• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.85-109
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• 2018

Recommender system recommends the items expected to be purchased by a customer in the future according to his or her previous purchase behaviors. It has been served as a tool for realizing one-to-one personalization for an e-commerce service company. Traditional recommender systems, especially the recommender systems based on collaborative filtering (CF), which is the most popular recommendation algorithm in both academy and industry, are designed to generate the items list for recommendation by using 'overall rating' - a single criterion. However, it has critical limitations in understanding the customers' preferences in detail. Recently, to mitigate these limitations, some leading e-commerce companies have begun to get feedback from their customers in a form of 'multicritera ratings'. Multicriteria ratings enable the companies to understand their customers' preferences from the multidimensional viewpoints. Moreover, it is easy to handle and analyze the multidimensional ratings because they are quantitative. But, the recommendation using multicritera ratings also has limitation that it may omit detail information on a user's preference because it only considers three-to-five predetermined criteria in most cases. Under this background, this study proposes a novel hybrid recommendation system, which selectively uses the results from 'traditional CF' and 'CF using multicriteria ratings'. Our proposed system is based on the premise that some people have holistic preference scheme, whereas others have composite preference scheme. Thus, our system is designed to use traditional CF using overall rating for the users with holistic preference, and to use CF using multicriteria ratings for the users with composite preference. To validate the usefulness of the proposed system, we applied it to a real-world dataset regarding the recommendation for POI (point-of-interests). Providing personalized POI recommendation is getting more attentions as the popularity of the location-based services such as Yelp and Foursquare increases. The dataset was collected from university students via a Web-based online survey system. Using the survey system, we collected the overall ratings as well as the ratings for each criterion for 48 POIs that are located near K university in Seoul, South Korea. The criteria include 'food or taste', 'price' and 'service or mood'. As a result, we obtain 2,878 valid ratings from 112 users. Among 48 items, 38 items (80%) are used as training dataset, and the remaining 10 items (20%) are used as validation dataset. To examine the effectiveness of the proposed system (i.e. hybrid selective model), we compared its performance to the performances of two comparison models - the traditional CF and the CF with multicriteria ratings. The performances of recommender systems were evaluated by using two metrics - average MAE(mean absolute error) and precision-in-top-N. Precision-in-top-N represents the percentage of truly high overall ratings among those that the model predicted would be the N most relevant items for each user. The experimental system was developed using Microsoft Visual Basic for Applications (VBA). The experimental results showed that our proposed system (avg. MAE = 0.584) outperformed traditional CF (avg. MAE = 0.591) as well as multicriteria CF (avg. AVE = 0.608). We also found that multicriteria CF showed worse performance compared to traditional CF in our data set, which is contradictory to the results in the most previous studies. This result supports the premise of our study that people have two different types of preference schemes - holistic and composite. Besides MAE, the proposed system outperformed all the comparison models in precision-in-top-3, precision-in-top-5, and precision-in-top-7. The results from the paired samples t-test presented that our proposed system outperformed traditional CF with 10% statistical significance level, and multicriteria CF with 1% statistical significance level from the perspective of average MAE. The proposed system sheds light on how to understand and utilize user's preference schemes in recommender systems domain.

• Asia pacific journal of information systems
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• v.20 no.3
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• pp.139-166
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• 2010

The supply chain management (SCM) systems have emerged as strong managerial tools for manufacturing firms in enhancing competitive strength. Despite of large investments in the SCM systems, many companies are not fully realizing the promised benefits from the systems. A review of literature on adoption, implementation and success factor of IOS (inter-organization systems), EDI (electronic data interchange) systems, shows that this issue has been examined from multiple theoretic perspectives. And many researchers have attempted to identify the factors which influence the success of system implementation. However, the existing studies have two drawbacks in revealing the determinants of systems implementation success. First, previous researches raise questions as to the appropriateness of research subjects selected. Most SCM systems are operating in the form of private industrial networks, where the participants of the systems consist of two distinct groups: focus companies and vendors. The focus companies are the primary actors in developing and operating the systems, while vendors are passive participants which are connected to the system in order to supply raw materials and parts to the focus companies. Under the circumstance, there are three ways in selecting the research subjects; focus companies only, vendors only, or two parties grouped together. It is hard to find researches that use the focus companies exclusively as the subjects probably due to the insufficient sample size for statistic analysis. Most researches have been conducted using the data collected from both groups. We argue that the SCM success factors cannot be correctly indentified in this case. The focus companies and the vendors are in different positions in many areas regarding the system implementation: firm size, managerial resources, bargaining power, organizational maturity, and etc. There are no obvious reasons to believe that the success factors of the two groups are identical. Grouping the two groups also raises questions on measuring the system success. The benefits from utilizing the systems may not be commonly distributed to the two groups. One group's benefits might be realized at the expenses of the other group considering the situation where vendors participating in SCM systems are under continuous pressures from the focus companies with respect to prices, quality, and delivery time. Therefore, by combining the system outcomes of both groups we cannot measure the system benefits obtained by each group correctly. Second, the measures of system success adopted in the previous researches have shortcoming in measuring the SCM success. User satisfaction, system utilization, and user attitudes toward the systems are most commonly used success measures in the existing studies. These measures have been developed as proxy variables in the studies of decision support systems (DSS) where the contribution of the systems to the organization performance is very difficult to measure. Unlike the DSS, the SCM systems have more specific goals, such as cost saving, inventory reduction, quality improvement, rapid time, and higher customer service. We maintain that more specific measures can be developed instead of proxy variables in order to measure the system benefits correctly. The purpose of this study is to find the determinants of SCM systems success in the perspective of vendor companies. In developing the research model, we have focused on selecting the success factors appropriate for the vendors through reviewing past researches and on developing more accurate success measures. The variables can be classified into following: technological, organizational, and environmental factors on the basis of TOE (Technology-Organization-Environment) framework. The model consists of three independent variables (competition intensity, top management support, and information system maturity), one mediating variable (collaboration), one moderating variable (government support), and a dependent variable (system success). The systems success measures have been developed to reflect the operational benefits of the SCM systems; improvement in planning and analysis capabilities, faster throughput, cost reduction, task integration, and improved product and customer service. The model has been validated using the survey data collected from 122 vendors participating in the SCM systems in Korea. To test for mediation, one should estimate the hierarchical regression analysis on the collaboration. And moderating effect analysis should estimate the moderated multiple regression, examines the effect of the government support. The result shows that information system maturity and top management support are the most important determinants of SCM system success. Supply chain technologies that standardize data formats and enhance information sharing may be adopted by supply chain leader organization because of the influence of focal company in the private industrial networks in order to streamline transactions and improve inter-organization communication. Specially, the need to develop and sustain an information system maturity will provide the focus and purpose to successfully overcome information system obstacles and resistance to innovation diffusion within the supply chain network organization. The support of top management will help focus efforts toward the realization of inter-organizational benefits and lend credibility to functional managers responsible for its implementation. The active involvement, vision, and direction of high level executives provide the impetus needed to sustain the implementation of SCM. The quality of collaboration relationships also is positively related to outcome variable. Collaboration variable is found to have a mediation effect between on influencing factors and implementation success. Higher levels of inter-organizational collaboration behaviors such as shared planning and flexibility in coordinating activities were found to be strongly linked to the vendors trust in the supply chain network. Government support moderates the effect of the IS maturity, competitive intensity, top management support on collaboration and implementation success of SCM. In general, the vendor companies face substantially greater risks in SCM implementation than the larger companies do because of severe constraints on financial and human resources and limited education on SCM systems. Besides resources, Vendors generally lack computer experience and do not have sufficient internal SCM expertise. For these reasons, government supports may establish requirements for firms doing business with the government or provide incentives to adopt, implementation SCM or practices. Government support provides significant improvements in implementation success of SCM when IS maturity, competitive intensity, top management support and collaboration are low. The environmental characteristic of competition intensity has no direct effect on vendor perspective of SCM system success. But, vendors facing above average competition intensity will have a greater need for changing technology. This suggests that companies trying to implement SCM systems should set up compatible supply chain networks and a high-quality collaboration relationship for implementation and performance.

• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.1-10
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• 2013

RFID(Radio Frequency Identification) system is non-contact identification technology. A basic RFID system consists of a reader, and a set of tags. RFID tags can be divided into active and passive tags. Active tags with power source allows their own operation execution and passive tags are small and low-cost. So passive tags are more suitable for distribution industry than active tags. A reader processes the information receiving from tags. RFID system achieves a fast identification of multiple tags using radio frequency. RFID systems has been applied into a variety of fields such as distribution, logistics, transportation, inventory management, access control, finance and etc. To encourage the introduction of RFID systems, several problems (price, size, power consumption, security) should be resolved. In this paper, we proposed an algorithm to significantly alleviate the collision problem caused by simultaneous responses of multiple tags. In the RFID systems, in anti-collision schemes, there are three methods: probabilistic, deterministic, and hybrid. In this paper, we introduce ALOHA-based protocol as a probabilistic method, and Tree-based protocol as a deterministic one. In Aloha-based protocols, time is divided into multiple slots. Tags randomly select their own IDs and transmit it. But Aloha-based protocol cannot guarantee that all tags are identified because they are probabilistic methods. In contrast, Tree-based protocols guarantee that a reader identifies all tags within the transmission range of the reader. In Tree-based protocols, a reader sends a query, and tags respond it with their own IDs. When a reader sends a query and two or more tags respond, a collision occurs. Then the reader makes and sends a new query. Frequent collisions make the identification performance degrade. Therefore, to identify tags quickly, it is necessary to reduce collisions efficiently. Each RFID tag has an ID of 96bit EPC(Electronic Product Code). The tags in a company or manufacturer have similar tag IDs with the same prefix. Unnecessary collisions occur while identifying multiple tags using Query Tree protocol. It results in growth of query-responses and idle time, which the identification time significantly increases. To solve this problem, Collision Tree protocol and M-ary Query Tree protocol have been proposed. However, in Collision Tree protocol and Query Tree protocol, only one bit is identified during one query-response. And, when similar tag IDs exist, M-ary Query Tree Protocol generates unnecessary query-responses. In this paper, we propose Adaptive M-ary Query Tree protocol that improves the identification performance using m-bit recognition, collision information of tag IDs, and prediction technique. We compare our proposed scheme with other Tree-based protocols under the same conditions. We show that our proposed scheme outperforms others in terms of identification time and identification efficiency.

• Management & Information Systems Review
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• v.34 no.5
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• pp.191-212
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• 2015

Unlike the existing researches related to salespersons, this study intends to place the focus on salespersons' psychological characteristic as an element affecting their selling behavior. This is because employees' psychological characteristic is very likely to affect their devotion and commitment to relationship with customers and long-term production by a company. In particular, salespersons are likely to get a feeling of fatigue or loss, or make a cynical or cold response to customers because of frequent interaction with them, and to show emotional indifference in an attempt to keep their distance from customers. But the likelihood can vary depending on salespersons' own psychological characteristic; in particular, the occurrence of these phenomena is very likely to vary significantly depending on relationship belief in interpersonal relations. In the field of psychology, under way are researches related to personal psychological characteristics to improve the quality of interpersonal relations and to maximize personal performance and enhance situational adaptability during this process; it is a personal relationship belief that is recently mentioned as such a psychological characteristic. For salespersons having frequent interaction with customers, particularly, relationship belief can be a very important element in forming relations with customers. So this study aims at determining how salespersons' relationship belief affects negative emotion regulation strategies and prosocial behavior to customer. As a result, salespersons' relationship belief was found to have effects on their negative emotion regulation strategies and prosocial behavior to customer. Negative emotion regulation strategies was found to have effects on prosocial behavior. Salespersons with intimate relationship belief try to use active regulation, support-seeking regulation and salespersons with controlling relationship belief try to use avoidant/distractive regulation. Intimate relationship belief was found to have more prosocial behavior, controlling relationship belief was found to have less prosocial behavior to customer. salespersons' negative emotion regulation strategies was found to have effects on their prosocial behavior to customer. Active, support-seeking influence prosocial behavior to customer positively, avoidant/distractive regulation influence prosocial behavior to customer negatively.

• Journal of the Korea Fashion and Costume Design Association
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• v.4 no.1
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• pp.111-130
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• 2002

This study performed a structural questionnaire survey and non-structural interview of the ladies apparel manufacturers in Daegu on the qualification for the employees, skills required for job performance, job training, automatic manufacturing systems, and the use of computer. The results are as follows. 1. Almost all of the apparel manufacturing systems were Pair System, except one Line System in one company. In terms of outsourcing, most of the manufacturers answered “yes,” and in 1998 the outsourcing process was sewing, but in the year 2002, outsourcing has been increased :12 manufacturers(57.1%) outsourcing most of the processes except patterning, 3(14.3%) outsourcing the finish of sewing. 2. The workforce of 1998 and that of 2002 shows a significant difference(P＜. 01) between office work and management. The number of office workers has decreased from 15 down to 5.3 people. On the other hand, that of the management has slightly increased from 5.3 to 9.2 people. The number of the manual workers has decreased from 32.2 to 28.7 people. And the number of tailoring and patterning workers has slightly decreased, but the number has increased in sewing from 3.7 to 7.0 people. 3. The wage of an employee shows a significant difference between a sewing assistant(P＜. 01) and a production manager(P＜. 05), and the wage of a sewing assistant, in particular, has slightly raised from ￦905,000 to ￦1,054,000. 4. The qualifications required of employees are “cooperative human relations”(30.8％), “diligence,” and “ability for job analysis”(26.9％), and “positive thinking” (15.4％) in 1998, and “ability for job analysis”(38.5％), “cooperative human relations”(34.6％), and “positive thinking” (15.4％) in 2002. The areas for job openings are significantly different(P＜. 01) depending on the year. Job openings in the design section has increased from 1(3.8％) to 16 manufacturers (61.5％), and decreased in tailoring section from 22(84.6％) to 2 manufacturers(7.7％). Job openings in the sewing section have increased form 2(7.7％) to 6 manufacturers (23.1％). In terms of sex of the employees, there is a significant difference(P＜. 001). 19 companies(73.1％) wanted “male” in 1998, but 8 companies(30.8％) answered that they want “female” and 17 companies(65.4％) answered that “it does not matter.” About the educational background, there was a significant difference between the years. The number of the companies that want junior college graduates with an associate degree has increased(15 companies(57.7％). There was a significant difference(P＜. 05) in major of the employee. The number of the companies that want fashion majors has increased from 5(19.2％) to 20(76.9％). 5. In terms of job skills required, there was no significant difference. In 1998, “production skills” (46.2％) and “ability for job analysis” (26.9％) were required, and in 2002, “ability for job analysis” (42.3％) and “emotional skills” (26.9％). 6. In regard to training for job skills, “fashion professional training” has slightly decreased from 65.4％ in 1998 to 46.2％ in 2002, however, “training for job analysis” has slightly increased from 30.8％ in 1998 to 46.2％ in 2002, which indicates the fact that “fashion professional training” and “ability for job analysis” have been emphasized. 7. The number of the manufacturers purchased apparel CAD has increased from 1(3.8％) to 3(11.5％), and the number of the manufacturers that have no plan for purchase has increased from 16(61.5％) in 1998 to 15(57.7％), still taking up a big proportion. 8. About the use of computers in manufacturing, there is a significant difference(P＜. 05). The number of the manufacturers using computer has increased from 5(19.2％) to 15(57.7％) and that of the manufacturers which do not use computers has decreased from 17(57.7％) to 8(30.8％). 9. In the interviews with the owners of the manufacturers, they pointed that schools should give more weight on practical training courses, the invitation of experts in the specific field, complex production systems, training courses for sewing, field trip courses, and furthering specialty education, personality and vocational education.

• Journal of Venture Innovation
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• v.1 no.1
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• pp.1-14
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• 2018

The 4th Industrial Revolution is making a big change for our company like the tsunami. The CPS system, which is represented by the digital age, is based on the data accumulated in the physical domain and is making business that was not imagined in the past through digital technology. As a result, the business model of the 4th Industrial Revolution era is different from the previous one. In this study, we analyze the trends and the issues of business innovation theory research. Then, the business innovation model of the digital age was compared with the previous period. Based on this, we have searched for a business model suitable for the 4th Industrial Revolution era. The existing business models have many difficulties to explain the model of the digital era. Even though more empirical research should be supported, Michael Porter's diamond model is most suitable for four cases of business models by applying them. Type A sharing outcome with customer is a model that pay differently according to the basis of customer performance. Type B Value Chain Digitalization model provides products and services to customers with faster and lower cost by digitalizing products, services and SCM. Type C Digital Platform is the model that brings the biggest ripple effect. It is a model that can secure profitability by creating new market by creating the sharing economy based on digital platform. Finally, Type D Sharing Resources is a model for building a competitive advantage model by collaborating with partners in related industries. This is the most effective way to complement each other's core competencies and their core competencies. Even though numerous Unicorn companies have differentiated digital competitiveness with many digital technologies in their respective industries in the 4th Industrial Revolution era, there is a limit to the number of pieces to be listed. In future research, it is necessary to identify the business model of the digital age through more specific empirical analysis. In addition, since digital business models may be different in each industry, it is also necessary to conduct comparative analysis between industries

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.3
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• pp.125-140
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• 2018

According to statistics, it is shown that domestic SMEs rely on bank loans for the majority of fund procurement. From financial information shortage (Thin file) that does not provide information necessary for credit evaluation from banks such as financial statements. In order to overcome these problems, recently, in alternative finance such as P2P, using differentiated information such as demographics, trading information and the like utilizing Fintech instead of existing financial information, small funds A new credit evaluation method has been expanding to provide SMEs with small amounts of money. In this paradigm of environmental change, in this research, credit evaluation which can expand fund supply to SMEs by utilizing big data based on trade area information such as sales fluctuation, location conditions etc. In this research, we try to find such a solution. By analyzing empirically the big data generated in the trade area, we verify the effectiveness as a credit evaluation factor and try to derive the main parameters necessary for the business performance evaluation of the founder of SMEs. In this research, for 17,116 material businesses in Seoul City that operate the service industry from 2009 to February 2018, we collect trade area information generated for each business location from Big Data specialized company NICE Zini Data Co., Ltd.. We collected and analyzed the data on the locations and commercial areas of the facilities that were difficult to obtain from SMEs and analyzed the data that affected the Corporate financial Distress. It is possible to refer to the variable of the existing unused big data and to confirm the possibility of utilizing it for efficient financial support for SMEs, This is to ensure that commercial lenders, even in general commercial banks, are made to be more prominent in one sector of the financing of SMEs. In this research, it is not the traditional financial information about raising fund of SMEs who have basically the problem of information asymmetry, but a trade area analysis variable is derived, and this variable is evaluated by credit evaluation There is differentiation of research in that it verified through analysis of big data from Trading-area whether or not there is an effect on.

• Journal of Plant Biotechnology
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• v.42 no.2
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• pp.71-76
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• 2015

Developed countries have set seed industry as a new growth engine, which demands strong support from the government. Multinational seed companies such as Monsanto and DuPont have made huge financial investment to secure their major roles in the global market. To spur domestic seed industry performance, Korean government laid out the foundation for developing seed industry through policy promotion in the late 2000s. In this paper, I look at the current state of the domestic and international seed market to provide information for improving the efficiency of the propulsion of the Golden Seed Project (GSP) along with its vision. The increasing size of global giant companies has been regarded to monopolize the world seed industry wherein ten renowned companies occupy 73% of the overall global market. In effect, this causes a price hike due to limited seed choices. Domestic seed market has been stuck in a range due to a sustained low agricultural production resulting in decreased seed demand and market size. Though breeding technologies for rice and vegetables are world-class, the technologies for top global crops such as cabbage, paprika, and forage are insufficient therefore professionals in this field are not easily employed. Moreover, there is a lack in appropriate infrastructure set up in the universities which adds to ineffective training of professionals. Being a key-supporting industry for agriculture, seed industry should be granted with strong and sustainable investment support from the government. In view thereof, GSP, which started in 2012, ambitions to spur researches outlined by excellent professionals in universities and seed companies aimed to drive seed export volume and quality and attain domestic seed self-sufficiency through adoption of export- and import-substitution seed types (10 varieties each) development strategies. To develop Korea's seed industry excellent achievement of GSP's goals should be drawn successfully and to do this beside development of high quality seeds, support programs for promotion of seed exports are also needed.

• The Korean Journal of Financial Management
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• v.25 no.1
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• pp.51-84
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• 2008

We investigate the signaling effect of repurchase preceding new equity issue using Korean data. In a short time span, firms announce stock repurchases and equity offerings. The proximity of two events in Korean firms indicates that those are not independent of each other. In this paper, we test the signaling effect of repurchase on equity offerings on the two measures. One is announcement effect, which is measured as CAR(0, +2). The other is the effectiveness which is measured as CAR(0, +30) because the price movement during this window influences on the price of new issues. Previous studies that stock repurchase convey positive signal to equity offerings-Billet and Xue(2004) and Jung(2004)-construct sample without the limit of time interval between two events. This causes the unclear relation between those because of the long time interval. In this study we consider only samples of being within one year each other to reduce this problem and clarify the signal of repurchase on equity offerings. Korean firms are allowed to repurchase own shares with two different method. One is direct repurchase as same as open market repurchase. The other is stock stabilization fund and stock trust fund which trust company or bank buy and sell their shares on the behalf of firms. Generally, the striking different characteristic between direct repurchase and indirect repurchase is following. Direct repurchase is applied by more strict regulation than indirect repurchase. Therefore, the direct repurchase is more informative signal to the equity offering than the indirect repurchase. We construct two sample firms- firms with direct repurchase preceding-equity offerings and indirect repurchase-preceding equity offering, and one control firms-equity offerings only firms-to investigate the announcement effect and the effectiveness of repurchases. Our findings are as follows. Direct repurchase favorably affect the price of new issues favorably. CAR(0, +2) of firms with direct repurchase is not different from that of equity offerings only firms but CAR(0, +30) is higher than that of equity offerings only firms. For firms with indirect repurchase and equity offerings, Both the announcement effect and the effectiveness does not exist. Jung(2004) suggest the possibilities of how indirect stock repurchase can be regarded as one of unfair trading practices on based on the survey results that financial managers of some of KSE listed firms have been asked of their opinion on the likelihood of the stock repurchase being used in unfair trading. This is not objective empirical evidence but opinion of financial managers. To investigate whether firms announce false signal before equity offerings to boost the price of new issues, we calculate the long-run performance following equity offerings. If firms have announced repurchase to boost the price of new issues intentionally, they would undergo the severe underperformance. The empirical results do not show the severer underperformance of both sample firms than equity offerings only firms. The suggestion of false signaling of repurchase preceding equity offerings is not supported by our evidence.

• 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.