• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2007.05a
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• pp.234-245
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• 2007

The empirical studies on the implementation of data warehousing systems (DWS) are lacking while there exist a number of studies on the implementation of IS. This study intends to examine the factors affecting the implementation success of DWS. The study adopts the empirical analysis of the sample of 112 responses from DWS practitioners. The study results suggest several implications for researchers and practitioners. First, when the support from top management becomes great, the implementation success of DWS in organizational aspects is more likely. When the support from top management exists, users are more likely to be encouraged to use DWS, and organizational resistance to use DWS is well coped with increasing the possibility of implementation success of DWS. The support of resource increases the implementation success of DWS in project aspects while it is not significantly related to the implementation success of DWS in organizational aspects. The support of funds, human resources, and other efforts enhances the possibility of successful implementation of project; the project does not exceed the time and resource budgets and meet the functional requirements. The effect of resource support, however, is not significantly related to the organizational success. The user involvement in systems implementation affects the implementation success of DWS in organizational and project aspects. The success of DWS implementation is significantly related to the users' commitment to the project and the proactive involvement in the implementation tasks. users' task. The observation of the behaviors of competitors which possibly increases data quality does not affect the implementation success of DWS. This indicates that the quality of data such as data consistency and accuracy is not ensured through the understanding of the behaviors of competitors, and this does not affect the data integration and the successful implementation of DWS projects. The prototyping for the DWS implementation positively affects the implementation success of DWS. This indicates that the extent of understanding requirements and the communication among project members increases the implementation success of DWS. Developing the prototypes for DWS ensures the acquirement of accurate or integrated data, the flexible processing of data, and the adaptation into new organizational conditions. The extent of consulting activities in DWS projects increases the implementation success of DWS in project aspects. The continuous support for consulting activities and technology transfer enhances the adherence to the project schedule preventing the exceeding use of project budget and ensuring the implementation of intended system functions; this ultimately leads to the successful implementation of DWS projects. The research hypothesis that the capability of project teams affects the implementation success of DWS is rejected. The technical ability of team members and human relationship skills themselves do not affect the successful implementation of DWS projects. The quality of the system which provided data to DWS affects the implementation success of DWS in technical aspects. The standardization of data definition and the commitment to the technical standard increase the possibility of overcoming the technical problems of DWS. Further, the development technology of DWS affects the implementation success of DWS. The hardware, software, implementation methodology, and implementation tools contribute to effective integration and classification of data in various forms. In addition, the implementation success of DWS in organizational and project aspects increases the data quality and system quality of DWS while the implementation success of DWS in technical aspects does not affect the data quality and system quality of DWS. The data and systems quality increases the effective processing of individual tasks, and reduces the decision making times and efforts enhancing the perceived benefits of DWS.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.131-145
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• 2010

The framework for making a coordinated decision for large-scale facilities has become an important issue in supply chain(SC) management research. The competitive business environment requires companies to continuously search for the ways to achieve high efficiency and lower operational costs. In the areas of production/distribution planning, many researchers and practitioners have developedand evaluated the deterministic models to coordinate important and interrelated logistic decisions such as capacity management, inventory allocation, and vehicle routing. They initially have investigated the various process of SC separately and later become more interested in such problems encompassing the whole SC system. The accurate quotation of ATP(Available-To-Promise) plays a very important role in enhancing customer satisfaction and fill rate maximization. The complexity for intelligent manufacturing system, which includes all the linkages among procurement, production, and distribution, makes the accurate quotation of ATP be a quite difficult job. In addition to, many researchers assumed ATP model with integer time. However, in industry practices, integer times are very rare and the model developed using integer times is therefore approximating the real system. Various alternative models for an ATP system with time lags have been developed and evaluated. In most cases, these models have assumed that the time lags are integer multiples of a unit time grid. However, integer time lags are very rare in practices, and therefore models developed using integer time lags only approximate real systems. The differences occurring by this approximation frequently result in significant accuracy degradations. To introduce the ATP model with time lags, we first introduce the dynamic production function. Hackman and Leachman's dynamic production function in initiated research directly related to the topic of this paper. They propose a modeling framework for a system with non-integer time lags and show how to apply the framework to a variety of systems including continues time series, manufacturing resource planning and critical path method. Their formulation requires no additional variables or constraints and is capable of representing real world systems more accurately. Previously, to cope with non-integer time lags, they usually model a concerned system either by rounding lags to the nearest integers or by subdividing the time grid to make the lags become integer multiples of the grid. But each approach has a critical weakness: the first approach underestimates, potentially leading to infeasibilities or overestimates lead times, potentially resulting in excessive work-inprocesses. The second approach drastically inflates the problem size. We consider an optimized ATP system with non-integer time lag in supply chain management. We focus on a worldwide headquarter, distribution centers, and manufacturing facilities are globally networked. We develop a mixed integer programming(MIP) model for ATP process, which has the definition of required data flow. The illustrative ATP module shows the proposed system is largely affected inSCM. The system we are concerned is composed of a multiple production facility with multiple products, multiple distribution centers and multiple customers. For the system, we consider an ATP scheduling and capacity allocationproblem. In this study, we proposed the model for the ATP system in SCM using the dynamic production function considering the non-integer time lags. The model is developed under the framework suitable for the non-integer lags and, therefore, is more accurate than the models we usually encounter. We developed intelligent ATP System for this model using genetic algorithm. We focus on a capacitated production planning and capacity allocation problem, develop a mixed integer programming model, and propose an efficient heuristic procedure using an evolutionary system to solve it efficiently. This method makes it possible for the population to reach the approximate solution easily. Moreover, we designed and utilized a representation scheme that allows the proposed models to represent real variables. The proposed regeneration procedures, which evaluate each infeasible chromosome, makes the solutions converge to the optimum quickly.

• Management & Information Systems Review
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• v.38 no.4
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• pp.137-157
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• 2019

Companies are trying to use enterprise SNS for collaboration and speedy decision-making. This study verified the mediating effect of communication between enterprise SNS and job performance, and proved the moderating effect of nationality between enterprise SNS and communication. This study collected survey data of 81 Korean and 81 Chinese from employees who have used enterprise SNS in Korea and China. As results of data analysis, first, enterprise SNS improved job performance through speedy information sharing and error reduction. Second, communication mediated the effect of enterprise SNS on job performance. Third, enterprise SNS increased the level of organizational communication through decreasing the burden of offline face-to-face communication. Compared with Chinese corporate organizations, Korean corporate organizations have high power distances, centralized control, and high superior authority. Therefore, in the off-line communication situation, the subordinate feels the social pressure to follow the command of the superior. Thus communication is one-way and closed. In this Korean organizational situation, corporate SNS can be used as a means to bypass rigid offline communication. In the online communication environment of non face-to-face corporate SNS, anxiety and stress of face-to-face communication can be reduced, so communication between the upper and lower sides can flow more smoothly. The contribution of this paper is that it proved that enterprise SNS promotes communication and improve job performance by reducing the anxiety or stress of offline communication, while according to prior research successful adoption of many types of information systems requires the fit between an organization and its organizational culture.

• Korean Journal of Comparative Education
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• v.27 no.1
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• pp.145-169
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• 2017

The purpose of this study is to investigate the influences of educational policy transfer on transitional countries by analyzing the impacts of the Bologna Process on the educational systems of former socialist countries in Europe including Russia, Ukraine, and Hungary. For this purpose, documents published by European Union and its associated educational institutions, as well as academic institutions and scholars were analyzed to evaluate the changes made not only in the systemic level but also institutional and personal levels. The Bologna Process, instigated by the rise of knowledge economy and globalization, is purported to be the most influential educational reform conducted by the member countries since the formation of EU. However, unlike its original intentions to promote the voluntary participation of universities and students, the Bologna Process strengthened the structure of centralized bureaucracy in the educational systems, and restricted the freedom of professors, since most of the universities in these countries relied on governments for their funding. This indicates that in analyzing the influences of educational policy transfer in transitional countries, it is important to analyze the roles and motivations of actors participating in the decision-making processes. Moreover, Bologna Process reforms, made under the direction and control of government, were often turned into cases in which administrators hurriedly implement new policies against the will of faculty members and students, thus impeding the efficient localization of the reforms. This case, thus, implies that while educational reforms driven by policy transfer can change external systems and policies of universities, the fundamental reforms in the minds of faculty and inner workings of organization can only come about after a careful consideration of the societal and cultural values embedded within society.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.35-55
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• 2013

Information technology is a critical resource necessary for any company hoping to support and realize its strategic goals, which contribute to growth promotion and sustainable development. The selection of information technology and its strategic use are imperative for the enhanced performance of every aspect of company management, leading a wide range of companies to have invested continuously in information technology. Despite researchers, managers, and policy makers' keen interest in how information technology contributes to organizational performance, there is uncertainty and debate about the result of information technology investment. In other words, researchers and managers cannot easily identify the independent factors that can impact the investment performance of information technology. This is mainly owing to the fact that many factors, ranging from the internal components of a company, strategies, and external customers, are interconnected with the investment performance of information technology. Using an agent-based simulation technique, this research extracts factors expected to affect investment performance on information technology, simplifies the analyses of their relationship with economic modeling, and examines the performance dependent on changes in the factors. In terms of economic modeling, I expand the model that highlights the way in which product quality moderates the relationship between information technology investments and economic performance (Thatcher and Pingry, 2004) by considering the cost of information technology investment and the demand creation resulting from product quality enhancement. For quality enhancement and its consequences for demand creation, I apply the concept of information quality and decision-maker quality (Raghunathan, 1999). This concept implies that the investment on information technology improves the quality of information, which, in turn, improves decision quality and performance, thus enhancing the level of product or service quality. Additionally, I consider the effect of word of mouth among consumers, which creates new demand for a product or service through the information diffusion effect. This demand creation is analyzed with an agent-based simulation model that is widely used for network analyses. Results show that the investment on information technology enhances the quality of a company's product or service, which indirectly affects the economic performance of that company, particularly with regard to factors such as consumer surplus, company profit, and company productivity. Specifically, when a company makes its initial investment in information technology, the resultant increase in the quality of a company's product or service immediately has a positive effect on consumer surplus, but the investment cost has a negative effect on company productivity and profit. As time goes by, the enhancement of the quality of that company's product or service creates new consumer demand through the information diffusion effect. Finally, the new demand positively affects the company's profit and productivity. In terms of the investment strategy for information technology, this study's results also reveal that the selection of information technology needs to be based on analysis of service and the network effect of customers, and demonstrate that information technology implementation should fit into the company's business strategy. Specifically, if a company seeks the short-term enhancement of company performance, it needs to have a one-shot strategy (making a large investment at one time). On the other hand, if a company seeks a long-term sustainable profit structure, it needs to have a split strategy (making several small investments at different times). The findings from this study make several contributions to the literature. In terms of methodology, the study integrates both economic modeling and simulation technique in order to overcome the limitations of each methodology. It also indicates the mediating effect of product quality on the relationship between information technology and the performance of a company. Finally, it analyzes the effect of information technology investment strategies and information diffusion among consumers on the investment performance of information technology.

• The KIPS Transactions:PartD
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• v.11D no.1
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• pp.183-194
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• 2004

The existing management job of research expenses has been divided into three parts： budget planning, budget draw-up, and exact settlement of budget. However, it caused some problems. Under this current circumstance it is required to obtain research expenses steadily, to operate efficiently and to use them clearly to solve such problems. As a result of a study on data warehouse development process of existing system integration company (Inmon, IBM) to reflect current trend described above, data warehouse development process of Inmon uses systematic and gradual access as a classical development cycle method. It causes overlap and feedback to the previous step in the process of each step Is requested. And another problem that it is difficult to toil what function refers and corrects data because functions and data are separated during performing development process at data warehouse development process of IBM is caused. Integrated management data warehouse development process of research expenses in the enterprise environment which applies UML at planning and analysis step, design step and implement and test step is suggested in this paper. Information retrieval agent uses existing budget plan DB, budget draw-up DB and budget settlement DB to find out information that a user wants to know. Information retrieval agent collects and saves information at integration database and information integration agent extracts, transports, transforms and loads the data. Information integration agent reduces a user's efforts to access to a number of information sources and check each of them. It also screens out data that a user may not need. As a result, integrated management data warehouse development process of research expenses in the enterprise environment reflects a user's requirements as much as possible and provides various types of information to make a decision which is needed to establish the policy of research expense management. It helps an end user approach his/her desired analysis information quickly and get various data from the comprehensive viewpoint rather than the fragmentary viewpoint. Furthermore, as it integrated three systems into one, it is possible to share data, to integrate the system, to reduce operating expenses and to simplify supporting environment for the decision making.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• International Commerce and Information Review
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• v.4 no.2
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• pp.253-277
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• 2002

Nowaday, the advanced technology in information and communication has been leading the dramatic change of transaction paradigm expansion from physical basis to electronic one. As we know, financial services support most of financial exchange between two business parties. So the expansion of electronic transaction paradigm affects to every financial institutions which provide financial services. Thus, financial institutions have accepted e-Finance systems and providing internet financial services to live in the competition. The purpose of this study is to contribute the qualitative enhancement of its customer service, rapid diffusion and accurate strategy establishment for e-Finance industry in the user side. Through the literature review and factor and reliability analysis, this study selects six diffusion factors such as efficiency of perceived e-Finance, reliability and safety of e-Finance in perceived e-Finance itself's characteristic; confidence, technical factors and the customer service quality of e-Finance system in perception on e-Finance System; inclination to innovation in the personal characteristic. According to result of hypothesis verification by using logistics regression analysis, technical factors and the customer service quality of e-Finance system in perception on e-Finance System and inclination to innovation in the personal characteristic gave statistically positive effect to the diffusion decision at the significant level 0.05 and 0.01. However efficiency of perceived e-Finance, reliability and safety of e-Finance in perceived e-Finance itself's characteristic didn't affect to diffusion decision and confidence of e-Finance system in perception on e-Finance System didn't have any statistical significancy. This study can be used as a basic material for the forward empirical study of diffusion factors in the user side and be able to apply to company and government policy making or embodiment, determination for customer service quality degree of financial institutions. But this study has some limitations like didn't touch satisfaction factors and its effect, only deal domestic customers and didn't use multi-regression analysis.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.99-122
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• 2019

According to the recent IDC (International Data Corporation) report, as from 2025, the total volume of data is estimated to reach ten times higher than that of 2016, corresponding to 163 zettabytes. then the main body of generating information is moving more toward corporations than consumers. So-called "the wave of Big-data" is arriving, and the following aftermath affects entire industries and firms, respectively and collectively. Therefore, effective management of vast amounts of data is more important than ever in terms of the firm. However, there have been no previous studies that measure the effects of big data investment, even though there are number of previous studies that quantitatively the effects of IT investment. Therefore, we quantitatively analyze the Big-data investment effects, which assists firm's investment decision making. This study applied the Event Study Methodology, which is based on the efficient market hypothesis as the theoretical basis, to measure the effect of the big data investment of firms on the response of market investors. In addition, five sub-variables were set to analyze this effect in more depth: the contents are firm size classification, industry classification (finance and ICT), investment completion classification, and vendor existence classification. To measure the impact of Big data investment announcements, Data from 91 announcements from 2010 to 2017 were used as data, and the effect of investment was more empirically observed by observing changes in corporate value immediately after the disclosure. This study collected data on Big Data Investment related to Naver 's' News' category, the largest portal site in Korea. In addition, when selecting the target companies, we extracted the disclosures of listed companies in the KOSPI and KOSDAQ market. During the collection process, the search keywords were searched through the keywords 'Big data construction', 'Big data introduction', 'Big data investment', 'Big data order', and 'Big data development'. The results of the empirically proved analysis are as follows. First, we found that the market value of 91 publicly listed firms, who announced Big-data investment, increased by 0.92%. In particular, we can see that the market value of finance firms, non-ICT firms, small-cap firms are significantly increased. This result can be interpreted as the market investors perceive positively the big data investment of the enterprise, allowing market investors to better understand the company's big data investment. Second, statistical demonstration that the market value of financial firms and non - ICT firms increases after Big data investment announcement is proved statistically. Third, this study measured the effect of big data investment by dividing by company size and classified it into the top 30% and the bottom 30% of company size standard (market capitalization) without measuring the median value. To maximize the difference. The analysis showed that the investment effect of small sample companies was greater, and the difference between the two groups was also clear. Fourth, one of the most significant features of this study is that the Big Data Investment announcements are classified and structured according to vendor status. We have shown that the investment effect of a group with vendor involvement (with or without a vendor) is very large, indicating that market investors are very positive about the involvement of big data specialist vendors. Lastly but not least, it is also interesting that market investors are evaluating investment more positively at the time of the Big data Investment announcement, which is scheduled to be built rather than completed. Applying this to the industry, it would be effective for a company to make a disclosure when it decided to invest in big data in terms of increasing the market value. Our study has an academic implication, as prior research looked for the impact of Big-data investment has been nonexistent. This study also has a practical implication in that it can be a practical reference material for business decision makers considering big data investment.