• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.163-178
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• 2014

Following research proposes "Virtualization of Smart Cards (ViSCa)" which is a security system that aims to provide a multi-device platform for the deployment of services that require a strong security protocol, both for the access & authentication and execution of its applications and focuses on analyzing Virtualization of Smart Cards (ViSCa) platform-based mobile payment service by comparing with other similar cases. At the present day, the appearance of new ICT, the diffusion of new user devices (such as smartphones, tablet PC, and so on) and the growth of internet penetration rate are creating many world-shaking services yet in the most of these applications' private information has to be shared, which means that security breaches and illegal access to that information are real threats that have to be solved. Also mobile payment service is, one of the innovative services, has same issues which are real threats for users because mobile payment service sometimes requires user identification, an authentication procedure and confidential data sharing. Thus, an extra layer of security is needed in their communication and execution protocols. The Virtualization of Smart Cards (ViSCa), concept is a holistic approach and centralized management for a security system that pursues to provide a ubiquitous multi-device platform for the arrangement of mobile payment services that demand a powerful security protocol, both for the access & authentication and execution of its applications. In this sense, Virtualization of Smart Cards (ViSCa) offers full interoperability and full access from any user device without any loss of security. The concept prevents possible attacks by third parties, guaranteeing the confidentiality of personal data, bank accounts or private financial information. The Virtualization of Smart Cards (ViSCa) concept is split in two different phases: the execution of the user authentication protocol on the user device and the cloud architecture that executes the secure application. Thus, the secure service access is guaranteed at anytime, anywhere and through any device supporting previously required security mechanisms. The security level is improved by using virtualization technology in the cloud. This virtualization technology is used terminal virtualization to virtualize smart card hardware and thrive to manage virtualized smart cards as a whole, through mobile cloud technology in Virtualization of Smart Cards (ViSCa) platform-based mobile payment service. This entire process is referred to as Smart Card as a Service (SCaaS). Virtualization of Smart Cards (ViSCa) platform-based mobile payment service virtualizes smart card, which is used as payment mean, and loads it in to the mobile cloud. Authentication takes place through application and helps log on to mobile cloud and chooses one of virtualized smart card as a payment method. To decide the scope of the research, which is comparing Virtualization of Smart Cards (ViSCa) platform-based mobile payment service with other similar cases, we categorized the prior researches' mobile payment service groups into distinct feature and service type. Both groups store credit card's data in the mobile device and settle the payment process at the offline market. By the location where the electronic financial transaction information (data) is stored, the groups can be categorized into two main service types. First is "App Method" which loads the data in the server connected to the application. Second "Mobile Card Method" stores its data in the Integrated Circuit (IC) chip, which holds financial transaction data, which is inbuilt in the mobile device secure element (SE). Through prior researches on accept factors of mobile payment service and its market environment, we came up with six key factors of comparative analysis which are economic, generality, security, convenience(ease of use), applicability and efficiency. Within the chosen group, we compared and analyzed the selected cases and Virtualization of Smart Cards (ViSCa) platform-based mobile payment service.

• The Journal of the Korea Contents Association
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• v.15 no.6
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• pp.445-454
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• 2015

The purpose of this study is to analyze the influence of the financial structure of pharmaceutical companies on R&D investment. 358 pharmaceutical firms listed in the KOSDAQ market from 2000 to 2012. Financial statements and comments in general and internal transactions were extracted from TS-2000 of the Korea Listed Company Association (KLCA), and data related to stock price was extracted from KISVALUE-III of NICE Information Service Co., Ltd. STATA 12.0 was used as the statistical package for panel analysis. The summary of the findings and the interpretation of the significance of this are as follows: First, the shareholding ratio of major shareholders and foreigners had a positive influence on R&D investment. Second, the ratio of outside directors had a negative influence on R&D investment. Third, the shareholding ratio of institutional investors did not have a significant influence on R&D investment.

• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.87-99
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• 2013

As Internet advances, websites with simpel HTML pages are changing to complex web application systems with enormous contents and various services. With this trend, it is noted that situations where Web accessibility of the old and the handicapped is inhibited are growing. To solve this problem, The Disability Discrimination Act has been enacted since April 2013. This act triggers massive website reorganization efforts. However, in order for the huge and sophisticated web applications and web sites to ensure a web accessibility, a framework is required to throughout the web site development. Based on thorough review of website development methodologies, web accessibility compliance standards, and various web accessibility issues related to website characteristics, this study proposes a practice oriented "Web Accessibility Compliance Framework". The current study also examines the usefulness and value of this framework by applying it to the internet banking development project of W bank and receiving a certificate for high quality website complying web accessibility standards.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.43-65
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• 2010

Facing a complex environment driven by a decade, many companies are adopting new strategic frameworks such as Customer Relationship Management system to achieve sustainable profitability as well as overcome serious competition for survival. In many business areas, CRM system advanced a great deal in a matter of continuous compensating the defect and overall integration. However, pharmaceutical companies in Korea were slow to accept them for usesince they still have a tendency of holding fast to traditional way of sales and marketing based on individual networks of sales representatives. In the circumstance, this article tried to empirically address current status of CRM system as well as the effects of the system on the performance of pharmaceutical companies by applying BSC method's four perspectives, from financial, customer, learning and growth and internal process. Survey by e-mail and post to employers and employees who were working in pharma firms were undergone for the purpose. Total 113 cases among collected 140 ones were used for the statistical analysis by SPSS ver. 15 package. Reliability, Factor analysis, regression were done. This study revealed that CRM system had a significant effect on improving financial and non-financial performance of pharmaceutical companies as expected. Proposed regression model fits well and among them, CRM marketing information system shed the light on substantial impact on companies' outcome given profitability, growth and investment. Useful analytical information by CRM marketing information system appears to enable pharmaceutical firms to set up effective marketing and sales strategies, these result in favorable financial performance by enhancing values for stakeholderseventually, not to mention short-term profit and/or mid-term potential to growth. CRM system depicted its influence on not only financial performance, but also non-financial fruit of pharmaceutical companies. Further analysis for each component showed that CRM marketing information system were able to demonstrate statistically significant effect on the performance like the result of financial outcome. CRM system is believed to provide the companies with efficient way of customers managing by valuable standardized business process prompt coping with specific customers' needs. It consequently induces customer satisfaction and retentionto improve performance for long period. That is, there is a virtuous circle for creating value as the cornerstone for sustainable growth. However, the research failed to put forward to evidence to support hypothesis regarding favorable influence of CRM sales representative's records assessment system and CRM customer analysis system on the management performance. The analysis is regarded to reflect the lack of understanding of sales people and respondents between actual work duties and far-sighted goal in strategic analysis framework. Ordinary salesmen seem to dedicate short-term goal for the purpose of meeting sales target, receiving incentive bonus in a manner-of-fact style, as such, they tend to avail themselves of personal network and sales and promotional expense rather than CRM system. The study finding proposed a link between CRM information system and performance. It empirically indicated that pharmaceutical companies had been implementing CRM system as an effective strategic business framework in order for more balanced achievements based on the grounded understanding of both CRM system and integrated performance. It suggests a positive impact of supportive CRM system on firm performance, especially for pharmaceutical industry through the initial empirical evidence. Also, it brings out unmet needs for more practical system design, improvement of employees' awareness, increase of system utilization in the field. On the basis of the insight from this exploratory study, confirmatory research by more appropriate measurement tool and increased sample size should be further examined.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.6
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• pp.91-101
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• 2019

Tourism or travel business consists of a set of services for people who visit exotic places. Payment is usually marking the end of the series of activities relating to tourism, and it becomes the linkage for the next activity. With the recent advancement of mobile Fintech technologies, we have learned that more convenient and more secure financial transactions are improving the quality of tourism. It should be noted that tourism counts on information technology heavily in terms of mobile Internet and smart devices use, which yields to a wide business opportunities for Fintech startups. However, payment information has not been highlighted for additional marketing promotion activities. The lack of research into information technology-based business models that extend Fintech services related to payment in venture start-up studies hinders the understanding of the possibility of creating new business through the value creation process after payment. This study attempts to investigate this issue based on the theory of smart tourism and service-dominant logic with developing a new information system. More specifically, marketing promotion activities after payment for Chinese tourists visiting Korea are examined. Specifically, WeChat Pay and instant tax refund service were considered while the system was developed by following desing science research methodology. This study is meaningful in that it finds a new possibility of Fintech business model by applying scientific and academic methods, and it reminds the necessity of service automation system centered on instant tax refund.

• The KIPS Transactions:PartA
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• v.19A no.1
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• pp.51-60
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• 2012

IC(Integrated circuits)card, generally be named smard card, embedded MPU(Micro Processor Unit) of small-size, memory, EEPROM, Card Operating System(COS) and security algorithm. The IC card is used in almost all industry such as a finance(credit, bank, stock etc.), a traffic, a communication, a medical, a electronic passport, a membership management and etc. Recently, a application field of IC card is on the increase by method for payments of T-commerce, as T-commerce is becoming a new growth engine of the broadcating industry by trend of broadcasting and telecommunication convergence, smart mechanization of TV. For example, we can pay in IC credit card(or IC cash card) on T-Commerce. or we can be provided TV banking service in IC cash card such as ATM. However, so far, T-commerce payment services have weakness in security such as storage and disclosure of card information as well as dropping sharply about custom ease because of taking advantage of card information input method using remote control. To solve this problem, This paper developed processing payment module for implementing TV electronic payment system using IC credit card payment standard, EMV.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.155-170
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• 2018

Predicting a company's financial bankruptcy is traditionally one of the most crucial forecasting problems in business analytics. In previous studies, prediction models have been proposed by applying or combining statistical and machine learning-based techniques. In this paper, we propose a novel intelligent prediction model based on the simulated annealing which is one of the well-known optimization techniques. The simulated annealing is known to have comparable optimization performance to the genetic algorithms. Nevertheless, since there has been little research on the prediction and classification of business decision-making problems using the simulated annealing, it is meaningful to confirm the usefulness of the proposed model in business analytics. In this study, we use the combined model of simulated annealing and machine learning to select the input features of the bankruptcy prediction model. Typical types of combining optimization and machine learning techniques are feature selection, feature weighting, and instance selection. This study proposes a combining model for feature selection, which has been studied the most. In order to confirm the superiority of the proposed model in this study, we apply the real-world financial data of the Korean companies and analyze the results. The results show that the predictive accuracy of the proposed model is better than that of the naïve model. Notably, the performance is significantly improved as compared with the traditional decision tree, random forests, artificial neural network, SVM, and logistic regression analysis.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.309-323
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• 2023

As the role of online reviews has become increasingly crucial, numerous studies have been conducted to utilize helpful reviews. Helpful reviews, perceived by customers, have been verified in various research studies to be influenced by factors such as ratings, review length, review content, and so on. The determination of a review's helpfulness is generally based on the number of 'helpful' votes from consumers, with more 'helpful' votes considered to have a more significant impact on consumers' purchasing decisions. However, recently written reviews that have not been exposed to many customers may have relatively few 'helpful' votes and may lack 'helpful' votes altogether due to a lack of participation. Therefore, rather than relying on the number of 'helpful' votes to assess the helpfulness of reviews, we aim to classify them based on review content. In addition, the text of the review emerges as the most influential factor in review helpfulness. This study employs text mining techniques, including topic modeling and sentiment analysis, to analyze the diverse impacts of content and emotions embedded in the review text. In this study, we propose a review helpfulness prediction model based on review content, utilizing movie reviews from IMDb, a global movie information site. We construct a review helpfulness prediction model by using an explainable Graph Neural Network (GNN), while addressing the interpretability limitations of the machine learning model. The explainable graph neural network is expected to provide more reliable information about helpful or non-helpful reviews as it can identify connections between reviews.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.187-201
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• 2011

KOSPI200 index is the Korean stock price index consisting of actively traded 200 stocks in the Korean stock market. Its base value of 100 was set on January 3, 1990. The Korea Exchange (KRX) developed derivatives markets on the KOSPI200 index. KOSPI200 index futures market, introduced in 1996, has become one of the most actively traded indexes markets in the world. Traders can make profit by entering a long position on the KOSPI200 index futures contract if the KOSPI200 index will rise in the future. Likewise, they can make profit by entering a short position if the KOSPI200 index will decline in the future. Basically, KOSPI200 index futures trading is a short-term zero-sum game and therefore most futures traders are using technical indicators. Advanced traders make stable profits by using system trading technique, also known as algorithm trading. Algorithm trading uses computer programs for receiving real-time stock market data, analyzing stock price movements with various technical indicators and automatically entering trading orders such as timing, price or quantity of the order without any human intervention. Recent studies have shown the usefulness of artificial intelligent systems in forecasting stock prices or investment risk. KOSPI200 index data is numerical time-series data which is a sequence of data points measured at successive uniform time intervals such as minute, day, week or month. KOSPI200 index futures traders use technical analysis to find out some patterns on the time-series chart. Although there are many technical indicators, their results indicate the market states among bull, bear and flat. Most strategies based on technical analysis are divided into trend following strategy and non-trend following strategy. Both strategies decide the market states based on the patterns of the KOSPI200 index time-series data. This goes well with Markov model (MM). Everybody knows that the next price is upper or lower than the last price or similar to the last price, and knows that the next price is influenced by the last price. However, nobody knows the exact status of the next price whether it goes up or down or flat. So, hidden Markov model (HMM) is better fitted than MM. HMM is divided into discrete HMM (DHMM) and continuous HMM (CHMM). The only difference between DHMM and CHMM is in their representation of state probabilities. DHMM uses discrete probability density function and CHMM uses continuous probability density function such as Gaussian Mixture Model. KOSPI200 index values are real number and these follow a continuous probability density function, so CHMM is proper than DHMM for the KOSPI200 index. In this paper, we present an artificial intelligent trading system based on CHMM for the KOSPI200 index futures system traders. Traders have experienced on technical trading for the KOSPI200 index futures market ever since the introduction of the KOSPI200 index futures market. They have applied many strategies to make profit in trading the KOSPI200 index futures. Some strategies are based on technical indicators such as moving averages or stochastics, and others are based on candlestick patterns such as three outside up, three outside down, harami or doji star. We show a trading system of moving average cross strategy based on CHMM, and we compare it to a traditional algorithmic trading system. We set the parameter values of moving averages at common values used by market practitioners. Empirical results are presented to compare the simulation performance with the traditional algorithmic trading system using long-term daily KOSPI200 index data of more than 20 years. Our suggested trading system shows higher trading performance than naive system trading.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.