• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1165-1178
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• 2018

Collapse of an Antarctic ice shelf and its flow velocity changes has the potential to reduce the restraining stress to the seaward flow of the Antarctic Ice Sheet, which can cause sea level rising. In this study, variations in ice velocity from 2000 to 2017 for the Nansen Ice Shelf in East Antarctica that experienced a large-scale collapse in April 2016 were analyzed using Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) images. To extract ice velocity, image matching based on orientation correlation was applied to the image pairs of blue, green, red, near-infrared, panchromatic, and the first principal component image of the Landsat multispectral data, from which the results were combined. The Landsat multispectral image matching produced reliable ice velocities for at least 14% wider area on the Nansen Ice Shelf than for the case of using single band (i.e., panchromatic) image matching. The ice velocities derived from the Landsat multispectral image matching have the error of $2.1m\;a^{-1}$ compared to the in situ Global Positioning System (GPS) observation data. The region adjacent to the Drygalski Ice Tongue showed the fastest increase in ice velocity between 2000 and 2017. The ice velocity along the central flow line of the Nansen Ice Shelf was stable before 2010 (${\sim}228m\;a^{-1}$). In 2011-2012, when a rift began to develop near the ice front, the ice flow was accelerated (${\sim}255m\;a^{-1}$) but the velocity was only about 11% faster than 2010. Since 2014, the massive rift had been fully developed, and the ice velocity of the upper region of the rift slightly decreased (${\sim}225m\;a^{-1}$) and stabilized. This means that the development of the rift and the resulting collapse of the ice front had little effect on the ice velocity of the Nansen Ice Shelf.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.19-38
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• 2017

Collaborative filtering (CF) algorithm has been popularly used for implementing recommender systems. Until now, there have been many prior studies to improve the accuracy of CF. Among them, some recent studies adopt 'hybrid recommendation approach', which enhances the performance of conventional CF by using additional information. In this research, we propose a new hybrid recommender system which fuses CF and the results from the social network analysis on trust and distrust relationship networks among users to enhance prediction accuracy. The proposed algorithm of our study is based on memory-based CF. But, when calculating the similarity between users in CF, our proposed algorithm considers not only the correlation of the users' numeric rating patterns, but also the users' in-degree centrality values derived from trust and distrust relationship networks. In specific, it is designed to amplify the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the trust relationship network. Also, it attenuates the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the distrust relationship network. Our proposed algorithm considers four (4) types of user relationships - direct trust, indirect trust, direct distrust, and indirect distrust - in total. And, it uses four adjusting coefficients, which adjusts the level of amplification / attenuation for in-degree centrality values derived from direct / indirect trust and distrust relationship networks. To determine optimal adjusting coefficients, genetic algorithms (GA) has been adopted. Under this background, we named our proposed algorithm as SNACF-GA (Social Network Analysis - based CF using GA). To validate the performance of the SNACF-GA, we used a real-world data set which is called 'Extended Epinions dataset' provided by 'trustlet.org'. It is the data set contains user responses (rating scores and reviews) after purchasing specific items (e.g. car, movie, music, book) as well as trust / distrust relationship information indicating whom to trust or distrust between users. The experimental system was basically developed using Microsoft Visual Basic for Applications (VBA), but we also used UCINET 6 for calculating the in-degree centrality of trust / distrust relationship networks. In addition, we used Palisade Software's Evolver, which is a commercial software implements genetic algorithm. To examine the effectiveness of our proposed system more precisely, we adopted two comparison models. The first comparison model is conventional CF. It only uses users' explicit numeric ratings when calculating the similarities between users. That is, it does not consider trust / distrust relationship between users at all. The second comparison model is SNACF (Social Network Analysis - based CF). SNACF differs from the proposed algorithm SNACF-GA in that it considers only direct trust / distrust relationships. It also does not use GA optimization. The performances of the proposed algorithm and comparison models were evaluated by using average MAE (mean absolute error). Experimental result showed that the optimal adjusting coefficients for direct trust, indirect trust, direct distrust, indirect distrust were 0, 1.4287, 1.5, 0.4615 each. This implies that distrust relationships between users are more important than trust ones in recommender systems. From the perspective of recommendation accuracy, SNACF-GA (Avg. MAE = 0.111943), the proposed algorithm which reflects both direct and indirect trust / distrust relationships information, was found to greatly outperform a conventional CF (Avg. MAE = 0.112638). Also, the algorithm showed better recommendation accuracy than the SNACF (Avg. MAE = 0.112209). To confirm whether these differences are statistically significant or not, we applied paired samples t-test. The results from the paired samples t-test presented that the difference between SNACF-GA and conventional CF was statistical significant at the 1% significance level, and the difference between SNACF-GA and SNACF was statistical significant at the 5%. Our study found that the trust/distrust relationship can be important information for improving performance of recommendation algorithms. Especially, distrust relationship information was found to have a greater impact on the performance improvement of CF. This implies that we need to have more attention on distrust (negative) relationships rather than trust (positive) ones when tracking and managing social relationships between users.

• Journal of the Korean Geotechnical Society
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• v.35 no.11
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• pp.111-119
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• 2019

The high-speed railway system is mainly composed of tunnel, bridge, and viaduct to meet the straightness needed for keeping the high speed up to 400 km/s. Seismic fragility for the high-speed railway infrastructure can be assessed as two ways: one way is studying each element of infrastructure analytically or numerically, but it requires lots of research efforts due to wide range of railway system. On the other hand, empirical method can be used to access the fragility of an entire system efficiently, which requires case history data. In this study, we collect the 2004 M_W 6.6 Niigata earthquake case history data to develop empirical seismic fragility function for a railway system. Five types of intensity measures (IMs) and damage levels are assigned to all segments of target system for which the unit length is 200 m. From statistical analysis, probability of exceedance for a certain damage level (DL) is calculated as a function of IM. For those probability data points, log-normal CDF is fitted using MLE method, which forms fragility function for each damage level of exceedance. Evaluating fragility functions calculated, we observe that T=3.0 spectral acceleration (SA_T3.0) is superior to other IMs, which has lower standard deviation of log-normal CDF and low error of the fit. This indicates that long-period ground motion has more impacts on railway infrastructure system such as tunnel and bridge. It is observed that when SA_T3.0 = 0.1 g, P(DL>1) = 2%, and SA_T3.0 = 0.2 g, P(DL>1) = 23.9%.

• Journal of Korean Society of Disaster and Security
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• v.16 no.2
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• pp.15-32
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• 2023

There are some difficulties such as safety problem and need of manpower in measuring discharge by submerging the instruments because of many floating debris and very fast flow in the river during the flood season. As an alternative, microwave water surface current meters have been increasingly used these days, which are easy to measure the discharge in the field without contacting the water surface directly. But it is also hard to apply the method in the sudden and rapidly changing field conditions. Therefore, the estimation of the discharge using the surface velocity in flood conditions requires a theoretical and economical approach. In this study, the measurements from microwave water surface current meter and rating curve were collected and then analyzed by the discharge estimation method using the surface velocity. Generally, the measured and converted discharge are analyzed to be similar in all methods at a hydraulic radius of 3 m or over or a mean velocity of 2 ㎧ or more. Besides, the study computed the discharge by the index velocity method and the velocity profile method with the maximum surface velocity in the section where the maximum velocity occurs at the high water level range of the rating curve among the target locations. As a result, the mean relative error with the converted discharge was within 10%. That is, in flood season, the discharge estimation method using one maximum surface velocity measurement, index velocity method, and velocity profile method can be applied to develop high-level extrapolation, therefore, it is judged that the reliability for the range of extrapolation estimation could be improved. Therefore, the discharge estimation method using the surface velocity is expected to become a fast and efficient discharge measurement method during the flood season.

• Korean Journal of Remote Sensing
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• v.30 no.4
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• pp.525-536
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• 2014

In this study, a method to assess and monitor hydrological drought using remote sensing was investigated for use in regions with limited observation data, and was applied to the Upper Namhangang basin in South Korea, which was seriously affected by the 2008-2009 drought. Drought information may be obtained more easily from meteorological data based on water balance than hydrological data that are hard to estimate. Air temperature data at 2 m above ground level (AGL) were estimated using remotely sensed data, evapotranspiration was estimated from the air temperature, and the correlations between precipitation minus evapotranspiration (P-PET) and streamflow percentiles were examined. Land Surface Temperature data with $1{\times}1km$ spatial resolution as well as Atmospheric Profile data with $5{\times}5km$ spatial resolution from MODIS sensor on board Aqua satellite were used to estimate monthly maximum and minimum air temperature in South Korea. Evapotranspiration was estimated from the maximum and minimum air temperature using the Hargreaves method and the estimates were compared to existing data of the University of Montana based on Penman-Monteith method showing smaller coefficient of determination values but smaller error values. Precipitation was obtained from TRMM monthly rainfall data, and the correlations of 1-, 3-, 6-, and 12-month P-PET percentiles with streamflow percentiles were analyzed for the Upper Namhan-gang basin in South Korea. The 1-month P-PET percentile during JJA (r = 0.89, tau = 0.71) and SON (r = 0.63, tau = 0.47) in the Upper Namhan-gang basin are highly correlated with the streamflow percentile with 95% confidence level. Since the effect of precipitation in the basin is especially high, the correlation between evapotranspiration percentile and streamflow percentile is positive. These results indicate that remote sensing-based P-PET estimates can be used for the assessment and monitoring of hydrological drought. The high spatial resolution estimates can be used in the decision-making process to minimize the adverse impacts of hydrological drought and to establish differentiated measures coping with drought.

• Journal of Korea Port Economic Association
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• v.40 no.2
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• pp.65-78
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• 2024

This study investigates the correlation between financial constraints and investment activities in Korean logistics firms. A sample of 340 companies engaged in the transportation sector, as per the 2021 KSIC, was selected for analysis. Financial data obtained from the DART were used to compile a panel dataset spanning from 1996 to 2021, totaling 6,155 observations. The research model was validated, and tests for heteroscedasticity and autocorrelation in the error terms were conducted considering the panel data structure. The relationship between investment activities in the previous period and current investment activities was analyzed using panel Generalized Method of Moments(GMM). The validation results of the research indicate that Korean logistics firms tend to increase investment activities as their level of financial constraints improves. Specifically, a positive relationship between the level of financial constraints and investment activities was consistently observed across all models. These findings suggest that investment decision-making varies based on the financial constraints faced by companies, aligning with previous research indicating that investment activities of constrained firms are subdued. Moreover, while the results from the model examining whether investment activities in the previous period affect current investment activities indicated an influence of investment activities from the previous period on current investment activities, the investment activities from two periods ago did not show a significant relationship with current investment activities. Among the control variables, firm size and cash flow variables exhibited positive relationships, while debt size and asset diversification variables showed negative relationships. Thus, larger firm size and smoother cash flows were associated with more proactive investment activities, while high debt levels and extensive asset diversification appeared to constrain investment activities in logistics companies. These results interpret that under financial constraints, internal funding sources such as cash flows exhibit positive relationships, whereas external capital sources such as debt demonstrate negative relationships, consistent with empirical findings from previous research.

• Journal of Global Scholars of Marketing Science
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• v.17 no.2
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• pp.55-73
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• 2007

Festivals are an indispensable feature of cultural tourism(Formica & Uysal, 1998). Cultural tourism festivals are increasingly being used as instruments promoting tourism and boosting the regional economy. So much research related to festivals is undertaken from a variety of perspectives. Plans to revisit a particular festival have been viewed as an important research topic both in academia and the tourism industry. Therefore festivals have frequently been leveled as cultural events. Cultural tourism festivals have become a crucial component in constituting the attractiveness of tourism destinations(Prentice, 2001). As a result, a considerable number of tourist studies have been carried out in diverse cultural tourism festivals(Backman et al., 1995; Crompton & Mckay, 1997; Park, 1998; Clawson & Knetch, 1996). Much of previous literature empirically shows the close linkage between tourist satisfaction and behavioral intention in festivals. The main objective of this study is to investigate the effects of evaluation attributes of cultural tourism festivals on satisfaction and behavioral intention. accomplish the research objective, to find out evaluation items of cultural tourism festivals through the literature study an empirical study. Using a varimax rotation with Kaiser normalization, the research obtained four factors in the 18 evaluation attributes of cultural tourism festivals. Some empirical studies have examined the relationship between behavioral intention and actual behavior. To understand between tourist satisfaction and behavioral intention, this study suggests five hypotheses and hypothesized model. In this study, the analysis is based on primary data collected from visitors who participated in '2006 Gwangju Kimchi Festival'. In total, 700 self-administered questionnaires were distributed and 561 usable questionnaires were obtained. Respondents were presented with the 18 satisfactions item on a scale from 1(strongly disagree) to 7(strongly agree). Dimensionality and stability of the scale were evaluated by a factor analysis with varimax rotation. Four factors emerged with eigenvalues greater than 1, which explained 66.40% of the total variance and Cronbach' alpha raging from 0.876 to 0.774. And four factors named: advertisement and guides, programs, food and souvenirs, and convenient facilities. To test and estimate the hypothesized model, a two-step approach with an initial measurement model and a subsequent structural model for Structural Equation Modeling was used. The AMOS 4.0 analysis package was used to conduct the analysis. In estimating the model, the maximum likelihood procedure was used.In this study Chi-square test is used, which is the most common model goodness-of-fit test. In addition, considering the literature about the Structural Equation Modeling, this study used, besides Chi-square test, more model fit indexes to determine the tangibility of the suggested model: goodness-of-fit index(GFI) and root mean square error of approximation(RMSEA) as absolute fit indexes; normed-fit index(NFI) and non-normed-fit index(NNFI) as incremental fit indexes. The results of T-test and ANOVAs revealed significant differences(0.05 level), therefore H1(Tourist Satisfaction level should be different from Demographic traits) are supported. According to the multiple Regressions analysis and AMOS, H2(Tourist Satisfaction positively influences on revisit intention), H3(Tourist Satisfaction positively influences on word of mouth), H4(Evaluation Attributes of cultural tourism festivals influences on Tourist Satisfaction), and H5(Tourist Satisfaction positively influences on Behavioral Intention) are also supported. As the conclusion of this study are as following: First, there were differences in satisfaction levels in accordance with the demographic information of visitors. Not all visitors had the same degree of satisfaction with their cultural tourism festival experience. Therefore it is necessary to understand the satisfaction of tourists if the experiences that are provided are to meet their expectations. So, in making festival plans, the organizer should consider the demographic variables in explaining and segmenting visitors to cultural tourism festival. Second, satisfaction with attributes of evaluation cultural tourism festivals had a significant direct impact on visitors' intention to revisit such festivals and the word of mouth publicity they shared. The results indicated that visitor satisfaction is a significant antecedent of their intention to revisit such festivals. Festival organizers should strive to forge long-term relationships with the visitors. In addition, it is also necessary to understand how the intention to revisit a festival changes over time and identify the critical satisfaction factors. Third, it is confirmed that behavioral intention was enhanced by satisfaction. The strong link between satisfaction and behavioral intentions of visitors areensured by high quality advertisement and guides, programs, food and souvenirs, and convenient facilities. Thus, examining revisit intention from a time viewpoint may be of a great significance for both practical and theoretical reasons. Additionally, festival organizers should give special attention to visitor satisfaction, as satisfied visitors are more likely to return sooner. The findings of this research have several practical implications for the festivals managers. The promotion of cultural festivals should be based on the understanding of tourist satisfaction for the long- term success of tourism. And this study can help managers carry out this task in a more informed and strategic manner by examining the effects of demographic traits on the level of tourist satisfaction and the behavioral intention. In other words, differentiated marketing strategies should be stressed and executed by relevant parties. The limitations of this study are as follows; the results of this study cannot be generalized to other cultural tourism festivals because we have not explored the many different kinds of festivals. A future study should be a comparative analysis of other festivals of different visitor segments. Also, further efforts should be directed toward developing more comprehensive temporal models that can explain behavioral intentions of tourists.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.185-202
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• 2012

.Since the value of information has been realized in the information society, the usage and collection of information has become important. A facial expression that contains thousands of information as an artistic painting can be described in thousands of words. Followed by the idea, there has recently been a number of attempts to provide customers and companies with an intelligent service, which enables the perception of human emotions through one's facial expressions. For example, MIT Media Lab, the leading organization in this research area, has developed the human emotion prediction model, and has applied their studies to the commercial business. In the academic area, a number of the conventional methods such as Multiple Regression Analysis (MRA) or Artificial Neural Networks (ANN) have been applied to predict human emotion in prior studies. However, MRA is generally criticized because of its low prediction accuracy. This is inevitable since MRA can only explain the linear relationship between the dependent variables and the independent variable. To mitigate the limitations of MRA, some studies like Jung and Kim (2012) have used ANN as the alternative, and they reported that ANN generated more accurate prediction than the statistical methods like MRA. However, it has also been criticized due to over fitting and the difficulty of the network design (e.g. setting the number of the layers and the number of the nodes in the hidden layers). Under this background, we propose a novel model using Support Vector Regression (SVR) in order to increase the prediction accuracy. SVR is an extensive version of Support Vector Machine (SVM) designated to solve the regression problems. The model produced by SVR only depends on a subset of the training data, because the cost function for building the model ignores any training data that is close (within a threshold ${\varepsilon}$) to the model prediction. Using SVR, we tried to build a model that can measure the level of arousal and valence from the facial features. To validate the usefulness of the proposed model, we collected the data of facial reactions when providing appropriate visual stimulating contents, and extracted the features from the data. Next, the steps of the preprocessing were taken to choose statistically significant variables. In total, 297 cases were used for the experiment. As the comparative models, we also applied MRA and ANN to the same data set. For SVR, we adopted '${\varepsilon}$-insensitive loss function', and 'grid search' technique to find the optimal values of the parameters like C, d, ${\sigma}^2$, and ${\varepsilon}$. In the case of ANN, we adopted a standard three-layer backpropagation network, which has a single hidden layer. The learning rate and momentum rate of ANN were set to 10%, and we used sigmoid function as the transfer function of hidden and output nodes. We performed the experiments repeatedly by varying the number of nodes in the hidden layer to n/2, n, 3n/2, and 2n, where n is the number of the input variables. The stopping condition for ANN was set to 50,000 learning events. And, we used MAE (Mean Absolute Error) as the measure for performance comparison. From the experiment, we found that SVR achieved the highest prediction accuracy for the hold-out data set compared to MRA and ANN. Regardless of the target variables (the level of arousal, or the level of positive / negative valence), SVR showed the best performance for the hold-out data set. ANN also outperformed MRA, however, it showed the considerably lower prediction accuracy than SVR for both target variables. The findings of our research are expected to be useful to the researchers or practitioners who are willing to build the models for recognizing human emotions.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1565-1576
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• 2023

An atmospheric correction algorithm based on the radiative transfer model is required to obtain remote-sensing reflectance (R_rs) from the Geostationary Ocean Color Imager-II (GOCI-II) observed at the top-of-atmosphere. This R_rs derived from the atmospheric correction is utilized to estimate various marine environmental parameters such as chlorophyll-a concentration, total suspended materials concentration, and absorption of dissolved organic matter. Therefore, an atmospheric correction is a fundamental algorithm as it significantly impacts the reliability of all other color products. However, in clear waters, for example, atmospheric path radiance exceeds more than ten times higher than the water-leaving radiance in the blue wavelengths. This implies atmospheric correction is a highly error-sensitive process with a 1% error in estimating atmospheric radiance in the atmospheric correction process can cause more than 10% errors. Therefore, the quality assessment of R_rs after the atmospheric correction is essential for ensuring reliable ocean environment analysis using ocean color satellite data. In this study, a Quality Assurance (QA) algorithm based on in-situ R_rs data, which has been archived into a database using Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Bio-optical Archive and Storage System (SeaBASS), was applied and modified to consider the different spectral characteristics of GOCI-II. This method is officially employed in the National Oceanic and Atmospheric Administration (NOAA)'s ocean color satellite data processing system. It provides quality analysis scores for R_rs ranging from 0 to 1 and classifies the water types into 23 categories. When the QA algorithm is applied to the initial phase of GOCI-II data with less calibration, it shows the highest frequency at a relatively low score of 0.625. However, when the algorithm is applied to the improved GOCI-II atmospheric correction results with updated calibrations, it shows the highest frequency at a higher score of 0.875 compared to the previous results. The water types analysis using the QA algorithm indicated that parts of the East Sea, South Sea, and the Northwest Pacific Ocean are primarily characterized as relatively clear case-I waters, while the coastal areas of the Yellow Sea and the East China Sea are mainly classified as highly turbid case-II waters. We expect that the QA algorithm will support GOCI-II users in terms of not only statistically identifying R_rs resulted with significant errors but also more reliable calibration with quality assured data. The algorithm will be included in the level-2 flag data provided with GOCI-II atmospheric correction.

• Journal of Global Scholars of Marketing Science
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• v.18 no.4
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• pp.1-32
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• 2008

Brown and Dacin (1997) have investigated the relationship between corporate associations and product evaluations. Their study focused on the effects of associations with a company's corporate ability (CA) and its corporate social responsibility (CSR) on consumers' product evaluations. Their study has found that both of CA and CSR influenced product evaluation but CA association has a stronger effect than CSR associations. Brown and Dacin (1997) have, however, claimed that there are few researches on how corporate association impacts product responses. Accordingly, some of researchers have found the variables to moderate or to mediate the relationship between the corporate association and the product responses. In particular, there has been existed a few of studies that tested the influence of the reputation on the product-relevant perceived risk, but the effects of two types of the corporate association on the product-relevant perceived risk were not identified so far. The primary goal of this article is to identify and empirically examine some variables to moderate the effects of CA association and CSR association on the perceived risk of the product. In this articles, we take the concept of the corporate associations that Brown and Dacin (1997) had proposed. CA association is those association related to the company's expertise in producing and delivering its outputs and CSR association reflected the organization's status and activities with respect to its perceived societal obligations. Also, this study defines the risk, which is the uncertainty or loss of the product and corporate that consumers have taken in a particular purchase decision or after having purchased. The risk is classified into product-relevant performance risk and financial risk. Performance risk is the possibility or the consequence of a product not functioning at some expected level and financial risk is the monetary loss one perceives to be incurring if a product does not function at some expected level. In relation to consumer's knowledge, expert consumers have much of the experiences or knowledge of the product in consumer position and novice consumers does not. The model tested in this article are shown in Figure 1. The model indicates that both of CA association and CSR association influence on performance risk and financial risk. In addition, the effects of CA and CSR are moderated by product category knowledge (product knowledge) and product category involvement (product involvement). In this study, the relationships between the corporate association and product-relevant perceived risk are hypothesized as the following form. For example, Hypothesis 1a($H_{1a}$) is represented that CA association has a positive influence on the performance risk of consumer. Also, the hypotheses that identified some variables to moderate the effects of two types of corporate association on the perceived risk of the product are laid down. One of the hypotheses of the interaction effect is Hypothesis 3a($H_{3a}$), it is described that consumer's knowledges of the product moderates the negative relationship between CA association and product-relevant performance risk. A field experiment was conducted in order to examine our model. The company tested was not real but imagined to meet the internal validity. Water purifiers were used for our study. Four scenarios have been developed and described as the imaginary company: Type A with both of superior CA and CSR, Type B with superior CSR and inferior CA, Type C with superior CA and inferior CSR, and Type D with both inferior of CA and CSR. The respondents of this study were classified into four groups. One type of four scenarios (Type A, B, C, or D) in its questionnaire was given to the respondent who filled out questions. Data were collected by means of a self-administered questionnaire to the respondents, chosen in convenience. A total of 300 respondents filled out the questionnaire but 207 were used for further analysis. Table 1 indicates that the scales in this study are reliable because the range of coefficients of Cronbach's $\alpha$ are from 0.85 to 0.92. The composite reliability is in the range of 0,85 to 0,92 and average variance extracted is in 0.72-0.98 range that is higher than the base level of 0.6. As shown in Table 2, the values for CFI, NNFI, root-mean-square error approximation (RMSEA), and standardized root-mean-square residual (SRMR) are acceptably close to the standards suggested by Hu and Bentler (1999):.95 for CFI and NNFI,.06 for RMSEA, and.08 for SRMR. We also tested discriminant validity provided by Fornell and Larcker (1981). As shown in Table 2, we found strong evidence for discriminant validity between each possible pair of latent constructs in all samples. Given that these batteries of overall goodness-of-fit indices were accurate and that the model was developed on theoretical bases, and given the high level of consistency across samples, this enables us to proceed the previously defined scales. We used the moderated hierarchical regression analysis to test the influence of the corporate association(CA and CSR associations) on product-relevant perceived risk(performance and financial risks) and to identify the variables moderating the relationship between the corporate association and product-relevant performance risk. In this study, dependent variables are performance and financial risk. CA and CSR associations are described the independent variables. The moderating variables are product category knowledge and product category involvement. The results are, as expected, found that CA association has statistically a significant influence on the perceived risk of the product, but CSR association does not. Product category knowledge and involvement moderate the relationship between the CA association and the perceived risk of the product. However, the effect of CSR association on the perceived risk of the product is not moderated by the consumers' knowledge and involvement. For this result, it is necessary for a corporate to inform its customers CA association more than CSR association so that they could be felt to be the reduction of the perceived risk. The important theoretical contribution of this research is the meanings that two types of corporate association that Brown and Dacin(1997), and Brown(1998) have proposed replicated the difference of the effects on product evaluation. According to Hunter(2001), it was an important affair to accomplish the validity of a particular study and we had to take about ten studies to deduce a strict study. Next, there is the contribution of the this study to find that the effects of corporate association on the perceived risk of the product are varied by the moderator variables. In particular, the moderating effect of knowledge on the relationship between corporate association and product-relevant perceived risk has not been tested in Korea. In the managerial implications of this research, we suggest the necessity to stress the ability that corporate manufactures the product well(CA association) than the accomplishment of corporate's social obligation(CSR association). This study suffers from various limitations that imply future research directions. The moderating effects of product category knowledge and involvement on the relationship between corporate association and perceived risk need to be replicated. Next, future research could explore whether the mediated effects of the perceived risk has the relationship between corporate association and consumer's product purchase. In addition, to ensure the external validity of the study will be needed to use realistic company, not artificial.