• Journal of the Korean Society of Clothing and Textiles
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• v.36 no.6
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• pp.601-615
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• 2012

In this study, two representative measures in the job-focused approach and the employee-focused approach of emotional labor are explored to examine dimensionality and the predictability of each emotional labor measure on key consequences that include job stress, customer orientation, and job satisfaction. Data obtained from 193 department store apparel saleswomen were submitted for analysis. The results show that the emotional dissonance and emotional effort of the Kruml and Geddes measure are good predictors for job stress, customer orientation, and job satisfaction. In a test of the Davies measure, job stress is predicted by emotional dissonance and frequency while customer orientation is predicted by duration, variety, and the frequency of emotional expression in jobs. Duration is also a key predictor for job satisfaction. The result confirm the dimensionality and predictability of two emotional labor measures as well as suggests the need for the careful selection and refinement of appropriate measures according to consequences.

• Journal of the Korean Society of Clothing and Textiles
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• v.7 no.1
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• pp.17-25
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• 1983

The purpose of this study were to explore specific buying practices of college women and to determine if there were significant differences in shopping patterns between grade level. The participants consisted of 415 college women in grades freshmen through seniors. The data were collected by questionnaires. The statistical analysis of the obtained data included Caculation of the Frenquency Distribution and Chi-Square test. The specific findings of this study were as follows: 1. More than half of the students had purchasing dependence in their clothing purchase. 2. Most of the students planned their clothing purchase in advance, 3. College women interact minimally with sales people. They feel salespeople are not courteous and often dishonest in order to increase sales. They also feel that sales person does not have much knowledge about products. 4. Magazines and store displays affected college students clothing purchases more than other fashion stimulants. 5. Seniors had greater purchashing independence than freshmen. 6. Freshmen considered becoming-ness more important while seniors considered price more important in the selection of clothes.

• Journal of Fashion Business
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• v.20 no.4
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• pp.110-127
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• 2016

The purpose of this study was to analyze the brand preference, the clothing pursuit benefits, and the purchasing behavior of Chinese women. A survey questionnaire was distributed to Chinese women in their 20s and 30s. The surveys period was from 9th January 9th to $21^{st}$ February 2016. The respondents were 343 Chinese women living in Henan. The collected data was analyzed by frequency analysis, factor analysis, t-test, one-way analysis of variance, and Duncan's multiple range of verification. The key results of this study are herein summarized. The brands examined, listed in order of preference, are Ochirly, Only, Zara, Uniqlo, La Chapelle, H&M, Peace bird, Vero Moda, JNBY, and The Basic House. The five most preferred brands were then selected and further analyzed. For purchasing, the design and price were considered to be important, while for product evaluation, importance was given to the style, price, quality, and color. Factors important in the clothing pursuit benefits were found to be price pursuit, trend pursuit, brand pursuit, individual pursuit, comfort pursuit, and quality pursuit. Also, clothing pursuit benefits depends on the average monthly income, monthly clothing purchasing cost, and the education level of the individual. Lastly, we observed that the sources and store selection made noticeable difference in clothing pursuit benefits.

• Journal of the Korea Fashion and Costume Design Association
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• v.20 no.1
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• pp.1-16
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• 2018

The purposes of this study were to investigate actual use, design preference, and purchasing behaviors of bedding among married women in their 30s to 60s, and to determine the differences by age groups on these variables. The subjects were 623 married women and the research method was survey. The data were analyzed by descriptive statistics, cross tab analysis, multiple response analysis, ANOVA, and Duncan's multiple range test, using SPSS program. The results were as follows. First, on the actual use of bedding, the possession quantity of the bed cover and mattress was 2~4 while bedclothes and pillow was 7~8. The period of use of bedding was about 2~4 years and the frequency of washing was about 2~3 times a month. Second, married women generally preferred white and pastel tones, floral patterns, cotton fabrics, and a clean and comfortable image on bedding designs. Third, on the purchasing behaviors of bedding, married women considered functional damage and health & sanitary aspects as important purchase purposes. The most important selection criterion was fabric. Price, tactility, functionality, and manageability were also important criteria. Married women generally used the internet and store displays as important information sources, and considered bedding specialty stores as important purchase places. They generally spent about 200,000~300,000 won a year to purchase bedding. Fourth, the actual use, design preference, and purchasing behaviors of bedding showed many differences by age group. Therefore, it is needed to establish product development and marketing strategy of bedding, considering customers'age variable.

• Science of Emotion and Sensibility
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• v.23 no.1
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• pp.3-16
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• 2020

Recently, as the fashion e-commerce market has expanded, the proportion of online shops that are growing rapidly has increased and with them so too has competition. Most retailers operating online shops need their own competitiveness, and accordingly, the need to develop their logistics service quality components is increasing. This study investigated the quality of the logistics services, which is a factor of the logistics service quality of the internet shop. It influences customer satisfaction and repurchase intention by collecting samples from the customers using online fashion shops. Two hundred customers who shop online were surveyed to extract the data. The sample was subjected to basic statistical analysis using the SPSS 25.0 package, and factor analysis, t-test, ANOVA, and correlation analysis were performed. The results of this study showed that the information quality of proactive return, promptness of the return process, and reliability of the return cost had a positive impact on customer satisfaction, and it had a significant influence on the customer's repurchase intention to the online store. A selection of shops showed high amounts of return reasons, high customer satisfaction, and high repurchase, whereas, in general, many others scored poorly across these criteria. This suggests that a retailer operating online should consider pages for receiving information plus sales content in addition to the quality and constituent factors of its logistics services for returns that influence repurchase and satisfaction.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.