• Journal of Distribution Research
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• v.14 no.1
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• pp.27-44
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• 2009

This research classified the customer service factor of on-line shopping mall into tangibility, reliability, responsiveness, and empathy and analyzed the effect that the factors affect to consumer's purchase and re-purchase. If we present suggestions on the basis of these results of study, we would provide next two points: First, purchasers have utilized online shopping mall who pursued free from hard sell that being done in off-line and convenience of purchase affected more by reliability and responsiveness such as the fame of shopping mall that visit, reliability of security, and quick product search than the Customer of After-purchase Feedback influence for online purchasers decision factor out of consumer's purchase and re-purchase by on-line shopping mall customer service factor. Second, This study analyzed that online re-purchaser recognized the Customer of After-purchase Feedback factor high and built their loyalty through friendly emotion of on-line shopping mall and satisfaction of shopping mall service, and recommendation. In addition, they behave themselves as an affirmative messenger that is role of the Customer of After-purchase Feedback that make active opinion presentation and participation through community by important adjustment impact that empathy factor of on-line shopping mall customer service.

• Journal of Digital Convergence
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• v.15 no.11
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• pp.23-35
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• 2017

Since the beginning of 2002, KONEPS(Korea ON-line E-Procurement System) which is known with GePS(Government e-Procurement Service), has become a significant role in Korea public procurement service. This paper focuses on the amendments of the related laws and the practical improvement of inconveniences and inefficiencies in user's' position during the use of KONEPS. We analyzed the case of government procurement by major countries, the detailed process and function of KONEPS, and confirmed the revised procurement laws since the commencement of KONEPS. When a user of KONEPS makes an online purchase at the g2b shopping mall to purchase procurement goods, we analyzed focused on the inconvenience of option selection, user feedback after purchasing, unification of the purchase procedure, compared with general online shopping purchase. This paper suggests practical ways to improve the inconveniences and inefficiencies that arise in the process of procurement and use from KONEPS. It is necessary to study on the construction contracts and the empirical studies based on the questionnaire on the functions of procurement law and research model for the cases used in this study in the future.

• Science of Emotion and Sensibility
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• v.12 no.4
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• pp.433-442
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• 2009

People are tending to buy products through the Internet rather than purchasing them from the store. Some of the consumers give their feedback on line such as reviews, replies, comments, and blogs after they purchased the products. People are also likely to get some information through the Internet. Therefore, companies and public institutes have been facing this situation where they need to collect and analyze reviews or public opinions for them because many consumers are interested in other's opinions when they are about to make a purchase. However, most of the people's reviews on web site are too numerous, short and redundant. Under these circumstances, the emotion scanning system of text documents on the web is rising to the surface. Extracting writer's opinions or subjective ideas from text exists labeled words like GI(General Inquirer) and LKB(Lexical Knowledge base of near synonym difference) in English, however Korean language is not provided yet. In this paper, we labeled positive, negative, and neutral attribute at 4 POS(part of speech) which are noun, adjective, verb, and adverb in Korean dictionary. We extract construction patterns of emotional words and relationships among words in sentences from a large training set, and learned them. Based on this knowledge, comments and reviews regarding products are classified into two classes polarities with positive and negative using SO-PMI, which found the optimal condition from a combination of 4 POS. Lastly, in the design of the system, a flexible user interface is designed to add or edit the emotional words, the construction patterns related to emotions, and relationships among the words.

• 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.