• Asia Marketing Journal
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• v.16 no.1
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• pp.149-168
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• 2014

This study investigates the effect of perceived risk on attitude toward mobile Social Network Services (SNSs). First, we understand that perceived risk of SNSs is a multidimensional concept, and we study the relationship between attitude and perceived risk such as social risk, performance risk, and privacy risk in SNS environments. Subsequently, the relationships between these multidimensional concepts of perceived risk and attitude are investigated. The result indicates that social, performance, and privacy risk have negative effects on attitude. In addition, the moderated effect of individual characteristic variables such as hedonic value and self-construal are confirmed as mitigating factors that alleviate the negative impact of perceived risk. The Findings show that customers who perceive SNSs to be risky are more likely to have a negative attitude toward SNSs. However, the negative impact of perceived risk on their attitude toward SNSs is alleviated in customers with high hedonic value. Similarly, the negative impact of perceived risk on their attitude toward SNS is weaker with customers in interdependent self-construal. This paper presents effective segmentation variables, such as consumer's motivation (hedonic value) and psychological variable (self-construal), which mitigate the risk perception of customers. Therefore, it provides practical guidelines for the marketing managers in terms of who to target and what kind of strategies to implement in terms of these segmentation variables to approach consumers more efficiently.

• Journal of Fashion Business
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• v.22 no.3
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• pp.39-56
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• 2018

Since trusting in other consumers may refer to having similar needs and preferences on fashion goods with them, interpersonal trust can be a reliable and practical criterion for market segmentation online. Therefore, this study aims to identify fashion online consumers based on interpersonal trust. This study segments fashion consumers based on interpersonal trust and experience and describes characteristics of each segment by examining demo-psychographic and behavioral variables as well as clothing consumption values. An online survey was conducted to collect data from 426 adult consumers who had bought fashion goods from their patronized e-tailer in the past one month. They completed a self-administered questionnaire inquiring about interpersonal trust, trust in e-tailers, purchase intentions, clothing consumption values, and their purchasing behavior online. Two-step cluster analysis generated four segments: distrustful doers, trusting doers, inactive trusters, and distrusters. They exhibited different characteristics in gender, online experiences, interpersonal trust, clothing consumption values, trust in the e-tailers, and attitude toward the e-tailers. Providing a new effective segmentation base, this study suggests that fashion marketers identify customers with a high level of trust in other customers and develop an encouraging environment that customers can interact with others in order to increase the effectiveness of trust. Because customers with a higher level of interpersonal trust are likely to have stronger trust in e-tailers with, more favorable attitude and purchase intention, and highly perceive the value of clothing consumption than those who have a lower level of interpersonal trust.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.2
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• pp.125-132
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• 2004

In this paper, we propose a new two-stage segmentation method for the effective object recognition which uses region-growing algorithm and k-means clustering method. At first, an image is segmented into many small regions via region growing algorithm. And then the segmented small regions are merged in several regions so that the regions of an object may be included in the same region using typical k-means clustering method. This paper also establishes similarity measurement which is useful for object recognition in an image. Similarity is measured by fuzzy system whose input variables are compactness, magnitude of biasness and orientation of biasness of the object image, which are geometrical features of the object. To verify the effectiveness of the proposed two-stage segmentation method and similarity measurement, experiments for object recognition were made and the results show that they are applicable to object recognition under normal circumstance as well as under abnormal circumstance of being.

• Journal of the Korean Society of Clothing and Textiles
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• v.27 no.1
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• pp.100-110
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• 2003

The purpose of this study were 1) to find out the structural elements in classifying clothes images, and 2) to segment the consumer market for women's street clothes based on clothes image preferences and to identify the group differences in psychological variables, purchasing behavior variables and demographic variables. The sample was taken from 1106 middle class women who were in thier 30's∼40's living in Gwangju city. Consumers were classified into six groups: active image group (35.4%), feminine image group (25.9%). daring image group (16.5%), elegant image group (10.8%), dressy image group (8.9%) and brisk image group (3.5%). Women in their 30's∼40's preferred elegant image, daring image, active image and feminine image. Elegant image oriented group: This group is the lowest education level group and has the highest rating of housewife. This group has the lowest scores use of person information search, Daring image oriented group: Woman in their 30's prefers daring image. This group thinks practical benefit sought is less important than self-expression benefit sought. This group has the highest scores use of non-person information search, Active image oriented group: This group is practical benefit seeking group. and purchases the lowest amount of clothes. The amount of average household income is the lowest. Feminine image oriented group: The amount of average household income is the highest. This group perceives more youth$.$fashion benefit sought and self-expression benefit sought than elegant image oriented group. ANOVA, $\chi$$^2$-test revealed differences among groups according to benefit sought use of information sources, purchasing behavior variables and demographic variables.

• Journal of the Korean Society of Food Culture
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• v.39 no.1
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• pp.30-37
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• 2024

Consumption market research was conducted on gradually increasing vegetarians using various selection attributes. Factors were extracted to identify vegetarian selection attributes and to divide the study cohort into groups, continuous variables (health, animal welfare, eco-friendliness, religion, familiarity, convenience, stability, and cost) and categorical variables (age, marital status, vegetarian duration, and vegetarian frequency) were simultaneously subjected to two-step cluster analysis. Cluster 1 contained high proportions of 20-29 and 30-39 year-olds, which are MZ-generation age groups. A high proportion had a vegetarian duration of 1-3 years, and the popular reasons for vegetarian selection were animal welfare and eco-friendliness. Cluster 2 contained high proportions of 50-59 and 40-49 year-olds, and many in this cluster were married, and mean vegetarian duration was ≥15 years. In addition, significant differences were observed between Clusters 1 and 2 in terms of religion, health, familiarity, cost, stability, and convenience. This study should contribute significantly to predicting vegetarian consumers' selection decisions and consumption behaviors and provide reliable marketing data for foodservice companies that develop vegetarian foods.

• The Journal of the Korea Contents Association
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• v.8 no.10
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• pp.351-360
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• 2008

The purpose of this study is to segment visitors for beer franchise restaurants. Total of 308 questionnaires were distributed to the beer franchise restaurant customers of Kangnam area, Seoul. To accomplish the aim of this study, author took the following three steps. Firstly, literature review was done to find out selective attributes for restaurant and bar and market segmentation. secondly, Selective attributes of beer franchise were analyzed and five factors, -'additional service', 'side dish quality', 'service class', 'accessibility' and 'outlet image'- were extracted. Lastly, customers of beer franchise shop were divided into two groups by the selective attributes, -'simple visiting group' and 'active lover group'. Those variables-loyalty, intent to recommend and overall satisfaction- have significant influence on 'active lover group'. Other than that, other variables -demographic characteristics and customer's behavior characteristics- didn't play important role to distinct two groups. As a result, only basic information for setting up marketing strategy of each market was provided.

• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.531-538
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• 2005

In k-means clustering, we standardize variables before clustering and iterate two steps: units allocation by Euclidean sense and centroids updating. In applications to DB marketing where clusters are to be used as customer segments with similar consumption behaviors, we frequently acquire additional variables on the customers or the units through marketing campaigns a posteriori. Hence we need to modify the clusters originally formed after each campaign. The aim of this study is to propose a revision method of k-means clusters, incorporating added information by weighting clustering variables. We illustrate the proposed method in an empirical case.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.

• Journal of the Korean Society of Fashion and Beauty
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• v.3 no.1 s.4
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• pp.20-28
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• 2005

As the size and the diversity of the beauty industry grow, the need for systematic marketing strategy in beauty shop also increases. This study aims at finding 1)how to segment the market of beauty industry based on lifestyle variable and 2)how to set up the corresponding mix strategy on target market. The case of 'B beauty shop' shows that the validity of lifestyle-based segementation model is verified as the shop has posted a drastic financial outcome during last 5 years. In addition, this study implies that the corelation research between lifestyle variables and marketing mix strategy should be included as an important factor in launching a beauty-related business in segmented market.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.1
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• pp.26-33
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• 2006

The potential needs as well as visible needs of customer should be considered in order to research and analyze of the customer data. The methods to analyze customer data is classified into customer segmentation, clustering analysis model, forecasting customer response probability model, analysis of the customer break rate model and new customer analysis model by the purpose. In this study, we developed the CW-CLV (Correlation Weight Customer Lifetime Value)method that used AHP(Analytic Hierarchy Process)rule for enhance the reliability of customer data and quantitative analysis of the customer segmentation, based on CLV(Customer Lifetime Value). We suggest to new variables and methodology from determined CW-CLV coefficients, because all of companies respect to the diversified customers classification and complexity of consumers needs. Finally, we unfolded any company's scheduling added new methodology using simulation and leaded conclusion about the new methodology.