• 한국의류학회지
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• 제32권2호
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• pp.247-258
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• 2008

The purposes of this study were to investigate influences of consumer's shopping value, brand alliances and apparel product involvement on purchasing behavior. The subjects of 172 hedonic shopping value and 208 utilitarian shopping value were chosen to participated for data collection. The data was analyzed by factor analysis, cluster analysis, and ANOVA. The main results of study were summarized as follows: 1. When an Internet Private Brand(PB) did not form brand alliance with a National brand(NB), utilitarian shopping value consumers did not differ in preference irrespective of whether product involvement was high or not. However, when the PB formed brand alliance with well-known NB, they showed higher preference for the high involvement apparel product than the low involvement product. 2. When an Internet PB did not form brand alliance with well-known NB, the utilitarian shopping value consumers' word-of-mouth intention did not differ between the high involvement apparel product and low involvement apparel product. 3. It was revealed that when an Internet PB did not form brand alliance, the utilitarian shopping value consumers showed higher intention to purchase than that of the low involvement product.

• 한국정밀공학회지
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• 제21권9호
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• pp.85-94
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• 2004

As over 70% of the total life cycle cost (LCC) of a product is committed at the early design stage, designers are in an important position to substantially reduce the LCC of the products they design by giving due to life cycle implications of their design decisions. During early design stages, there may be competing concepts with dramatic differences. In addition, the detailed information is scarce and decisions must be made quickly. Thus, both the overhead in developing parametric LCC models fur a wide range of concepts, and the lack of detailed information make the application of traditional LCC models impractical. A different approach is needed, because a traditional LCC method is to be incorporated in the very early design stages. This paper explores an approximate method for providing the preliminary LCC, Learning algorithms trained to use the known characteristics of existing products might allow the LCC of new products to be approximated quickly during the conceptual design phase without the overhead of defining new LCC models. Artificial neural networks are trained to generalize product attributes and LCC data from pre-existing LCC studies. Then the product designers query the trained artificial model with new high-level product attribute data to quickly obtain an LCC for a new product concept. Foundations fur the learning LCC approach are established, and then an application is provided.

• The Journal of Asian Finance, Economics and Business
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• 제8권3호
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• pp.1275-1286
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• 2021

The purpose of this study is to employ the theory of consumption value and consumers' innovative personality characteristics to explain the adoption of new personal electronics devices in Vietnamese market. This study adapts a quantitative survey-based approach to test hypotheses about relationship between consumption value, product specific innovativeness and new product adoption. The study uses a quantitative data set of 915 consumers who owned one mobile electronic device at least in Ho Chi Minh city, one of the biggest cities of Vietnam. The data was collected through personal interview and convenient sampling method. The conceptual model was tested using PLS structural equation model. The findings of this study suggest that both consumption value and product specific innovativeness influence the adoption of new electronic products. The results also reveal that product specific innovativeness mediates the relationship between consumption value and new product adoption. The study further identified that consumption value was taken as a second-order multidimensions construct with five components, namely functional value, epistemic value, economic value, social value and emotional value. As a result, the research suggests some implications to enhance marketers' capabilities to develop strategies for launching new hi-tech products in an emerging market as Vietnam.

• 유통과학연구
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• 제19권1호
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• pp.49-60
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• 2021

Purpose: A good physical appearance greatly affects a person's self-confidence, especially when the media constantly depicts that beautiful men and women are those with perfect bodies, which later forms the perception that being fat or too thin is not attractive. That is in line with the increasing knowledge and the need for nutritious foods and drinks for diets. Therefore, this study aims to see whether there is a relationship between the Weight Rejuvenation Program Everyday product innovation towards millennial purchase decision and the motivation of having an ideal body as a moderating effect. Research design, data, and methodology: Distributed online Google form questionnaires to 96 audiences who commented on "Mute" web series. The respondents consisted of 63 women and 33 men from the millennial generation who lived in Greater Jakarta and were classified as the middle to upper economic class. After all of the data were collected, they were processed using Structural Equation Modeling Partial Least Squares. Results: Product innovation had a significant influence on the purchasing decisions of the millennial consumer, but motivation did not have the moderating function in the relationships between product innovation and purchase decision. Conclusions: The main factor for product innovation that can be accepted by millennials is the product quality that remains good.

• 한국전자거래학회지
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• 제21권2호
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• pp.151-161
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• 2016

개방, 공유, 참여를 특징으로 하는 웹 2.0 시대로 들어서면서 인터넷 사용자들의 데이터 생산 및 공유가 쉬워졌다. 이에 따른 데이터의 기하급수적인 증가와 함께 디지털 정보의 대부분인 비정형적 데이터(Unstructured Data)의 양도 증가하고 있다. 인터넷에서 정해진 형식 없이 자연어 형태로 만들어진 비정형 데이터 중, 특정 상품들에 대해 개인이 평가한 리뷰들은 해당 기업이나 해당 상품에 관심이 있는 잠재적 고객에게 필요한 데이터이다. 많은 양의 리뷰 데이터에서 상품에 대한 유용한 정보를 얻기 위해서는 데이터 수집, 저장, 전처리, 분석, 및 결론 도출의 과정이 필요하다. 따라서 본 연구는 R을 이용한 텍스트 마이닝(Text Mining) 기법을 사용하여 텍스트 형식의 비정형 데이터에서 자연어 처리 기술 및 문서 처리 기술을 적용하여 정형화된 데이터 값을 도출하는 방법에 대해 소개한다. 또한, 도출된 정형화된 리뷰 정보를 데이터 마이닝 기법에 적용하여 목적에 맞게 맞춤화된 리뷰 정보를 도출시키는 방안을 제시하고자 한다.

• 경영정보학연구
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• 제23권1호
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• pp.155-172
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• 2021

최근 온라인 상품 구매의 증가로 인해 사용자의 선호에 맞는 상품을 추천해주는 시스템이 지속적으로 연구되고 있다. 추천 시스템은 사용자들에게 개인화된 상품 추천 서비스를 제공하는 시스템으로 사용자가 상품에 남긴 평점을 이용한 협업 필터링(Collaborative Filtering)이 가장 널리 쓰이는 추천 방법이다. 협업 필터링에서 상품 간의 유사도 계산은 시간이 많이 소요되는데, 특히 리뷰 데이터와 같은 빅데이터를 사용할 경우 더욱 많은 시간을 소요한다. 그래서 본 연구에서는 리뷰 데이터 마이닝을 이용하여 상품 간의 유사도 계산을 빠르게 수행할 수 있으면서 정확도를 높일 있도록 2단계(2-Phase) 방법을 이용한 하이브리드 추천시스템 방식을 제안한다. 이를 위해 온라인 전자책 상거래 상점인 아마존 킨들 스토어(Amazon Kindle Store)의 약 98만 개의 온라인 소비자 평점과 리뷰 데이터를 수집하였다. 실험 결과 본 연구에서 제안한 사용자의 평점과 리뷰를 단계적으로 반영한 하이브리드 추천 방식이 전통적인 추천 방식과 비교하여 추천 시간은 비슷하였으나 높은 정확도를 나타내는 것을 확인하였다. 따라서 제안한 방법을 사용하면 사용자가 선호하는 상품을 빠르고 정확하게 추천함으로써 고객의 만족을 높여서 기업의 매출 증대에 기여할수 있을 것으로 기대된다.

• Journal of Information Technology Applications and Management
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• 제27권1호
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• pp.173-186
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• 2020

This study examines the effect of congruity between product and TV reality show on purchase intention of Chinese consumers. A total of 110 respondents from Chinese consumers were collected using online surveys. The results were analyzed by SPSS 22.0. Multiple Linear Regression and process analysis were used to test the hypotheses. This article found that congruity between product and TV reality show, attitude toward product placement and recall of product affected consumers' purchase intention. In addition, the frequency of consumer watching TV reality show and familiarity of product moderated the attitude toward product placement and recall of product. This study provides useful implications for sponsors to select product placement as one of their marketing promotion tool.

• 한국의류학회지
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• 제36권8호
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• pp.802-813
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• 2012

This study establishes how the clothes consumption value, need for uniqueness, and use information sources could influence the importance of apparel product attributes and the importance of store attributes. Data were collected through a survey of adults in their 20's and 30's with 48 questionnaires for statistical analysis. The collected data were processed with the programs AMOS 16.0 and SPSS 18.0 for windows and reliability analysis, correlation analysis, factor analysis, and structural equation analysis were conducted to analyze the data. The results in this research are follows. First, the clothes consumption value influences the importance of apparel product attributes both directly and indirectly and the importance of store attributes indirectly through use information sources. Second, the need for uniqueness influences the importance of apparel product attributes indirectly and importance of store attributes both directly and indirectly through clothes consumption value and use information sources. The implications of these findings and suggestions for future study are also discussed.

• 디지털산업정보학회논문지
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• 제6권2호
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• pp.71-80
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• 2010

This thesis is purposed on developing security product co-evaluation statistics administrate program which is can administrate or analysis CC accreditation product using by DEVS modeling via portal site of member of CCRA. Via developing security product evaluation statistics administrate program, it can analysis the trend of all countries of the world in many ways, and noticed the ways of evaluation and accreditation of most countries via scheme analysis. Except this, it can analysis the situation of accreditation trend of any countries via data analysis of ICCC 2009. Also, For trend analysis to evaluation technique of CCRA member, it analyzed up to date technology and policy of the evaluation organization and the Certification Authority of most countries. And it peformed analysis the most trend of information security of evaluation authorization in CCRA member countries. In this program, It provide the function of trend statistics analysis which can statically analyzed the evaluation accreditation trends of most countries and automatical statistics by categorization ( by Product, Class and statistics in national) and report creation functions which can easily extraction and use the needed data. It has been updated the related informations until latest accredited product using by CC(Common Criteria) portal home page's data.

• 산업경영시스템학회지
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• 제44권3호
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• pp.117-124
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• 2021

The development of IOT technology and artificial intelligence technology is promoting the smartization of manufacturing system. In this study, data extracted from acceleration sensor and current sensor were obtained through experiments in the cutting process of SKD11, which is widely used as a material for special mold steel, and the amount of tool wear and product surface roughness were measured. SVR (Support Vector Regression) is applied to predict the roughness of the product surface in real time using the obtained data. SVR, a machine learning technique, is widely used for linear and non-linear prediction using the concept of kernel. In particular, by applying GSVQR (Generalized Support Vector Quantile Regression), overestimation, underestimation, and neutral estimation of product surface roughness are performed and compared. Furthermore, surface roughness is predicted using the linear kernel and the RBF kernel. In terms of accuracy, the results of the RBF kernel are better than those of the linear kernel. Since it is difficult to predict the amount of tool wear in real time, the product surface roughness is predicted with acceleration and current data excluding the amount of tool wear. In terms of accuracy, the results of excluding the amount of tool wear were not significantly different from those including the amount of tool wear.