• Journal of Smart Tourism
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• v.2 no.4
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• pp.35-43
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• 2022

Tourism businesses use chat services to provide immediate customer support and to help users navigate within a website, but there are more outcomes of this interaction that should be examined. The current study aimed to discover if the online travel chat service quality and information value of the online travel chat service lead to user satisfaction with the service and visit intention to a recommended destination by Korea Tourism Organization's 1330 Live Chat. The results indicate that information value (functional and innovation) and online travel chat service quality (reliability, assurance, and security) lead to satisfaction with the live chat service and visit intention to a recommended destination. The results can benefit practitioners who want to expand and improve their customer service interaction and recommendations, and to scholars who study the relationship between customer services in tourism recommendation and sales context.

• 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.

• Korean Management Science Review
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• v.20 no.1
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• pp.25-36
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• 2003

Now that many organizations have invested a tremendous amount of money and efforts to operate Web sites on the Internet, there is a strong demand to understand the effectiveness of such investments. In other words, one of most frequent and important questions about their Web sites is "Will the current Web site management policy be effective enough to have more visitors come to our Web site\ulcorner" In this paper, a system which predicts the degree of user interest in the future to Web sites is constructed. The degree of user interest to a Web site is defined to be the visit counts for the Web site in the system. With higher the visit counts, the related site is considered to be more interesting. However, the figures of the visit counts themselves cannot explain properly the degree of user Interest in the future to the related Web sites (i.e. the effectiveness of the related Web sites). Therefore, the system also uses mechanisms which use the concept of the Moving Averages, which have been used frequently in the stock exchanges. In this paper. two prediction mechanisms are proposed and compared. The first mechanism uses the Golden Cross/the Dead Cross of the Moving Averages, while the second mechanism uses the changes of upward/downward direction of the Moving Averages. Experimental results show that the two prediction mechanisms proposed in this paper predict the degree of user interest in the future to the related Web sites very well in most cases. However, the first one is considered to be better than the second one In the sense that the second one is too much sensitive to the changes of visit counts.it counts.

• Management & Information Systems Review
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• v.32 no.1
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• pp.43-62
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• 2013

In order to know the characteristics of high visit web sites that many people have visited, 37 high visit websites of plastic surgery were compared to 69 benchmark sites of same industry. We selected 36 web site attributes that can be measured objectively from existing studies and composed the data set of 36 attributes multiplied by 106 websites. For analysis, Multiple Discriminant Analysis(MDA) and Decision Tree Technique are conducted for searching what attributes divide two group definitely. The result of this study shows the dividing attributes fall into 3 categories like 'Community', 'Mobile', 'Up to date'. Thus, we are able to conclude that high visit plastic surgery web sites are community centric site but not contents centric, response a change to mobile environment rapidly and are maintained with tide up to date. The methodology employed in this study provides an efficient way of improving satisfaction of visitors of plastic surgery website.

• Journal of the Korean Society for information Management
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• v.36 no.3
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• pp.149-174
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• 2019

This study analyzes the actual use data of the websites of university libraries, analyzes the users' usage behavior, and proposes improvement measures for the websites. The study analyzed users' traffic and analyzed their usage behavior from January 2018 to December 2018 on the C University website. The website's analysis tool used 'Google Analytics'. The web traffic variables were analyzed in five categories: user general characteristics, user environment analysis, visit analysis, inflow analysis, site analysis, and site analysis based on the metrics of sessions, users, page views, pages per session, average session time, and bounce rate. As a result, 1) In the analysis results of general characteristics of users, there was some access to the website not only in Korea but also in China. 2) In the user experience analysis, the main browser type appeared as Internet Explorer. The next place was Chrome, with a bounce rate of Safari, third and fourth, double that of the Explore or Chrome. In terms of screen resolution, 1920x1080 resolution accounted for the largest percentage, with access in a variety of other environments. 3) Direct inflow was the highest in the inflow media analysis. 4) The site analysis showed the most page views out of 4,534,084 pages, followed by the main page, followed by the lending/extension/history/booking page, the academic DB page, and the collection page.

• Convergence Security Journal
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• v.15 no.7
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• pp.9-19
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• 2015

The Android operating system is exposed to a phishing attack of stealing private information that a user enters into a web page. We have discovered two security vulnerabilities of the phishing attack. First, an always-on-top scheme allows malware to place a transparent user interface (UI) on the current top screen and intercept a user input. Second, the Android provides some APIs that allow malware to obtain the information of a currently visited web page. This paper introduces a phishing that attacks a web page by exploiting the two vulnerabilities. The attack detects a visit to a security-relevant web page and steals private information from the web page. Our experiments on popular web sites reveal that the attack is significantly accurate and dangerous.

• Journal of the Korean Society for Library and Information Science
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• v.38 no.2
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• pp.49-74
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• 2004

In this study, It designated to grasp and analyse the status and problem regarding whole information service system in the object of specialized information center in scientific and technological fields which is designated and supported by KISTI(Korea Institute of Science and Technology Information). And with this basis, it presented the efficient information service plan which reflected and adopted recent information technology, of specialized information center in scientific and technical fields To acquisite and analyse about the status and problem regarding whole information service system of specialized information center in scientific and technical fields, it executed a preceding research analysis, a questionnaire investigation, conversation which leads a direct visit to corresponding agency, website analysis of home page related corresponding agency, and the brainstorming method which leads the conference of the regular discussion with specialist in related fields in parallel.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.573-581
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• 2021

This paper examines the effect of e-service quality on the users of the Facebook marketplace. Users can always have stickiness intention. Stickiness intention is regarded as repetitive visits to and use of a certain website because of a commitment to continue using that website. Hence, we examine and explain the influence of e-service quality variables on stickiness intention. The variables used for e-service quality include efficiency, fulfillment, system availability, and privacy. The researchers related stickiness intention variable to online media users who always use the Facebook marketplace longer than other marketplaces, and users who visit the Facebook marketplace more often than other marketplaces. The method of data analysis was using inferential statistics GeSCA method. The GeSCA method is a Structural Equation Modeling (SEM) technique that can directly analyze latent variables, indicators, and measurement errors. The results of the GeSCA method before the COVID-19 pandemic states that an increase in e-service quality by 77.5% will increase stickiness intention by 61.2%. The results of the GeSCA method after the COVID-19 pandemic states that an increase in e-service quality by 85.2% would increase stickiness intention by 81.1%. This indicates that Facebook marketplace users had more stickiness intention for the Facebook marketplace.

• Journal of Digital Convergence
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• v.11 no.4
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• pp.147-152
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• 2013

Now, plastic surgery has become the industry for beauty. In order to know the characteristics of high-visit web sites that many people have visited, 33 high visit websites of plastic surgery were compared to 60 benchmark sites of same industry. We selected 34 web site attributes that can be measured objectively from existing studies. For analysis, Multiple Discriminant Analysis(MDA) is conducted for searching what attributes divide two group definitely. The result of this study shows the dividing attributes fall into 2 categories like 'Community', 'Up to date'. Thus, we are able to conclude that high-visit plastic surgery web sites are community-centric site but not contents-centric and are maintained with tide up to date. The methodology employed in this study provides an efficient way of improving satisfaction of visitors of plastic surgery website.

• Journal of Information Technology Applications and Management
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• v.11 no.4
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• pp.49-60
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• 2004

While the World-Wide Web offers an incredibly rich base of information, organized as a hypertext it does not provide a uniform and efficient way to retrieve specific information. Therefore, it is needed to develop an efficient web crawler for gathering useful information in acceptable amount of time. In this paper, we studied the order in which the web crawler visit URLs to rapidly obtain more important web pages. We also developed an internet agent for efficient web crawling using hyperlink structure and fitness of hypertext documents. As a result of experiment on a website. it is shown that proposed agent outperforms other web crawlers using BackLink and PageRank algorithm.