• Journal of Distribution Science
• /
• v.9 no.2
• /
• pp.31-36
• /
• 2011

I conducted empirical analyses of what happens when an offline channel expands to an online channel and whether the pre-existing offline channel's competitive assets (e.g. brand reputation and level of service satisfaction) can be linked to online channel preference. I found that an offline channel's brand reputation and level of service satisfaction can have a direct influence on offline channel preference and a second-hand influence on online channel preference. Thus, if the competitiveness of the online channel is strong enough and its customers have a higher preference for the offline channel, they will be committed and loyal to the company. The resultant enhanced competitiveness of the offline channel will present opportunities for both present and future success. The main results are the following. First, the management of the distribution channel service quality is more important than that of the brand reputation. Customers' experiences of service and subjective evaluations are not important only as the leading factors in the long-term brand reputation management but also as influential factors in channel preference. SoThus, given that the service quality of the pre-existing channel is not the customers' main concern, a strategy of improving the level of service satisfaction aimed at present customers is more valuable than a wide brand positioning strategy aimed at general and new customers. Second, when an offline channel company establishes an internet shopping mall on an online channel, it is highly likely that the preference and subjective evaluation of the present customers will influence the online channel. This applies not only to the special case of an expansion from an offline intermediary channel to an online one, but also to an online channel acting as an expansion of the business model of a conventional manufacturing or service company: both cases are vertical integrations of marketing channels in an expansion of the distribution channel. My theory applies to a wide range of contexts. Third and finally, any business strategy can grasp the meaning of 'channel expansion. Fundamentally, it is an expansion of the sales activity channel and marketing activity. However, it is also a way of enhancing marketing and sales competitiveness through an expansion to an online or offline channel. The expansion of an offline company to an online channel could be seen not as improvement but as an innovation of the business process by which two goals are achieved with one technique. The former is expected to increase the sales of the offline company, and the latter is also expected to increase sales while also contributing to cost reduction.

• Journal of Intelligence and Information Systems
• /
• v.17 no.4
• /
• pp.157-173
• /
• 2011

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.

• Journal of Intelligence and Information Systems
• /
• v.17 no.4
• /
• pp.109-130
• /
• 2011

One of the major problems in the area of data mining is the size of the data, as most data set has huge volume these days. Streams of data are normally accumulated into data storages or databases. Transactions in internet, mobile devices and ubiquitous environment produce streams of data continuously. Some data set are just buried un-used inside huge data storage due to its huge size. Some data set is quickly lost as soon as it is created as it is not saved due to many reasons. How to use this large size data and to use data on stream efficiently are challenging questions in the study of data mining. Stream data is a data set that is accumulated to the data storage from a data source continuously. The size of this data set, in many cases, becomes increasingly large over time. To mine information from this massive data, it takes too many resources such as storage, money and time. These unique characteristics of the stream data make it difficult and expensive to store all the stream data sets accumulated over time. Otherwise, if one uses only recent or partial of data to mine information or pattern, there can be losses of valuable information, which can be useful. To avoid these problems, this study suggests a method efficiently accumulates information or patterns in the form of rule set over time. A rule set is mined from a data set in stream and this rule set is accumulated into a master rule set storage, which is also a model for real-time decision making. One of the main advantages of this method is that it takes much smaller storage space compared to the traditional method, which saves the whole data set. Another advantage of using this method is that the accumulated rule set is used as a prediction model. Prompt response to the request from users is possible anytime as the rule set is ready anytime to be used to make decisions. This makes real-time decision making possible, which is the greatest advantage of this method. Based on theories of ensemble approaches, combination of many different models can produce better prediction model in performance. The consolidated rule set actually covers all the data set while the traditional sampling approach only covers part of the whole data set. This study uses a stock market data that has a heterogeneous data set as the characteristic of data varies over time. The indexes in stock market data can fluctuate in different situations whenever there is an event influencing the stock market index. Therefore the variance of the values in each variable is large compared to that of the homogeneous data set. Prediction with heterogeneous data set is naturally much more difficult, compared to that of homogeneous data set as it is more difficult to predict in unpredictable situation. This study tests two general mining approaches and compare prediction performances of these two suggested methods with the method we suggest in this study. The first approach is inducing a rule set from the recent data set to predict new data set. The seocnd one is inducing a rule set from all the data which have been accumulated from the beginning every time one has to predict new data set. We found neither of these two is as good as the method of accumulated rule set in its performance. Furthermore, the study shows experiments with different prediction models. The first approach is building a prediction model only with more important rule sets and the second approach is the method using all the rule sets by assigning weights on the rules based on their performance. The second approach shows better performance compared to the first one. The experiments also show that the suggested method in this study can be an efficient approach for mining information and pattern with stream data. This method has a limitation of bounding its application to stock market data. More dynamic real-time steam data set is desirable for the application of this method. There is also another problem in this study. When the number of rules is increasing over time, it has to manage special rules such as redundant rules or conflicting rules efficiently.

• The KIPS Transactions:PartC
• /
• v.18C no.5
• /
• pp.343-360
• /
• 2011

The objective of this paper is to analyze and characterize to simulate routing observations on end-to-end routing circuits and a ping experiment of a virtual network after modeling, such as IPv6 migration, an OSPFv3 routing experiment based on an IPv6 environment, and a ping experiment for IPv4/IPv6 dual-stack networks and IPv6 network for OSPFv3 routing using IPv6 planning and operations in an OPNET Modeler. IPv6 deployment based largely on the integrated wired and wireless network was one of the research tasks at hand. The previous studies' researchers recommended that future research work be done on the explicit features of both OSPFv3 and EIGRP protocols in the IPv4/IPv6 environment, and more research should be done to explore how to improve the end-to-end IPv6 performance. Also, most related work was performed with an IPv4 environment but lacked studies related to the OSPFv3 virtual network based on an end-to-end IPv6 environment. Hence, this research continues work in previous studies in analyzing IPv6 migration, an OSPFv3 routing experiment based on IPv6, and a ping experiment for IPv4/IPv6 dual-stack networks and IPv6 network for OSPFv3 routing. In the not too distant future, before enabling the default IPv6, it would help to understand network design and deployment based on an IPv6 environment through IPv6 planning and operations for the end-user perspective such as success or failure of connection on IPv6 migration, exploration of an OSPFv3 routing circuit based on an end-to-end IPv6 environment, and a ping experiment for IPv4/IPv6 dual-stack networks and IPv6 network for OSPFv3 routing. We were able to observe an optimal route for modeling of an end-to-end virtual network through simulation results as well as find what appeared to be a fast ping response time VC server to ensure Internet quality of service better than an HTTP server.

• Journal of Global Scholars of Marketing Science
• /
• v.20 no.2
• /
• pp.123-134
• /
• 2010

Considering the benefits for both consumers and suppliers, firms are taking advantage of the Internet as a medium to communicate with and sell products to their consumers. This trend makes the online shopping environment a growing field for both researchers and practitioners. This paper contributes by testing a model of online consumer behavior with websites varying in levels of hedonism. Unlike past studies, we included all three types of emotions (arousal, pleasure, and dominance) and flow into the model. In this study, we assumed that website interfaces, such as background colors, music, and fonts impact the three types of emotions at the initial exposure to the site (Mazaheri, Richard, and Laroche, 2011). In turn, these emotions influence flow and consumers' perceptions of the site atmospherics-perception of site informativeness, effectiveness, and entertainment. This assumption is consistent with Zajonc (1980) who argued that affective reactions are independent of perceptual and cognitive operations and can influence responses. We, then, propose that the perceptions of site atmospherics along with flow, influence customers' attitudes toward the website and toward the product, site involvement, and purchase intentions. In addition, we studied the moderating impact of the level of hedonism of websites on all the relationship in the model. Thus, the path coefficients were compared between "high" and "low" hedonic websites. We used 39 real websites from 12 product categories (8 services and 4 physical goods) to test the model. Among them, 20 were perceived as high hedonic and 19 as low hedonic by the respondents. The result of EQS 6.1 support the overall model: $\chi^2$=1787 (df=504), CFI=.994; RMSEA=.031. All the hypotheses were significant. In addition, the results of multi-groups analyses reveal several non-invariant structural paths between high and low hedonic website groups. The findings supported the model regarding the influence of the three types of emotions on customers' perceptions of site atmospherics, flow, and other customer behavior variables. It was found that pleasure strongly influenced site attitudes and perceptions of site entertainment. Arousal positively impacted the other two types of emotions, perceptions of site informativeness, and site involvement. Additionally, the influence of arousal on flow was found to be highly significant. The results suggested a strong association between dominance and customers' perceptions of site effectiveness. Dominance was also found to be associated with site attitudes and flow. Moreover, the findings suggested that site involvement and attitudes toward the product are the most important antecedents of purchase intentions. Site informativeness and flow also significantly influenced purchase intentions. The results of multi-group analysis supported the moderating impacts of hedonism of the websites. Compared to low (high) hedonic sites, the impacts of utilitarian (hedonic) attributes on other variables were stronger in high (low) hedonic websites. Among the three types of emotions, dominance (controlling feelings) effects were stronger in high hedonic sites and pleasure effects were stronger in low hedonic sites. Moreover, the impact of site informativeness was stronger for high hedonic websites compared to their low-hedonic counterparts. On the other hand, the influence of effectiveness of information on perceptions of site informativeness and the impact of site involvement on product attitudes were stronger for low hedonic websites than for high hedonic ones.

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

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

• Journal of Global Scholars of Marketing Science
• /
• v.20 no.1
• /
• pp.4-14
• /
• 2010

Collaboration strategies entail information sharing and other varied forms of cooperation that are mutually beneficial to the company and stakeholder groups. This study addresses the specific types of collaboration used in the fashion industry while also examining strategies that have been most successful for fashion companies and perceived benefits of collaboration from the customer perspective. In the present study we define fashion companies and brands as collaborators and their partners or stakeholders as collaboratees. We define collaboration as a cooperative relationship where more than two companies, brands or individuals provide customers with beneficial outcomes utilizing their own competitive advantages on an equal basis. Collaboration strategies entail information sharing and other varied forms of cooperation that are mutually beneficial to the company and stakeholder groups. Through collaboration, fashion companies have pursued both tangible differentiation, such as design and technology applications, and intangible differentiation such as emotional and psychological benefits to customers. As a result, collaboration within the fashion industry has become an important, value creating concept. This qualitative study utilized case studies and in-depth interview methodologies to examine customers' attitudes concerning collaboration in the fashion industry. A total of 173 collaboration cases were identified in Korean and international markets from 1998 through December 2008, focusing on fashion companies. Cases were collected from documented data including websites and industry data bases and top ranked portal search sites such as: Rankey.com; Naver, Daum, and Nate; and representative fashion information websites, Samsungdesignnet and Firstviewkorea. Cases were collected between November 2008 and February 2009. Cases were selected for the analysis where one or more partners were associated with the production of fashion products (excluding textile production), retail fashion products, or designer services. Additional collaboration case information was obtained from news articles, periodicals, internet portal sites and fashion information sites as conducted in prior studies (Jeong and Kim 2008; Park and Park 2004; Yoon 2005). In total, 173 cases were selected for analysis that clearly exhibited the benefits and outcomes of collaboration efforts and strategies between fashion companies and stakeholders. Findings show that the overall results show that for both partners (collaborator and collaboratee) participating in collaboration, that the major benefits are reduction of costs and risks by sharing resource such as design power, image, costs, technology and targets, and creation of synergy. Regarding types of collaboration outcomes, product/design was most important (55%), followed by promotion (21%), price (20%), and place (4%). This result shows that collaboration plays an important role in giving life to products and designs, particularly in the fashion industry which seeks for creative and newness. To be successful in collaboration efforts, results of the depth interviews in this study confirm that fashion companies should have a clear objective on why they are doing the collaboration. After setting the objective, they should select collaboratees that match their brand image and target market, make quality co-products that have definite concepts and differentiating factors, and also pay attention to increasing brand awareness. Based on depth interviews with customers, customer benefits were categorized into six factors: pursuit for individual character; pursuit for brand; pursuit for scarcity; pursuit for fashion; pursuit for economic efficiency; and pursuit for sociality. Customers also placed more importance on image, reputation, and trust of brands regarding the cases shown in the interviews. They also commented that strong branding should come first before other marketing strategies. However, success factors recognized by experts and customers in this study showed different results by subcategories. Thus, target customers and target market should be studied from various dimensions to develop appropriate strategies for successful collaboration.

• Journal of Global Scholars of Marketing Science
• /
• v.17 no.4
• /
• pp.113-139
• /
• 2007

This study aims to, looking from a standpoint of network, has investigated the shipping industry's B2B e-marketplace, the characteristics that can earn electronic trust from the users, and characteristics of the web-site. It has examined the mechanism whereby electronic trust be earned and how it affects web-site involvement and service transaction intention. Ultimately, The study attempts to make proposals whereby such trust can lead for a cooperative trading community in the shipping industry's B2B e-marketplace The Covalence structural equation modeling was designed and empirically tested for the shipping industry's B2B e-marketplace. The shipping industry employees were given questionnaires and data were analyzed. Except for perceived security of the three characteristic factors on the web-site, the perceived site quality and characteristics factors in operation only affected co-variables. Transaction Fairness was determined to be the most important factor among exogenous factors increasing electronic trust. With regards to transaction rules, if a transaction is beneficial only to one side, then no long term transaction will not take place. If the concerned parties properly recognize that transaction fairness is crucial to electronic transaction, then it will enormously contribute to successful operations of shipping e-marketplace. Also, Perceived efficiency in transaction also affects electronic trust. This reduces transaction costs and speeds up and simplifies the transaction process. It has reduced greater time and costs than existing off-line transaction, and would positively affect electronic trust. By making an open forum for participants to obtain information for transaction, they can gather useful information, and at the same time, the web-site operator can provide information, which, in turn, will increase electronic trust in electronic transaction. Furthermore, such formation of trust in electronic transaction influences shipping companies in such a way that they will want to continuously participate in the transaction, raising web-site involvement. The result of increased trust is that shipping companies in the future will do business with each other and form a foundation for continuous transactions amongst themselves. Consequently, the formation of trust in electronic transaction greatly influences web-site involvement and service transaction intention. The results of the study have again proved that in order to maintain continuous business relationship with the current clients, electronic trust in virtual space, which operates the shipping industry's B2B e-marketplace, is important for the interested parties.

• Korean journal of communication and information
• /
• v.34
• /
• pp.196-243
• /
• 2006

The popularity of Korea pop culture, which called as the name of "Korea wave", has started to spread out in China and other Asian states from late-1990's. The study about "Korea wave" until now, however, have prevailed within an economic point of view. So, I would like to clarify that this dissertation raises a question in exiting argument and explains the identity of "Korea wave" by investigating the details of pop culture contents of Korea, and understanding of chinese receiver. It shows that chinese receiver, watching the movie , has estimated in the affirmative viewpoint after I have analyzed a reply of movie web-site in China. The main features of this analysis prove that there are a lot of good estimation when chinese receiver have seen that movie because it has been well-matched with emotion and fun of story and attraction in the movie. In that order, Some Chinese netizen evaluated that there are some negative point of view as the main actress has a strange and crazy behavior. I have also found that Korea pop culture contents has not given to them good image and chinese receiver had a tendency to view objectively to classify with strength and weakness. Analysis to contrast understanding of Chinese netizen with Korea netizen showed that Korea netizen emphasized fun of story, however, Chinese netizen showed that they had a lot of opinion to be fresh and realistic relatively. In conclusion, I would like herewith to identify that there are some differences between Chinese netizen and Korean netizen after contacting the movie. The reason has showed that understanding about the same object can be a great deal of various consideration in two more diverse cultures which have many different social-cultural and historical situation.

• Progress in Medical Physics
• /
• v.26 no.3
• /
• pp.119-126
• /
• 2015

The purpose of this study is to analyze motion-induced dose error generated by each tumor motion parameters of irregular tumor motion in helical tomotherapy. To understand the effect of the irregular tumor motion, a simple analytical model was simulated. Moving cases that has tumor motion were divided into a slightly irregular tumor motion case, a large irregular tumor motion case and a patient case. The slightly irregular tumor motion case was simulated with a variability of 10% in the tumor motion parameters of amplitude (amplitude case), period (period case), and baseline (baseline case), while the large irregular tumor motion case was simulated with a variability of 40%. In the phase case, the initial phase of the tumor motion was divided into end inhale, mid exhale, end exhale, and mid inhale; the simulated dose profiles for each case were compared. The patient case was also investigated to verify the motion-induced dose error in 'clinical-like' conditions. According to the simulation process, the dose profile was calculated. The moving case was compared with the static case that has no tumor motion. In the amplitude, period, baseline cases, the results show that the motion-induced dose error in the large irregular tumor motion case was larger than that in the slightly irregular tumor motion case or regular tumor motion case. Because the offset effect was inversely proportion to irregularity of tumor motion, offset effect was smaller in the large irregular tumor motion case than the slightly irregular tumor motion case or regular tumor motion case. In the phase case, the larger dose discrepancy was observed in the irregular tumor motion case than regular tumor motion case. A larger motion-induced dose error was also observed in the patient case than in the regular tumor motion case. This study analyzed motion-induced dose error as a function of each tumor motion parameters of irregular tumor motion during helical tomotherapy. The analysis showed that variability control of irregular tumor motion is important. We believe that the variability of irregular tumor motion can be reduced by using abdominal compression and respiratory training.