• The Journal of the Korea Contents Association
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• v.20 no.6
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• pp.342-353
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• 2020

The purpose of this study is to identify priority factors for improving service quality of cosmetic stores in drug stores(DRS) and department stores(DES) and to provide basic data for improving service quality of cosmetic stores by analyzing the service quality based on the Kano model and the Potential Customer Satisfaction Improvement (PCSI) Index. As a result, most items of quality factors of cosmetic stores in both stores were evaluated as attractive quality factors. As a result of PCSI Index comparison, the quality factors of 'Reliability', 'Responsiveness', and 'Empathy' items for DRS and 'Empathy' and 'Reliability' items for DES had higher priority for improvement. That is, if these factors are improved, there is a high potential to improve customer satisfaction. Through this study, practical implications were provided by identifying service quality factor classification and priorities for customer satisfaction improvement of DRS and DES. This is expected to contribute to the guidelines for improving customer satisfaction in the future.

• Journal of the Korean Society of Food Culture
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• v.23 no.5
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• pp.605-615
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• 2008

The principal objective of this study was to categorize service attributes on the basis of the asymmetric and non-linear relationship existing between service attributes and customer satisfaction. Researchers generally assume that service attribute performances and customer satisfaction are both symmetrical and linear. That is to say, improvements in attribute performance will inevitably result in increased customer satisfaction. However, this is not always the case. Certain attributes have been shown not to create satisfaction even when improved, and others do not create dissatisfaction even when their performance ratings become negative. Understanding this relationship is crucial not only to researchers, but also to service managers. Service managers can arrange their priorities with regard to which attributes must be improved or promoted first, in an environment of limited technical, financial, and human resources. Many studies into this asymmetric and non-linear relationship have recently been conducted, beginning with Herzberg's motivation-hygiene theory (1976) and the disconfirmation theory, which was eventually developed into Kano's model (1984). This study attempted to determine the impact level of service attributes on incidents of satisfaction or dissatisfaction. It used 30 service attributes generated by Park (2008) in the CIT research into family restaurants. The data were collected from 600 participants, 300 incidences of satisfaction and 300 incidents of dissatisfaction, via an online survey. The t-test was used to confirm the difference between the satisfaction group's and dissatisfaction group's attributes. 11 attributes were found to be significant at a level of p>0.05. This indicates that the 11 attributes exerted different impacts on satisfaction and dissatisfaction, which confirmed the asymmetric and non-linear relationship. 14 attributes were categorized into the core service, 1 attribute into the quality service, 7 attributes into the basic service, and 8 attributes into the neutral service. Strategic customer service management was recommended for the 'A' family restaurant as an example, on the basis of the asymmetric and non-linear relationship and the characteristics of the four service factors.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.129-142
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• 2016

Customer product reviews have become one of the important factors for purchase decision makings. Customers believe that reviews written by others who have already had an experience with the product offer more reliable information than that provided by sellers. However, there are too many products and reviews, the advantage of e-commerce can be overwhelmed by increasing search costs. Reading all of the reviews to find out the pros and cons of a certain product can be exhausting. To help users find the most useful information about products without much difficulty, e-commerce companies try to provide various ways for customers to write and rate product reviews. To assist potential customers, online stores have devised various ways to provide useful customer reviews. Different methods have been developed to classify and recommend useful reviews to customers, primarily using feedback provided by customers about the helpfulness of reviews. Most shopping websites provide customer reviews and offer the following information: the average preference of a product, the number of customers who have participated in preference voting, and preference distribution. Most information on the helpfulness of product reviews is collected through a voting system. Amazon.com asks customers whether a review on a certain product is helpful, and it places the most helpful favorable and the most helpful critical review at the top of the list of product reviews. Some companies also predict the usefulness of a review based on certain attributes including length, author(s), and the words used, publishing only reviews that are likely to be useful. Text mining approaches have been used for classifying useful reviews in advance. To apply a text mining approach based on all reviews for a product, we need to build a term-document matrix. We have to extract all words from reviews and build a matrix with the number of occurrences of a term in a review. Since there are many reviews, the size of term-document matrix is so large. It caused difficulties to apply text mining algorithms with the large term-document matrix. Thus, researchers need to delete some terms in terms of sparsity since sparse words have little effects on classifications or predictions. The purpose of this study is to suggest a better way of building term-document matrix by deleting useless terms for review classification. In this study, we propose neutrality index to select words to be deleted. Many words still appear in both classifications - useful and not useful - and these words have little or negative effects on classification performances. Thus, we defined these words as neutral terms and deleted neutral terms which are appeared in both classifications similarly. After deleting sparse words, we selected words to be deleted in terms of neutrality. We tested our approach with Amazon.com's review data from five different product categories: Cellphones & Accessories, Movies & TV program, Automotive, CDs & Vinyl, Clothing, Shoes & Jewelry. We used reviews which got greater than four votes by users and 60% of the ratio of useful votes among total votes is the threshold to classify useful and not-useful reviews. We randomly selected 1,500 useful reviews and 1,500 not-useful reviews for each product category. And then we applied Information Gain and Support Vector Machine algorithms to classify the reviews and compared the classification performances in terms of precision, recall, and F-measure. Though the performances vary according to product categories and data sets, deleting terms with sparsity and neutrality showed the best performances in terms of F-measure for the two classification algorithms. However, deleting terms with sparsity only showed the best performances in terms of Recall for Information Gain and using all terms showed the best performances in terms of precision for SVM. Thus, it needs to be careful for selecting term deleting methods and classification algorithms based on data sets.

• Journal of IKEEE
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• v.8 no.2 s.15
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• pp.216-223
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• 2004

The customer's bulletin board is the important channel to get opinions from customers directly. The effective management of the bulletin board for the customer improves the reliance by providing the best replies and by accepting opinions of the customer and furthermore, that can raise the customer's reliance of the whole shopping mall is the important eCRM method. But, the present mostly customer's bulletin board is been replied without any classifying about many kinds of question. Consequently, The shopping mall should do systematic management of the best professional reply about many kinds of question. In order to resolve this problem, we implement a classifier called Naive Bayesian classifier is classified automatically bulletin board for eCRM of shopping mall.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.2
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• pp.207-216
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• 2000

This paper introduces an effective system design method to develop a customer oriented product using a design optimization process and to select a set of critical design paramenters,. The process results in the development of a successful product satisfying customer needs and reducing development risk. The proposed scheme adopted a five step QFD(Quality Function Deployment) in order to extract design parameters from customer needs and evaluated their priority using risk factors for extracted design parameters. In this process we determine critical design parameters and allocate them to subsystem designers. Subsequently design engineers develop and test the product based on these parameters. These design parameters capture the characteristics of customer needs in terms of performance cost and schedule in the process of QFD, The subsequent risk management task ensures the minimum risk approach in the presence of design parameter uncertainty. An application of this approach was demonstrated in the development of weld quality monitoring system. Dominant design parameters affect linearity characteristics of weld defect feature vectors. Therefore it simplifies the algorithm for adopting pattern classification of feature vectors and improves the accuracy of recognition rate of weld defect and the real time response of the defect detection in the performance. Additionally the development cost decreases by using DSP board for low speed because of reducing CPU's load adopting algorithm in classifying weld defects. It also reduces the cost by using the single sensor to measure weld defects. Furthermore the synergy effect derived from the critical design parameters improves the detection rate of weld defects by 15% when compared with the implementation using the non-critical design parameters. It also result in 30% saving in development cost./ The overall results are close to 95% customer level showing the effectiveness of the proposed development approach.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.608-613
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• 2006

There are many e-showing mall because of revitalization of e-commerce system. It is necessary to recommending system of products that is for saving time and effort of customer. In this paper, we propose the system that is applying classification among data mining techniques to analysis of log data of customer. This log data contains access of user and purchasing of products. The proposed system operates in two phases. The first phase is composed of data filter module and association extraction module among web pages. The second phase is composed of personalization module and rule generation module. Customer can easily know the recommended sites because the proposed system can present rank of the recommended web pages to customer. As a result, the proposed system can efficiently do recommending of products to customer.

• The Journal of the Korea Contents Association
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• v.12 no.7
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• pp.346-357
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• 2012

This study classified the various quality features of coffee shop by Kano model with customers' perspective. Also both satisfaction coefficient and dissatisfaction coefficient are calculated to analyse the relative influence of quality features on customer satisfaction. This study also dragged the potential customer satisfaction improvement index to scrutinize the quality improvement possibility for coffee shops. The analysis results showed that low price, luxurious interior, restfulness of table and chair, usability of wireless internet are belonged to the Attractive quality. On the other hand, cleanliness and hygiene, quality to price are identified as the One-dimensional quality. The current satisfaction level for both 'Caffe Bene' and 'Starbucks' are measured to draw the potential customer satisfaction improvement index. The result showed that low price and quality to price appeared to be the highest in its quality improvement possibility. The findings of this study help understanding the quality features to focus on and strengthening the competitiveness for coffee shops.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.347-354
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• 1999

The credit rating represents an assessment of the relative level of risk associated with the timely payments required by the debt obligation. In this paper, we present a new approach to credit rating of customers based on the rough set theory. The concept of a rough set appeared to be an effective tool for the analysis of customer information systems representing knowledge gained by experience. The customer information system describes a set of customers by a set of multi-valued attributes, called condition attributes. The customers are classified into groups of risk subject to an expert's opinion, called decision attribute. A natural problem of knowledge analysis consists then in discovering relationships, in terms of decision rules, between description of customers by condition attributes and particular decisions. The rough set approach enables one to discover minimal subsets of condition attributes ensuring an acceptable quality of classification of the customers analyzed and to derive decision rules from the customer information system which can be used to support decisions about rating new customers. Using the rough set approach one analyses only facts hidden in data, it does not need any additional information about data and does not correct inconsistencies manifested in data; instead, rules produced are categorized into certain and possible. A real problem of the evaluation of the evaluation of credit rating by a department store is studied using the rough set approach.

• Korean Journal of Human Ecology
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• v.16 no.1
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• pp.89-101
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• 2007

This study deals with classification of various approaches regarding consumer experience, core characteristics, and contribution of experiential marketing. This study classifies seven approaches regarding consumer experience into 4 broad views (1)experience as experiential brand concept, 2)experience as a behavior, 3)experience as a behavior focusing on an affect, 4)experience as a holistic experience. Each of these 4 views of experience as well as the seven approaches is further explained in details. Five core characteristics of experiential marketing are suggested: l)forming a deep relationship between a customer and a brand, 2)being related to personal final values, 3)holistic experience with a brand, 4)fun, pleasure, and immersion, and 5)keeping customers through customer satisfaction and giving impression to a customer. Five propositions based on these core characteristics are suggested. The contribution of experiential marketing is suggested.

• The Journal of the Korea Contents Association
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• v.18 no.5
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• pp.464-471
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• 2018

The business card is regarded as the simplest means as well as a tool the most likely to use as a means of sales. Every day, we are exchanging business cards with many customers, but the paper based business card is easy to discard and difficult for searching information on the business card. As a solution, if we take a photographed business card with a smart phone and make it into a database, we can easily obtain customer information we wanted for sales at any time. In this study, we develop an application solution based on electronic business card database that supports sales management. The system operates in a cloud environment and has various decision support functions such as customer's human network management, customer classification, and finding prospective customers.