Hyoung Yong Lee

Analysis of Trust in the E-Commerce Adoption

Understanding user acceptance of the Internet, especially the usage intention of virtual communities, is important in explaining the fact that virtual communities have been growing at an exponential rate in recent years. This paper studies the trust of virtual communities to better understand and manage the activities of E-commerce. A theoretical model proposed in this paper is to clarify the factors as they are related to the Technology Acceptance Model. In particular the relationship between trust and Intentions is hypothesized. Using the Technology Acceptance Model, this research showed that the importance of trust in virtual communities. According to the research, different ways of stimulating the members are necessary in order to facilitate participation in activities of virtual communities. The effect of trust in members on intention to use is stronger than that of trust in service providers. The intention to purchase is more sensitive to trust in service providers than trust in members.

VCR: Virtual community recommender using the technology acceptance model and the user's needs type

A recommender system is a kind of automated and sophisticated decision support system that is needed to provide a personalized solution in a brief form without going through a complicated search process. There have been a substantial number of studies to make recommender systems more accurate and efficient, however, most of them have a common critical limitation - these systems are used as virtual salespeople, rather than as marketing tools. A crucial reason for this phenomenon is that the models suggested by prior studies only focus on a users behavioral outcomes without consideration of the embedded procedure. In this study, we propose a novel recommender system based on users behavioral model. Our proposed system, labeled VCR-virtual community recommender, recommends optimal virtual communities for an active user by case-based reasoning (CBR) using behavioral factors suggested in the technology acceptance model (TAM) and its extended models. In addition, it refines its recommendation results by considering the users needs type at the point of usage. To test the usefulness of our recommendation model, we conducted two-step validation-empirical validation for the collected data set, and practical validation to investigate the actual satisfaction level of users. Experimental results showed that our model outperformed all comparative models from the perspective of user satisfaction.

Using neural networks to support early warning system for financial crisis forecasting

This study deals with the construction process of a daily financial condition indicator (DFCI), which can be used as an early warning signal using neural networks and nonlinear programming. One of the characteristics in the proposed indicator is to establish an alarm zone in the DFCI, which plays a role of predicting a potential financial crisis. The previous financial condition indicators based on statistical methods are developed such that they examine whether a crisis will be break out within 24 months. In this study, however, the alarm zone makes it possible for the DFCI to forecast an unexpected crisis on a daily basis and then issue an early warning signal. Therefore, DFCI involves daily monitoring of the evolution of the stock price index, foreign exchange rate and interest rate, which tend to exhibit unusual behaviors preceding a possible crisis. Using nonlinear programming, the procedure of DFCI construction is completed by integrating three sub-DFCIs, based on each financial variable, into the final DFCI. The DFCI for Korean financial market will be established as an empirical study. This study then examines the predictability of alarm zone for the financial crisis forecasting in Korea.

Using Hybrid Data Mining Techniques for Facilitating Cross-Selling of a Mobile Telecom Market to Develop Customer Classification Model

As the competition between mobile telecom operators becomes severe, it becomes critical for operators to diversify their business areas. Especially, the mobile operators are turning from traditional voice communication to mobile value-added services (VAS), which are new services to generate more ARPU (average revenue per user). That is, cross-selling is critical for mobile telecom operators to expand their revenues and profits. In this study, we propose a customer classification model. Our model uses the cumulated data on the existing customers including the patterns for using old products or services to find prospects for purchasing. The data mining techniques are applied to our proposed model in two steps. In the first step, several classification techniques are applied independently. In the second step, our model compromises all these probabilities by using genetic algorithm. To validate the usefulness of our model, we applied it to a real-world mobile telecom companys case in Korea.

Using Rough Set to Support Investment Strategies of Rule-Based Trading with Real-Time Data in Futures Market

Investment strategies in stock market have gained unprecedented popularity in major financial markets around the world. However, it is a very difficult problem because of the fluctuation of the stock market. This study presents usefulness of rough set on the rule base to develop real-time investment strategies using technical analysis in futures market. This study consists of four phases. In the first phase, meaningful technical indicators are selected to reflect market movements. In the second phase, rough set is used to extract trading rules for identification of buy and sell patterns in the stock market. In the third phase, the investment strategies are developed in order to apply selected trading rules using rule-based reasoning to unpredictable stock market. Finally, investment strategies on the basis of rule base are evaluated by real-time trading. This study then examines the profitability of the proposed model.

Machine Learning Algorithm Selection for Forecasting Behavior of Global Institutional Investors

Recently Son et al. [32] proposed early warning system (EWS) monitoring the behaviors of global institutional investors (GII) against their possible massive pullout from the local emerging stock market. They used machine learning algorithm for lag l classifier to forecast the behavior of GII. The main aim of this article is to implement various machine learning algorithms in constructing the EWS and to compare their performances to select the proper one. Our results address various important issues for machine learning forecasting problem. In particular, a proper machine learning algorithm will be recommended for both long term and short term forecasting. This is empirically studied for the Korean stock market.