• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.481-493
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• 2023

In high-density urban areas, the urban heat island effect increases urban temperatures, leading to negative impacts such as worsened air pollution, increased cooling energy consumption, and increased greenhouse gas emissions. In urban environments where it is difficult to secure additional green spaces, rooftop greening is an efficient greenhouse gas reduction strategy. In this study, we not only analyzed the current status of the urban heat island effect but also utilized high-resolution satellite data and spatial information to estimate the available rooftop greening area within the study area. We evaluated the mitigation effect of the urban heat island phenomenon and carbon sequestration capacity through temperature predictions resulting from rooftop greening. To achieve this, we utilized WorldView-2 satellite data to classify land cover in the urban heat island areas of Busan city. We developed a prediction model for temperature changes before and after rooftop greening using machine learning techniques. To assess the degree of urban heat island mitigation due to changes in rooftop greening areas, we constructed a temperature change prediction model with temperature as the dependent variable using the random forest technique. In this process, we built a multiple regression model to derive high-resolution land surface temperatures for training data using Google Earth Engine, combining Landsat-8 and Sentinel-2 satellite data. Additionally, we evaluated carbon sequestration based on rooftop greening areas using a carbon absorption capacity per plant. The results of this study suggest that the developed satellite-based urban heat island assessment and temperature change prediction technology using Random Forest models can be applied to urban heat island-vulnerable areas with potential for expansion.

• Archives of design research
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• v.18 no.3 s.61
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• pp.85-94
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• 2005

Most of product designers use 3D CAD system as a inevitable design tool nowadays and many new products are developed through a concurrent engineering process. However, it is very difficult for novice designers to get the sense of reality from modeling objects shown in the computer screens. Such a intangibility problem comes from the lack of haptic interactions and contextual information about the real space because designers tend to do 3D modeling works only in a virtual space of 3D CAD system. To address this problem, this research investigate the possibility of a interactive quantified structure simulation for product design using AR(augmented reality) which can register a 3D CAD modeling object on the real space. We built a quantified structure simulation system based on AR and conducted a series of experiments to measure how accurately human perceive and adjust the size of virtual objects under varied experimental conditions in the AR environment. The experiment participants adjusted a virtual cube to a reference real cube within 1.3% relative error(5.3% relative StDev). The results gave the strong evidence that the participants can perceive the size of a virtual object very accurately. Furthermore, we found that it is easier to perceive the size of a virtual object in the condition of presenting plenty of real reference objects than few reference objects, and using LCD panel than HMD. We tried to apply the simulation system to identify preference characteristics for the appearance design of a home-service robot as a case study which explores the potential application of the system. There were significant variances in participants' preferred characteristics about robot appearance and that was supposed to come from the lack of typicality of robot image. Then, several characteristic groups were segmented by duster analysis. On the other hand, it was interesting finding that participants have significantly different preference characteristics between robot with arm and armless robot and there was a very strong correlation between the height of robot and arm length as a human body.

• Journal of Climate Change Research
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• v.3 no.2
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• pp.117-128
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• 2012

The Government of South Korea has continued its effort to fixate virtuous circle of economic growth and climate change response to cope with international demands and pressure to commitment for greenhouse gas reduction effectively. Nationally, Korean Government has established "Enforcement of the Framework Act on Low carbon, Green Growth"(2010. 4. 13) to implement national mid-term GHG mitigation goal(30% reduction by 2020 compare to BAU), which established the foundation for phased GHG mitigation by setting up the sectoral and industrial goal, adopting GHG and Energy Target Management System. Also, follow-up measures are taken such as planning and control of mid-term and short-term mitigation target by detailed analysis of potential mitigation of sector and industry, building up the infrastructure for periodic and systematic analysis of target management. Likewise, it is required to establish more accurate, reliable and detailed sectoral GHG inventory for successfully establishment and implement the frame act. In comparison to the $CO_2$ emission, Especially fluorinated greenhouse gases (HFCs, PFCs, $SF_6$) are lacking research to build the greenhouse gas inventories to identify emissions sources and collection of the applicable collection activities data. In this study, with the refrigeration and air conditioning sector being used to fluorine refrigerant(HFCs) as the center, greenhouse gas emission estimation methodology for evaluating the feasibility of using this methodology look over and mobile air conditioning, fixed air conditioning, household refrigeration equipment, commercial refrigeration equipment for the greenhouse gas emissions were calculated. First look at in terms of methodology, refrigeration and air conditioning sector GHG emissions in developing country-specific emission factors and activity data of the industrial sector the construction of the DB is not enough, it's 2006 IPCC Guidelines Tier 2a (emission factor approach) rather than the Tier 2b (mass balance approach) deems appropriate, and each detail by process, sectoral activity data more accurate, if DB is built Tier 2a (emission factor approach) can be applied will also be judged. Refrigeration and air conditioning sector in 2009 due to the use of refrigerant greenhouse gas emissions ($CO_2eq.$) assessment results, portable air conditioner 1,974,646 ton to year, fixed-mount air conditioner 1,011,754 ton to year, household refrigeration unit 4,396 ton to year, commercial refrigeration equipment 1,263 ton to year was estimated to total 2,992,037 tons.

• Progress in Medical Physics
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• v.12 no.1
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• pp.51-58
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• 2001

Synchrotron radiation (SR) has several advantages over convetional x-rays, including its phase, collimation, and high flux. A synchrotron radiation beamline 5C1 at Pohang Light Source (PLS) was recently built for imaging applications. We have shown that a SR imaging system is useful in imaging microscopic structures. SR with broad-band energy spectrum were adjusted to an object by Si wafers and their energy were approximately ranging from 6 keV to 30 keV. SR were passed through an object and finally transformed into visible lights by CdWO$_4$ scintillator screen. The visible lights which were reflected at an angle of 90 degrees by gold plated mirror were detected by a CCD camera and the image data were acquired using image acquisition system. A high-resolution phantom, capacitor, adult tooth, child tooth, cancerous breast tissue, and mouse lumbar vertebra were imaged with SR imaging system. The Objects were rotated within the field of view of the CCD detector, and their projection image data were obtained at 250 steps over 180 degrees rotation. Image reconstructions were carried out in a PC by using IDLTM(Research systems, Inc., US) program. The spatial resolution of the images acquired by the SR imaging system was measured with a high-resolution chart manufactured for several micrometer resolution. The specimens were also imaged with conventional x-ray radiography system to compare the image quality of radiography obtained with the SR imaging system. The results showed more structural details and high contrast images with SR imaging system than conventional x-ray radiography system. The SR imaging system may have a potential for imaging in biological researches, material applications, and clinical radiography.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.177-193
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• 2014

Over the past decade, there has been a rapid diffusion of electronic commerce and a rising number of interconnected networks, resulting in an escalation of security threats and privacy concerns. Electronic commerce has a built-in trade-off between the necessity of providing at least some personal information to consummate an online transaction, and the risk of negative consequences from providing such information. More recently, the frequent disclosure of private information has raised concerns about privacy and its impacts. This has motivated researchers in various fields to explore information privacy issues to address these concerns. Accordingly, the necessity for information privacy policies and technologies for collecting and storing data, and information privacy research in various fields such as medicine, computer science, business, and statistics has increased. The occurrence of various information security accidents have made finding experts in the information security field an important issue. Objective measures for finding such experts are required, as it is currently rather subjective. Based on social network analysis, this paper focused on a framework to evaluate the process of finding experts in the information security field. We collected data from the National Discovery for Science Leaders (NDSL) database, initially collecting about 2000 papers covering the period between 2005 and 2013. Outliers and the data of irrelevant papers were dropped, leaving 784 papers to test the suggested hypotheses. The co-authorship network data for co-author relationship, publisher, affiliation, and so on were analyzed using social network measures including centrality and structural hole. The results of our model estimation are as follows. With the exception of Hypothesis 3, which deals with the relationship between eigenvector centrality and performance, all of our hypotheses were supported. In line with our hypothesis, degree centrality (H1) was supported with its positive influence on the researchers' publishing performance (p<0.001). This finding indicates that as the degree of cooperation increased, the more the publishing performance of researchers increased. In addition, closeness centrality (H2) was also positively associated with researchers' publishing performance (p<0.001), suggesting that, as the efficiency of information acquisition increased, the more the researchers' publishing performance increased. This paper identified the difference in publishing performance among researchers. The analysis can be used to identify core experts and evaluate their performance in the information privacy research field. The co-authorship network for information privacy can aid in understanding the deep relationships among researchers. In addition, extracting characteristics of publishers and affiliations, this paper suggested an understanding of the social network measures and their potential for finding experts in the information privacy field. Social concerns about securing the objectivity of experts have increased, because experts in the information privacy field frequently participate in political consultation, and business education support and evaluation. In terms of practical implications, this research suggests an objective framework for experts in the information privacy field, and is useful for people who are in charge of managing research human resources. This study has some limitations, providing opportunities and suggestions for future research. Presenting the difference in information diffusion according to media and proximity presents difficulties for the generalization of the theory due to the small sample size. Therefore, further studies could consider an increased sample size and media diversity, the difference in information diffusion according to the media type, and information proximity could be explored in more detail. Moreover, previous network research has commonly observed a causal relationship between the independent and dependent variable (Kadushin, 2012). In this study, degree centrality as an independent variable might have causal relationship with performance as a dependent variable. However, in the case of network analysis research, network indices could be computed after the network relationship is created. An annual analysis could help mitigate this limitation.

• Journal of the Korean Geographical Society
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• v.30 no.1
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• pp.68-92
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• 1995

Recentry the structure of the retail trade have been chanaed with its environmantal changes. Some studies may be necessary on the changing process of environment and fundamental structure analyses of the retail trade. This study analyzes the location of retail trades, inhabitants' behavior in retail tredes and their desirable utilization scheme of them in Kwangju-si. Some study methods, contents and coming-out results are as follows: 1. Retail trades can be classified into independent stores, chain-stores (supermarket, voluntary chain and frenchiise system and convenience store), department stores, cooperative associations, traditional, markets mail-order marketing, automatic vending and others by service levels, selling-items, prices, managements, methods of retailing and store or nonstore type. 2. In Kwangju, the environment of retail trades is related to the consumers of population structure: chanes in consumers pattern, trends toward agings and nuclear family, increase of leisur: time and female advances to society. Rapid structural shift in retail trade has also been occurred due to these social changes. Traditionl and premodern markets until 1970s altere to supermarkets or department stores in 1980s, and various types, large enterprises and foreign capitals came into being in 1990s. 3. The locational characteristics of retail trades are resulted from the spatial analysis of the total population distribution, and from the calculation of segregation index in the light of potential demand. The densely-populated areas occurs in newly-built apartment housing complex which is distributed with a ring-shaped pattern around the old urban core. The numbers and rates of the aged over sixty in Kwangsan-gu and the circumference area of Mt.Moodeung, are larger and higher where rural elements are remarkable. A relation between population distribution and retail trade are analysed by the index of population per shop. The index of the population number per shop is lower in urban center, as a whole, being more convenient for consumers. In newly-formed apartment complex areas, on the other, the index more than 1,000 per shop, meeting not the demands for consumers. Because both the younger and the aged are numerous in these areas, the retail trade pattern pertinent to both are needed. Urban fringes including Kwangsan-gu and the vicinity of Mt.Moodeung have some problems owing to the most of population number per shop (more than 1, 500) and the most extensive as well. 4. The regional characteristic of retail trade is analyzed through the location quotient of shops by locational patterns and centerality index. Chungkum-dong is the highest-order central place in CBD. It is the core of retail trades, which has higher-ordered specialty store including three big department stores, supermarkets and large stores. Taegum-dong, Chungsu-dong, Taeui-dong, and Numun-dong that are neiahbored to Chungkum-dong fall on the second group. They have a central commercial section where large chain stores, specialty shopping streets, narrow-line retailing shops (furniture, amusement service, and gallary), supermarkets and daily markets are located. The third group is formed on the axis of state roads linking to Naju-kun, Changseong-kun, Tamyang-kun, Hwasun-kun and forme-Songjeong-eup. It is related to newly, rising apartment housing complex along a trunk road, and characterized by markets and specialty stores. The fourth group has neibourhood-shopping centers including older residential area and Songjeong-eup area with independent stores and supermarkets as main retailing functions. The last group contains inner residential area and outer part of a city including Songjeong-eup. Outer part of miscellaneous shops being occasionally found is rural rather than urban (Fig. 7). 5. The residents' behaviors using retail trade are analyzed by factors of goods and facilities. Department stores are very high level in preference for higher-order shopping-goods such as clothes for full dress in view of both diversity and quality of goods(28.9%). But they have severe traffic congestions, and high competitions for market ranges caused by their sma . 64.0% of respondents make combined purpose trips together with banking and shopping. 6. For more efficiency of retail-trading, it is necessary to induce spatial distribution policy with regard to opportunity frequency of goods selection by central place, frontier regions and age groups. Also we must consider to analyze competition among different types of retail trade and analyze the consumption behaviors of working females and younger-aged groups, in aspects of time and space. Service improvement and the rationalization of management should be accomplished in such as cooperative location (situation) must be under consideration in relations to other functions such as finance, leisure & sports, and culture centers. Various service systems such as installment, credit card and peremium ticket, new used by enterprises, must also be carried service improvement. The rationalization and professionalization in for the commercial goods are bsically requested.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.111-126
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• 2017

Recommender systems based on association rule mining significantly contribute to seller's sales by reducing consumers' time to search for products that they want. Recommendations based on the frequency of transactions such as orders can effectively screen out the products that are statistically marketable among multiple products. A product with a high possibility of sales, however, can be omitted from the recommendation if it records insufficient number of transactions at the beginning of the sale. Products missing from the associated recommendations may lose the chance of exposure to consumers, which leads to a decline in the number of transactions. In turn, diminished transactions may create a vicious circle of lost opportunity to be recommended. Thus, initial sales are likely to remain stagnant for a certain period of time. Products that are susceptible to fashion or seasonality, such as clothing, may be greatly affected. This study was aimed at expanding association rules to include into the list of recommendations those products whose initial trading frequency of transactions is low despite the possibility of high sales. The particular purpose is to predict the strength of the direct connection of two unconnected items through the properties of the paths located between them. An association between two items revealed in transactions can be interpreted as the interaction between them, which can be expressed as a link in a social network whose nodes are items. The first step calculates the centralities of the nodes in the middle of the paths that indirectly connect the two nodes without direct connection. The next step identifies the number of the paths and the shortest among them. These extracts are used as independent variables in the regression analysis to predict future connection strength between the nodes. The strength of the connection between the two nodes of the model, which is defined by the number of nodes between the two nodes, is measured after a certain period of time. The regression analysis results confirm that the number of paths between the two products, the distance of the shortest path, and the number of neighboring items connected to the products are significantly related to their potential strength. This study used actual order transaction data collected for three months from February to April in 2016 from an online commerce company. To reduce the complexity of analytics as the scale of the network grows, the analysis was performed only on miscellaneous goods. Two consecutively purchased items were chosen from each customer's transactions to obtain a pair of antecedent and consequent, which secures a link needed for constituting a social network. The direction of the link was determined in the order in which the goods were purchased. Except for the last ten days of the data collection period, the social network of associated items was built for the extraction of independent variables. The model predicts the number of links to be connected in the next ten days from the explanatory variables. Of the 5,711 previously unconnected links, 611 were newly connected for the last ten days. Through experiments, the proposed model demonstrated excellent predictions. Of the 571 links that the proposed model predicts, 269 were confirmed to have been connected. This is 4.4 times more than the average of 61, which can be found without any prediction model. This study is expected to be useful regarding industries whose new products launch quickly with short life cycles, since their exposure time is critical. Also, it can be used to detect diseases that are rarely found in the early stages of medical treatment because of the low incidence of outbreaks. Since the complexity of the social networking analysis is sensitive to the number of nodes and links that make up the network, this study was conducted in a particular category of miscellaneous goods. Future research should consider that this condition may limit the opportunity to detect unexpected associations between products belonging to different categories of classification.

• Journal of Environmental Impact Assessment
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• v.26 no.2
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• pp.93-104
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• 2017

CCS (Carbon Capture and Storage) is a technical process to capture $CO_2$ from industrial and energy-based sources, to transfer and sequestrate impressed $CO_2$ in geological formations, oceans, or mineral carbonates. However, potential $CO_2$ leakage exists and causes environmental problems. Thus, this study was conducted to analyze the spatial and temporal variations of $CO_2$ fluxes and concentrations after artificial $CO_2$ release. The Environmental Impact Evaluation Test Facility (EIT) was built in Eumseong, Korea in 2015. Approximately 34kg $CO_2$ /day/zone were injected at Zones 2, 3, and 4 among the total of 5 zones from October 26 to 30, 2015. $CO_2$ fluxes were measured every 30 minutes at the surface at 0m, 1.5m, 2.5m, and 10m from the $CO_2$ releasing well using LI-8100A until November 13, 2015, and $CO_2$ concentrations were measured once a day at 15cm, 30cm, and 60cm depths at every 0m, 1.5m, 2.5m, 5m, and 10m from the well using GA5000 until November 28, 2015. $CO_2$ flux at 0m from the well started increasing on the fifth day after $CO_2$ release started, and continued to increase until November 13 even though the artificial $CO_2$ release stopped. $CO_2$ fluxes measured at 2.5m, 5.0m, and 10m from the well were not significantly different with each other. On the other hand, soil $CO_2$ concentration was shown as 38.4% at 60cm depth at 0m from the well in Zone 3 on the next day after $CO_2$ release started. Soil $CO_2$ was horizontally spreaded overtime, and detected up to 5m away from the well in all zones until $CO_2$ release stopped. Also, soil $CO_2$ concentrations at 30cm and 60cm depths at 0m from the well were measured similarly as $50.6{\pm}25.4%$ and $55.3{\pm}25.6%$, respectively, followed by 30cm depth ($31.3{\pm}17.2%$) which was significantly lower than those measured at the other depths on the final day of $CO_2$ release period. Soil $CO_2$ concentrations at all depths in all zones were gradually decreased for about 1 month after $CO_2$ release stopped, but still higher than those of the first day after $CO_2$ release stared. In conclusion, the closer the distance from the well and the deeper the depth, the higher $CO_2$ fluxes and concentrations occurred. Also, long-term monitoring should be required because the leaked $CO_2$ gas can remains in the soil for a long time even if the leakage stopped.

• Asia pacific journal of information systems
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• v.22 no.1
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• pp.29-52
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• 2012

Consumers differ in the way they make a purchase. An audio mania would willingly make a bold, yet serious, decision to buy a top-of-the-line home theater system, while he is not interested in replacing his two-decade-old shabby car. On the contrary, an automobile enthusiast wouldn't mind spending forty thousand dollars to buy a new Jaguar convertible, yet cares little about his junky component system. It is product involvement that helps us explain such differences among individuals in the purchase style. Product involvement refers to the extent to which a product is perceived to be important to a consumer (Zaichkowsky, 2001). Product involvement is an important factor that strongly influences consumer's purchase decision-making process, and thus has been of prime interest to consumer behavior researchers. Furthermore, researchers found that involvement is closely related to perceived risk (Dholakia, 2001). While abundant research exists addressing how product involvement relates to overall perceived risk, little attention has been paid to the relationship between involvement and different types of perceived risk in an electronic commerce setting. Given that perceived risk can be a substantial barrier to the online purchase (Jarvenpaa, 2000), research addressing such an issue will offer useful implications on what specific types of perceived risk an online firm should focus on mitigating if it is to increase sales to a fullest potential. Meanwhile, past research has focused on such consumer responses as information search and dissemination as a consequence of involvement, neglecting other behavioral responses like online merchant selection. For one example, will a consumer seriously considering the purchase of a pricey Guzzi bag perceive a great degree of risk associated with online buying and therefore choose to buy it from a digital storefront rather than from an online marketplace to mitigate risk? Will a consumer require greater trust on the part of the online merchant when the perceived risk of online buying is rather high? We intend to find answers to these research questions through an empirical study. This paper explores the impact of enduring product involvement and perceived risks on required trust level, and further on online merchant choice. For the purpose of the research, five types or components of perceived risk are taken into consideration, including financial, performance, delivery, psychological, and social risks. A research model has been built around the constructs under consideration, and 12 hypotheses have been developed based on the research model to examine the relationships between enduring involvement and five components of perceived risk, between five components of perceived risk and required trust level, between enduring involvement and required trust level, and finally between required trust level and preference toward an e-tailer. To attain our research objectives, we conducted an empirical analysis consisting of two phases of data collection: a pilot test and main survey. The pilot test was conducted using 25 college students to ensure that the questionnaire items are clear and straightforward. Then the main survey was conducted using 295 college students at a major university for nine days between December 13, 2010 and December 21, 2010. The measures employed to test the model included eight constructs: (1) enduring involvement, (2) financial risk, (3) performance risk, (4) delivery risk, (5) psychological risk, (6) social risk, (7) required trust level, (8) preference toward an e-tailer. The statistical package, SPSS 17.0, was used to test the internal consistency among the items within the individual measures. Based on the Cronbach's ${\alpha}$ coefficients of the individual measure, the reliability of all the variables is supported. Meanwhile, the Amos 18.0 package was employed to perform a confirmatory factor analysis designed to assess the unidimensionality of the measures. The goodness of fit for the measurement model was satisfied. Unidimensionality was tested using convergent, discriminant, and nomological validity. The statistical evidences proved that the three types of validity were all satisfied. Now the structured equation modeling technique was used to analyze the individual paths along the relationships among the research constructs. The results indicated that enduring involvement has significant positive relationships with all the five components of perceived risk, while only performance risk is significantly related to trust level required by consumers for purchase. It can be inferred from the findings that product performance problems are mostly likely to occur when a merchant behaves in an opportunistic manner. Positive relationships were also found between involvement and required trust level and between required trust level and online merchant choice. Enduring involvement is concerned with the pleasure a consumer derives from a product class and/or with the desire for knowledge for the product class, and thus is likely to motivate the consumer to look for ways of mitigating perceived risk by requiring a higher level of trust on the part of the online merchant. Likewise, a consumer requiring a high level of trust on the merchant will choose a digital storefront rather than an e-marketplace, since a digital storefront is believed to be trustworthier than an e-marketplace, as it fulfills orders by itself rather than acting as an intermediary. The findings of the present research provide both academic and practical implications. The first academic implication is that enduring product involvement is a strong motivator of consumer responses, especially the selection of a merchant, in the context of electronic shopping. Secondly, academicians are advised to pay attention to the finding that an individual component or type of perceived risk can be used as an important research construct, since it would allow one to pinpoint the specific types of risk that are influenced by antecedents or that influence consequents. Meanwhile, our research provides implications useful for online merchants (both online storefronts and e-marketplaces). Merchants may develop strategies to attract consumers by managing perceived performance risk involved in purchase decisions, since it was found to have significant positive relationship with the level of trust required by a consumer on the part of the merchant. One way to manage performance risk would be to thoroughly examine the product before shipping to ensure that it has no deficiencies or flaws. Secondly, digital storefronts are advised to focus on symbolic goods (e.g., cars, cell phones, fashion outfits, and handbags) in which consumers are relatively more involved than others, whereas e- marketplaces should put their emphasis on non-symbolic goods (e.g., drinks, books, MP3 players, and bike accessories).