• KDI Journal of Economic Policy
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• v.12 no.1
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• pp.31-68
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• 1990

The concentration of economic power takes the form of one or a few firms controlling a substantial portion of the economic resources and means in a certain economic area. At the same time, to the extent that these firms are owned by a few individuals, resource allocation can be manipulated by them rather than by the impersonal market mechanism. This will impair allocative efficiency, run counter to a decentralized market system and hamper the equitable distribution of wealth. Viewed from the historical evolution of Western capitalism in general, the concentration of economic power is a paradox in that it is a product of the free market system itself. The economic principle of natural discrimination works so that a few big firms preempt scarce resources and market opportunities. Prominent historical examples include trusts in America, Konzern in Germany and Zaibatsu in Japan in the early twentieth century. In other words, the concentration of economic power is the outcome as well as the antithesis of free competition. As long as judgment of the economic system at large depends upon the value systems of individuals, therefore, the issue of how to evaluate the concentration of economic power will inevitably be tinged with ideology. We have witnessed several different approaches to this problem such as communism, fascism and revised capitalism, and the last one seems to be the only surviving alternative. The concentration of economic power in Korea can be summarily represented by the "jaebol," namely, the conglomerate business group, the majority of whose member firms are monopolistic or oligopolistic in their respective markets and are owned by particular individuals. The jaebol has many dimensions in its size, but to sketch its magnitude, the share of the jaebol in the manufacturing sector reached 37.3% in shipment and 17.6% in employment as of 1989. The concentration of economic power can be ascribed to a number of causes. In the early stages of economic development, when the market system is immature, entrepreneurship must fill the gap inherent in the market in addition to performing its customary managerial function. Entrepreneurship of this sort is a scarce resource and becomes even more valuable as the target rate of economic growth gets higher. Entrepreneurship can neither be readily obtained in the market nor exhausted despite repeated use. Because of these peculiarities, economic power is bound to be concentrated in the hands of a few entrepreneurs and their business groups. It goes without saying, however, that the issue of whether the full exercise of money-making entrepreneurship is compatible with social mores is a different matter entirely. The rapidity of the concentration of economic power can also be traced to the diversification of business groups. The transplantation of advanced technology oriented toward mass production tends to saturate the small domestic market quite early and allows a firm to expand into new markets by making use of excess capacity and of monopoly profits. One of the reasons why the jaebol issue has become so acute in Korea lies in the nature of the government-business relationship. The Korean government has set economic development as its foremost national goal and, since then, has intervened profoundly in the private sector. Since most strategic industries promoted by the government required a huge capacity in technology, capital and manpower, big firms were favored over smaller firms, and the benefits of industrial policy naturally accrued to large business groups. The concentration of economic power which occured along the way was, therefore, not necessarily a product of the market system. At the same time, the concentration of ownership in business groups has been left largely intact as they have customarily met capital requirements by means of debt. The real advantage enjoyed by large business groups lies in synergy due to multiplant and multiproduct production. Even these effects, however, cannot always be considered socially optimal, as they offer disadvantages to other independent firms-for example, by foreclosing their markets. Moreover their fictitious or artificial advantages only aggravate the popular perception that most business groups have accumulated their wealth at the expense of the general public and under the behest of the government. Since Korea stands now at the threshold of establishing a full-fledged market economy along with political democracy, the phenomenon called the concentration of economic power must be correctly understood and the roles of business groups must be accordingly redefined. In doing so, we would do better to take a closer look at Japan which has experienced a demise of family-controlled Zaibatsu and a success with business groups(Kigyoshudan) whose ownership is dispersed among many firms and ultimately among the general public. The Japanese case cannot be an ideal model, but at least it gives us a good point of departure in that the issue of ownership is at the heart of the matter. In setting the basic direction of public policy aimed at controlling the concentration of economic power, one must harmonize efficiency and equity. Firm size in itself is not a problem, if it is dictated by efficiency considerations and if the firm behaves competitively in the market. As long as entrepreneurship is required for continuous economic growth and there is a discrepancy in entrepreneurial capacity among individuals, a concentration of economic power is bound to take place to some degree. Hence, the most effective way of reducing the inefficiency of business groups may be to impose competitive pressure on their activities. Concurrently, unless the concentration of ownership in business groups is scaled down, the seed of social discontent will still remain. Nevertheless, the dispersion of ownership requires a number of preconditions and, consequently, we must make consistent, long-term efforts on many fronts. We can suggest a long list of policy measures specifically designed to control the concentration of economic power. Whatever the policy may be, however, its intended effects will not be fully realized unless business groups abide by the moral code expected of socially responsible entrepreneurs. This is especially true, since the root of the problem of the excessive concentration of economic power lies outside the issue of efficiency, in problems concerning distribution, equity, and social justice.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.183-203
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• 2018

News articles are the most suitable medium for examining the events occurring at home and abroad. Especially, as the development of information and communication technology has brought various kinds of online news media, the news about the events occurring in society has increased greatly. So automatically summarizing key events from massive amounts of news data will help users to look at many of the events at a glance. In addition, if we build and provide an event network based on the relevance of events, it will be able to greatly help the reader in understanding the current events. In this study, we propose a method for extracting event networks from large news text data. To this end, we first collected Korean political and social articles from March 2016 to March 2017, and integrated the synonyms by leaving only meaningful words through preprocessing using NPMI and Word2Vec. Latent Dirichlet allocation (LDA) topic modeling was used to calculate the subject distribution by date and to find the peak of the subject distribution and to detect the event. A total of 32 topics were extracted from the topic modeling, and the point of occurrence of the event was deduced by looking at the point at which each subject distribution surged. As a result, a total of 85 events were detected, but the final 16 events were filtered and presented using the Gaussian smoothing technique. We also calculated the relevance score between events detected to construct the event network. Using the cosine coefficient between the co-occurred events, we calculated the relevance between the events and connected the events to construct the event network. Finally, we set up the event network by setting each event to each vertex and the relevance score between events to the vertices connecting the vertices. The event network constructed in our methods helped us to sort out major events in the political and social fields in Korea that occurred in the last one year in chronological order and at the same time identify which events are related to certain events. Our approach differs from existing event detection methods in that LDA topic modeling makes it possible to easily analyze large amounts of data and to identify the relevance of events that were difficult to detect in existing event detection. We applied various text mining techniques and Word2vec technique in the text preprocessing to improve the accuracy of the extraction of proper nouns and synthetic nouns, which have been difficult in analyzing existing Korean texts, can be found. In this study, the detection and network configuration techniques of the event have the following advantages in practical application. First, LDA topic modeling, which is unsupervised learning, can easily analyze subject and topic words and distribution from huge amount of data. Also, by using the date information of the collected news articles, it is possible to express the distribution by topic in a time series. Second, we can find out the connection of events in the form of present and summarized form by calculating relevance score and constructing event network by using simultaneous occurrence of topics that are difficult to grasp in existing event detection. It can be seen from the fact that the inter-event relevance-based event network proposed in this study was actually constructed in order of occurrence time. It is also possible to identify what happened as a starting point for a series of events through the event network. The limitation of this study is that the characteristics of LDA topic modeling have different results according to the initial parameters and the number of subjects, and the subject and event name of the analysis result should be given by the subjective judgment of the researcher. Also, since each topic is assumed to be exclusive and independent, it does not take into account the relevance between themes. Subsequent studies need to calculate the relevance between events that are not covered in this study or those that belong to the same subject.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.143-163
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• 2016

The demographics of Internet users are the most basic and important sources for target marketing or personalized advertisements on the digital marketing channels which include email, mobile, and social media. However, it gradually has become difficult to collect the demographics of Internet users because their activities are anonymous in many cases. Although the marketing department is able to get the demographics using online or offline surveys, these approaches are very expensive, long processes, and likely to include false statements. Clickstream data is the recording an Internet user leaves behind while visiting websites. As the user clicks anywhere in the webpage, the activity is logged in semi-structured website log files. Such data allows us to see what pages users visited, how long they stayed there, how often they visited, when they usually visited, which site they prefer, what keywords they used to find the site, whether they purchased any, and so forth. For such a reason, some researchers tried to guess the demographics of Internet users by using their clickstream data. They derived various independent variables likely to be correlated to the demographics. The variables include search keyword, frequency and intensity for time, day and month, variety of websites visited, text information for web pages visited, etc. The demographic attributes to predict are also diverse according to the paper, and cover gender, age, job, location, income, education, marital status, presence of children. A variety of data mining methods, such as LSA, SVM, decision tree, neural network, logistic regression, and k-nearest neighbors, were used for prediction model building. However, this research has not yet identified which data mining method is appropriate to predict each demographic variable. Moreover, it is required to review independent variables studied so far and combine them as needed, and evaluate them for building the best prediction model. The objective of this study is to choose clickstream attributes mostly likely to be correlated to the demographics from the results of previous research, and then to identify which data mining method is fitting to predict each demographic attribute. Among the demographic attributes, this paper focus on predicting gender, age, marital status, residence, and job. And from the results of previous research, 64 clickstream attributes are applied to predict the demographic attributes. The overall process of predictive model building is compose of 4 steps. In the first step, we create user profiles which include 64 clickstream attributes and 5 demographic attributes. The second step performs the dimension reduction of clickstream variables to solve the curse of dimensionality and overfitting problem. We utilize three approaches which are based on decision tree, PCA, and cluster analysis. We build alternative predictive models for each demographic variable in the third step. SVM, neural network, and logistic regression are used for modeling. The last step evaluates the alternative models in view of model accuracy and selects the best model. For the experiments, we used clickstream data which represents 5 demographics and 16,962,705 online activities for 5,000 Internet users. IBM SPSS Modeler 17.0 was used for our prediction process, and the 5-fold cross validation was conducted to enhance the reliability of our experiments. As the experimental results, we can verify that there are a specific data mining method well-suited for each demographic variable. For example, age prediction is best performed when using the decision tree based dimension reduction and neural network whereas the prediction of gender and marital status is the most accurate by applying SVM without dimension reduction. We conclude that the online behaviors of the Internet users, captured from the clickstream data analysis, could be well used to predict their demographics, thereby being utilized to the digital marketing.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.51-69
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• 2020

Now, it is a stylized fact that a small number of technology firms such as Apple, Alphabet, Microsoft, Amazon, Facebook and a few others have become larger and dominant players in an industry. Coupled with the rise of these leading firms, we have also observed that a large number of young firms have become an acquisition target in their early IPO stages. This indeed results in a sharp decline in the number of new entries in public exchanges although a series of policy reforms have been promulgated to foster competition through an increase in new entries. Given the observed industry trend in recent decades, a number of studies have reported increased concentration in most developed countries. However, it is less understood as to what caused an increase in industry concentration. In this paper, we uncover the mechanisms by which industries have become concentrated over the last decades by tracing the changes in industry concentration associated with a firm's status change in its early IPO stages. To this end, we put emphasis on the case in which firms are acquired shortly after they went public. Especially, with the transition to digital-based economies, it is imperative for incumbent firms to adapt and keep pace with new ICT and related intelligent systems. For instance, after the acquisition of a young firm equipped with AI-based solutions, an incumbent firm may better respond to a change in customer taste and preference by integrating acquired AI solutions and analytics skills into multiple business processes. Accordingly, it is not unusual for young ICT firms become an attractive acquisition target. To examine the role of M&As involved with young firms in reshaping the level of industry concentration, we identify a firm's status in early post-IPO stages over the sample periods spanning from 1990 to 2016 as follows: i) being delisted, ii) being standalone firms and iii) being acquired. According to our analysis, firms that have conducted IPO since 2000s have been acquired by incumbent firms at a relatively quicker time than those that did IPO in previous generations. We also show a greater acquisition rate for IPO firms in the ICT sector compared with their counterparts in other sectors. Our results based on multinomial logit models suggest that a large number of IPO firms have been acquired in their early post-IPO lives despite their financial soundness. Specifically, we show that IPO firms are likely to be acquired rather than be delisted due to financial distress in early IPO stages when they are more profitable, more mature or less leveraged. For those IPO firms with venture capital backup have also become an acquisition target more frequently. As a larger number of firms are acquired shortly after their IPO, our results show increased concentration. While providing limited evidence on the impact of large incumbent firms in explaining the change in industry concentration, our results show that the large firms' effect on industry concentration are pronounced in the ICT sector. This result possibly captures the current trend that a few tech giants such as Alphabet, Apple and Facebook continue to increase their market share. In addition, compared with the acquisitions of non-ICT firms, the concentration impact of IPO firms in early stages becomes larger when ICT firms are acquired as a target. Our study makes new contributions. To our best knowledge, this is one of a few studies that link a firm's post-IPO status to associated changes in industry concentration. Although some studies have addressed concentration issues, their primary focus was on market power or proprietary software. Contrast to earlier studies, we are able to uncover the mechanism by which industries have become concentrated by placing emphasis on M&As involving young IPO firms. Interestingly, the concentration impact of IPO firm acquisitions are magnified when a large incumbent firms are involved as an acquirer. This leads us to infer the underlying reasons as to why industries have become more concentrated with a favor of large firms in recent decades. Overall, our study sheds new light on the literature by providing a plausible explanation as to why industries have become concentrated.

• Proceedings of the KOR-KST Conference
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• 1995.02a
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• pp.101-113
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• 1995

The objective of this research is to test the feasibility of developing a statewide truck traffic forecasting methodology for Wisconsin by using Origin-Destination surveys, traffic counts, classification counts, and other data that are routinely collected by the Wisconsin Department of Transportation (WisDOT). Development of a feasible model will permit estimation of future truck traffic for every major link in the network. This will provide the basis for improved estimation of future pavement deterioration. Pavement damage rises exponentially as axle weight increases, and trucks are responsible for most of the traffic-induced damage to pavement. Consequently, forecasts of truck traffic are critical to pavement management systems. The pavement Management Decision Supporting System (PMDSS) prepared by WisDOT in May 1990 combines pavement inventory and performance data with a knowledge base consisting of rules for evaluation, problem identification and rehabilitation recommendation. Without a r.easonable truck traffic forecasting methodology, PMDSS is not able to project pavement performance trends in order to make assessment and recommendations in the future years. However, none of WisDOT's existing forecasting methodologies has been designed specifically for predicting truck movements on a statewide highway network. For this research, the Origin-Destination survey data avaiiable from WisDOT, including two stateline areas, one county, and five cities, are analyzed and the zone-to'||'&'||'not;zone truck trip tables are developed. The resulting Origin-Destination Trip Length Frequency (00 TLF) distributions by trip type are applied to the Gravity Model (GM) for comparison with comparable TLFs from the GM. The gravity model is calibrated to obtain friction factor curves for the three trip types, Internal-Internal (I-I), Internal-External (I-E), and External-External (E-E). ~oth "macro-scale" calibration and "micro-scale" calibration are performed. The comparison of the statewide GM TLF with the 00 TLF for the macro-scale calibration does not provide suitable results because the available 00 survey data do not represent an unbiased sample of statewide truck trips. For the "micro-scale" calibration, "partial" GM trip tables that correspond to the 00 survey trip tables are extracted from the full statewide GM trip table. These "partial" GM trip tables are then merged and a partial GM TLF is created. The GM friction factor curves are adjusted until the partial GM TLF matches the 00 TLF. Three friction factor curves, one for each trip type, resulting from the micro-scale calibration produce a reasonable GM truck trip model. A key methodological issue for GM. calibration involves the use of multiple friction factor curves versus a single friction factor curve for each trip type in order to estimate truck trips with reasonable accuracy. A single friction factor curve for each of the three trip types was found to reproduce the 00 TLFs from the calibration data base. Given the very limited trip generation data available for this research, additional refinement of the gravity model using multiple mction factor curves for each trip type was not warranted. In the traditional urban transportation planning studies, the zonal trip productions and attractions and region-wide OD TLFs are available. However, for this research, the information available for the development .of the GM model is limited to Ground Counts (GC) and a limited set ofOD TLFs. The GM is calibrated using the limited OD data, but the OD data are not adequate to obtain good estimates of truck trip productions and attractions .. Consequently, zonal productions and attractions are estimated using zonal population as a first approximation. Then, Selected Link based (SELINK) analyses are used to adjust the productions and attractions and possibly recalibrate the GM. The SELINK adjustment process involves identifying the origins and destinations of all truck trips that are assigned to a specified "selected link" as the result of a standard traffic assignment. A link adjustment factor is computed as the ratio of the actual volume for the link (ground count) to the total assigned volume. This link adjustment factor is then applied to all of the origin and destination zones of the trips using that "selected link". Selected link based analyses are conducted by using both 16 selected links and 32 selected links. The result of SELINK analysis by u~ing 32 selected links provides the least %RMSE in the screenline volume analysis. In addition, the stability of the GM truck estimating model is preserved by using 32 selected links with three SELINK adjustments, that is, the GM remains calibrated despite substantial changes in the input productions and attractions. The coverage of zones provided by 32 selected links is satisfactory. Increasing the number of repetitions beyond four is not reasonable because the stability of GM model in reproducing the OD TLF reaches its limits. The total volume of truck traffic captured by 32 selected links is 107% of total trip productions. But more importantly, ~ELINK adjustment factors for all of the zones can be computed. Evaluation of the travel demand model resulting from the SELINK adjustments is conducted by using screenline volume analysis, functional class and route specific volume analysis, area specific volume analysis, production and attraction analysis, and Vehicle Miles of Travel (VMT) analysis. Screenline volume analysis by using four screenlines with 28 check points are used for evaluation of the adequacy of the overall model. The total trucks crossing the screenlines are compared to the ground count totals. L V/GC ratios of 0.958 by using 32 selected links and 1.001 by using 16 selected links are obtained. The %RM:SE for the four screenlines is inversely proportional to the average ground count totals by screenline .. The magnitude of %RM:SE for the four screenlines resulting from the fourth and last GM run by using 32 and 16 selected links is 22% and 31 % respectively. These results are similar to the overall %RMSE achieved for the 32 and 16 selected links themselves of 19% and 33% respectively. This implies that the SELINICanalysis results are reasonable for all sections of the state.Functional class and route specific volume analysis is possible by using the available 154 classification count check points. The truck traffic crossing the Interstate highways (ISH) with 37 check points, the US highways (USH) with 50 check points, and the State highways (STH) with 67 check points is compared to the actual ground count totals. The magnitude of the overall link volume to ground count ratio by route does not provide any specific pattern of over or underestimate. However, the %R11SE for the ISH shows the least value while that for the STH shows the largest value. This pattern is consistent with the screenline analysis and the overall relationship between %RMSE and ground count volume groups. Area specific volume analysis provides another broad statewide measure of the performance of the overall model. The truck traffic in the North area with 26 check points, the West area with 36 check points, the East area with 29 check points, and the South area with 64 check points are compared to the actual ground count totals. The four areas show similar results. No specific patterns in the L V/GC ratio by area are found. In addition, the %RMSE is computed for each of the four areas. The %RMSEs for the North, West, East, and South areas are 92%, 49%, 27%, and 35% respectively, whereas, the average ground counts are 481, 1383, 1532, and 3154 respectively. As for the screenline and volume range analyses, the %RMSE is inversely related to average link volume. 'The SELINK adjustments of productions and attractions resulted in a very substantial reduction in the total in-state zonal productions and attractions. The initial in-state zonal trip generation model can now be revised with a new trip production's trip rate (total adjusted productions/total population) and a new trip attraction's trip rate. Revised zonal production and attraction adjustment factors can then be developed that only reflect the impact of the SELINK adjustments that cause mcreases or , decreases from the revised zonal estimate of productions and attractions. Analysis of the revised production adjustment factors is conducted by plotting the factors on the state map. The east area of the state including the counties of Brown, Outagamie, Shawano, Wmnebago, Fond du Lac, Marathon shows comparatively large values of the revised adjustment factors. Overall, both small and large values of the revised adjustment factors are scattered around Wisconsin. This suggests that more independent variables beyond just 226; population are needed for the development of the heavy truck trip generation model. More independent variables including zonal employment data (office employees and manufacturing employees) by industry type, zonal private trucks 226; owned and zonal income data which are not available currently should be considered. A plot of frequency distribution of the in-state zones as a function of the revised production and attraction adjustment factors shows the overall " adjustment resulting from the SELINK analysis process. Overall, the revised SELINK adjustments show that the productions for many zones are reduced by, a factor of 0.5 to 0.8 while the productions for ~ relatively few zones are increased by factors from 1.1 to 4 with most of the factors in the 3.0 range. No obvious explanation for the frequency distribution could be found. The revised SELINK adjustments overall appear to be reasonable. The heavy truck VMT analysis is conducted by comparing the 1990 heavy truck VMT that is forecasted by the GM truck forecasting model, 2.975 billions, with the WisDOT computed data. This gives an estimate that is 18.3% less than the WisDOT computation of 3.642 billions of VMT. The WisDOT estimates are based on the sampling the link volumes for USH, 8TH, and CTH. This implies potential error in sampling the average link volume. The WisDOT estimate of heavy truck VMT cannot be tabulated by the three trip types, I-I, I-E ('||'&'||'pound;-I), and E-E. In contrast, the GM forecasting model shows that the proportion ofE-E VMT out of total VMT is 21.24%. In addition, tabulation of heavy truck VMT by route functional class shows that the proportion of truck traffic traversing the freeways and expressways is 76.5%. Only 14.1% of total freeway truck traffic is I-I trips, while 80% of total collector truck traffic is I-I trips. This implies that freeways are traversed mainly by I-E and E-E truck traffic while collectors are used mainly by I-I truck traffic. Other tabulations such as average heavy truck speed by trip type, average travel distance by trip type and the VMT distribution by trip type, route functional class and travel speed are useful information for highway planners to understand the characteristics of statewide heavy truck trip patternS. Heavy truck volumes for the target year 2010 are forecasted by using the GM truck forecasting model. Four scenarios are used. Fo~ better forecasting, ground count- based segment adjustment factors are developed and applied. ISH 90 '||'&'||' 94 and USH 41 are used as example routes. The forecasting results by using the ground count-based segment adjustment factors are satisfactory for long range planning purposes, but additional ground counts would be useful for USH 41. Sensitivity analysis provides estimates of the impacts of the alternative growth rates including information about changes in the trip types using key routes. The network'||'&'||'not;based GMcan easily model scenarios with different rates of growth in rural versus . . urban areas, small versus large cities, and in-state zones versus external stations. cities, and in-state zones versus external stations.

• Journal of Distribution Research
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• v.15 no.3
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• pp.71-110
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• 2010

1. Introduction Today Internet is recognized as an important way for the transaction of products and services. According to the data surveyed by the National Statistical Office, the on-line transaction in 2007 for a year, 15.7656 trillion, shows a 17.1%(2.3060 trillion won) increase over last year, of these, the amount of B2C has been increased 12.0%(10.2258 trillion won). Like this, because the entry barrier of on-line market of Korea is low, many retailers could easily enter into the market. So the bigger its scale is, but on the other hand, the tougher its competition is. Particularly due to the Internet and innovation of IT, the existing market has been changed into the perfect competitive market(Srinivasan, Rolph & Kishore, 2002). In the early years of on-line business, they think that the main reason for success is a moderate price, they are awakened to its importance of on-line service quality with tough competition. If it's not sure whether customers can be provided with what they want, they can use the Web sites, perhaps they can trust their products that had been already bought or not, they have a doubt its viability(Parasuraman, Zeithaml & Malhotra, 2005). Customers can directly reserve and issue their air tickets irrespective of place and time at the Web sites of travel agencies or airlines, but its empirical studies about these Web sites for reserving and issuing air tickets are insufficient. Therefore this study goes on for following specific objects. First object is to measure service quality and service recovery of Web sites for reserving and issuing air tickets. Second is to look into whether above on-line service quality and on-line service recovery have an impact on overall service quality. Third is to seek for the relation with overall service quality and customer satisfaction, then this customer satisfaction and loyalty intention. 2. Theoretical Background 2.1 On-line Service Quality Barnes & Vidgen(2000; 2001a; 2001b; 2002) had invented the tool to measure Web sites' quality four times(called WebQual). The WebQual 1.0, Step one invented a measuring item for information quality based on QFD, and this had been verified by students of UK business school. The Web Qual 2.0, Step two invented for interaction quality, and had been judged by customers of on-line bookshop. The WebQual 3.0, Step three invented by consolidating the WebQual 1.0 for information quality and the WebQual2.0 for interactionquality. It includes 3-quality-dimension, information quality, interaction quality, site design, and had been assessed and confirmed by auction sites(e-bay, Amazon, QXL). Furtheron, through the former empirical studies, the authors changed sites quality into usability by judging that usability is a concept how customers interact with or perceive Web sites and It is used widely for accessing Web sites. By this process, WebQual 4.0 was invented, and is consist of 3-quality-dimension; information quality, interaction quality, usability, 22 items. However, because WebQual 4.0 is focusing on technical part, it's usable at the Website's design part, on the other hand, it's not usable at the Web site's pleasant experience part. Parasuraman, Zeithaml & Malhorta(2002; 2005) had invented the measure for measuring on-line service quality in 2002 and 2005. The study in 2002 divided on-line service quality into 5 dimensions. But these were not well-organized, so there needed to be studied again totally. So Parasuraman, Zeithaml & Malhorta(2005) re-worked out the study about on-line service quality measure base on 2002's study and invented E-S-QUAL. After they invented preliminary measure for on-line service quality, they made up a question for customers who had purchased at amazon.com and walmart.com and reassessed this measure. And they perfected an invention of E-S-QUAL consists of 4 dimensions, 22 items of efficiency, system availability, fulfillment, privacy. Efficiency measures assess to sites and usability and others, system availability measures accurate technical function of sites and others, fulfillment measures promptness of delivering products and sufficient goods and others and privacy measures the degree of protection of data about their customers and so on. 2.2 Service Recovery Service industries tend to minimize the losses by coping with service failure promptly. This responses of service providers to service failure mean service recovery(Kelly & Davis, 1994). Bitner(1990) went on his study from customers' view about service providers' behavior for customers to recognize their satisfaction/dissatisfaction at service point. According to them, to manage service failure successfully, exact recognition of service problem, an apology, sufficient description about service failure and some tangible compensation are important. Parasuraman, Zeithaml & Malhorta(2005) approached the service recovery from how to measure, rather than how to manage, and moved to on-line market not to off-line, then invented E-RecS-QUAL which is a measuring tool about on-line service recovery. 2.3 Customer Satisfaction The definition of customer satisfaction can be divided into two points of view. First, they approached customer satisfaction from outcome of comsumer. Howard & Sheth(1969) defined satisfaction as 'a cognitive condition feeling being rewarded properly or improperly for their sacrifice.' and Westbrook & Reilly(1983) also defined customer satisfaction/dissatisfaction as 'a psychological reaction to the behavior pattern of shopping and purchasing, the display condition of retail store, outcome of purchased goods and service as well as whole market.' Second, they approached customer satisfaction from process. Engel & Blackwell(1982) defined satisfaction as 'an assessment of a consistency in chosen alternative proposal and their belief they had with them.' Tse & Wilton(1988) defined customer satisfaction as 'a customers' reaction to discordance between advance expectation and ex post facto outcome.' That is, this point of view that customer satisfaction is process is the important factor that comparing and assessing process what they expect and outcome of consumer. Unlike outcome-oriented approach, process-oriented approach has many advantages. As process-oriented approach deals with customers' whole expenditure experience, it checks up main process by measuring one by one each factor which is essential role at each step. And this approach enables us to check perceptual/psychological process formed customer satisfaction. Because of these advantages, now many studies are adopting this process-oriented approach(Yi, 1995). 2.4 Loyalty Intention Loyalty has been studied by dividing into behavioral approaches, attitudinal approaches and complex approaches(Dekimpe et al., 1997). In the early years of study, they defined loyalty focusing on behavioral concept, behavioral approaches regard customer loyalty as "a tendency to purchase periodically within a certain period of time at specific retail store." But the loyalty of behavioral approaches focuses on only outcome of customer behavior, so there are someone to point the limits that customers' decision-making situation or process were neglected(Enis & Paul, 1970; Raj, 1982; Lee, 2002). So the attitudinal approaches were suggested. The attitudinal approaches consider loyalty contains all the cognitive, emotional, voluntary factors(Oliver, 1997), define the customer loyalty as "friendly behaviors for specific retail stores." However these attitudinal approaches can explain that how the customer loyalty form and change, but cannot say positively whether it is moved to real purchasing in the future or not. This is a kind of shortcoming(Oh, 1995). 3. Research Design 3.1 Research Model Based on the objects of this study, the research model derived is