• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.19-32
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• 2010

Volatility plays a central role in both academic and practical applications, especially in pricing financial derivative products and trading volatility strategies. This study presents a novel mechanism based on generalized autoregressive conditional heteroskedasticity (GARCH) models that is able to enhance the performance of intelligent volatility trading systems by predicting Korean stock market volatility more accurately. In particular, we embedded the concept of the volatility asymmetry documented widely in the literature into our model. The newly developed Korean stock market volatility index of KOSPI 200, VKOSPI, is used as a volatility proxy. It is the price of a linear portfolio of the KOSPI 200 index options and measures the effect of the expectations of dealers and option traders on stock market volatility for 30 calendar days. The KOSPI 200 index options market started in 1997 and has become the most actively traded market in the world. Its trading volume is more than 10 million contracts a day and records the highest of all the stock index option markets. Therefore, analyzing the VKOSPI has great importance in understanding volatility inherent in option prices and can afford some trading ideas for futures and option dealers. Use of the VKOSPI as volatility proxy avoids statistical estimation problems associated with other measures of volatility since the VKOSPI is model-free expected volatility of market participants calculated directly from the transacted option prices. This study estimates the symmetric and asymmetric GARCH models for the KOSPI 200 index from January 2003 to December 2006 by the maximum likelihood procedure. Asymmetric GARCH models include GJR-GARCH model of Glosten, Jagannathan and Runke, exponential GARCH model of Nelson and power autoregressive conditional heteroskedasticity (ARCH) of Ding, Granger and Engle. Symmetric GARCH model indicates basic GARCH (1, 1). Tomorrow's forecasted value and change direction of stock market volatility are obtained by recursive GARCH specifications from January 2007 to December 2009 and are compared with the VKOSPI. Empirical results indicate that negative unanticipated returns increase volatility more than positive return shocks of equal magnitude decrease volatility, indicating the existence of volatility asymmetry in the Korean stock market. The point value and change direction of tomorrow VKOSPI are estimated and forecasted by GARCH models. Volatility trading system is developed using the forecasted change direction of the VKOSPI, that is, if tomorrow VKOSPI is expected to rise, a long straddle or strangle position is established. A short straddle or strangle position is taken if VKOSPI is expected to fall tomorrow. Total profit is calculated as the cumulative sum of the VKOSPI percentage change. If forecasted direction is correct, the absolute value of the VKOSPI percentage changes is added to trading profit. It is subtracted from the trading profit if forecasted direction is not correct. For the in-sample period, the power ARCH model best fits in a statistical metric, Mean Squared Prediction Error (MSPE), and the exponential GARCH model shows the highest Mean Correct Prediction (MCP). The power ARCH model best fits also for the out-of-sample period and provides the highest probability for the VKOSPI change direction tomorrow. Generally, the power ARCH model shows the best fit for the VKOSPI. All the GARCH models provide trading profits for volatility trading system and the exponential GARCH model shows the best performance, annual profit of 197.56%, during the in-sample period. The GARCH models present trading profits during the out-of-sample period except for the exponential GARCH model. During the out-of-sample period, the power ARCH model shows the largest annual trading profit of 38%. The volatility clustering and asymmetry found in this research are the reflection of volatility non-linearity. This further suggests that combining the asymmetric GARCH models and artificial neural networks can significantly enhance the performance of the suggested volatility trading system, since artificial neural networks have been shown to effectively model nonlinear relationships.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.123-139
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• 2019

The Korean NPL market was formed by the government and foreign capital shortly after the 1997 IMF crisis. However, this market is short-lived, as the bad debt has started to increase after the global financial crisis in 2009 due to the real economic recession. NPL has become a major investment in the market in recent years when the domestic capital market's investment capital began to enter the NPL market in earnest. Although the domestic NPL market has received considerable attention due to the overheating of the NPL market in recent years, research on the NPL market has been abrupt since the history of capital market investment in the domestic NPL market is short. In addition, decision-making through more scientific and systematic analysis is required due to the decline in profitability and the price fluctuation due to the fluctuation of the real estate business. In this study, we propose a prediction model that can determine the achievement of the benchmark yield by using the NPL market related data in accordance with the market demand. In order to build the model, we used Korean NPL data from December 2013 to December 2017 for about 4 years. The total number of things data was 2291. As independent variables, only the variables related to the dependent variable were selected for the 11 variables that indicate the characteristics of the real estate. In order to select the variables, one to one t-test and logistic regression stepwise and decision tree were performed. Seven independent variables (purchase year, SPC (Special Purpose Company), municipality, appraisal value, purchase cost, OPB (Outstanding Principle Balance), HP (Holding Period)). The dependent variable is a bivariate variable that indicates whether the benchmark rate is reached. This is because the accuracy of the model predicting the binomial variables is higher than the model predicting the continuous variables, and the accuracy of these models is directly related to the effectiveness of the model. In addition, in the case of a special purpose company, whether or not to purchase the property is the main concern. Therefore, whether or not to achieve a certain level of return is enough to make a decision. For the dependent variable, we constructed and compared the predictive model by calculating the dependent variable by adjusting the numerical value to ascertain whether 12%, which is the standard rate of return used in the industry, is a meaningful reference value. As a result, it was found that the hit ratio average of the predictive model constructed using the dependent variable calculated by the 12% standard rate of return was the best at 64.60%. In order to propose an optimal prediction model based on the determined dependent variables and 7 independent variables, we construct a prediction model by applying the five methodologies of discriminant analysis, logistic regression analysis, decision tree, artificial neural network, and genetic algorithm linear model we tried to compare them. To do this, 10 sets of training data and testing data were extracted using 10 fold validation method. After building the model using this data, the hit ratio of each set was averaged and the performance was compared. As a result, the hit ratio average of prediction models constructed by using discriminant analysis, logistic regression model, decision tree, artificial neural network, and genetic algorithm linear model were 64.40%, 65.12%, 63.54%, 67.40%, and 60.51%, respectively. It was confirmed that the model using the artificial neural network is the best. Through this study, it is proved that it is effective to utilize 7 independent variables and artificial neural network prediction model in the future NPL market. The proposed model predicts that the 12% return of new things will be achieved beforehand, which will help the special purpose companies make investment decisions. Furthermore, we anticipate that the NPL market will be liquidated as the transaction proceeds at an appropriate price.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.113-125
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• 2013

Every company wants to know customer's requirement and makes an effort to meet them. Cause that, communication between customer and company became core competition of business and that important is increasing continuously. There are several strategies to find customer's needs, but VOC (Voice of customer) is one of most powerful communication tools and VOC gathering by several channels as telephone, post, e-mail, website and so on is so meaningful. So, almost company is gathering VOC and operating VOC system. VOC is important not only to business organization but also public organization such as government, education institute, and medical center that should drive up public service quality and customer satisfaction. Accordingly, they make a VOC gathering and analyzing System and then use for making a new product and service, and upgrade. In recent years, innovations in internet and ICT have made diverse channels such as SNS, mobile, website and call-center to collect VOC data. Although a lot of VOC data is collected through diverse channel, the proper utilization is still difficult. It is because the VOC data is made of very emotional contents by voice or text of informal style and the volume of the VOC data are so big. These unstructured big data make a difficult to store and analyze for use by human. So that, the organization need to automatic collecting, storing, classifying and analyzing system for unstructured big VOC data. This study propose an intelligent VOC analyzing system based on opinion mining to classify the unstructured VOC data automatically and determine the polarity as well as the type of VOC. And then, the basis of the VOC opinion analyzing system, called domain-oriented sentiment dictionary is created and corresponding stages are presented in detail. The experiment is conducted with 4,300 VOC data collected from a medical website to measure the effectiveness of the proposed system and utilized them to develop the sensitive data dictionary by determining the special sentiment vocabulary and their polarity value in a medical domain. Through the experiment, it comes out that positive terms such as "칭찬, 친절함, 감사, 무사히, 잘해, 감동, 미소" have high positive opinion value, and negative terms such as "퉁명, 뭡니까, 말하더군요, 무시하는" have strong negative opinion. These terms are in general use and the experiment result seems to be a high probability of opinion polarity. Furthermore, the accuracy of proposed VOC classification model has been compared and the highest classification accuracy of 77.8% is conformed at threshold with -0.50 of opinion classification of VOC. Through the proposed intelligent VOC analyzing system, the real time opinion classification and response priority of VOC can be predicted. Ultimately the positive effectiveness is expected to catch the customer complains at early stage and deal with it quickly with the lower number of staff to operate the VOC system. It can be made available human resource and time of customer service part. Above all, this study is new try to automatic analyzing the unstructured VOC data using opinion mining, and shows that the system could be used as variable to classify the positive or negative polarity of VOC opinion. It is expected to suggest practical framework of the VOC analysis to diverse use and the model can be used as real VOC analyzing system if it is implemented as system. Despite experiment results and expectation, this study has several limits. First of all, the sample data is only collected from a hospital web-site. It means that the sentimental dictionary made by sample data can be lean too much towards on that hospital and web-site. Therefore, next research has to take several channels such as call-center and SNS, and other domain like government, financial company, and education institute.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.143-159
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• 2017

Due to the rapid development of IT in recent years, not only personal information but also the key technologies and information leakage that companies have are becoming important issues. For the enterprise, the core technology that the company possesses is a very important part for the survival of the enterprise and for the continuous competitive advantage. Recently, there have been many cases of technical infringement. Technology leaks not only cause tremendous financial losses such as falling stock prices for companies, but they also have a negative impact on corporate reputation and delays in corporate development. In the case of SMEs, where core technology is an important part of the enterprise, compared to large corporations, the preparation for technological leakage can be seen as an indispensable factor in the existence of the enterprise. As the necessity and importance of Information Security Management (ISM) is emerging, it is necessary to check and prepare for the threat of technology infringement early in the enterprise. Nevertheless, previous studies have shown that the majority of policy alternatives are represented by about 90%. As a research method, literature analysis accounted for 76% and empirical and statistical analysis accounted for a relatively low rate of 16%. For this reason, it is necessary to study the management model and prediction model to prevent leakage of technology to meet the characteristics of SMEs. In this study, before analyzing the empirical analysis, we divided the technical characteristics from the technology value perspective and the organizational factor from the technology control point based on many previous researches related to the factors affecting the technology leakage. A total of 12 related variables were selected for the two factors, and the analysis was performed with these variables. In this study, we use three - year data of "Small and Medium Enterprise Technical Statistics Survey" conducted by the Small and Medium Business Administration. Analysis data includes 30 industries based on KSIC-based 2-digit classification, and the number of companies affected by technology leakage is 415 over 3 years. Through this data, we conducted a randomized sampling in the same industry based on the KSIC in the same year, and compared with the companies (n = 415) and the unaffected firms (n = 415) 1:1 Corresponding samples were prepared and analyzed. In this research, we will conduct an empirical analysis to search for factors influencing technology leakage, and propose an early warning system through data mining. Specifically, in this study, based on the questionnaire survey of SMEs conducted by the Small and Medium Business Administration (SME), we classified the factors that affect the technology leakage of SMEs into two factors(Technology Characteristics, Organization Characteristics). And we propose a model that informs the possibility of technical infringement by using Support Vector Machine(SVM) which is one of the various techniques of data mining based on the proven factors through statistical analysis. Unlike previous studies, this study focused on the cases of various industries in many years, and it can be pointed out that the artificial intelligence model was developed through this study. In addition, since the factors are derived empirically according to the actual leakage of SME technology leakage, it will be possible to suggest to policy makers which companies should be managed from the viewpoint of technology protection. Finally, it is expected that the early warning model on the possibility of technology leakage proposed in this study will provide an opportunity to prevent technology Leakage from the viewpoint of enterprise and government in advance.

• Asian-Australasian Journal of Animal Sciences
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• v.22 no.6
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• pp.836-842
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• 2009

The objectives of this study were to evaluate potential benefits of intercropping of corn with runner bean for a smallsized farming system, based on land equivalent ratio (LER) and silage yield and quality of corn intercropped with runner bean (Phaseolus vulgaris L.), in arid conditions of Turkey under an irrigation system. This experiment was established as a split-plot design in a randomized complete block, with three replications and carried out over two (consecutive) years in 2006 and 2007. Seven different mixtures (runner bean, B and silage corn sole crop, C, 10% B+90% C, 20% B+80% C, 30% B+70% C, 40% B+60%C, and 50% B+50%C) of silage corn-runner bean were intercropped. All of the mixtures were grown under irrigation. The corn-runner bean fields were planted in the second week of May and harvested in the first week of September in both years. Green beans were harvested three times each year and green bean yields were recorded each time. After the 3rd harvest of green bean, residues of bean and corn together were randomly harvested from a 1 $m^{2}$ area by hand using a clipper when the bean started to dry and corn was at the dough stage. Green mass yields of each plot were recorded. Silages were prepared from each plot (triplicate) in 1 L mini-silos. After 60 d ensiling, subsamples were taken from this material for determination of dry matter (DM), pH, organic acids, chemical composition, and in vitro DM digestibility of silages. The LER index was also calculated to evaluate intercrop efficiencies with respect to sole crops. Average pH, acetic, propionic and butyric acid concentrations were similar but lactic acid and ammonia-N levels were significantly different (p<0.05) among different mixtures of bean intercropped with corn. Ammonia-N levels linearly increased from 0.90% to 2.218 as the percentage of bean increased in the mixtures up to a 50:50 seeding ratio. While average CP content increased linearly from 6.47 to 12.45%, and average NDF and ADF contents decreased linearly from 56.17 to 44.88 and from 34.92 to 33.51%, respectively, (p<0.05) as the percentage of bean increased in the mixtures up to a 50:50 seeding ratio, but DM and OM contents did not differ among different mixtures of bean intercropped with corn (p>0.05). In vitro OM digestibility values differed significantly among bean-corn mixture silages (p<0.05). Fresh bean, herbage DM, IVOMD, ME yields, and LER index were significantly influenced by percentage of bean in the mixtures (p<0.01). As the percentage of bean increased in the mixtures up to a 50:50 seeding ratio, yields of fresh bean (from 0 to 24,380 kg/ha) and CP (from 1,258.0 to 1,563.0 kg/ha) and LER values (from 1.0 to 1.775) linearly increased, but yields of herbage DM (from 19,670 to 12,550 kg/ha), IVOMD (from 12,790 to 8,020 kg/ha) and ME (46,230 to 29,000 Mcal/ha) yields decreased (p<0.05). In conclusion, all of the bean-corn mixtures provided a good silage and better CP concentrations. Even though forage yields decreased, the LER index linearly increased as the percentage of bean increased in the mixture up to a 50:50 seeding ratio, which indicates a greater utilization of land. Therefore, a 50:50 seeding ratio seemed to be best for optimal utilization of land in this study and to provide greater financial stability for labor-intensive, small farmers.

• Journal of Global Scholars of Marketing Science
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• v.20 no.1
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• pp.80-88
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• 2010

There has been considerable research examining the relationship between stockholders equity and various marketing strategies. These include studies linking stock price performance to advertising, customer service metrics, new product introductions, research and development, celebrity endorsers, brand perception, brand extensions, brand evaluation, company name changes, and sports sponsorships. Another facet of marketing investments which has received heightened scrutiny for its purported influence on stockholder equity is television advertisement embedded within specific sporting events such as the Super Bowl. Research indicates that firms which advertise in Super Bowls experience stock price gains. Given this reported relationship between advertising investment and increased shareholder value, for both general and special events, it is surprising that relatively little research attention has been paid to investigating the relationship between advertising in the Olympic Games and its subsequent impact on stockholder equity. While attention has been directed at examining the effectiveness of sponsoring the Olympic Games, much less focus has been placed on the financial soundness of advertising during the telecasts of these Games. Notable exceptions to this include Peters (2008), Pfanner (2008), Saini (2008), and Keller Fay Group (2009). This paper presents a study of Olympic advertisers who ran TV ads on NBC in the American telecasts of the 2000, 2004, and 2008 Summer Olympic Games. Five hypothesis were tested: H1: The stock prices of firms which advertised on American telecasts of the 2008, 2004 and 2000 Olympics (referred to as O-Stocks), will outperform the S&P 500 during this same period of time (i.e., the Monday before the Games through to the Friday after the Games). H2: O-Stocks will outperform the S&P 500 during the medium term, that is, for the period of the Monday before the Games through to the end of each Olympic calendar year (December 31st of 2000, 2004, and 2008 respectively). H3: O-Stocks will outperform the S&P 500 in the longer term, that is, for the period of the Monday before the Games through to the midpoint of the following years (June 30th of 2001, 2005, and 2009 respectively). H4: There will be no difference in the performance of these O-Stocks vs. the S&P 500 in the Non-Olympic time control periods (i.e. three months earlier for each of the Olympic years). H5: The annual revenue of firms which advertised on American telecasts of the 2008, 2004 and 2000 Olympics will be higher for those years than the revenue for those same firms in the years preceding those three Olympics respectively. In this study, we recorded stock prices of those companies that advertised during the Olympics for the last three Summer Olympic Games (i.e. Beijing in 2008, Athens in 2004, and Sydney in 2000). We identified these advertisers using Google searches as well as with the help of the television network (i.e., NBC) that hosted the Games. NBC held the American broadcast rights to all three Olympic Games studied. We used Internet sources to verify the parent companies of the brands that were advertised each year. Stock prices of these parent companies were found using Yahoo! Finance. Only companies that were publicly held and traded were used in the study. We identified changes in Olympic advertisers' stock prices over the four-week period that included the Monday before through the Friday after the Games. In total, there were 117 advertisers of the Games on telecasts which were broadcast in the U.S. for 2008, 2004, and 2000 Olympics. Figure 1 provides a breakdown of those advertisers, by industry sector. Results indicate the stock of the firms that advertised (O-Stocks) out-performed the S&P 500 during the period of interest and under-performed the S&P 500 during the earlier control periods. These same O-Stocks also outperformed the S&P 500 from the start of these Games through to the end of each Olympic year, and for six months beyond that. Price pressure linkage, signaling theory, high involvement viewers, and corporate activation strategies are believed to contribute to these positive results. Implications for advertisers and researchers are discussed, as are study limitations and future research directions.

• Korean Journal of Agricultural Science
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• v.18 no.2
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• pp.164-179
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• 1991

Korean agriculture has encountered two problems. One is internal income disparity between rural and urbarn area and the other is external Uruguay Round trade problems as an abolition of direct and indirect import barriers, reduction in export subsidies and to reduce internal price supports. These problems will be brought severe farm problems such as decreasing farm household income and repressing agricultural growth in the near future. Considering the above inevitable facts Korean government has implemented several development projects such as rural industrial area development project, rural special production area development project, leisuresight seeing farm development project, traditional food development project, unskilled labor training project for off-farm employment and so on, to increase farm household income through off-farm income increase. This study was mainly concentrated on the identification of operational problems and post evaluation of the rural special production area development projects which aimed at increasing non-farm incomes and giving employment opportunity for rural farmers in small factories processing regional special farm products and mine products. The main findings and problems to be solved for the successful project implementation are as followed ; 1. Total number of the special production area development projects as of the end of 1991 was amount to 138, and total number of farm household participated were estimated at 2,079, and total amount of off-farm income per farm household was reached to 3,011 thousand won. 2. The total number of processed special products have increased from 21 items in 1981 to 56 items in 1991. On the other hand the total number of farm household participated in the projects have decreased from 2,518 to 2,079 during same period. 3. Total amount of investment for the projects has increased from 1,429 million won in 1981 to 24,760 million won in 1991 but the rate of G'T loan of the total investment has reduced from 24.5% to 5.2% during same period. 4. 138 special production area development project are classified into 6 kinds of commodity groups such as 19 of general industrial good production areas, 52 of folks-industrial art objects production areas, 39 of food processing areas, 9 of fiber and texstile processing areas, 18 of agricultural and fishery inputs processing areas and 1 of stone processing area. 5. The total production value in 1990 was estimated 20,169 million won of which export was amount to 2,627 million won. 6. The finacial rate of return of the UNGOK KUGIJA Tea processing Project operated by UNGOK coops and BAKSAN ginseng tea processing project were estimated at 45.4% (B/C Ratio=1.17, NPV=152.5 million won) and 17.7% (B/C Ratio=1.12, NPV=120.2 million won) respectively. 7. More favorite terms and condition of the loan including collateral problems have to be given to farmers participated. Heavy investment and G'T subsidy policies should be started for the successful project implementation anf farm household income increase. 8. To expand market demand of the rural special goods G'T have to provide special program of TV or other mass media for commodity propaganda and the total cost concerned must be supported by G'T subsidy. 9. The special farm products as GUGUJA,MOSI'Ramie', Ginseng. SOGOKJU,HEMP,Mushroom.DUGYUNJU and Chesnut processing projects have to be propelled and expanded for off-farm income increase in Chung Nam Province. 10. Direct operational pattern of the special production area by coops is more favorable to farmers and recommendable considering with off-farm income increase and market demand creation throughout Korea. 11. In rural area, special organizations for project appraisal are not exist. Accordingly special training program, project appraisal, formulation and preparation for civil servants concerned have to be prepared for project selection and sound implementation under limited budget and financial support.

• Management & Information Systems Review
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• v.36 no.4
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• pp.21-32
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• 2017

Motivated by the recent cases of negligent social responsibility as manifested by foreign luxury fashion brands in Korea, this study investigates whether agency costs depend on the sustainability of different types of corporate governance. Agency costs refer either to vertical costs arising from the relationship between stockholders and managers, or to horizontal costs associated with the potential conflicts between majority and minority stockholders. The firms with luxury fashion brand could spend large sums of money on maintenance of magnificent brand image, thereby increasing the agency cost. On the contrary, the firms may hold down wasteful spending to report a gaudily financial achievement. This results in mitigation of the agency cost. Agency costs are measured by the value of the principal component. First, three ratios are constructed: asset turnover, operating expense to sales, and earnings before interest, tax, and depreciation. Then, the scores of each of these ratios for individual firms in the sample are differenced from the ratios for the benchmark firm of S-OIL. S-OIL was designated as the best superior governance model firm for 2013 by CGS. We perform regression analysis of each agency cost index, luxury fashion brand dummy and a set of control variables. The regression results indicate that the agency costs of the firms with luxury fashion brand exceed those of control group in the fashion industry in the part of operating expenses, but the agency cost falls short of those of control group in the part of EBITD, thus the aggregate agency costs are not differential of those of the control group. In sensitivity test, the results are same that the agency cost of the firms are higher than those of the matching control group with PSM(propensity matching method). These results are corroborated by an additional analysis comparing the group of the companies with the best brands with the control group. The results raise doubts about the effectiveness of management of the firms with luxury fashion brand. This study has a limitation that the research has performed only for 2013 and this paper suggests that there is room for improvement in the current research methodology.

• Asia pacific journal of information systems
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• v.19 no.1
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• pp.147-165
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• 2009

Virtual community(VC) will increasingly be organized as commercial enterprises, with the objective of earning an attractive financial return by providing members with valuable resources and environment. For example, Cyworld.com in Korea uses several community services to enable customers of Cyworld to take control of their own value as potential purchasers of products and services. Although initial adoption is important for online network service success, it does not necessarily result in the desired managerial performance unless the initial usage is continuously related to the continuous usage and purchase. Particularly, the customer who receives relevant online services and is well equipped with online network services, will trust the online service provider and perceive less risk and experience more activities such as continuous usage and purchase. Thus, how to promote continued online service usage or, alternatively, how to prevent discontinuance is a critical issue for VC service providers to consider. By aggregating a wide range of information and online environments for customers and providing trust to its members, the service providers of virtual communities help to reduce the perceived risk of continuous usage and purchase. Drill down, online service managers realize that achieving strong and sustained customers who continuously use online service and purchase on it is crucial. Therefore, the research into this online service continuance will identify the relationship between the initial usage and the continuous usage and purchase. The research of continuous usage or post adoption has recently emerged as an important issue in the IS literature. Individuals' information systems(IS) continuous usage decisions are congruent with consumers' repeat purchase decisions. The TAM(Technology Acceptance Model) paradigm has been strongly confirmed across a wide range from product purchase on EC to online service usage contexts. The analysis of IS usage based on TAM has proven to be successful across almost online service contexts. However, most of previous studies have focused on only an area (i.e., VC or EC). Just little research has tried to analyze the relationship between VC and EC. The effect of some factors on user intention, captured through several theories such as TAM, has been demonstrated. Yet, few studies have explored the salient relationships of VC users' EC acceptance. To fill this gap between VC and EC research, this paper attempts to develop a research model that extends the TAM perspective in view of the additional contributions of trust in the service provider and trust in members on some factors that affect EC and VC adoption. In this extension, we applied the TAM-to-TAM(T2T) model, and analyzed the transfer effect of trust between these two TAMs. The research model was empirically tested on the context of a social network service. The model was to extend TAM with the trust concept for the virtual community environment from the perspective of tasks. By building an extended model of TAM and examining the relationships between trust and the existing variables of TAM, it is aimed to explain a user's continuous intention to use VC and purchase on EC. The unit of analysis in this paper is an individual user of a virtual community. The population of interest is the individual with the experiences in virtual community. The data for this paper was made available via a Web survey of VC users. In total, 281 cases were gathered for about one week, but there were some missing values in the sample and there were some inappropriate cases. Thus, only 248 cases were finally analyzed. We chose the structural equation analysis to test the hypotheses and it is better suited for explaining complex relationships than the other methods. In this test, AMOS was used to test the Structural Equation Model (SEM). Noticeable results have been found in the T2T model regarding the factors affecting the intention to use of virtual community and loyalty. Our result showed that trust transfer plays a key role in forming the two adoption beliefs. Overall, this study preliminarily confirms the salience of trust transfer in online service.

• 한국벤처창업학회:학술대회논문집
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• 2007.04a
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• pp.405-433
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• 2007

This study is conducted to examine how purchase behaviors of consumers have affected by the promotion of coupons in internet shopping mall. This study was conducted with the purpose of identifying the differences in purchase behavior based on consumer' s perception and experience of internet shopping mall coupons, and based on consumers' perception of cost and value of coupons, using a theoretic framework presented in previously conducted studies. The results of this study can be summarized as follows. First, based on the perception of coupons, there were significant differences in intent to use and intent to re-use at the time when coupons are offered, and at the time when coupons are offered, no significant differences were found between the level of interest and the importance of coupon at the time of visiting the shopping mall; however, significant differences were found in the overall purchase behavior based on perception of coupons. Second, when overall differences m purchase behavior based on experience in coupon use was observed, having experience in using coupons showed a higher average than did having no experience in using coupons, showing a significant difference. It was found that compared to those without experience in using coupons, those with experience with coupons had higher intent to use at the time when coupon is offered, intent to re-use at the time when coupon is offered, and higher level of purchase behavior in the importance of coupons at the time of visiting the shopping mall. Third, when relationship between purchase behaviors, cost of coupon, and perception of convenience was observed, a clear static relationship was found. This suggests that as the cost and perception of convenience of coupon increases, purchase behavior also increases. Such result suggests that there is a difference in purchase behavior based on experience in coupon use. When relationship of purchase behavior by variables of cost of coupon and perception of convenience is observed, it has a positive relationship with the perception that the use of coupon includes saving money, financial help, enjoyment of use, habitual use, has a short effective date, and has a negative relationship with the perception that it saves little money and is a waste of time. Therefore, it can be seen that purchase behavior has the highest relationship with enjoyment of coupon use and habitual coupon use. Such results suggest that purchase behavior will be significantly influenced based on cost of coupon and perception of convenience.