• Journal of Korean Public Health Nursing
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• v.3 no.2
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• pp.5-46
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• 1989

The current medical payment Insurance Rates in Korea stipulate charges for medical treatment by the doctor, pharmaceutist, medical technician and maternity nurse. But unfortunately didn't specify those charges for nursing done by the professional nurse. Only basic nursing fee is accounted insufficiently in current medical insurance fee schedule. therefore, Being face with covering entire people by medical insurance by 1991, It seems that the problems pertaining to operating the hospital and medical insurance system would be incessantly expanded in that no mention is made of medical charges rendered by major medical producer service in the current system, For that reason, this study made an attempt to clarify the importance the professional nursing puts of the current medical payment. The purpose of this study was to accounting nursing fee which diveded into the current medical fee schedule. (Method) 1. Data collection; Importance and difficulties in nursing activities was conducted in 'S' National University Hospital. Total nursing activities were selected 72 items which included direct care and indirect care. This study was conducted to evaluating the degree of importance and difficulties according to nursing activities through questionnaire to 204 RN. and so relative difficulties (acuity) were computered because the nursing cost level of each nursing service was differently established by the equivalent coefficient according to degree of relative difficulty and time required. 2. Calculation of cost according to nursing activities; After 47 nursing activities were selected in General surgery nursing units, calculation of nursing cost was as follows Cost of Nursing activity = (relative difficulty X Average hourly wage and benefits of nurse) + material cost of nursing -t- Average nursing administration cost So, Calculated cost by nursing activities was compared to current non-insured and insurance rate. 3. Calculation of nursing cost by K - DRG ; Total of 578 patients who were hospitalized in General Surgery units from January to March 1988 ware classified by K - DRG After estimation of total nursing cost based on the K-DRG, verified the appropriateness of basic nursing fee in medical insurance rate (Results) 1. Analysis of degree of importance and difficulties were 4.16 and 3.67 based on 5 point scale. This score were judged that it is worthy specifying the nursing fee 2. The nursing cost of 47 nursing service items in general surgery patients showed that the average cost of nursing activity was \1374.5 and The lowest cost was \217 of 'oral administration nursing' item, The highest cost was \11,025 of 'saline enematill clear' item 3. The result of comparison between the calculated cost by nursing activities against the current non-insured and insurance rate showed that 13 items(27.7%) involved to payment of insurance rate, 9 items(19.1%) involved to non-insured rate, remainder 25 items (53.2%) were not charged anywhere of total 47 nursing activities 4. When calculated cost by nursing activities was 100. current insurance rate was 62.3, non-insured rate was 176.6. Therefore this showed that most of non-insured rate were higher than calculated nursing cost. The insurance rate, however, were lower than it. Reim-bursement was imputed to non-insured patients. So the current rate system became estrainged from cost system. When Remainder 25 items of nursing activities compared' to \1390 of daily basic nursing fee per patient belonged to payment as a insurance fee schedule, basic nursing fee schedule was 1-2% of calculated cost of nursing activities. Therefore it showed that nursing fee was not counted adequately in it. 5. Nursing cost by K-DRG estimated in chart review based on counting number of nursing activities and length of stay The result showed that average amount of total nursing cost was \183828.1 Comparison of nursing cost calculated by K- DRG and basic nursing fee schedule showed that only 12.3% of nursing cost was charged (Conclusion) From the above research result, It is fact that nursing prime cost should be estimated more accurately and included adequately in current medical payment system. The payment system of nursing activities should be introduced not only nursing activities of drug administration and injection fee belonged to insurance fee schedule but also most nursing activities belonged not to mekical fee schedule. Even if introducing payment system of nursing activities, It should be estimated scientific method of Accounting nursing cost So nurses could offer nursing care of good quality, thereby they could make a great contribution not merely to the convalescence of the patient but to the promotion of the people's health.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.1-18
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• 2015

Collaborative filtering(CF) algorithm has been popularly used for recommender systems in both academic and practical applications. A general CF system compares users based on how similar they are, and creates recommendation results with the items favored by other people with similar tastes. Thus, it is very important for CF to measure the similarities between users because the recommendation quality depends on it. In most cases, users' explicit numeric ratings of items(i.e. quantitative information) have only been used to calculate the similarities between users in CF. However, several studies indicated that qualitative information such as user's reviews on the items may contribute to measure these similarities more accurately. Considering that a lot of people are likely to share their honest opinion on the items they purchased recently due to the advent of the Web 2.0, user's reviews can be regarded as the informative source for identifying user's preference with accuracy. Under this background, this study proposes a new hybrid recommender system that combines with users' review mining. Our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and his/her text reviews on the items when calculating similarities between users. In specific, our system creates not only user-item rating matrix, but also user-item review term matrix. Then, it calculates rating similarity and review similarity from each matrix, and calculates the final user-to-user similarity based on these two similarities(i.e. rating and review similarities). As the methods for calculating review similarity between users, we proposed two alternatives - one is to use the frequency of the commonly used terms, and the other one is to use the sum of the importance weights of the commonly used terms in users' review. In the case of the importance weights of terms, we proposed the use of average TF-IDF(Term Frequency - Inverse Document Frequency) weights. To validate the applicability of the proposed system, we applied it to the implementation of a recommender system for smartphone applications (hereafter, app). At present, over a million apps are offered in each app stores operated by Google and Apple. Due to this information overload, users have difficulty in selecting proper apps that they really want. Furthermore, app store operators like Google and Apple have cumulated huge amount of users' reviews on apps until now. Thus, we chose smartphone app stores as the application domain of our system. In order to collect the experimental data set, we built and operated a Web-based data collection system for about two weeks. As a result, we could obtain 1,246 valid responses(ratings and reviews) from 78 users. The experimental system was implemented using Microsoft Visual Basic for Applications(VBA) and SAS Text Miner. And, to avoid distortion due to human intervention, we did not adopt any refining works by human during the user's review mining process. To examine the effectiveness of the proposed system, we compared its performance to the performance of conventional CF system. The performances of recommender systems were evaluated by using average MAE(mean absolute error). The experimental results showed that our proposed system(MAE = 0.7867 ~ 0.7881) slightly outperformed a conventional CF system(MAE = 0.7939). Also, they showed that the calculation of review similarity between users based on the TF-IDF weights(MAE = 0.7867) leaded to better recommendation accuracy than the calculation based on the frequency of the commonly used terms in reviews(MAE = 0.7881). The results from paired samples t-test presented that our proposed system with review similarity calculation using the frequency of the commonly used terms outperformed conventional CF system with 10% statistical significance level. Our study sheds a light on the application of users' review information for facilitating electronic commerce by recommending proper items to users.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.23-38
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• 2012

There is a large difference between purchasing patterns in an online shopping mall and in an offline market. This difference may be caused mainly by the difference in accessibility of online and offline markets. It means that an interval between the initial purchasing decision and its realization appears to be relatively short in an online shopping mall, because a customer can make an order immediately. Because of the short interval between a purchasing decision and its realization, an online shopping mall transaction usually contains fewer items than that of an offline market. In an offline market, customers usually keep some items in mind and buy them all at once a few days after deciding to buy them, instead of buying each item individually and immediately. On the contrary, more than 70% of online shopping mall transactions contain only one item. This statistic implies that traditional data mining techniques cannot be directly applied to online market analysis, because hardly any association rules can survive with an acceptable level of Support because of too many Null Transactions. Most market basket analyses on online shopping mall transactions, therefore, have been performed by expanding the co-occurrence criteria of traditional association rule mining. While the traditional co-occurrence criteria defines items purchased in one transaction as concurrently purchased items, the expanded co-occurrence criteria regards items purchased by a customer during some predefined period (e.g., a day) as concurrently purchased items. In studies using expanded co-occurrence criteria, however, the criteria has been defined arbitrarily by researchers without any theoretical grounds or agreement. The lack of clear grounds of adopting a certain co-occurrence criteria degrades the reliability of the analytical results. Moreover, it is hard to derive new meaningful findings by combining the outcomes of previous individual studies. In this paper, we attempt to compare expanded co-occurrence criteria and propose a guideline for selecting an appropriate one. First of all, we compare the accuracy of association rules discovered according to various co-occurrence criteria. By doing this experiment we expect that we can provide a guideline for selecting appropriate co-occurrence criteria that corresponds to the purpose of the analysis. Additionally, we will perform similar experiments with several groups of customers that are segmented by each customer's average duration between orders. By this experiment, we attempt to discover the relationship between the optimal co-occurrence criteria and the customer's average duration between orders. Finally, by a series of experiments, we expect that we can provide basic guidelines for developing customized recommendation systems. Our experiments use a real dataset acquired from one of the largest internet shopping malls in Korea. We use 66,278 transactions of 3,847 customers conducted during the last two years. Overall results show that the accuracy of association rules of frequent shoppers (whose average duration between orders is relatively short) is higher than that of causal shoppers. In addition we discover that with frequent shoppers, the accuracy of association rules appears very high when the co-occurrence criteria of the training set corresponds to the validation set (i.e., target set). It implies that the co-occurrence criteria of frequent shoppers should be set according to the application purpose period. For example, an analyzer should use a day as a co-occurrence criterion if he/she wants to offer a coupon valid only for a day to potential customers who will use the coupon. On the contrary, an analyzer should use a month as a co-occurrence criterion if he/she wants to publish a coupon book that can be used for a month. In the case of causal shoppers, the accuracy of association rules appears to not be affected by the period of the application purposes. The accuracy of the causal shoppers' association rules becomes higher when the longer co-occurrence criterion has been adopted. It implies that an analyzer has to set the co-occurrence criterion for as long as possible, regardless of the application purpose period.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.4
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• pp.115-133
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• 2021

This study examines the concept and related technologies of the 4th industrial revolution that has been mixed so far and examines the socio-economic changes and influences resulting from it, and the cases of responding to the 4th industrial revolution in major countries. Based on this, by deriving SWOT factors and calculating the importance of each factor for Korean venture companies to prepare for the forth industrial revolution, it was intended to help the government and policymakers in suggesting directions for establishing related policies. Furthermore, the purpose of this study was to suggest a direction for securing global competitiveness to Korean venture entrepreneurs and to help with basic and systematic analysis for further academic in-depth research. For this study, a total of 21 items derived through extensive literature research and data research to understand what are the necessary competency factors for internal and external environmental changes in order for Korean venture companies to have global competitiveness in the era of the 4th Industrial Revolution. After reviewing SWOT factors by three expert groups and confirming them through Delphi survey, the importance of each item was analyzed by using AHP, a systematic decision-making technique. As a result of the analysis, it was shown that Strength(48%), Opportunity(25%), Threat(16%), Weakness(11%) were considered important in order. In terms of sub-items, 'quick and flexible commercialization capability', 'platform/big data/non-face-to-face service activation', and 'ICT infrastructure and it's utilization' were shown to be of the comparatively high importance. On the other hand, in the lower three items, 'macro-economic stability and social infrastructure', 'difficulty in entering overseas markets due to global protectionism', and 'absolutely inferior in foreign investment' were found to have low priority. As a result of the correlation verification by item to see differences in opinions by industry, academia, and policy expert groups, there was no significant difference of opinion, as industry and academic experts showed a high correlation and industry experts and policy experts showed a moderate correlation. The correlation between the academic and policy experts was not statistically significant (p<0.01), so it was analyzed that there was a difference of opinion on importance. This was due to the fact that policy experts highly valued 'quick and flexible commercialization', which are strengths, and 'excellent educational system and high-quality manpower' and 'creation of new markets' which are opportunity items, while academic experts placed great importance on 'support part of government policy', which are strengths. The implication of this study is that in order for Korean venture companies to secure competitiveness in the field of the 4th industrial revolution, it is necessary to have a policy that preferentially supports the relevant items of strengths and opportunity factors. The difference in the details of strength factors and opportunity factors, which shows a high level of variability, suggests that it is necessary to actively review it and reflect it in the policy.

• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.55-66
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• 2021

The mobile application (APP) market is growing at an unprecedented speed. Amid such growth, the global platform providers are mandating exclusive in-app payments and charging 30% for platform commission fees. A serious tension has arisen between mobile global platform providers and local content providers. The present study attempts to analyze the domestic mobile game market and in-app payment commission fees. This study estimates the size of the domestic mobile game market and platform commission fees by directly using publicly available financial statements and footnote information of some representative listed mobile game firms. Also, the study analyzes the cost structures of the same sample firms and attempts to draw some implications on sustainable growths of the mobile game ecosystem. We estimated that, in 2019, the domestic mobile game market is around 4.9 trillion Won and the ensuing in-app payment commission fees market was 1.5 trillion Won. High market share firms display a proportional increase in in-app payment commission fees in relation to sales growth. This, in turn, makes the in-app payment commission fees a primary cost item far exceeding employee salaries and R&D expenses. During the same period, low market share firms generated a mere profit or experienced net loss. Analysis of the cost structure reveals that these firms are even more liable to higher in-app payment commission fee cost structure than high market share. Most constituents of the mobile game ecosystem are small business entrepreneurs. By employing a micro-level analysis, the study estimates that, in 2019, a representative median firm generates 530 million Won in sales. At the same time, it spends 190 million Won in employee salaries, 50 Won million in R&D and 190 million Won in in-app payment commission fees, respectively. In the absence of other cost items, these three cost items alone account for 73.8% of sales revenue. The results imply that a sustainable growth of the local mobile game market heavily depends upon the cost structure of such representative median firm, the in-app payment commission fees being the primary cost item of such firm.

• Journal of Food Hygiene and Safety
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• v.33 no.6
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• pp.438-446
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• 2018

The purpose of this study was to investigate food safety awareness, knowledge, and behavior in college students, to provide basic data for the increase in food safety awareness. Data were collected from 252 college students in Gyeonggi region, using a self-administered questionnaire. In results of concern about food safety, subjects responded 3.48 of 5.00 and have knowledge about food safety education revealing significantly higher awareness and concern than subjects without knowledge about food safety education. Food safety awareness of distributed food was 2.55, considered unsafe. Among reasons in perceiving food as unsafe, 62.3% of subjects expressed distrust about safety relative food production. As for risk factors relative to food safety, subjects responded that the highest risk factor was food additives (2.35), followed by heavy metal (2.38) and endocrine disrupters (2.38). Correlation analysis resulting in risk factors for food had positive correlation with each other, heavy metal revealed highest correlation with pesticide residue (r = 0.674), than with endocrine disrupters (r = 0.672). Also, genetically modified food revealed high correlation with radiation irradiated food. Regression analysis demonstrated that concern about food safety significantly influenced pro-actively engaging in food safety education. Meanwhile, 63.5% of subjects correctly responded to food safety knowledge items. The item 'the heavy metals are contaminated the most, in the roots of vegetables' revealed the lowest correct answer rate (38.1%). In food safety behavior, the item 'always wash hands before handling food and meal's revealed 3.85, and subjects with awareness and concern about food safety education, responded in significantly higher numbers than subject without awareness and concern about food safety. The most neglected concern was relative to frozen food thawed at room temperature. Together, students recognize that distributed foods are unsafe, and students with awareness and concern about food safety education showed higher knowledge compared to without awareness and concern experience about food safety eduction. So, systematic education using accurate and objective data is required to reduce anxiety and raise the level of awareness and concern about food safety.

• The Journal of Korean society of community based occupational therapy
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• v.10 no.3
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• pp.63-74
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• 2020

Objective : The purpose of this study is to investigate the influence of internal shame and self-control on interpersonal relationships in stroke patients, and to provide evidence and information necessary for clinical trials by analyzing the relationship. Methods : For this study, 150 stroke patients receiving occupational therapy services at institutions where occupational therapists work in Jeollanam-do and Chungnam regions were targeted through email and mail from March 1, 2019 to April 30, 2019. The questionnaire was conducted using general characteristics, Relationship Change Scales(RCS), Self-Control Scales(SCS), and Internalized Shame Scale(ISS) questionnaire. Descriptive statistical analysis was performed for the general characteristics of the study subjects, and t-test and one-way batch variance analysis (ANOVA) were used to compare interpersonal relationships according to general characteristics. The relationship between internalized shame, self-control, and interpersonal competence was analyzed by Pearson's correlation coefficient, and multiple regression analysis was performed to determine the factors affecting interpersonal relationships of stroke patients. Results : As a result of comparing interpersonal competence according to general characteristics, significant differences were found in terms of age and education level. Interpersonal relationships and internalized shame, internalized shame and self-control showed a negative correlation, and self-control and interpersonal relationships had a positive correlation, but self-control was the sub-factors of interpersonal relationships such as openness, sensitivity, intimacy, It was not statistically significant with the communication item. In addition, the items of inadequacy (β =-0.32) and adventure seeking (β =-0.23), which are sub-areas of internalized shame, affect the negative direction, and physical activity (β =0.22), which is the sub-area of self-control and the self-centered (β =0.24) item was found to have an effect on the positive direction. Conclusion : Therefore, additional research is needed that can operate a rehabilitation treatment program that applies various psychological factors for the formation of interpersonal relationships among stroke patients.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.23-48
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• 2021

The Korean economy has achieved continuous economic growth for the past several decades thanks to the government's export strategy policy. This increase in exports is playing a leading role in driving Korea's economic growth by improving economic efficiency, creating jobs, and promoting technology development. Traditionally, the main factors affecting Korea's exports can be found from two perspectives: economic factors and industrial structural factors. First, economic factors are related to exchange rates and global economic fluctuations. The impact of the exchange rate on Korea's exports depends on the exchange rate level and exchange rate volatility. Global economic fluctuations affect global import demand, which is an absolute factor influencing Korea's exports. Second, industrial structural factors are unique characteristics that occur depending on industries or products, such as slow international division of labor, increased domestic substitution of certain imported goods by China, and changes in overseas production patterns of major export industries. Looking at the most recent studies related to global exchanges, several literatures show the importance of cultural aspects as well as economic and industrial structural factors. Therefore, this study attempted to develop a forecasting model by considering cultural factors along with economic and industrial structural factors in calculating the import volume of each country from Korea. In particular, this study approaches the influence of cultural factors on imports of Korean products from the perspective of PUSH-PULL framework. The PUSH dimension is a perspective that Korea develops and actively promotes its own brand and can be defined as the degree of interest in each country for Korean brands represented by K-POP, K-FOOD, and K-CULTURE. In addition, the PULL dimension is a perspective centered on the cultural and psychological characteristics of the people of each country. This can be defined as how much they are inclined to accept Korean Flow as each country's cultural code represented by the country's governance system, masculinity, risk avoidance, and short-term/long-term orientation. The unique feature of this study is that the proposed final prediction model can be selected based on Design Principles. The design principles we presented are as follows. 1) A model was developed to reflect interest in Korea and cultural characteristics through newly added data sources. 2) It was designed in a practical and convenient way so that the forecast value can be immediately recalled by inputting changes in economic factors, item code and country code. 3) In order to derive theoretically meaningful results, an algorithm was selected that can interpret the relationship between the input and the target variable. This study can suggest meaningful implications from the technical, economic and policy aspects, and is expected to make a meaningful contribution to the export support strategies of small and medium-sized enterprises by using the import forecasting model.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.

• Journal of Global Scholars of Marketing Science
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• v.20 no.1
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• pp.59-69
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• 2010

Green or an environmental consciousness has been a major issue for businesses and government offices, as well as consumers, worldwide. In response to this movement, the Korean government announced, in the early 2000s, the era of "Green Growth" as a way to encourage green-related business activities. The Korean fashion industry, in various levels of involvement, presents diverse eco-friendly products as a part of the green movement. These apparel products include organic products and recycled clothing. For these companies to be successful, they need information about who are the consumers who consider green issues (e.g., environmental sustainability) as part of their personal values when making a decision for product purchase, use, and disposal. These consumers can be considered as eco-sumers. Previous studies have examined consumers' purchase intention for or with eco-friendly products. In addition, studies have examined influential factors used to identify the eco-sumers or green consumers. However, limited attention was paid to eco-sumers' disposal or recycling behavior of clothes in comparison with their green product purchases. Clothing disposal behaviors are ways that consumer can get rid of unused clothing and in clue temporarily lending the item or permanently eliminating the item by "handing down" (e.g., giving it to a younger sibling), donating, exchanging, selling, or simply throwing it away. Accordingly, examining purchasing behaviors of eco-friendly fashion items in conjunction with clothing disposal behaviors should improve understanding of a consumer's clothing consumption behavior from the environmental perspective. The purpose of this exploratory study is to provide descriptive information about Korean eco-sumers who have ecologically-favorable lifestyles and behaviors when buying and disposing of clothes. The objectives of this study are to (a) categorize Koreans on the basis of clothing disposal behaviors; (b) investigate the differences in demographics, lifestyles, and clothing consumption values among segments; and (c) compare the purchase intention of eco-friendly fashion items and influential factors among segments. A self-administered questionnaire was developed based on previous studies. The questionnaire included 10 items of clothing disposal behavior, 22 items of LOHAS (Lifestyles of Health and Sustainability) characteristics, and 19 items of consumption values, measured by five-point Likert-type scales. In addition, the purchase intention of two eco-friendly fashion items and 11 attributes of each item were measured by seven-point Likert type scales. Two polyester fleece pullovers, made from fabric created from recycled bottles with the PET identification code, were selected from one Korean brand and one US imported brand among outdoor sportswear brands. A brief description of each product with a color picture was provided in the survey. Demographic variables (i.e., gender, age, marital status, education level, income, occupation) were also included. The data were collected through a professional web survey agency during May 2009. A total of 600 final usable questionnaires were analyzed. The age of respondents ranged from 20 to 49 years old with a mean age of 34 years. Fifty percent of the respondents were males and about 58% were married, and 62% reported having earned university degrees. Principal components factor analysis with varimax rotation was used to identify the underlying dimensions of the clothing disposal behavior scale, and three factors were generated (i.e., reselling behavior, donating behavior, non-recycling behavior). To categorize the respondents on the basis of clothing disposal behaviors, k-mean cluster analysis was used, and three segments were obtained. These consumer segments were labeled as 'Resale Group', 'Donation Group', and 'Non-Recycling Group.' The classification results indicated approximately 98 percent of the original cases were correctly classified. With respect to demographic characteristics among the three segments, significant differences were found in gender, marital status, occupation, and age. LOHAS characteristics were reduced into the following five factors: self-satisfaction, family orientation, health concern, environmental concern, and voluntary service. Significant differences were found in the LOHAS factors among the three clusters. Resale Group and Donation Group showed a similar predisposition to LOHAS issues while the Non-Recycling Group presented the lowest mean scores on the LOHAS factors compared to the other segments. The Resale and Donation Groups described themselves as enjoying or being satisfied with their lives and spending spare-time with family. In addition, these two groups cared about health and organic foods, and tried to conserve energy and resources. Principal components factor analysis generated clothing consumption values into the following three factors: personal values, social value, and practical value. The ANOVA test with the factors showed differences primarily between the Resale Group and the other two groups. The Resale Group was more concerned about personal value and social value than the other segments. In contrast, the Non-Recycling Group presented the higher level of social value than did Donation Group. In a comparison of the intention to purchase eco-friendly products, the Resale Group showed the highest mean score on intent to purchase Product A. On the other hand, the Donation Group presented the highest intention to purchase for Product B among segments. In addition, the mean scores indicated that the Korean product (Product B) was more preferable for purchase than the U.S. product (Product A). Stepwise regression analysis was used to identify the influence of product attributes on the purchase intention of eco product. With respect to Product A, design, price and contribution to environmental preservation were significant to predict purchase intention for the Resale Group, while price and compatibility with my image factors were significant for the Donation Group. For the Non-Recycling Group, design, price compatibility with the factors of my image, participation to eco campaign, and contribution to environmental preservation were significant. Price appropriateness was significant for each of the three clusters. With respect to Product B, design, price and compatibility with my image factors were important, but different attributes were associated significantly with purchase intention for each of the three groups. The influence of LOHAS characteristics and clothing consumption values on intention to purchase Products A and B were also examined. The LOHAS factor of health concern and the personal value factor were significant in the relationships with the purchase intention; however, the explanatory powers were low in the three segments. Findings showed that each group as classified by clothing disposal behaviors showed differences in the attributes of a product, personal values, and the LOHAS characteristics that influenced their purchase intention of eco-friendly products. Findings would enable organizations to understand eco-friendly behavior and to design appropriate strategic decisions to appeal eco-sumers.