• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.127-141
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• 2017

Advances in Internet technologies and the proliferation of mobile devices enabled consumers to approach a wide range of goods and services, while causing an adverse effect that they have hard time reaching their congenial items even if they devote much time to searching for them. Accordingly, businesses are using the recommender systems to provide tools for consumers to find the desired items more easily. Association Rule Mining (ARM) technology is advantageous to recommender systems in that ARM provides intuitive form of a rule with interestingness measures (support, confidence, and lift) describing the relationship between items. Given an item, its relevant items can be distinguished with the help of the measures that show the strength of relationship between items. Based on the strength, the most pertinent items can be chosen among other items and exposed to a given item's web page. However, the diversity of the measures may confuse which items are more recommendable. Given two rules, for example, one rule's support and confidence may not be concurrently superior to the other rule's. Such discrepancy of the measures in distinguishing one rule's superiority from other rules may cause difficulty in selecting proper items for recommendation. In addition, in an online environment where a web page or mobile screen can provide a limited number of recommendations that attract consumer interest, the prudent selection of items to be included in the list of recommendations is very important. The exposure of items of little interest may lead consumers to ignore the recommendations. Then, such consumers will possibly not pay attention to other forms of marketing activities. Therefore, the measures should be aligned with the probability of consumer's acceptance of recommendations. For this reason, this study proposes a model-based approach to combine those measures into one unified measure that can consistently determine the ranking of recommended items. A regression model was designed to describe how well the measures (independent variables; i.e., support, confidence, and lift) explain consumer's acceptance of recommendations (dependent variables, hit rate of recommended items). The model is intuitive to understand and easy to use in that the equation consists of the commonly used measures for ARM and can be used in the estimation of hit rates. The experiment using transaction data from one of the Korea's largest online shopping malls was conducted to show that the proposed model can improve the hit rates of recommendations. From the top of the list to 13th place, recommended items in the higher rakings from the proposed model show the higher hit rates than those from the competitive model's. The result shows that the proposed model's performance is superior to the competitive model's in online recommendation environment. In a web page, consumers are provided around ten recommendations with which the proposed model outperforms. Moreover, a mobile device cannot expose many items simultaneously due to its limited screen size. Therefore, the result shows that the newly devised recommendation technique is suitable for the mobile recommender systems. While this study has been conducted to cover the cross-selling in online shopping malls that handle merchandise, the proposed method can be expected to be applied in various situations under which association rules apply. For example, this model can be applied to medical diagnostic systems that predict candidate diseases from a patient's symptoms. To increase the efficiency of the model, additional variables will need to be considered for the elaboration of the model in future studies. For example, price can be a good candidate for an explanatory variable because it has a major impact on consumer purchase decisions. If the prices of recommended items are much higher than the items in which a consumer is interested, the consumer may hesitate to accept the recommendations.

• Journal of Korea Multimedia Society
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• v.14 no.1
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• pp.54-75
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• 2011

PoC(Push-to-talk Over Cellular) is an integrated technology of group voice calls, video calls and internet based multimedia services. If a PoC user can not participate in the PoC session for various reasons such as an emergency situation, lack of battery capacity, then the user can use the PoC Box which has a similar functionality to the MM Box in the MMS(Multimedia Messaging Service). The RTSP(Real-Time Streaming Protocol) method is recommended to be used when there is a transmission session between the PoC box and a terminal. Since the existing VOD service uses a wired network, the packet size of RTSP-based VOD service is huge, however, the PoC service has wireless communication environments which have general characteristics to be used in RTSP method. Packet loss in a wired communication environments is relatively less than that in wireless communication environment, therefore, a buffering latency occurs in PoC service due to a play-out delay which means an asynchronous play of audio & video contents. Those problems make a user to be difficult to find the information they want when the media contents are played-out. In this paper, the following techniques and methods were proposed and their performance and superiority were verified through testing: cross-over dual reception buffering technique, advance partition multi-reception buffering technique, and on-demand multi-reception buffering technique, which are designed for effective picking up of information in media content being transmitted in short amount of time using RTSP when a user searches for media, as well as for reduction in playback delay; and same-priority packetization transmission method and priority-based packetization transmission method, which are media data packetization methods for transmission. From the simulation of functional evaluation, we could find that the proposed multiple receiving buffering and packetizing methods are superior, with respect to the media retrieval inclination, to the existing single receiving buffering method by 6-9 points from the viewpoint of effectiveness and excellence. Among them, especially, on-demand multiple receiving buffering technology with same-priority packetization transmission method is able to manage the media search inclination promptly to the requests of users by showing superiority of 3-24 points above compared to other combination methods. In addition, users could find the information they want much quickly since large amount of informations are received in a focused media retrieval period within a short time.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.153-169
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• 2011

Today using Internet environment is considered absolutely essential for establishing corporate marketing strategy. Companies have promoted their products and services through various ways of on-line marketing activities such as providing gifts and points to customers in exchange for participating in events, which is based on customers' membership data. Since companies can use these membership data to enhance their marketing efforts through various data analysis, appropriate website membership management may play an important role in increasing the effectiveness of on-line marketing campaign. Despite the growing interests in proper membership management, however, there have been difficulties in identifying inappropriate members who can weaken on-line marketing effectiveness. In on-line environment, customers tend to not reveal themselves clearly compared to off-line market. Customers who have malicious intent are able to create duplicate IDs by using others' names illegally or faking login information during joining membership. Since the duplicate members are likely to intercept gifts and points that should be sent to appropriate customers who deserve them, this can result in ineffective marketing efforts. Considering that the number of website members and its related marketing costs are significantly increasing, it is necessary for companies to find efficient ways to screen and exclude unfavorable troublemakers who are duplicate members. With this motivation, this study proposes an approach for managing duplicate membership based on the social network analysis and verifies its effectiveness using membership data gathered from real websites. A social network is a social structure made up of actors called nodes, which are tied by one or more specific types of interdependency. Social networks represent the relationship between the nodes and show the direction and strength of the relationship. Various analytical techniques have been proposed based on the social relationships, such as centrality analysis, structural holes analysis, structural equivalents analysis, and so on. Component analysis, one of the social network analysis techniques, deals with the sub-networks that form meaningful information in the group connection. We propose a method for managing duplicate memberships using component analysis. The procedure is as follows. First step is to identify membership attributes that will be used for analyzing relationship patterns among memberships. Membership attributes include ID, telephone number, address, posting time, IP address, and so on. Second step is to compose social matrices based on the identified membership attributes and aggregate the values of each social matrix into a combined social matrix. The combined social matrix represents how strong pairs of nodes are connected together. When a pair of nodes is strongly connected, we expect that those nodes are likely to be duplicate memberships. The combined social matrix is transformed into a binary matrix with '0' or '1' of cell values using a relationship criterion that determines whether the membership is duplicate or not. Third step is to conduct a component analysis for the combined social matrix in order to identify component nodes and isolated nodes. Fourth, identify the number of real memberships and calculate the reliability of website membership based on the component analysis results. The proposed procedure was applied to three real websites operated by a pharmaceutical company. The empirical results showed that the proposed method was superior to the traditional database approach using simple address comparison. In conclusion, this study is expected to shed some light on how social network analysis can enhance a reliable on-line marketing performance by efficiently and effectively identifying duplicate memberships of websites.

• Journal of Global Scholars of Marketing Science
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• v.20 no.2
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• pp.123-134
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• 2010

Considering the benefits for both consumers and suppliers, firms are taking advantage of the Internet as a medium to communicate with and sell products to their consumers. This trend makes the online shopping environment a growing field for both researchers and practitioners. This paper contributes by testing a model of online consumer behavior with websites varying in levels of hedonism. Unlike past studies, we included all three types of emotions (arousal, pleasure, and dominance) and flow into the model. In this study, we assumed that website interfaces, such as background colors, music, and fonts impact the three types of emotions at the initial exposure to the site (Mazaheri, Richard, and Laroche, 2011). In turn, these emotions influence flow and consumers' perceptions of the site atmospherics-perception of site informativeness, effectiveness, and entertainment. This assumption is consistent with Zajonc (1980) who argued that affective reactions are independent of perceptual and cognitive operations and can influence responses. We, then, propose that the perceptions of site atmospherics along with flow, influence customers' attitudes toward the website and toward the product, site involvement, and purchase intentions. In addition, we studied the moderating impact of the level of hedonism of websites on all the relationship in the model. Thus, the path coefficients were compared between "high" and "low" hedonic websites. We used 39 real websites from 12 product categories (8 services and 4 physical goods) to test the model. Among them, 20 were perceived as high hedonic and 19 as low hedonic by the respondents. The result of EQS 6.1 support the overall model: $\chi^2$=1787 (df=504), CFI=.994; RMSEA=.031. All the hypotheses were significant. In addition, the results of multi-groups analyses reveal several non-invariant structural paths between high and low hedonic website groups. The findings supported the model regarding the influence of the three types of emotions on customers' perceptions of site atmospherics, flow, and other customer behavior variables. It was found that pleasure strongly influenced site attitudes and perceptions of site entertainment. Arousal positively impacted the other two types of emotions, perceptions of site informativeness, and site involvement. Additionally, the influence of arousal on flow was found to be highly significant. The results suggested a strong association between dominance and customers' perceptions of site effectiveness. Dominance was also found to be associated with site attitudes and flow. Moreover, the findings suggested that site involvement and attitudes toward the product are the most important antecedents of purchase intentions. Site informativeness and flow also significantly influenced purchase intentions. The results of multi-group analysis supported the moderating impacts of hedonism of the websites. Compared to low (high) hedonic sites, the impacts of utilitarian (hedonic) attributes on other variables were stronger in high (low) hedonic websites. Among the three types of emotions, dominance (controlling feelings) effects were stronger in high hedonic sites and pleasure effects were stronger in low hedonic sites. Moreover, the impact of site informativeness was stronger for high hedonic websites compared to their low-hedonic counterparts. On the other hand, the influence of effectiveness of information on perceptions of site informativeness and the impact of site involvement on product attitudes were stronger for low hedonic websites than for high hedonic ones.

• Journal of Global Scholars of Marketing Science
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• v.20 no.1
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• pp.4-14
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• 2010

Collaboration strategies entail information sharing and other varied forms of cooperation that are mutually beneficial to the company and stakeholder groups. This study addresses the specific types of collaboration used in the fashion industry while also examining strategies that have been most successful for fashion companies and perceived benefits of collaboration from the customer perspective. In the present study we define fashion companies and brands as collaborators and their partners or stakeholders as collaboratees. We define collaboration as a cooperative relationship where more than two companies, brands or individuals provide customers with beneficial outcomes utilizing their own competitive advantages on an equal basis. Collaboration strategies entail information sharing and other varied forms of cooperation that are mutually beneficial to the company and stakeholder groups. Through collaboration, fashion companies have pursued both tangible differentiation, such as design and technology applications, and intangible differentiation such as emotional and psychological benefits to customers. As a result, collaboration within the fashion industry has become an important, value creating concept. This qualitative study utilized case studies and in-depth interview methodologies to examine customers' attitudes concerning collaboration in the fashion industry. A total of 173 collaboration cases were identified in Korean and international markets from 1998 through December 2008, focusing on fashion companies. Cases were collected from documented data including websites and industry data bases and top ranked portal search sites such as: Rankey.com; Naver, Daum, and Nate; and representative fashion information websites, Samsungdesignnet and Firstviewkorea. Cases were collected between November 2008 and February 2009. Cases were selected for the analysis where one or more partners were associated with the production of fashion products (excluding textile production), retail fashion products, or designer services. Additional collaboration case information was obtained from news articles, periodicals, internet portal sites and fashion information sites as conducted in prior studies (Jeong and Kim 2008; Park and Park 2004; Yoon 2005). In total, 173 cases were selected for analysis that clearly exhibited the benefits and outcomes of collaboration efforts and strategies between fashion companies and stakeholders. Findings show that the overall results show that for both partners (collaborator and collaboratee) participating in collaboration, that the major benefits are reduction of costs and risks by sharing resource such as design power, image, costs, technology and targets, and creation of synergy. Regarding types of collaboration outcomes, product/design was most important (55%), followed by promotion (21%), price (20%), and place (4%). This result shows that collaboration plays an important role in giving life to products and designs, particularly in the fashion industry which seeks for creative and newness. To be successful in collaboration efforts, results of the depth interviews in this study confirm that fashion companies should have a clear objective on why they are doing the collaboration. After setting the objective, they should select collaboratees that match their brand image and target market, make quality co-products that have definite concepts and differentiating factors, and also pay attention to increasing brand awareness. Based on depth interviews with customers, customer benefits were categorized into six factors: pursuit for individual character; pursuit for brand; pursuit for scarcity; pursuit for fashion; pursuit for economic efficiency; and pursuit for sociality. Customers also placed more importance on image, reputation, and trust of brands regarding the cases shown in the interviews. They also commented that strong branding should come first before other marketing strategies. However, success factors recognized by experts and customers in this study showed different results by subcategories. Thus, target customers and target market should be studied from various dimensions to develop appropriate strategies for successful collaboration.

• Journal of Distribution Science
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• v.8 no.4
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• pp.17-23
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• 2010

The Banking industry is expanding rapidly. To keep the competitive advantages, participating companies concentrate their resource to provide the distinguishable services by increasing the service quality. This study is to find that how three kinds of service quality(process, output, and service environment) affect on the customer satisfaction. In this paper, WPCSI (Weighted Potential Customer Satisfaction Index) was developed using Kano model and PCSI. Kano's model of service quality classification was used to improve customer satisfaction, customer satisfaction index was calculated. Customer satisfaction index was calculated using the existing potential for improving customer satisfaction index (PCSI Index) to complement the limitations of the weighted potential improve customer satisfaction index (WPCSI) were used. Analysis using PCSI improve the quality of service levels may be useful in assessing. However, this figure is a marginal degree of importance on customers and quality characteristics have been overlooked but has its problems. A service provided to customers with some important differences depending on the interpretation of the scope for improvement is to be classified. In other words, the level of customer satisfaction and the satisfaction of the current difference between the comparison factor for the company to provide information about the priority of the improvement was not significant. Companies are also considered important that the customer does not consider the uniform quality of service provided can be fallible. In this study, the weighted potential to improve it improve customer satisfaction index (WPCSI) proposed a new customer satisfaction index. This is for customers to recognize the importance of quality characteristics by weighting factors, to identify practical and improved priority to provide more useful information than has been. Weighted potentially improve customer satisfaction index (WPCSI) presented in this study by the customers aware of the importance of considering the quality factor is an exponent. The results, 'Employees' working ability', 'provided the desired service level', 'staff to handle this task quickly enough' to the customer of the factors had significant effects on satisfaction are met. On the other hand 'aggressiveness on the product description of employees', 'service environment as a whole, beautiful enough to' meet and shows no significant difference between satisfaction. But 'aggressiveness on the product description of employees' and reverse (逆) were attributable to the quality. Small dogs and overly aggressive products that encourage the customer dissatisfaction that can result in widening should be careful because the quality factor can be said. As a result, WPCSI is more effect to find critical factors which can affect customer satisfaction than PCSI. After that, we discuss effects and advantages of customer satisfaction using WPCSI. This study, along with these positive aspects, the limitations are implied. First, this study directly to the bank so that I could visit any other way for customers, utilizing the Internet or mobile to take advantage of the respondents were excluded from the analysis. Second, in survey questionnaires can help improve understanding of the measures will be taken. In addition to the survey targeted mainly focused on Seoul, according to a sample, so sampling can cause problems is the viscosity revealed intends.

• Journal of the Korean Society for Library and Information Science
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• v.34 no.1
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• pp.265-295
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• 2000

Education of library and information science must focus on practical education acted upon as a laboratory room in the characteristics of learning, because it cultivates a librarian as an information expert who can conduct professional affairs and services, applying traditional theory to the practical business of library and information. This dissertation suggested a new paradigm of an instructional media center as an advanced laboratory room which faithfully can run the curriculum of a library and information science for cultivating librarians, information experts who can satisfy the 21C information society. To carry out this purpose, I considered the various opinions of professors and librarians, after investigating and analyzing facilities and furnishings of laboratory rooms and teaching and learning data related to departments of library and information science in 32 universities. These contents can be summarized as follows : 1) Constructional media centers connected to education of library and information science sets laboratory rooms for practical classification and cataloging classes; laboratory rooms for film media which can utilize advanced media, listening tools, and practical materials; information management laboratory rooms which can experience the various information research methods through the Internet, cultivate the ability of information application, and teach the curriculum of library and information science related to computers. 2) Arrangement plans linked to laboratory rooms for classification and cataloging, one for film media, and one for information proceedings are as follows: , , and . 3) The size of each room is $162m^2$ (49.1pying); the number of persons to be admitted is about 40 to 50; each room has one media expert and one assistant as operating manager of exclusive responsibility. 4) Instructional & learning data which must be contained as instructional media of library and information science include computers, marginal tools related to it, listening materials, supplies for ordering books, teaching aids containing various equipment and tools, textbooks for practice, books connected to classification and cataloging for practice, and textbooks related to practical subjects and reference books. 5) Industrial media centers belonging to library and information science require for practice, general furnishings like bookshelves, and various material depository boxes.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2002.06a
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• pp.148-155
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• 2002

Multithreaded models improve the efficiency of parallel systems by combining inner parallelism, asynchronous data availability and the locality of von Neumann model. This model executes thread code which is generated by compiler and of which quality is given by the method of generation. But multithreaded models have the demerit that execution model is restricted to a specific platform. On the contrary, Java has the platform independency, so if we can translate from threads code to Java bytecode, we can use the advantages of multithreaded models in many platforms. Java executes Java bytecode which is intermediate language format for Java virtual machine. Java bytecode plays a role of an intermediate language in translator and Java virtual machine work as back-end in translator. But, Java bytecode which is translated from multithreaded models have the demerit that it is not secure. This paper, multhithread code whose feature of platform independent can execute in java virtual machine. We design and implement translator which translate from thread code of multithreaded code to Java bytecode and which check secure problems from Java bytecode.

• Journal of the Korean Society of Food Science and Nutrition
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• v.35 no.8
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• pp.1076-1087
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• 2006

The purpose of this study was to identify critical control points of service encounter by types of restaurants in order to manage moment of truth when customers encounter services. Questionnaires were collected from 812 customers (aged 15 years or older) who had used restaurants in Seoul, from October 24, 2005 to November 6, 2005. The main results of this study were as follows: Statistically significant differences were shown between importance and performance of interaction quality, physical environment quality and outcome quality. Significant differences were also shown in importance and performance of interaction and physical environment quality, and performance of outcome quality by restaurant types but no significant difference was indicated in importance of outcome quality by restaurant types. That is, the importance of outcome quality, which means the quality of food, was regarded as important by customers who use restaurants regardless of types of restaurants. The result of examining interaction quality showed that family restaurants managed waiting customers quite well and provided information on the Internet homepage. Performance of responding to customers with complaints was rated the highest in family restaurants. Regarding physical environment quality, importance and performance scores significantly differed by types of restaurants in order of fine-dining restaurants, family restaurants, and fast-food restaurants. In terms of service encounter quality, items whose importance scores were high but performance scores were low in importance-performance analysis matrix were 'quality of provided food is always uniform' and 'the space between other tables is enough' for fine-dining restaurants. In family restaurants, 'size of chairs or tables is enough', and 'the space between other tables is enough' were included in the items, while 'interior facilities are attractive', 'size of chairs and tables is enough', and 'the space between other tables is enough' were included in the items in case of fast-food restaurants. A difference was indicated depending on types of restaurants.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.93-107
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• 2014

In this paper, we report what we have observed with regard to user-perspective issue clustering based on multi-layered two-mode network analysis. This work is significant in the context of data collection by companies about customer needs. Most companies have failed to uncover such needs for products or services properly in terms of demographic data such as age, income levels, and purchase history. Because of excessive reliance on limited internal data, most recommendation systems do not provide decision makers with appropriate business information for current business circumstances. However, part of the problem is the increasing regulation of personal data gathering and privacy. This makes demographic or transaction data collection more difficult, and is a significant hurdle for traditional recommendation approaches because these systems demand a great deal of personal data or transaction logs. Our motivation for presenting this paper to academia is our strong belief, and evidence, that most customers' requirements for products can be effectively and efficiently analyzed from unstructured textual data such as Internet news text. In order to derive users' requirements from textual data obtained online, the proposed approach in this paper attempts to construct double two-mode networks, such as a user-news network and news-issue network, and to integrate these into one quasi-network as the input for issue clustering. One of the contributions of this research is the development of a methodology utilizing enormous amounts of unstructured textual data for user-oriented issue clustering by leveraging existing text mining and social network analysis. In order to build multi-layered two-mode networks of news logs, we need some tools such as text mining and topic analysis. We used not only SAS Enterprise Miner 12.1, which provides a text miner module and cluster module for textual data analysis, but also NetMiner 4 for network visualization and analysis. Our approach for user-perspective issue clustering is composed of six main phases: crawling, topic analysis, access pattern analysis, network merging, network conversion, and clustering. In the first phase, we collect visit logs for news sites by crawler. After gathering unstructured news article data, the topic analysis phase extracts issues from each news article in order to build an article-news network. For simplicity, 100 topics are extracted from 13,652 articles. In the third phase, a user-article network is constructed with access patterns derived from web transaction logs. The double two-mode networks are then merged into a quasi-network of user-issue. Finally, in the user-oriented issue-clustering phase, we classify issues through structural equivalence, and compare these with the clustering results from statistical tools and network analysis. An experiment with a large dataset was performed to build a multi-layer two-mode network. After that, we compared the results of issue clustering from SAS with that of network analysis. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The sample dataset contains 150 million transaction logs and 13,652 news articles of 5,000 panels over one year. User-article and article-issue networks are constructed and merged into a user-issue quasi-network using Netminer. Our issue-clustering results applied the Partitioning Around Medoids (PAM) algorithm and Multidimensional Scaling (MDS), and are consistent with the results from SAS clustering. In spite of extensive efforts to provide user information with recommendation systems, most projects are successful only when companies have sufficient data about users and transactions. Our proposed methodology, user-perspective issue clustering, can provide practical support to decision-making in companies because it enhances user-related data from unstructured textual data. To overcome the problem of insufficient data from traditional approaches, our methodology infers customers' real interests by utilizing web transaction logs. In addition, we suggest topic analysis and issue clustering as a practical means of issue identification.