• Korean Journal of Materials Research
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• v.11 no.8
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• pp.671-678
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• 2001

All solid-state thin film micro supercapacitor, which consists of $RuO_2$/LiPON/$RuO_2$ multi layer structure, was fabricated on Pt/Ti/Si substrate using a $RuO_2$ electrode. Bottom $RuO_2$ electrode was grown by dc reactive sputtering system with increasing $O_2/[Ar+O_2]$ ratio at room temperature, and a LiPON electrolyte film was subsequently deposited on the bottom $RuO_2$ electrode at pure nitrogen ambient by rf reactive sputtering system. Room temperature charge-discharge measurements based on a symmetric $RuO_2$/LiPON/$RuO_2$ structure clearly demonstrates the cyclibility dependence on the microstructure of the $RuO_2$ electrode. Using both glancing angle x-ray diffraction (GXRD) and transmission electron microscopy (TEM) analysis, it was found that the microstructure of the $RuO_2$ electrode was dependent on the oxygen flow ratio. In addition, x- ray photoelectron spectroscopy(XPS) examination shows that the Ru-O binding energy is affected by increasing oxygen flow ratio. Furthermore, TEM and AES depth profile analysis after cycling demonstrates that the interface layer formed by interfacial reaction between LiPON and $RuO_2$ act as a main factor in the degradation of the cyclibility of the thin film micro-supercapacitor.

• The Journal of Society for e-Business Studies
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• v.14 no.2
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• pp.71-94
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• 2009

The purpose of this study is to identify the effects of interactivity, computer playfulness, and computer anxiety on service satisfaction, attitude, and user intention based on the flow theory in order to understand factors improving effects of home-network system. Especially, Computer anxiety which is one of individual characteristics in negative aspect as well as computer playfulness was added to the research model to perform empirical research for grasping effects on attitude variables such as service satisfaction. The analysis resulted that the perceived interactivity and the computer playfulness had a positive relationship with service satisfaction, but on the contrary it was identified that the computer anxiety gave a negative effect on the service satisfaction. These results are evidences to explain high correlation of the perceived interactivity, the computer playfulness, and the computer anxiety with the service satisfaction. Besides, it was found that there was a meaningful relationship among the three attitude variables such as satisfaction, attitude, and intention to use. This research suggested an integrative research model using home-network technology where user satisfaction and use factors were included, expanded research area of the traditional interactivity, and presented a consistent result to support the relationship among the service satisfaction, the attitude, and the intention to use. In additions, practical issues were also provided by presenting user's criteria for selecting various services including electronic commerce and its using elements continuously.

• The Journal of Society for e-Business Studies
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• v.14 no.4
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• pp.1-17
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• 2009

E-Catalogs which describe products or services are one of the most important data for the electronic commerce. E-Catalogs are created, updated, and removed in order to keep up-to-date information in e-Catalog database. However, when the number of catalogs increases, information integrity is violated by the several reasons like catalog duplication and abnormal classification. Catalog search, duplication checking, and automatic classification are important functions to utilize e-Catalogs and keep the integrity of e-Catalog database. To implement these functions, probabilistic models that use statistics of index words extracted from e-Catalogs had been suggested and the feasibility of the methods had been shown in several papers. However, even though these functions are used together in the e-Catalog management system, there has not been enough consideration about how to share common data used for each function and how to effectively manage statistics of index words. In this paper, we suggest a method to implement these three functions by using simple SQL supported by relational database management system. In addition, we use materialized views to reduce the load for implementing an application that manages statistics of index words. This brings the efficiency of managing statistics of index words by putting database management systems optimize statistics updating. We showed that our method is feasible to implement three functions and effective to manage statistics of index words with empirical evaluation.

• Journal of Intelligence and Information Systems
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• v.9 no.3
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• pp.23-47
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• 2003

Traditional studies for customer relationship management (CRM) generally focus on static CRM in a specific time frame. The static CRM and customer behavior knowledge derived could help marketers to redirect marketing resources fur profit gain at that given point in time. However, as time goes, the static knowledge becomes obsolete. Therefore, application of CRM to an online retailer should be done dynamically in time. Customer-based analysis should observe the past purchase behavior of customers to understand their current and likely future purchase patterns in consumer markets, and to divide a market into distinct subsets of customers, any of which may conceivably be selected as a market target to be reached with a distinct marketing mix. Though the concept of buying-behavior-based CRM was advanced several decades ago, virtually little application of the dynamic CRM has been reported to date. In this paper, we propose a dynamic CRM model utilizing data mining and a Monitoring Agent System (MAS) to extract longitudinal knowledge from the customer data and to analyze customer behavior patterns over time for the Internet retailer. The proposed model includes an extensive analysis about a customer career path that observes behaviors of segment shifts of each customer: prediction of customer careers, identification of dominant career paths that most customers show and their managerial implications, and about the evolution of customer segments over time. furthermore, we show that dynamic CRM could be useful for solving several managerial problems which any retailers may face.

• The Korean Journal of Franchise Management
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• v.9 no.1
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• pp.27-39
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• 2018

Purpose - Recently, the mobile application field has been receiving astronomical attention from the past few years due to the growing number of mobile app downloads and withal due to the revenues being engendered. Especially delivery apps by mobile service market is experiencing rapid growth and competition is intensifying. Therefore, delivery apps' choice attributes has become important as a strategy for survival of franchise firms. Based on previous studies, this research proposed the theoretical framework about the structural relationships among customer satisfaction, trust and revisit intention on delivery apps' choice attributes. Research design, data, and methodology - This study examines the structural relationship between choice attributes of using the delivery app, satisfaction, trust, and revisit intention. More specifically, this study has been examined from the perspective of Generation Y who is enjoying electronic commerce and shopping with mobile phone. In this model, choice attributes of delivery app consists of three sub-dimensions such as service quality, system quality, interaction quality. So as to test the purposes of this study, research model and hypotheses were developed. After excluding 24 invalid respondent questionnaires, 201 valid questionnaires were coded and analyzed using frequency, confirmatory factor analysis, correlations analysis, and structural equation modeling with SPSS 21 and SmartPLS 3.0. Result - The results of the study are as follows. First, service quality and interaction quality had positive effects on satisfaction, and interaction quality had positive effects on trust, but system quality did not have a significant effect on both satisfaction and trust. Second, satisfaction had positive effects on both trust and revisit intention. Third, trust had positive effects on revisit intention. Conclusions - The implications of this study are following as: From the theoretical perspective, this study confirms the effect of delivery apps' choice attributes on satisfaction, trust, and revisit intention. In addition, it is significant that we examined the influence of choice attributes of delivery apps on their attitudes and behaviors of Generation Y familiar with mobile environment. Through this study, we hypothesized that the attributes of service quality and interaction quality of delivery apps have a significant effect on customer satisfaction, and this can be expected to provide meaningful implications for the development of franchise restaurant industry. To encourage continuous repurchase through customer satisfaction, franchise companies need to establish various strategic alliances with delivery app companies and new growth engines by providing diverse and high-quality services to customers in the smart age.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.1-18
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• 2015

Collaborative filtering(CF) algorithm has been popularly used for recommender systems in both academic and practical applications. A general CF system compares users based on how similar they are, and creates recommendation results with the items favored by other people with similar tastes. Thus, it is very important for CF to measure the similarities between users because the recommendation quality depends on it. In most cases, users' explicit numeric ratings of items(i.e. quantitative information) have only been used to calculate the similarities between users in CF. However, several studies indicated that qualitative information such as user's reviews on the items may contribute to measure these similarities more accurately. Considering that a lot of people are likely to share their honest opinion on the items they purchased recently due to the advent of the Web 2.0, user's reviews can be regarded as the informative source for identifying user's preference with accuracy. Under this background, this study proposes a new hybrid recommender system that combines with users' review mining. Our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and his/her text reviews on the items when calculating similarities between users. In specific, our system creates not only user-item rating matrix, but also user-item review term matrix. Then, it calculates rating similarity and review similarity from each matrix, and calculates the final user-to-user similarity based on these two similarities(i.e. rating and review similarities). As the methods for calculating review similarity between users, we proposed two alternatives - one is to use the frequency of the commonly used terms, and the other one is to use the sum of the importance weights of the commonly used terms in users' review. In the case of the importance weights of terms, we proposed the use of average TF-IDF(Term Frequency - Inverse Document Frequency) weights. To validate the applicability of the proposed system, we applied it to the implementation of a recommender system for smartphone applications (hereafter, app). At present, over a million apps are offered in each app stores operated by Google and Apple. Due to this information overload, users have difficulty in selecting proper apps that they really want. Furthermore, app store operators like Google and Apple have cumulated huge amount of users' reviews on apps until now. Thus, we chose smartphone app stores as the application domain of our system. In order to collect the experimental data set, we built and operated a Web-based data collection system for about two weeks. As a result, we could obtain 1,246 valid responses(ratings and reviews) from 78 users. The experimental system was implemented using Microsoft Visual Basic for Applications(VBA) and SAS Text Miner. And, to avoid distortion due to human intervention, we did not adopt any refining works by human during the user's review mining process. To examine the effectiveness of the proposed system, we compared its performance to the performance of conventional CF system. The performances of recommender systems were evaluated by using average MAE(mean absolute error). The experimental results showed that our proposed system(MAE = 0.7867 ~ 0.7881) slightly outperformed a conventional CF system(MAE = 0.7939). Also, they showed that the calculation of review similarity between users based on the TF-IDF weights(MAE = 0.7867) leaded to better recommendation accuracy than the calculation based on the frequency of the commonly used terms in reviews(MAE = 0.7881). The results from paired samples t-test presented that our proposed system with review similarity calculation using the frequency of the commonly used terms outperformed conventional CF system with 10% statistical significance level. Our study sheds a light on the application of users' review information for facilitating electronic commerce by recommending proper items to users.

• Journal of Arbitration Studies
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• v.15 no.2
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• pp.3-35
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• 2005

The meaning of Online ADR lies in the prompt and economical resolution of disputes by applying the information/communication element (Internet) to existing ADR. However, if the promptness and economical efficiency are overemphasized, the fairness and appropriateness of dispute resolution may be compromised and consequently Online ADR will be belittled and criticized as second-class trials. In addition, as communication is mostly made using texts in Online ADR it is difficult to investigate cases and to create atmosphere and induce dynamic feelings, which are possible in the process of dispute resolution through face-to-face contact. Despite such difficulties, Online ADR is expanding its area not only in online but also in offline due to its advantages such as promptness, low expenses and improved resolution methods, and is expected to develop rapidly as the electronic government decided to adopt it in the future. Accordingly, the following points must be focused on for the continuous First, in the legal and institutional aspects for the development of Online ADR, it is necessary to establish a framework law on ADR. A framework law on ADR comprehending existing mediation and arbitration should be established and it must include contents of Online ADR, which utilizes electronic communication means. However, it is too early to establish a separate law for Online ADR because Online ADR must develop based on the theoretical system of ADR. Second, although Online ADR is expanding rapidly, it may take time to be settled as a tool of dispute resolution. As discussed earlier, additionally, if the amount of money in dispute is large or the dispute is complicated, Online ADR may have a negative effect on the resolution of the dispute. Thus, it is necessary to apply Online ADR to trifle cases or domestic cases in the early stage, accumulating experiences and correcting errors. Moreover, in order to settle numerous disputes effectively, Online ADR cases should be analyzed systematically and cases should be classified by type so that similar disputes may be settled automatically. What is more, these requirements should reflected in developing Online ADR system. Third, the application of Online ADR is being expanded to consumer disputes, domain name disputes, commercial disputes, legal disputes, etc., millions of cases are settled through Online ADR, and 115 Online ADR sites are in operation throughout the world. Thus Online ADR requires not temporary but continuous attention, and mediators and arbitrators participating in Online ADR should be more intensively educated on negotiation and information technologies. In particular, government-led research projects should be promoted to establish Online ADR model and these projects should be supported by comprehensive researches on mediation, arbitration and Online ADR. Fourth, what is most important in the continuous development and expansion of Online ADR is to secure confidence in Online ADR and advertise Online ADR to users. For this, incentives and rewards should be given to specialists such as lawyers when they participate in Online ADR as mediators and arbitrators in order to improve their expertise. What is more, from the early stage, the government and public institutions should have initiative in promoting Online ADR so that parties involved in disputes recognize the substantial contribution of Online ADR to dispute resolution. Lastly, dispute resolution through Online ADR is performed by organizations such as Korea Institute for Electronic Commerce and Korea Consumer Protection Board and partially by Korean Commercial Arbitration Board. Online ADR is expected to expand its area to commercial disputes in offline in the future. In response to this, Korean Commercial Arbitration Board, which is an organization for commercial dispute resolution, needs to be restructured.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.361-381
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• 2023

Research on corporate bankruptcy prediction has been focused on financial information. Since the company's financial information is updated quarterly, there is a problem that timeliness is insufficient in predicting the possibility of a company's business closure in real time. Evaluated companies that want to improve this need a method of judging the soundness of a company that uses information other than financial information to judge the soundness of a target company. To this end, as information technology has made it easier to collect non-financial information about companies, research has been conducted to apply additional variables and various methodologies other than financial information to predict corporate bankruptcy. It has become an important research task to determine whether it has an effect. In this study, we examined the impact of electronic payment-related information, which constitutes non-financial information, when predicting the closure of business operators using electronic payment service and examined the difference in closure prediction accuracy according to the combination of financial and non-financial information. Specifically, three research models consisting of a financial information model, a non-financial information model, and a combined model were designed, and the closure prediction accuracy was confirmed with six algorithms including the Multi Layer Perceptron (MLP) algorithm. The model combining financial and non-financial information showed the highest prediction accuracy, followed by the non-financial information model and the financial information model in order. As for the prediction accuracy of business closure by algorithm, XGBoost showed the highest prediction accuracy among the six algorithms. As a result of examining the relative importance of a total of 87 variables used to predict business closure, it was confirmed that more than 70% of the top 20 variables that had a significant impact on the prediction of business closure were non-financial information. Through this, it was confirmed that electronic payment-related information of non-financial information is an important variable in predicting business closure, and the possibility of using non-financial information as an alternative to financial information was also examined. Based on this study, the importance of collecting and utilizing non-financial information as information that can predict business closure is recognized, and a plan to utilize it for corporate decision-making is also proposed.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.113-127
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• 2016

Among the techniques for recommendation, collaborative filtering (CF) is commonly recognized to be the most effective for implementing recommender systems. Until now, CF has been popularly studied and adopted in both academic and real-world applications. The basic idea of CF is to create recommendation results by finding correlations between users of a recommendation system. CF system compares users based on how similar they are, and recommend products to users by using other like-minded people's results of evaluation for each product. Thus, it is very important to compute evaluation similarities among users in CF because the recommendation quality depends on it. Typical CF uses user's explicit numeric ratings of items (i.e. quantitative information) when computing the similarities among users in CF. In other words, user's numeric ratings have been a sole source of user preference information in traditional CF. However, user ratings are unable to fully reflect user's actual preferences from time to time. According to several studies, users may more actively accommodate recommendation of reliable others when purchasing goods. Thus, trust relationship can be regarded as the informative source for identifying user's preference with accuracy. Under this background, we propose a new hybrid recommender system that fuses CF and social network analysis (SNA). The proposed system adopts the recommendation algorithm that additionally reflect the result analyzed by SNA. In detail, our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and trust relationship information between users when calculating user similarities. For this, our system creates and uses not only user-item rating matrix, but also user-to-user trust network. As the methods for calculating user similarity between users, we proposed two alternatives - one is algorithm calculating the degree of similarity between users by utilizing in-degree and out-degree centrality, which are the indices representing the central location in the social network. We named these approaches as 'Trust CF - All' and 'Trust CF - Conditional'. The other alternative is the algorithm reflecting a neighbor's score higher when a target user trusts the neighbor directly or indirectly. The direct or indirect trust relationship can be identified by searching trust network of users. In this study, we call this approach 'Trust CF - Search'. To validate the applicability of the proposed system, we used experimental data provided by LibRec that crawled from the entire FilmTrust website. It consists of ratings of movies and trust relationship network indicating who to trust between users. The experimental system was implemented using Microsoft Visual Basic for Applications (VBA) and UCINET 6. To examine the effectiveness of the proposed system, we compared the performance of our proposed method with one of conventional CF system. The performances of recommender system were evaluated by using average MAE (mean absolute error). The analysis results confirmed that in case of applying without conditions the in-degree centrality index of trusted network of users(i.e. Trust CF - All), the accuracy (MAE = 0.565134) was lower than conventional CF (MAE = 0.564966). And, in case of applying the in-degree centrality index only to the users with the out-degree centrality above a certain threshold value(i.e. Trust CF - Conditional), the proposed system improved the accuracy a little (MAE = 0.564909) compared to traditional CF. However, the algorithm searching based on the trusted network of users (i.e. Trust CF - Search) was found to show the best performance (MAE = 0.564846). And the result from paired samples t-test presented that Trust CF - Search outperformed conventional CF with 10% statistical significance level. Our study sheds a light on the application of user's trust relationship network information for facilitating electronic commerce by recommending proper items to users.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.157-175
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• 2012

It is difficult to evaluate the performance of process innovation of e-procurement which has large scale and complex processes. The existing evaluation methods for measuring the effects of process innovation have been mainly done with statistically quantitative methods by analyzing operational data or with qualitative methods by conducting surveys and interviews. However, these methods have some limitations to evaluate the effects because the performance evaluation of e-procurement process innovation should consider the interactions among participants who are active either directly or indirectly through the processes. This study considers the e-procurement process as a complex system and develops a simulation model based on MAS(Multi-Agent System) to evaluate the effects of e-procurement process innovation. Multi-agent based simulation allows observing interaction patterns of objects in virtual world through relationship among objects and their behavioral mechanism. Agent-based simulation is suitable especially for complex business problems. In this study, we used Netlogo Version 4.1.3 as a MAS simulation tool which was developed in Northwestern University. To do this, we developed a interaction model of agents in MAS environment. We defined process agents and task agents, and assigned their behavioral characteristics. The developed simulation model was applied to G2B system (KONEPS: Korea ON-line E-Procurement System) of Public Procurement Service (PPS) in Korea and used to evaluate the innovation effects of the G2B system. KONEPS is a successfully established e-procurement system started in the year 2002. KONEPS is a representative e-Procurement system which integrates characteristics of e-commerce into government for business procurement activities. KONEPS deserves the international recognition considering the annual transaction volume of 56 billion dollars, daily exchanges of electronic documents, users consisted of 121,000 suppliers and 37,000 public organizations, and the 4.5 billion dollars of cost saving. For the simulation, we analyzed the e-procurement of process of KONEPS into eight sub processes such as 'process 1: search products and acquisition of proposal', 'process 2 : review the methods of contracts and item features', 'process 3 : a notice of bid', 'process 4 : registration and confirmation of qualification', 'process 5 : bidding', 'process 6 : a screening test', 'process 7 : contracts', and 'process 8 : invoice and payment'. For the parameter settings of the agents behavior, we collected some data from the transactional database of PPS and some information by conducting a survey. The used data for the simulation are 'participants (government organizations, local government organizations and public institutions)', 'the number of bidding per year', 'the number of total contracts', 'the number of shopping mall transactions', 'the rate of contracts between bidding and shopping mall', 'the successful bidding ratio', and the estimated time for each process. The comparison was done for the difference of time consumption between 'before the innovation (As-was)' and 'after the innovation (As-is).' The results showed that there were productivity improvements in every eight sub processes. The decrease ratio of 'average number of task processing' was 92.7% and the decrease ratio of 'average time of task processing' was 95.4% in entire processes when we use G2B system comparing to the conventional method. Also, this study found that the process innovation effect will be enhanced if the task process related to the 'contract' can be improved. This study shows the usability and possibility of using MAS in process innovation evaluation and its modeling.