• The Korean Journal of Air & Space Law and Policy
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• v.25 no.2
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• pp.139-162
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• 2010

In terms of the international air transport, the open skies policy implies freedom in the sky or opening the sky. In the normative respect, the open skies policy is a kind of open-door policy which gives various forms of traffic right to other countries, but on the other hand it is a policy of free competition in the international air transport. Since the Airline Deregulation Act of 1978, the United States has signed an open skies agreement with many countries, starting with the Netherlands, so that competitive large airlines can compete in the international air transport market where there exist a lot of business opportunities. South Korea now has an open skies agreement with more than 20 countries. The frequent flyer program (FFP) is part of a broad-based marketing alliance which has been used as an airfare strategy since the U.S. government's airline deregulation. The membership-based program is an incentive plan that provides mileage points to customers for using airline services and rewards customer loyalty in tangible forms based on their accumulated points. In its early stages, the frequent flyer program was focused on marketing efforts to attract customers, but now in the environment of intense competition among airlines, the program is used as an important strategic marketing tool for enhancing business performance. Therefore, airline companies agree that they need to identify customer needs in order to secure loyal customers more effectively. The outcomes from an airline's frequent flyer program can have a variety of effects on international competition. First, the airline can obtain a more dominant position in the air flight market by expanding its air route networks. Second, the availability of flight products for customers can be improved with an increase in flight frequency. Third, the airline can preferentially expand into new markets and thus gain advantages over its competitors. However, there are few empirical studies on the airline frequent flyer program. Accordingly, this study aims to explore the effects of the program on international competition, after reviewing the types of strategic alliance between airlines. Making strategic airline alliances is a worldwide trend resulting from the open skies policy. South Korea also needs to be making open skies agreements more realistic to promote the growth and competition of domestic airlines. The present study is about the performance of the airline frequent flyer program and international competition under the open skies policy. With a sample of five global alliance groups (Star, Oneworld, Wings, Qualiflyer and Skyteam), the study was attempted as an empirical study of the effects that the resource structures and levels of information technology held by airlines in each group have on the type of alliance, and one-way analysis of variance and regression analysis were used to test hypotheses. The findings of this study suggest that both large airline companies and small/medium-size airlines in an alliance group with global networks and organizations are able to achieve high performance and secure international competitiveness. Airline passengers earn mileage points by using non-flight services through an alliance network with hotels, car-rental services, duty-free shops, travel agents and more and show high interests in and preferences for related service benefits. Therefore, Korean airline companies should develop more aggressive marketing programs based on multilateral alliances with other services including hotels, as well as with other airlines.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Journal of Global Scholars of Marketing Science
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• v.18 no.3
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• pp.59-88
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• 2008

Digital Convergence means integration between industry, technology, and contents, and in marketing, it usually comes with creation of new types of product and service under the base of digital technology as digitalization progress in electro-communication industries including telecommunication, home appliance, and computer industries. One can see digital convergence not only in instruments such as PC, AV appliances, cellular phone, but also in contents, network, service that are required in production, modification, distribution, re-production of information. Convergence in contents started around 1990. Convergence in network and service begins as broadcasting and telecommunication integrates and DMB(digital multimedia broadcasting), born in May, 2005 is the symbolic icon in this trend. There are some positive and negative expectations about DMB. The reason why two opposite expectations exist is that DMB does not come out from customer's need but from technology development. Therefore, customers might have hard time to interpret the real meaning of DMB. Time is quite critical to a high tech product, like DMB because another product with same function from different technology can replace the existing product within short period of time. If DMB does not positioning well to customer's mind quickly, another products like Wibro, IPTV, or HSPDA could replace it before it even spreads out. Therefore, positioning strategy is critical for success of DMB product. To make correct positioning strategy, one needs to understand how consumer interprets DMB and how consumer's interpretation can be changed via communication strategy. In this study, we try to investigate how consumer perceives a new product, like DMB and how AD strategy change consumer's perception. More specifically, the paper segment consumers into sub-groups based on their DMB perceptions and compare their characteristics in order to understand how they perceive DMB. And, expose them different printed ADs that have messages guiding consumer think DMB in specific ways, either cellular phone or personal TV. Research Question 1: Segment consumers according to perceptions about DMB and compare characteristics of segmentations. Research Question 2: Compare perceptions about DMB after AD that induces categorization of DMB in direction for each segment. If one understand and predict a direction in which consumer perceive a new product, firm can select target customers easily. We segment consumers according to their perception and analyze characteristics in order to find some variables that can influence perceptions, like prior experience, usage, or habit. And then, marketing people can use this variables to identify target customers and predict their perceptions. If one knows how customer's perception is changed via AD message, communication strategy could be constructed properly. Specially, information from segmented customers helps to develop efficient AD strategy for segment who has prior perception. Research framework consists of two measurements and one treatment, O1 X O2. First observation is for collecting information about consumer's perception and their characteristics. Based on first observation, the paper segment consumers into two groups, one group perceives DMB similar to Cellular phone and the other group perceives DMB similar to TV. And compare characteristics of two segments in order to find reason why they perceive DMB differently. Next, we expose two kinds of AD to subjects. One AD describes DMB as Cellular phone and the other Ad describes DMB as personal TV. When two ADs are exposed to subjects, consumers don't know their prior perception of DMB, in other words, which subject belongs 'similar-to-Cellular phone' segment or 'similar-to-TV' segment? However, we analyze the AD's effect differently for each segment. In research design, final observation is for investigating AD effect. Perception before AD is compared with perception after AD. Comparisons are made for each segment and for each AD. For the segment who perceives DMB similar to TV, AD that describes DMB as cellular phone could change the prior perception. And AD that describes DMB as personal TV, could enforce the prior perception. For data collection, subjects are selected from undergraduate students because they have basic knowledge about most digital equipments and have open attitude about a new product and media. Total number of subjects is 240. In order to measure perception about DMB, we use indirect measurement, comparison with other similar digital products. To select similar digital products, we pre-survey students and then finally select PDA, Car-TV, Cellular Phone, MP3 player, TV, and PSP. Quasi experiment is done at several classes under instructor's allowance. After brief introduction, prior knowledge, awareness, and usage about DMB as well as other digital instruments is asked and their similarities and perceived characteristics are measured. And then, two kinds of manipulated color-printed AD are distributed and similarities and perceived characteristics for DMB are re-measured. Finally purchase intension, AD attitude, manipulation check, and demographic variables are asked. Subjects are given small gift for participation. Stimuli are color-printed advertising. Their actual size is A4 and made after several pre-test from AD professionals and students. As results, consumers are segmented into two subgroups based on their perceptions of DMB. Similarity measure between DMB and cellular phone and similarity measure between DMB and TV are used to classify consumers. If subject whose first measure is less than the second measure, she is classified into segment A and segment A is characterized as they perceive DMB like TV. Otherwise, they are classified as segment B, who perceives DMB like cellular phone. Discriminant analysis on these groups with their characteristics of usage and attitude shows that Segment A knows much about DMB and uses a lot of digital instrument. Segment B, who thinks DMB as cellular phone doesn't know well about DMB and not familiar with other digital instruments. So, consumers with higher knowledge perceive DMB similar to TV because launching DMB advertising lead consumer think DMB as TV. Consumers with less interest on digital products don't know well about DMB AD and then think DMB as cellular phone. In order to investigate perceptions of DMB as well as other digital instruments, we apply Proxscal analysis, Multidimensional Scaling technique at SPSS statistical package. At first step, subjects are presented 21 pairs of 7 digital instruments and evaluate similarity judgments on 7 point scale. And for each segment, their similarity judgments are averaged and similarity matrix is made. Secondly, Proxscal analysis of segment A and B are done. At third stage, get similarity judgment between DMB and other digital instruments after AD exposure. Lastly, similarity judgments of group A-1, A-2, B-1, and B-2 are named as 'after DMB' and put them into matrix made at the first stage. Then apply Proxscal analysis on these matrixes and check the positional difference of DMB and after DMB. The results show that map of segment A, who perceives DMB similar as TV, shows that DMB position closer to TV than to Cellular phone as expected. Map of segment B, who perceive DMB similar as cellular phone shows that DMB position closer to Cellular phone than to TV as expected. Stress value and R-square is acceptable. And, change results after stimuli, manipulated Advertising show that AD makes DMB perception bent toward Cellular phone when Cellular phone-like AD is exposed, and that DMB positioning move towards Car-TV which is more personalized one when TV-like AD is exposed. It is true for both segment, A and B, consistently. Furthermore, the paper apply correspondence analysis to the same data and find almost the same results. The paper answers two main research questions. The first one is that perception about a new product is made mainly from prior experience. And the second one is that AD is effective in changing and enforcing perception. In addition to above, we extend perception change to purchase intention. Purchase intention is high when AD enforces original perception. AD that shows DMB like TV makes worst intention. This paper has limitations and issues to be pursed in near future. Methodologically, current methodology can't provide statistical test on the perceptual change, since classical MDS models, like Proxscal and correspondence analysis are not probability models. So, a new probability MDS model for testing hypothesis about configuration needs to be developed. Next, advertising message needs to be developed more rigorously from theoretical and managerial perspective. Also experimental procedure could be improved for more realistic data collection. For example, web-based experiment and real product stimuli and multimedia presentation could be employed. Or, one can display products together in simulated shop. In addition, demand and social desirability threats of internal validity could influence on the results. In order to handle the threats, results of the model-intended advertising and other "pseudo" advertising could be compared. Furthermore, one can try various level of innovativeness in order to check whether it make any different results (cf. Moon 2006). In addition, if one can create hypothetical product that is really innovative and new for research, it helps to make a vacant impression status and then to study how to form impression in more rigorous way.

• Asia Marketing Journal
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• v.14 no.2
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• pp.65-96
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• 2012

Brand has received much attention from considerable marketing research. When consumers consume product or services, they are exposed to a lot of brand related stimuli. These contain brand personality, brand experience, brand identity, brand communications and so on. A special kind of new crisis occasionally confronting companies' brand management today is the brand related rumor. An important influence on consumers' purchase decision making is the word-of-mouth spread by other consumers and most decisions are influenced by other's recommendations. In light of this influence, firms have reasonable reason to study and understand consumer-to-consumer communication such as brand rumor. The importance of brand rumor to marketers is increasing as the number of internet user and SNS(social network service) site grows. Due to the development of internet technology, people can spread rumors without the limitation of time, space and place. However relatively few studies have been published in marketing journals and little is known about brand rumors in the marketplace. The study of rumor has a long history in all major social science. But very few studies have dealt with the antecedents and consequences of any kind of brand rumor. Rumor has been generally described as a story or statement in general circulation without proper confirmation or certainty as to fact. And it also can be defined as an unconfirmed proposition, passed along from people to people. Rosnow(1991) claimed that rumors were transmitted because people needed to explain ambiguous and uncertain events and talking about them reduced associated anxiety. Especially negative rumors are believed to have the potential to devastate a company's reputation and relations with customers. From the perspective of marketer, negative rumors are considered harmful and extremely difficult to control in general. It is becoming a threat to a company's sustainability and sometimes leads to negative brand image and loss of customers. Thus there is a growing concern that these negative rumors can damage brands' reputations and lead them to financial disaster too. In this study we aimed to distinguish antecedents of brand rumor transmission and investigate the effects of brand rumor characteristics on rumor spread intention. We also found key components in personal acceptance of brand rumor. In contextualist perspective, we tried to unify the traditional psychological and sociological views. In this unified research approach we defined brand rumor's characteristics based on five major variables that had been found to influence the process of rumor spread intention. The five factors of usefulness, source credibility, message credibility, worry, and vividness, encompass multi level elements of brand rumor. We also selected product involvement as a control variable. To perform the empirical research, imaginary Korean 'Kimch' brand and related contamination rumor was created and proposed. Questionnaires were collected from 178 Korean samples. Data were collected from college students who have been experienced the focal product. College students were regarded as good subjects because they have a tendency to express their opinions in detail. PLS(partial least square) method was adopted to analyze the relations between variables in the equation model. The most widely adopted causal modeling method is LISREL. However it is poorly suited to deal with relatively small data samples and can yield not proper solutions in some cases. PLS has been developed to avoid some of these limitations and provide more reliable results. To test the reliability using SPSS 16 s/w, Cronbach alpha was examined and all the values were appropriate showing alpha values between .802 and .953. Subsequently, confirmatory factor analysis was conducted successfully. And structural equation modeling has been used to analyze the research model using smartPLS(ver. 2.0) s/w. Overall, R² of adoption of rumor is .476 and R² of intention of rumor transmission is .218. The overall model showed a satisfactory fit. The empirical results can be summarized as follows. According to the results, the variables of brand rumor characteristic such as source credibility, message credibility, worry, and vividness affect argument strength of rumor. And argument strength of rumor also affects rumor intention. On the other hand, the relationship between perceived usefulness and argument strength of rumor is not significant. The moderating effect of product involvement on the relations between argument strength of rumor and rumor W.O.M intention is not supported neither. Consequently this study suggests some managerial and academic implications. We consider some implications for corporate crisis management planning, PR and brand management. This results show marketers that rumor is a critical factor for managing strong brand assets. Also for researchers, brand rumor should become an important thesis of their interests to understand the relationship between consumer and brand. Recently many brand managers and marketers have focused on the short-term view. They just focused on strengthen the positive brand image. According to this study we suggested that effective brand management requires managing negative brand rumors with a long-term view of marketing decisions.

• Asia pacific journal of information systems
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• v.22 no.4
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• pp.101-123
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• 2012

Currently, Social Media, it has widely a renown keyword and its related social trends and businesses have been fastly applied into various contexts. Social media has become an important research area for scholars interested in online technologies and cyber space and their social impacts. Social media is not only including web-based services but also mobile-based application services that allow people to share various style information and knowledge through online connection. Social media users have tendency to common identity- and bond-attachment through interactions such as 'thumbs up', 'reply note', 'forwarding', which may have driven from various factors and may result in delivering information, sharing knowledge, and specific experiences et al. Even further, almost of all social media sites provide and connect unknown strangers depending on shared interests, political views, or enjoyable activities, and other stuffs incorporating the creation of contents, which provides benefits to users. As fast developing digital devices including smartphone, tablet PC, internet based blogging, and photo and video clips, scholars desperately have began to study regarding diverse issues connecting human beings' motivations and the behavioral results which may be articulated by the format of antecedents as well as consequences related to contents that people create via social media. Social media such as Facebook, Twitter, or Cyworld users are more and more getting close each other and build up their relationships by a different style. In this sense, people use social media as tools for maintain pre-existing network, creating new people socially, and at the same time, explicitly find some business opportunities using personal and unlimited public networks. In terms of theory in explaining this phenomenon, social capital is a concept that describes the benefits one receives from one's relationship with others. Thereby, social media use is closely related to the form and connected of people, which is a bridge that can be able to achieve informational benefits of a heterogeneous network of people and common identity- and bonding-attachment which emphasizes emotional benefits from community members or friend group. Social capital would be resources accumulated through the relationships among people, which can be considered as an investment in social relations with expected returns and may achieve benefits from the greater access to and use of resources embedded in social networks. Social media using for their social capital has vastly been adopted in a cyber world, however, there has been little explaining the phenomenon theoretically how people may take advantages or opportunities through interaction among people, why people may interactively give willingness to help or their answers. The individual consciously express themselves in an online space, so called, common identity- or bonding-attachments. Common-identity attachment is the focus of the weak ties, which are loose connections between individuals who may provide useful information or new perspectives for one another but typically not emotional support, whereas common-bonding attachment is explained that between individuals in tightly-knit, emotionally close relationship such as family and close friends. The common identify- and bonding-attachment are mainly studying on-offline setting, which individual convey an impression to others that are expressed to own interest to others. Thus, individuals expect to meet other people and are trying to behave self-presentation engaging in opposite partners accordingly. As developing social media, individuals are motivated to disclose self-disclosures of open and honest using diverse cues such as verbal and nonverbal and pictorial and video files to their friends as well as passing strangers. Social media context, common identity- and bond-attachment for self-presentation seems different compared with face-to-face context. In the realm of social media, social users look for self-impression by posting text messages, pictures, video files. Under the digital environments, people interact to work, shop, learn, entertain, and be played. Social media provides increasingly the kinds of intention and behavior in online. Typically, identity and bond social capital through self-presentation is the intentional and tangible component of identity. At social media, people try to engage in others via a desired impression, which can maintain through performing coherent and complementary communications including displaying signs, symbols, brands made of digital stuffs(information, interest, pictures, etc,). In marketing area, consumers traditionally show common-identity as they select clothes, hairstyles, automobiles, logos, and so on, to impress others in any given context in a shopping mall or opera. To examine these social capital and attachment, we combined a social capital theory with an attachment theory into our research model. Our research model focuses on the common identity- and bond-attachment how they are formulated through social capitals: cognitive capital, structural capital, relational capital, and individual characteristics. Thus, we examined that individual online kindness, self-rated expertise, and social relation influence to build common identity- and bond-attachment, and the attachment effects make an impact on both the willingness to help, however, common bond seems not to show directly impact on information sharing. As a result, we discover that the social capital and attachment theories are mainly applicable to the context of social media and usage in the individual networks. We collected sample data of 256 who are using social media such as Facebook, Twitter, and Cyworld and analyzed the suggested hypotheses through the Structural Equation Model by AMOS. This study analyzes the direct and indirect relationship between the social network service usage and outcomes. Antecedents of kindness, confidence of knowledge, social relations are significantly affected to the mediators common identity-and bond attachments, however, interestingly, network externality does not impact, which we assumed that a size of network was a negative because group members would not significantly contribute if the members do not intend to actively interact with each other. The mediating variables had a positive effect on toward willingness to help. Further, common identity attachment has stronger significant on shared information.

• Journal of Distribution Research
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• v.17 no.1
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• pp.87-115
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• 2012

Introduction: In these days, a loyalty program is one of the most common marketing mechanisms (Lacey & Sneath, 2006; Nues & Dreze, 2006; Uncles et al., 20003). In recent years, Coalition Loyalty Program is more noticeable as one of progressed forms. In the past, loyalty program was operating independently by single product brand or single retail channel brand. Now, companies using Coalition Loyalty Program share their programs as one single service and companies to participate to this program continue to have benefits from their existing program as well as positive spillover effect from the other participating network companies. Instead of consumers to earn or spend points from single retail channel or brand, consumers will have more opportunities to utilize their points and be able to purchase other participating companies products. Issues that are related to form of loyalty programs are essentially connected with consumers' perceived view on convenience of using its program. This can be a problem for distribution companies' strategic marketing plan. Although Coalition Loyalty Program is popular corporate marketing strategy to most companies, only few researches have been published. However, compared to independent loyalty program, coalition loyalty program operated by third parties of partnership has following conditions: Companies cannot autonomously modify structures of program for individual companies' benefits, and there is no guarantee to operate and to participate its program continuously by signing a contract. Thus, it is important to conduct the study on how coalition loyalty program affects companies' success and its process as much as conducting the study on effects of independent program. This study will complement the lack of coalition loyalty program study. The purpose of this study is to find out how consumer loyalty affects affiliated brands, its cause and mechanism. The past study about loyalty program only provided the variation of performance analysis, but this study will specifically focus on causes of results. In order to do these, this study is designed and to verify three primary objects as following; First, based on opinions of Switching Barriers (Fornell, 1992; Ping, 1993; Jones, et at., 2000) about causes of loyalty of coalition brand, 'brand attractiveness' and 'brand switching cost' are antecedents and causes of change in 'brand loyalty' will be investigated. Second, influence of consumers' perception and attitude prior to joining coalition loyalty program, influence of program in retail brands, brand attractiveness and spillover effect of switching cost after joining coalition program will be verified. Finally, the study will apply 'prior brand preference' as a variable and will provide a relationship between effects of coalition loyalty program and prior preference level. Hypothesis Hypothesis 1. After joining coalition loyalty program, more preferred brand (compared to less preferred brand) will increase influence on brand attractiveness to brand loyalty. Hypothesis 2. After joining coalition loyalty program, less preferred brand (compared to more preferred brand) will increase influence on brand switching cost to brand loyalty. Hypothesis 3. (1)Brand attractiveness and (2)brand switching cost of more preferred brand (before joining the coalition loyalty program) will influence more positive effects from (1)program attractiveness and (2)program switching cost of coalition loyalty program (after joining) than less preferred brand. Hypothesis 4. After joining coalition loyalty program, (1)brand attractiveness and (2)brand switching cost of more preferred brand will receive more positive impacts from (1)program attractiveness and (2)program switching cost of coalition loyalty program than less preferred brand. Hypothesis 5. After joining coalition loyalty program, (1)brand attractiveness and (2)brand switching cost of more preferred brand will receive less impacts from (1)brand attractiveness and (2)brand switching cost of different brands (having different preference level), which joined simultaneously, than less preferred brand. Method : In order to validate hypotheses, this study will apply experimental method throughout virtual scenario of coalition loyalty program if consumers have used or available for the actual brands. The experiment is conducted twice to participants. In a first experiment, the study will provide six coalition brands which are already selected based on prior research. The survey asked each brand attractiveness, switching cost, and loyalty after they choose high preference brand and low preference brand. One hour break was provided prior to the second experiment. In a second experiment, virtual coalition loyalty program "SaveBag" was introduced to participants. Participants were informed that "SaveBag" will be new alliance with six coalition brands from the first experiment. Brand attractiveness and switching cost about coalition program were measured and brand attractiveness and switching cost of high preference brand and low preference brand were measured as same method of first experiment. Limitation and future research This study shows limitations of effects of coalition loyalty program by using virtual scenario instead of actual research. Thus, future study should compare and analyze CLP panel data to provide more in-depth information. In addition, this study only proved the effectiveness of coalition loyalty program. However, there are two types of loyalty program, which are Single and Coalition, and success of coalition loyalty program will be dependent on market brand power and prior customer attitude. Therefore, it will be interesting to compare effects of two programs in the future.

• Cartoon and Animation Studies
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• s.33
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• pp.597-616
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• 2013

Character industry is not only knowledge-based industry chiefly with copyrights but also motive power for creative economy to take a role functionally over the fields of industries because it has industrial characteristic as complement product to promote sale value in manufacturing industry and service industry and increase profit on sales. Since 2003, the national policy related to character has aimed to maximize effect among connected industries, extend its business abroad, enforce copyrights through the improvement of marketing system, develop industrial infrastructure through raising quality of character products. With the result of this policy, the successful cases of connected contents have been crystallized and domestic character industry has stepped up methodically since 2007. It is needed to reset the scales of character industry and industrial stats because there are more know-how of self industry promotion and more related characters through strategy of market departmentalization starting with cartoon, animation, games, novels, movies and musicals. Especially, The Korea government set our target for 'Global Top Five Character Power' since 2009 and has started to carry out to find global star characters, support to establish network among connected industries, diversify promotion channels, and develop licensing business. Particularly, since 2013, There have been prospered the indoor character theme park with time management just like character experimental marketing or Kids cafes using characters, the demand market of digital character focusing on SNS emoticon, and the performance market for character musical consistently. Moreover, The domestic and foreign illegal black markets on off-line have been enlarged, so we need another policy alternative. To prepare for the era of exploding character demand market and diversifying platform, it is needed to set up a solid strategy that is required the elements of policy changes in character industry to vitalize character industry and support new character design and connected contents. the following shows that the elements of policy changes related to the existing policy, the current position of market. Nowadays, the elements of policy changes in domestic character industry are that variety of consumers in the digital character market according to platform diversification, Convergence contents of character goods for the Korean waves, legalization of the illegal black contents market, and controling the tendency of consumers in departmentalized market. This can help find the policy issue entirely deferent with the existing character powers like US, Japan or Europe. In its final analysis, the alternatives are the promotion of models with contract copyrights of domestic and foreign connected contents, the diversification of profit models of platform economy, the additive development of target market related to enlarging the Korean waves, and the strategy of character market for the age-specific tendency according to developing character demand market.

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

• Science of Emotion and Sensibility
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• v.15 no.2
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• pp.283-296
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• 2012

The goal of this study is to determine the social emotion model as an emotion sharing relationship and information sharing relationship based on the user's relations at social networking services. 26 social emotions were extracted by verification of compliance among 92 different emotions collected from the literature survey. The survey on the 26 emotion words was verified to the similarity of social relation types to the Likert 7-points scale. The principal component analysis of the survey data determined 12 representative social emotions in the emotion sharing relation and 13 representative social emotions in the information sharing relation. Multidimensional scaling developed the two-dimensional social emotion model of emotion sharing relation and of information sharing relation based on online communication environment. Meanwhile, insignificant factors in the suggest social emotion models were removed by the structural equation modeling analysis, statistically. The test result of validity analysis demonstrated the fitness of social emotion models at emotion sharing relationships (CFI: .887, TLI: .885, RMSEA: .094), social emotion model of information sharing relationships (CFI: .917, TLI: .900, RMSEA : 0.050). In conclusion, this study presents two different social emotion models based on two different relation types. The findings of this study will provide not only a reference of evaluating social emotions in designing social networking services but also a direction of improving social emotions.