of these analyses are not specific enough, and are subject to further revision. This study proceeds to look at various examinations undertaken on the existing risk analysis techniques. Through implementing them on certain products, investigations on the strengths and weaknesses were ascertained. This has allowed improvements on the existing techniques to be achieved as tell as the development of a new risk analysis technique, 'HuBRA(Human Behavior Risk Analysis)'. Finally the new technique was implemented on products to confirm its effectiveness.

• Fashion & Textile Research Journal
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• v.15 no.4
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• pp.543-553
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• 2013

This study analyzes the influence of price, product and promotion on clothing impulse buying according to internet shopping mall type. This study conducted a survey of 346 male and female consumers in their 20s-40s who have experienced clothing impulse buying from an internet shopping mall. Respondents were selected using convenience sampling through an online survey conducted in August 2012. For statistical analysis, descriptive statistics, reliability analysis, $X^2$-test, factor analysis, and regression analysis where carried out using SPSS for Windows 12.0. The results were as follows. First, there were significant differences for consumer gender, age, marital status, and education on clothing impulse buying according to shopping mall type. Second, there were differences for the influence of each component (price, product and promotion) on clothing impulse buying according to shopping mall type. Third, it was identified that influence of components (price, product and promotion) on clothing impulse buying were in the following order: low price, freshness, prize promotion, scarcity, open market price discount, prize promotion, freshness, fashionability, low price in general shopping malls, payment term benefit, freshness, and scarcity in fashion SOHO shopping malls. This study provides information for consumer management and promotion strategies according to each shopping mall type by understanding clothing impulse buying by consumers and factors according to shopping mall type.

• Journal of the East Asian Society of Dietary Life
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• v.16 no.3
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• pp.242-250
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• 2006

The purpose of this study was to identify tourist product factor combinations which confer the highest utility to tourists and to establish the relative importance of factors in terms of their contribution to total utility across gender. Among 250 questionnaires, 230 questionnaires were utilized for the analysis. $X^2$ analysis, Conjoint model, Max. Utility model, BTL model, Logit model, K-means cluster analysis, and one-way anova analysis were used for this study. The findings from this study were as follows. First, the Pearson's R and Kendall's tau statistics showed that the model fitted the data well across gender. Second, it was found that total respondents and three clusters regarded taste price as the very important factor across gender. Third, it was found that the male and female tourists most preferred product with light red color, shaped package, and highly pungent taste sold at a cheap price in factory. Fourth, it was found that the male tourists most preferred simulation product with light red color, shaped package, and highly pungent taste sold at a cheap price in factory. The female tourists most preferred simulation product with light red color, shaped package, and mild taste sold at a cheap price in factory. Finally, the results of the study provide some insights into the types of effective product designs that can be successfully developed by marketers.

• The Research Journal of the Costume Culture
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• v.27 no.2
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• pp.109-122
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• 2019

With the greater importance of lifestyle shops and experience marketing, research is needed for the effective experience marketing of lifestyle shops by fashion product type. This study establishes effective and differentiated experience marketing strategies for fashion lifestyle shops by examining the effect of the experience marketing satisfaction with lifestyle shops on purchase intention and loyalty. This study categorizes lifestyle shops by fashion product types into a suit-oriented lifestyle shop, a casual/leisure/sportswear-oriented lifestyle shop and a home fashion lifestyle shop, Then a survey was conducted on participants who had visited lifestyle shop. As an analysis method, factor analysis, reliability analysis and regression analysis were conducted. In this study, we identified the experience marketing factors of fashion lifestyle shops. This study found that the satisfaction from experience marketing factors of fashion lifestyle shops by fashion product types affected purchase intention and loyalty differently. Considering the results of this study, it will be able to increase not only purchase intention and loyalty but also brand sustainability by establishing, applying, and executing a differentiated strategy considering the detailed factors of experience marketing for each fashion product type when planning and operating a fashion lifestyle shop. Through this study, effective marketing of fashion lifestyle shops by fashion product type will be implemented.

• Advances in Computational Design
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• v.4 no.1
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• pp.43-52
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• 2019

Variation Analysis (VA) is used to simulate final product variation, taking into consideration part manufacturing and assembly variations. In VA, all the manufacturing and assembly processes are defined at the product design stage. Process Capability Data Bases (PCDB) provide information about measured variation from previous products and processes and allow the designer to apply this to the new product. A new challenge to this traditional approach is posed by the Industry 4.0 (I4.0) revolution, where Smart Manufacturing (SM) is applied. The manufacturing intelligence and adaptability characteristics of SM make present PCDBs obsolete. Current tolerance analysis methods, which are made for discrete assembly products, are also challenged. This paper discusses the differences expected in future factories relevant to VA, and the approaches required to meet this challenge. Current processes are mapped using I4.0 philosophy and gaps are analysed for potential approaches for tolerance analysis tools. Matching points of simulation capability and I4.0 intents are identified as opportunities. Applying conditional variations, incorporating levels of adjustability, and the un-suitability of present Monte Carlo simulation due to changed mass production characteristics, are considered as major challenges. Opportunities including predicting residual stresses in the final product and linking them to product deterioration, calculating non-dimensional performances and extending simulations for process manufactured products, such as drugs, food products etc. are additional winning aspects for next generation VA tools.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.586-591
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• 2001

In the cold forging, elastic deformation of the die has been investigated to improve the accuracy of cold forged parts with F.E.M analysis using DEFORM, and with experiments using strain gauges. In the experiments, initial billet was selected to easily find the effect of elastic deformation according to the forging modes, extrusion and upsetting type, and only extrusion type. Elastic deformation of the die can be obtained by the signal from the strain gauges and this signal can be amplified by data acquisition system during the process. In the F.E.M analysis, two types of analysis are used to predict elastic strain of the die. To improve an accuracy of forged product and die dimension, this study compared with strain distribution between experiment and F.E.M analysis. As a result, the history of the deformation of the die and elastic recovery of forged product can be obtained by the elastic strain analysis of forged product and die according to the forging modes.

• The Research Journal of the Costume Culture
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• v.23 no.6
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• pp.940-954
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• 2015

The purpose of this study is to identify the antecedents of repurchase intention toward fast fashion brands. Perceived quality, perceived price, deindividuation, and overly trendy styles, which are product characteristic variables, and fashion innovativeness, which is a consumer characteristic variable, were considered as antecedents. It was hypothesized that product and consumer characteristics influence repurchase intention toward fast fashion brands not only directly, but also indirectly through cognitive dissonance. Data were gathered by surveying university students in Seoul using convenience sampling. Three hundred and fifty-two questionnaires were used in the statistical analysis. SPSS was used for exploratory factor analysis, and AMOS was used for confirmatory factor analysis and path analysis. The factor analysis of product characteristics revealed four dimensions: "perceived quality", "perceived price", "deindividuation", and "overly trendy styles", and the factor analysis of consumer characteristics revealed one dimension. The factor analysis of cognitive dissonance revealed two dimensions, "regrets" and "perceived uncertainty." The hypothesized path test proved that perceived quality, deindividuation, overly trendy styles, and fashion innovativeness influence repurchase intention directly. Perceived price and deindividuation influence repurchase intention indirectly through the factor of cognitive dissonance, which is the perceived uncertainty, indicating the importance of cognitive dissonance. The results indicate effective marketing strategies should be used to decrease consumers' cognitive dissonance, and suggestions for future study are provided.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.21 no.6
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• pp.527-533
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• 2008

This paper proposes a product data model and associated process for CAE activities in context of integrated product development. The data model and process enable Product Lifecycle Management(PLM) systems to integrate currently separated CAE activities into the main product development process. The product view concept in the proposed product data model supports independent CAE activities including analysis of various alternatives based on shared product structures with design departments and seamless translation of the CAE result to design product views. The proposed model is validated through an implementation of a prototype PLM system that can integrate and synchronize CAE process with the company-wide product development process.