• Journal of Digital Convergence
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• v.16 no.6
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• pp.9-18
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• 2018

Recently, the necessity and interest to improve the tourism preparedness for enhancing the quality of foreign tourists is increasing, but the related research is insufficient. The purpose of this study is to identify the preferential improvement elements related to the tourism preparedness of foreign tourists. To do this, we applied the R-IPA analysis to analyze and compare the elements affecting the tourist preparedness according to tourist type and time period. As a result of R-IPA analysis for all tourists, the elements that need to maintain the current quality levels were food, security, transit, shopping, and tourist attractiveness and the elements that need to be improved but low priority were language communication, travel expenses, and tourist information service. As a result of R-IPA analysis by tourist type, for individual tourists it is necessary to maintain current quality levels of transit, food, shopping, tourist attractiveness, and security. For group tourists, it is necessary to maintain current quality levels of accommodation, shopping, tourist attractiveness, and tourist information service, but food needs to be urgent improvement.

• Journal of Global Scholars of Marketing Science
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• v.14
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• pp.17-37
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• 2004

This research is speak that is tendencious in comprehension of external consumer information search behavior using on-line external information source to consumers who use on-line that is used as corporations' main strategic means. That is, classify consumer groups which was atomized according to type inflict consumer's information search knowledge level and shopping value study which use on-line, and decision factors of information search that these groups can influence a difference or each group which use information sources grasped what it is. Result that investigate information search knowledge level difference about study finding on-line information source utilization used mainly portal site, comparison site, auction site. And, utilization shopping pursuit value group used information source by portal site, auction site, niche shopping mall site and hedonic shopping pursuit value group used information source by portal site, auction site, shopping mall site. It confirmed that all variables(i.e. consumer-based variable and web site-based variable) are influencing variously in on-line external information search types. Finally, we proposed different way to erect strategic model about consumers that use on-line with study finding that see.

• The Journal of Bigdata
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• v.7 no.2
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• pp.153-172
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• 2022

Online shopping has transformed and rapidly grown the entire market at the forefront of wholesale and retail services as an effective solution to issues such as digital transformation and social distancing policy (COVID-19 pandemic). Small business owners, who form the majority at the center of the online shopping industry, are constantly collecting policy changes and market trend information to overcome these problems and use them for marketing and other sales activities in order to overcome these problems and continue to grow. Objective and refined information that is more closely related to the business is also needed. Therefore, in this paper, through the collection and analysis of big data information, which is the core technology of digital transformation, key variables are set in product classification, sales trends, consumer preferences, and review information of online shopping malls, and a method of using them for competitor comparison analysis and business sustainability evaluation has been prepared and we would like to propose it as a service. If small and medium-sized businesses can benchmark competitors or excellent businesses based on big data and identify market trends and consumer tendencies, they will clearly recognize their level and position in business and voluntarily strive to secure higher competitiveness. In addition, if the sustainable growth of the online shopping mall operator can be confirmed as an indicator, more efficient policy establishment and risk management can be expected because it has an improved measurement method.

• Proceedings of the Korea Information Processing Society Conference
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• 2000.10b
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• pp.1493-1496
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• 2000

인터넷 사용의 급격한 증가와 방대한 자료로 인하여 검색엔진에 대한 요구가 높아지고 있으며, 인터넷을 통한 쇼핑이 확대됨에 따라 가격에 대한 정확한 검색과 필터링이 불가피하게 되었다. 현재 정보를 찾기 위한 많은 검색엔진이 존재하지만 실제로 사용자가 필요로 하는 정확한 정보를 찾아주지는 못하고 있다. 따라서 특화된 검색엔진이 필요하게되고, 이로 인해 가격비교 검색엔진이라는 특화된 비교 검색엔진을 제안한다. 구현에 사용된 데이터베이스는 MySQL이며 스크립트 언어는 PHP이다.

• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.1009-1014
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• 2009

The Internet has changed the daily lives of human being in Korea and elsewhere in the world. It has changed the paradigms of traditional commercial activities and created immense opportunities for new business models. Recently, there has been much attention to the internet shopping mall as a means of commercial transaction. To make internet shopping mall competitive, effective customer satisfaction service should be provided and it is necessary to dynamic analysis method for customers' purchasing pattern. In this paper we apply the sequential analysis to comparison of two kinds of sales through the analysis of customers' purchasing pattern.

• Journal of the Korea Fashion and Costume Design Association
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• v.16 no.3
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• pp.117-133
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• 2014

The purpose of this study was to compare with shopping behavior and satisfaction of who have visited Dongdaemun and Myeongdong and to provide the fundamental data for differentiation strategy of two fashion trading area. The survey was carried out targeting shoppers who were in Dongdaemun and Myeoongdong and then a total of 778 questionnaires were used for the data analysis; frequency, t-test, chi-squre independence test using SPSS. 20. The results of this study were as follows. Main shoppers in Dongdaemun were the teenagers and twenties, and in Myeongdong were twenties and thirtys. The shoppers who have visited Dongdaemun significantly considered 'store factor' such as store size and comfortability, store interior, store location and accessibility, and 'product factor' including material and quality, design, formfitting, and various sizes, while Myeongdong visitors thought 'promotion factor'such as business hours, one stop shopping, sale and event etc. as important factor. The degree of satisfaction for marketing mix of Myeongdong trade area was higher than Dongdaemun's one. The case of impulse buying in two trade area was not high, while intention to revisit Dongdaemun and Myenongdong was all higher than average. Consumers were more intended to recommend Myeongdong over Dongdaemun to others.

• Journal of Fashion Business
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• v.17 no.5
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• pp.57-69
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• 2013

The purpose of this study is to compare the traditional shopping model and virtual avatar fitting model with regards to credibility and favorable impression effects on shopping mall satisfaction, product preferences, and purchasing intentions of apparel e-commerce. Questionnaires are distributed to 10-30s years old consumers who live in Seoul. Data are analyzed by descriptive statistics, Cronbach's ${\alpha}$, and regression analysis. The results are that the provoked credibility and favorable impression from the traditional shopping model affects the consumers' shopping mall satisfaction and buying intention in descending order. In additional, the credibility from traditional shopping model affects the product preference. The provoked credibility from the virtual fitting model influences the consumers' product preferences, and buying intentions. The favorable impression from the virtual fitting model affects shopping mall satisfaction. In general, provoked credibility from virtual avatar fitting model and traditional shopping model play key roles which could influence the consumers' buying intention.

• Journal of Intelligence and Information Systems
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• v.9 no.3
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• pp.177-191
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• 2003

Recommender systems are a personalized information filtering technology to help customers find the products they would like to purchase. Collaborative filtering is the most successful recommendation technology. Web usage mining and clustering analysis are widely used in the recommendation field. In this paper, we propose several hybrid collaborative filtering-based recommender procedures to address the effect of web usage mining and cluster analysis. Through the experiment with real e-commerce data, it is found that collaborative filtering using web log data can perform recommendation tasks effectively, but using cluster analysis can perform efficiently.

• Journal of the Korean Society of Clothing and Textiles
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• v.31 no.3 s.162
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• pp.486-494
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• 2007

Consumers' great demand for foreign apparel products created a new rapidly growing type of distribution channel that substitute traditional roles of importers. Most of the merchandise selling at this new type of e-mall are apparel and accessories. But, little study focuses on this new e-shopping mall. This study was designed to examine SISM(surrogate internet shopping mall) shopping behavior of apparel by analyzing purchase motives and consumer satisfaction and compare these variables with GISM(general internet shopping mall) shopping behavior. 166 SISM consumers and 166 GISM consumers responded for the study. Descriptive statistics, t-tests, and regression were used far statistical analysis. Results indicated that there were significant mean differences of purchase motive and consumer satisfaction between SISM and GISM consumers. Regression analysis showed that purchase motives had significant influence on consumer satisfaction for SISM and GISM consumers.

• Journal of Intelligence and Information Systems
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• v.10 no.1
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• pp.13-25
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• 2004

In the online shopping environment, consumers suffer from the process of selecting an optimal supplier. Although comparison shopping agent-based web sites and consumers' online community sites support the selection process, they have limitations when considering diverse and dynamic purchase conditions as a whole, which is the cause of additional consumer effort for optimal supplier selection. This study provides a decision support model with a sensitivity analysis for selecting an optimal supplier considering purchase conditions as a whole. It screens suppliers with filtering factors and provides optimal suppliers through a sensitivity analysis from a Quadratic Programming model. We implemented a prototype system and showed that it could be an effective decision support system for selecting the optimal supplier in the online shopping environment.