• Information Systems Review
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• v.4 no.2
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• pp.133-153
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• 2002

Internet lowers the search cost to acquire information about products and sellers price information. Because consumers can easily compare prices among different Internet retailers, the price competition among merchants in Internet has been intensified. For online retailers, it is important to differentiate their sites from competitors based upon services rather than prices. This study investigates which factors influence on consumers price sensitivity in Internet book markets and examines whether consumers price sensitivity has any effect on the type of preferred Internet bookstore (hybrid or pure online book stores). Survey is conducted to empirically investigate the relationship among the various consumer attitudes and price sensitivity. The result shows that price search and price importance have effect on the choice of Internet bookstore. Consumers with higher price sensitivity preferred to purchase books from pure Internet bookstores rather than hybrid (click-and-mortar) bookstores. This study provides an exploratory framework that tries to understand consumer behavior about price sensitivity that might provide some marketing implications to lessen the price competition of Internet retailers.

• The Korea Journal of Herbology
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• v.36 no.5
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• pp.1-14
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• 2021

Objectives : From January 2020, when the first confirmed case of COVID-19 in Korea, the use of health information using the Internet is expected to increase. It is expected that there will be a significant change in the general public's interest in Korean herbal medicines for health care. Therefore, in this study, we tried to confirm the change in the search trend of Korean herbal medicines after the COVID-19 epidemic. Methods : Using the "Naver DataLab (http://datalab.naver.com)" service of a Korean portal site Naver, search volume was investigated with 606 Korean herbal medicines as keywords. The search period was from January 2020, right after the onset of COVID-19, to June 2021. The search results were sorted by the peak search volume and the total search volume. Results : 'Cheonsangap (천산갑, 穿山甲, Manitis Squama)' was the most searched Korean herbal medicine in the peak search volume and total search volume with least bias. Conclusions : The problem of supply and demand of Korean herbal medicines of high public interest was identified. Broadcasting and media exposure were the factors that had a big impact on the search volume for Korean herbal medicines. As it was confirmed that the search volume for Korean herbal medicines increased rapidly due to media exposure, it is necessary to provide correct information about Korean herbal medicines, improve public awareness, and manage stable supply and demand based on continuous search trend monitoring.

• Proceedings of the CALSEC Conference
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• 1999.07b
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• pp.575-586
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• 1999

The advent of the Internet and the enormous increase in volume of electronically stored information (SGML, Image, Sound, etc.) has led to substantial work on IR(Information Retrieval). To service on the WWW, construction and retrieval technology of SGML, which is the fundamental standard data format for CALS/EC, is needed specially. Due to such a change, it becomes essential to change the existing paradigm of conventional information retrieval systems and to adopt new Internet service system with search engine, SGML browser and advanced Internet technology on WWW. KIPRIS(Korea Industrial Property Rights Information Service), which is the specialized and integrated Internet service systems in the field of industrial property rights information service, is trying to be a guide for our country to establish its technological competitiveness with providing the online service of high quality. The objective of the paper identifies features and technologies of KIPRIS IR(Information Retrieval) system based on WWW as follows. First, it describes the development background and process of KIPRIS. Second, it presents a fundamental technology that consists of IR(Information Retrieval) concept, BRS(Bibliographical Retrieval System) search engine, SGML implementation technologies and the Internet/WWW technologies. Third, it provides information about system configuration, architecture, and the features and characteristics of KIPRIS. Finally, the implemented KIPRIS system is introduced.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.

• Journal of Korean Library and Information Science Society
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• v.53 no.3
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• pp.263-285
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• 2022

This study presented the factor analysis on constructing the new factors affecting the middle school students' online learning behaviors from the questionnaires employed among middle school students. A total of 204 students participated and the data were collected in South Korea. The sample of middle school ninth-grade students was selected and used through purposive sampling. Findings from the factor analysis provided evidence for the eight-factor solution for the 35-items accounting for 66.15% of the shared variance. A wide range of factors has been considered to identify students' online learning behaviors. The appropriate experience and use of e-learning in the middle school period is also important as it will be a critical stepstone for future education. This research provides information that has been taken into account for advancing online learning to enhance the quality of e-learning systems for middle school students. The study results provided eight new factors affecting the middle school students' online learning behaviors; that is 1) communication using social media as a learning tool, 2) intention to share information using ICT, 3) addiction of technology, 4) adoption of technology, 5) seeking information using ICT, 6) use of social media learning, 7) information search using ICT, and 8) immersion of technology. This study confirmed that middle school students prefer communication using social media as a learning tool, and value intention to share information using ICT for the most part. The data obtained based on factor analysis can highlight the online learning behaviors towards a mixture of social media learning and ICT to ensure a new educational platform for the future of e-learning. This research expects to be useful for both middle schools of online learning to better understand students' online learning behaviors and design online learning environments and information professionals to better assist students who particularly need digital literacy.

• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.3
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• pp.175-187
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• 2011

People could be effected by other's recommendations when they are in making decision to buy something. This phenomenon was called as 'word of mouth effect' and proved to be very significant to change consumer's attitude because of a lack of information about products or services what they needed. And also there are two kinds of views about Brand communication. One is that Brand communication would be weakening due to less cost to search information. the other is that Brand communication would be strengthen because of a lack of sensibility to product. Therefore, the purpose of this study is to examine the function of online word of month and the effect of brand communication by adopting a concept of BRQ. As The results, First, Online word of mouth have significantly effected on consumer's attitude even though those information are all texts and have been suspicious if it is true or not. Second, consumer brand relationship quality have a influence on consumer's attitude. In conclusion, This study would give implications for companies to build marketing strategies.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.265-274
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• 2021

In this paper, we propose a novel recommendation model based on Doc2vec using search keywords and product details. Until now, a lot of prior studies on recommender systems have proposed collaborative filtering (CF) as the main algorithm for recommendation, which uses only structured input data such as customers' purchase history or ratings. However, the use of unstructured data like online customer review in CF may lead to better recommendation. Under this background, we propose to use search keyword data and product detail information, which are seldom used in previous studies, for product recommendation. The proposed model makes recommendation by using CF which simultaneously considers ratings, search keywords and detailed information of the products purchased by customers. To extract quantitative patterns from these unstructured data, Doc2vec is applied. As a result of the experiment, the proposed model was found to outperform the conventional recommendation model. In addition, it was confirmed that search keywords and product details had a significant effect on recommendation. This study has academic significance in that it tries to apply the customers' online behavior information to the recommendation system and that it mitigates the cold start problem, which is one of the critical limitations of CF.

• Journal of Information Management
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• v.23 no.3
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• pp.45-73
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• 1992

STN International is operated in North America by CAS, a division of the American Chemical Society;by FIZ Karlsruhe in Eruope ; and by JICST in Japan. All three are not-for-profit scientific organizations. This paper describes Messenger software that is designed for fast and efficient information retrieval, the advanced front-end STN Express software that saves time and effort, and databases in STN.

• Journal of the Economic Geographical Society of Korea
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• v.25 no.1
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• pp.131-146
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• 2022

The purpose of this study is to examine the locational characteristics of cafes in Jeju Island and the changes. For the purpose, we identify the spatial distribution patterns of openings and closings by period from the first opening of cafes in Jeju Island to the present. In particular, we analyze the spatial distribution characteristics found in the locations of cafes that have been opened and closed after the outbreak of COVID-19, in which new stores have significantly increased. In addition, we identify the regional attributes and the influence of online that have affected the distribution of currently open cafes and cafes that have opened or closed during the COVID-19 outbreak. As a result of empirical analysis, Jeju Island is a tourist destination and island region with the characteristics of determining major destinations through information search, showing a different distribution form from the location of cafes in inland cities. In particular, as a result of frequency analysis by extracting keyword search volume for cafes in Jeju Island, online accessibility such as information search for new areas and places in Jeju Island has become more diversified and expanded after COVID-19. In addition, as a result of calculating the distance to cafes by road size, the relationship between physical location and road accessibility, which has traditionally been an important factor, was relatively low. This study is meaningful in that it revealed the distribution patterns and characteristics of cafe locations in Jeju Island by reflecting the influence of online and offline.

• Fashion & Textile Research Journal
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• v.15 no.1
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• pp.46-56
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• 2013

This study categorizes online fashion shopping malls according to consumer store patronage behavior as well as classifies consumer groups by online shopping mall patronage to understand the unique characteristics in each phases of purchase. A quantitative survey was conducted using 487 questionnaires from women in their 20s and 30s. The data were analyzed using frequency analysis, cross-tabulations, factor analysis, T-test, ANOVA, cluster analysis, and ${\chi}^2$-test. The findings of this study are as follows. First, online shopping malls were classified into three types of 'integrated mall', 'open market' and 'specialized fashion mall'. Second, based on one of the three types of categorization consumer groups patronizing each type turned out as follows: integrated mall patrons (141, 28.95%), open market patrons (226, 46.41%) and the specialized mall patrons (119, 24.64%). Third, the characteristics of each group had significant differences according to clothing shopping orientation, information search, shopping mall behavior, spending on online shopping, and e-loyalty.