• Journal of the Korean Society of Clothing and Textiles
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• v.31 no.2 s.161
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• pp.236-246
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• 2007

The purpose of the current study is to investigate the underlying reasons of fashion brand repurchase intention formation based on means-end chain theory and to identify whether the price level moderates the relationships between attribute evaluation and re-purchase intention. Questionnaire data from 291 consumers who had purchase experience of casual wear during the last 6 month period through off-line retail shops were analyzed. The results showed that hedonic shopping value had a positive impact on aesthetic attribute and utilitarian shopping value also had a positive impact on physical attribute of fashion product. While aesthetic attribute had a positive influence on brand repurchase intention, physical attribute had no effect on brand repurchase intention. In addition, there was no moderating effect of price level between the links from shopping value to fashion product attribute evaluation. When casual wear companies have to devise price related strategies, they need to pay attention to diverse promotion factors except for product attributes to realize price-based differentiation.

• Journal of the Korea Safety Management & Science
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• v.20 no.1
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• pp.41-55
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• 2018

For several decades, attribute classification methods using the asymmetrical relationship between an attribute performance and the satisfaction of that attribute have been explored by numerous researchers. In particular, the Kano model, which classifies quality attributes into 5 elements using simple questionnaire and two-dimensional evaluation table, has gained popularity: Attractive, One-dimensional, Must-be, Indifferent, and Reverse quality. As Kano's model is well accepted, many literatures have introduced categorization methods using the Kano's evaluation table at attribute level. However, they applied different terminologies and classification criteria and this causes confusion and misunderstanding. Therefore, a criterion for quality classification at attribute level is necessary. This study is aimed to suggest a new attribute classification method that sub-categorizes quality attributes using 5-point ordinal point and Kano's two-dimensional evaluation table through an extensive literature review. For this, the current study examines the intrinsic and extrinsic problems of the well-recognized Kano model that have been used for measuring customer satisfaction of products and services. For empirical study, the author conducted a comparative study between the results of Kano's model and the proposed method for an e-learning case (33 attributes). Results show that the proposed method is better in terms of ease of use and understanding of kano's results and this result will contribute to the further development of the attractive quality theory that enables to understand both the customers explicit and implicit needs.

• Journal of Technology Innovation
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• v.9 no.1
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• pp.1-19
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• 2001

Importance of technology evaluation cannot be overemphasized to support the effective investment policy. Most of the technology evaluation relies on either quantitative analysis for the value or qualitative comparison due to some attribute. When qualitative comparison is made, typically not only individual attribute. but also overall score is evaluated by the committee of experts. In this paper, we consider the qualitative evaluation procedure used in a venture incubating center and validate if the relationship between the average score of individual attribute and the overall score is significant. Additionally, we identify if the initially evaluated scores are good indicators for the realized future value of technology. Structural equation modeling approach is used and we expect that our approach can make important contributions on improving the currently used technology evaluation method.

• The Research Journal of the Costume Culture
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• v.28 no.2
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• pp.215-228
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• 2020

The purpose of this study is to evaluate the attributes of consumers global luxury fashion brands and to find the impact of consumer value, meaning the social and pleasure values derived from brands on product purchase involvement. The method in this study was analysis of a total of 298 responses to a questionnaire. The results indicate, first of all, that among the attribute evaluations of global luxury brands, traditionality and splurge affect purchasing involvement. However, prestige and originality were found to have no influence on purchasing involvement. Second, the responses to the questionnaire indicate that social and pleasure values had a statistically significant effect on purchase of luxury brands. Third, the value of cross-group attributes evaluation and consumption value to differences in purchase involvement has had statistically significant results. In conclusion, the traditional and Splurge natures of the luxury global fashion industry's brand, social consumption value and pleasure consumption value affect the level of purchase involvement. This underscores the importance of attribute Evaluation(traditional and splurge) and consumption value(social and pleasure) when planning consumer marketing strategies for global luxury fashion brands in the future.

• Journal of the Korean Society of Clothing and Textiles
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• v.30 no.5 s.153
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• pp.818-828
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• 2006

The purpose of this study was to clarify differences in the product attribute evaluation in relation to the objective knowledge, subjective knowledge and involvement of apparel. The measurement instruments were developed by researcher on the basis of previous studies in the same field. The subjects of this study were female adults who lived in Seoul, Kyunggi or Incheon areas and quota sampling using age and residential areas was employed. The data were obtained from 603 questionnaires. Data were statistically analyzed using SPSS 10 and LISREL 7.0. Major statistical methods were factor analysis, Cronbach's a coefficient, multiple regression analysis, and structural equation model analysis. The results were as follows: 1. Involvement was related to the consumer knowledge and the knowledge influenced evaluation of intrinsic attributes, social attributes, and economic attributes. 2. The dimensions of objective knowledge significantly influenced intrinsic attributes and economic attributes. The dimensions of subjective knowledge significantly influenced intrinsic attributes, social attributes and economic attributes. 3. Apparel involvement significantly influenced intrinsic attributes, social attributes and economic attributes. Consumers who have higher interest in apparel product but not in trends considered intrinsic attributes more importantly, whereas consumers who care trends considered social attribute more.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.34
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• pp.1-8
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• 1995

The practical problem of multi-attribute decision making are formed by the uncertain attribute that the attribute by the alternatives cannot be defined or judged crisphy but only as vague. In this case the final judgements are also represented by vague which have to be ordered to determine the optimal alternative. The problem is more complex if the evaluations of alternatives according to each attribute are expresed vague. This paper described the results of a study done to determine how well multi-attribute decision marking perform in helping a decision maker arrive at a preferred solution to a multi-attribute problem with vague attribute. Particular area of research has concentrated on the issue of combining quantitative and qualitative data supplied by estimation. Futher study considers some method for suitable evaluation of qualitative data.

• Asia pacific journal of information systems
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• v.18 no.3
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• pp.1-20
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• 2008

Based on the multi-attribute utility theory (MAUT), we present a survey method to measure consumer preferences. The multi-attribute utility theory has been used to make decisions in OR/MS field; however, we show that the method can be effectively used to estimate the demand for new services by measuring individual level utility function. Because conjoint method has been widely used to measure consumer preferences for new products and services, we compare the pros and cons of two consumer preference survey methods. Further, we illustrate how swing weighing method can be effectively used to elicit customer preferences especially for new telecommunications services, Multi-attribute utility theory is a compositional approach for modeling customer preference, in which researchers calculate overall service utility by summing up the evaluation results for each attribute. On the contrary, conjoint method is a decompositional approach, which requires holistic evaluations for profiles. Partworth for each attribute is derived or estimated based on the evaluation, and finally consumer preferences for each profile are calculated. However, if the profiles are quite new and unfamiliar to the survey respondents, they will find it very difficult to accurately evaluate the profiles. We believe that the multi-attribute utility theory-based survey method is more appropriate than the conjoint method, because respondents only need to assess attribute level preferences and not holistic assessment. We chose swing weighting method among many weight assessment methods in multi-attribute utility theory, because it is designed to perform in a simple and fast manner. As illustrated in Clemen and Reilly (2001), to assess swing weights, the first step is to create the worst possible outcome as a benchmark by setting the worst level on each of the attributes. Then, each of the succeeding rows "swings" one of the attributes from worst to best. Upon constructing the swing table, respondents rank order the outcomes (rows). The next step is to rate the outcomes in which the rating for the benchmark is set to be 0 and the rating for the best outcome to be 100, and the ratings for other outcomes are determined in the ranges between 0 and 100. In calculating weight for each attribute, ratings are normalized by the total sum of all ratings. To demonstrate the applicability of the approach, we elicited and analyzed individual-level customer preference for new telecommunication services-WiBro and HSDPA. We began with a randomly selected 800 interviewees, and reduced them to 432 because other remaining ones were related to the people who did not show strong intention for subscription to new telecommunications services. For each combination of content and handset, number of responses which favored WiBro and HSDPA were counted, respectively. It was assumed that interviewee favors a specific service when expected utility is greater than that of competing service(s). Then, the market share of each service was calculated by normalizing the total number of responses which preferred each service. Holistic evaluation of new and unfamiliar service is a tough challenge for survey respondents. We have developed a simple and easy method to assess individual level preference by estimating weight of each attribute. Swing method was applied for this purpose. We believe that estimating individual level preference will be quite flexibly used to predict market performance of new services in many different business environments.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.1
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• pp.75-84
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• 2021

For analyzing big data, the social network is increasingly being utilized through relational data, which means the connection characteristics between entities such as people and objects. When the relational data does not exist directly, a social network can be configured by calculating relational data such as attribute similarity from attribute data of entities and using it as links. In this paper, the composition method of the social network using the attribute similarity between entities as a connection relationship, and the clustering method using subgroups for the configured social network are suggested, and the clustering effectiveness of the clustering results is evaluated. The analysis results can vary depending on the type and characteristics of the data to be analyzed, the type of attribute similarity selected, and the criterion value. In addition, the clustering effectiveness may not be consistent depending on the its evaluation method. Therefore, selections and experiments are necessary for better analysis results. Since the analysis results may be different depending on the type and characteristics of the analysis target, options for clustering, etc., there is a limitation. In addition, for performance evaluation of clustering, a study is needed to compare the method of this paper with the conventional method such as k-means.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.879-894
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• 2003

We evaluated the efficiencies of applying attribute selection methods and prior discretization to supervised learning, modelled by C4.5 and Naive Bayes. Three databases were obtained from UCI data archive, which consisted of continuous attributes except for one decision attribute. Four methods were used for attribute selection : MDI, ReliefF, Gain Ratio and Consistency-based method. MDI and ReliefF can be used for both continuous and discrete attributes, but the other two methods can be used only for discrete attributes. Discretization was performed using the Fayyad and Irani method. To investigate the effect of noise included in the database, noises were introduced into the data sets up to the extents of 10 or 20%, and then the data, including those either containing the noises or not, were processed through the steps of attribute selection, discretization and classification. The results of this study indicate that classification of the data based on selected attributes yields higher accuracy than in the case of classifying the full data set, and prior discretization does not lower the accuracy.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2001.05a
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• pp.389-408
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• 2001

Evaluating the results of National Nuclear R&D projects has critical importance in nuclear management aspect. This paper uses multi-attribute utility theory as a basis for obtaining a value Index to assess the results of nuclear R&D projects and applies the theory to a specific Korean case study. To structure and quantify basic values for the . evaluation, we elicited important attributes, then refined and structured them into a hierarchy. A multi-attribute index is constructed as a multi-attribute utility function, based on value judgments provided by a group of technical experts, policy makers, and decision-makers. The implications of the results are also discussed. We found that the work and results can provide valuable insights for assessment of nuclear R&D consequences.