• 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 the Daesoon Academy of Sciences
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• v.36
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• pp.227-266
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• 2020

The purpose of this paper is to review the representative Buddhist policies enforced during the reign of Gwanghaegun (光海君), the 15th king of the Joseon Dynasty, and the aspects of the Buddhist community affected by them. Through this, the influence and dynamism of Buddhism during the reign of Gwanghaegun will be revealed. Some of the findings will run contrary to what is popularly known about Joseon Buddhism and the policy of Sungyueokbul (崇儒抑佛), 'Revering Confucianism and Supressing Buddhism.' During the Joseon Dynasty, Neo-Confucianism was taken as an ideological background, and consequently, Buddhism was ostracized by the ruling class who advocated the exclusion of heretical views. This also characterized King Gwanghaegun's reign during the Mid-Joseon Dynasty. In reality though, the ruling class held mixed opinions about Buddhism, and this influenced the Buddhist community in the Gwanghaegun Period. The military might of Japan demonstrated during the Japanese Invasion of Korea in 1592, led the ruling class to recognize Buddhism, and as a result, the status of Buddhism rose to a certain extent. Based on its elevated status and the aftermath of the Japanese Invasion of Korea, the Buddhist community engaged in social welfare activities inspired by the notion of requiting favors, and the Buddhist community gained recognition for providing relief services. As a result, the number of monks increased, and the economic situation improved as land ownership was granted to temples and monks. This is the means by which the Japanese Invasion of Korea influenced the Buddhist policies of the Gwanghaegun Period and changed the religious topography of Buddhism. During the reign of King Gwanghaegun, the ruling class regarded Buddhism as heretical, but offered posthumous titles to monks who engaged in meritorious services during the Japanese invasions of 1592~1598. Favorable and/or preferential treatment was also granted to some Buddhist monks. In addition, monks began to perform labor projects that demanded organizational and physical strength, such as those which related to national defense and architecture. However, throughout the Gwanghaegun Period, the monks were paid a certain amount of compensation for their labor, and the monks' responsibility for labor increased. This can be understood as a partial reconciliation with Buddhism or an acceptance of Buddhism rather than the suppression of Buddhism often presented by historians. As for policies which affected Buddhism, the Buddhist community showed signs of cooperation with the ruling class, the creation and reconstruction of temples, and the production of Buddhist art. Through close ties with the ruling class, Buddhism during the Gwanghaegun Period saw the Buddhist community actively responded policies that impacted Buddhism, and this allowed their religious orders to be maintained. In this way, it was also confirmed that the monk, Buhyu Seonsu (浮休 善修) and his disciple Byeogam Gakseong (碧巖 覺性), took up leadership roles in their Buddhist community. The Buddhist-aimed policies of Gwanghaegun were implemented against the backdrop of the Buddhist community, wherein the ruling class held mixed opinions regarding Buddhism. As such, both improvements and set backs for Buddhism could be observed during that time period. The ruling class actively utilized the organizational power of Buddhism for national defense and civil engineering after the Japanese invasions of 1592~1598. Out of gratitude, they implemented appropriate compensation for the Buddhists involved. The Buddhist community also responded to policies that affected them through exchanges with the ruling class. They succeeded in securing funds and support to repair and produce Buddhist temples and artworks. A thoughtful inspection of the policies towards and responses to Buddhism during the Gwanghaegun Period, shows that Buddhism actually enjoyed considerable organizational power and influence. This flies in the face of the general description of Joseon Buddhism as "Sungyueokbul (revering Confucianism and supressing Buddhism)."

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.67-88
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• 2018

Until recently, as we recognize the significance of sustainable growth and competitiveness of small-and-medium sized enterprises (SMEs), governmental support for tangible resources such as R&D, manpower, funds, etc. has been mainly provided. However, it is also true that the inefficiency of support systems such as underestimated or redundant support has been raised because there exist conflicting policies in terms of appropriateness, effectiveness and efficiency of business support. From the perspective of the government or a company, we believe that due to limited resources of SMEs technology development and capacity enhancement through collaboration with external sources is the basis for creating competitive advantage for companies, and also emphasize value creation activities for it. This is why value chain network analysis is necessary in order to analyze inter-company deal relationships from a series of value chains and visualize results through establishing knowledge ecosystems at the corporate level. There exist Technology Opportunity Discovery (TOD) system that provides information on relevant products or technology status of companies with patents through retrievals over patent, product, or company name, CRETOP and KISLINE which both allow to view company (financial) information and credit information, but there exists no online system that provides a list of similar (competitive) companies based on the analysis of value chain network or information on potential clients or demanders that can have business deals in future. Therefore, we focus on the "Value Chain Network System (VCNS)", a support partner for planning the corporate business strategy developed and managed by KISTI, and investigate the types of embedded network-based analysis modules, databases (D/Bs) to support them, and how to utilize the system efficiently. Further we explore the function of network visualization in intelligent value chain analysis system which becomes the core information to understand industrial structure ystem and to develop a company's new product development. In order for a company to have the competitive superiority over other companies, it is necessary to identify who are the competitors with patents or products currently being produced, and searching for similar companies or competitors by each type of industry is the key to securing competitiveness in the commercialization of the target company. In addition, transaction information, which becomes business activity between companies, plays an important role in providing information regarding potential customers when both parties enter similar fields together. Identifying a competitor at the enterprise or industry level by using a network map based on such inter-company sales information can be implemented as a core module of value chain analysis. The Value Chain Network System (VCNS) combines the concepts of value chain and industrial structure analysis with corporate information simply collected to date, so that it can grasp not only the market competition situation of individual companies but also the value chain relationship of a specific industry. Especially, it can be useful as an information analysis tool at the corporate level such as identification of industry structure, identification of competitor trends, analysis of competitors, locating suppliers (sellers) and demanders (buyers), industry trends by item, finding promising items, finding new entrants, finding core companies and items by value chain, and recognizing the patents with corresponding companies, etc. In addition, based on the objectivity and reliability of the analysis results from transaction deals information and financial data, it is expected that value chain network system will be utilized for various purposes such as information support for business evaluation, R&D decision support and mid-term or short-term demand forecasting, in particular to more than 15,000 member companies in Korea, employees in R&D service sectors government-funded research institutes and public organizations. In order to strengthen business competitiveness of companies, technology, patent and market information have been provided so far mainly by government agencies and private research-and-development service companies. This service has been presented in frames of patent analysis (mainly for rating, quantitative analysis) or market analysis (for market prediction and demand forecasting based on market reports). However, there was a limitation to solving the lack of information, which is one of the difficulties that firms in Korea often face in the stage of commercialization. In particular, it is much more difficult to obtain information about competitors and potential candidates. In this study, the real-time value chain analysis and visualization service module based on the proposed network map and the data in hands is compared with the expected market share, estimated sales volume, contact information (which implies potential suppliers for raw material / parts, and potential demanders for complete products / modules). In future research, we intend to carry out the in-depth research for further investigating the indices of competitive factors through participation of research subjects and newly developing competitive indices for competitors or substitute items, and to additively promoting with data mining techniques and algorithms for improving the performance of VCNS.