• Journal of Global Scholars of Marketing Science
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• v.13
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• pp.89-111
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• 2004

The authors address the effectiveness of the composite brand extension In the context of international brand alliance. In composite brand extension, four combinations of 4 domestic brands and one internationally well-known brand as header and modifier brands are used as the brand names for four experimental products. The results of analyses reveal that (1) degrees of brand attribute transferability between header and composite brands, and (2) the impact of header and modifier brands on a composite brand appear to be decreasing when the distance of product categories between header and modifier brands are farther. In addition, the authors demonstrate that the fitness between constituent brands and composite brands tends to have more influences on consumers' evaluation of a composite brand than does the complementarity when the distance of product categories between header and modifier brands are farther. Some implications and future research directions are also discussed.

• Asia Marketing Journal
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• v.17 no.1
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• pp.97-121
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• 2015

In this research, we examine whether and why temporal distance influences evaluations of two different types of brand extensions: concept-based extensions, defined as extensions primarily based on the importance or relevance of brand concepts to extension products; and similarity-based extensions, defined as extensions primarily based on the amount of feature similarity at the product-category level. In Study 1, we test the hypothesis that concept-based extensions are evaluated more favorably when they are framed to launch in the distant rather than in the near future, whereas similaritybased extensions are evaluated more favorably when they are framed to launch in the near rather than in the distant future. In Study 2, we confirm that this time-dependent differential evaluation is driven by the difference in construal level between the bases of the two types of extensions - i.e., brand-concept consistency and product-category feature similarity. As such, we find that conceptbased extensions are evaluated more favorably under the abstract than concrete mindset, whereas similarity-based extensions are evaluated more favorably under the concrete than abstract mindset. In Study 3, we extend to the case for a broad brand (i.e., brands that market products across multiple categories), finding that making accessible a specific product category of a broad parent brand influences evaluations of near-future, but not distant-future, brand extensions. Combined together, our findings suggest that temporal distance influences brand extension evaluation through its effect on the importance placed on brand concepts and feature similarity. That is, consumers rely on different bases to evaluate brand extensions, depending on their perception of when the extensions take place and on under what mindset they are placed. This research makes theoretical contributions to the brand extension research by identifying one important determinant to brand extension evaluation and also uncovering its underlying dynamics. It also contributes to expanding the scope of the construal level theory by putting forth a novel interpretation of two bases of perceived fit in terms of construal level. Marketers who are about to launch and advertise brand extensions may benefit by considering temporal-distance information in determining what content to deliver about extensions in their communication efforts. Conceptual relation of a parent brand to extensions needs to be emphasized in the distant future, whereas feature similarity should be highlighted in the near future.

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 2007.11a
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• pp.187-192
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• 2007

A project is being implemented to develop the long distance energy transport technology using the chemical reactions. This project can be classified into three main research categories covering heat recovery reaction, long distance energy transport, and heat generation reaction. In this study, the methanol is selected as a system material since it shows several unique superior characteristics as follows: gaseous state of reactant and product, large heat of reaction, high yields of reaction at relatively low temperature, and also steady and economical supply. Furthermore, it is anticipated that the outcomes of this study can be widely applied to the related industries. A feasibility study was carried out to evaluate the economics of this technology which study was based on the following case: 10,000 households, 15km distance energy transportation, utilization of waste heat from power plant.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.3
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• pp.37-49
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• 2015

To develop the contents making profit, we propose the program operation methods that can apply the needs of students and the demand of the times, and consider the capacity of the operating agency overall. First, we suggest the distance training motivation, the effective content type, appropriate interaction ratio, the effective teaching and learning methods and the assessment methods. Second, we suggest the development strategy of educational contents, assess quantitative the demand of students and the will of teacher overall, measure the potential success. Third by applying the strategies in the educational field, we product the 12 major development field. These fields are divided into categories A and B, category A is the very high success field and category B is the high potential success field. By applying the proposed strategy, You will select the most suitable contents here and now.

• Asia-Pacific Journal of Business
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• v.2 no.1
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• pp.49-73
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• 2011

With the market evaluation of economic globalization exchanges between different cultures, cultural trade has been developing at an accelerated speed, and also playing an important role in East Asian intra-regional trade. In this research the author used gravity trade model to explain the causal relationship between dependent variable trade flows and several independent variables applying with five categories cultural goods which classified in HS codes. Firstly for cultural heritage trade flow, the results indicated that economic masses of bilateral countries have no significant influences on it; GDP per capita of host country and adjacency factor with partner country have significant negative influences on it; Internet coverage ratio has improved cultural heritages exchanges in East Asian regions. Secondly for printed matter cultural goods trade flow, the distance factor has significant negative influence but common language has significant positive influence on it. Thirdly for recorded media cultural goods, only economic masses and GDP per capita of bilateral countries can improved their trade flows. Fourthly for visual arts cultural products trade flows, almost all variables we tested have significant influences on it. Fifthly for cinema photography cultural goods trade flow, the influenced factor are same with cultural heritage products except they have strong positive interaction relationship with economic masses and common language. At last, the paper figured out some important and potential sectors for cultural goods trade in East Asia and gave some suggestions to government and cultural goods product enterprises.

• Journal of Korean Classical Literature and Education
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• no.35
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• pp.5-46
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• 2017

This study aims to investigate the context of materials in literature textbooks and the awareness about the educational value of classical literature, as part of an interest in literature education phenomena. This study accepts the premise that textbooks affect the practice of classical literature education and, in particular, materials in textbooks are chosen according to the intentions, demands, and perspectives of education in specific social conditions. I divided the educational value of classical literature into two categories, classical and literary value, and investigated the actual conditions and context of materials of literature textbooks based on the 2009 revised curriculum and the 2011 revised curriculum. Classical literature is generally alienated and excluded; contemporary literature materials are mostly included and organized in the domains of 'the role of literature', 'reception and production of literature' and 'literature and life.' In addition, the tendency to heighten classical value and diminish literary value is deepening. In order to solve the problem that classical literature is only included as the product of the past, changes must be made not just to the curriculum, which are external changes, but to the awareness of the essence of classical literature, which are internal changes. Above all, generality as 'literature' and the sense of distance about space and time as 'classic' should be connected to various relationships which respond to problematic situations and the demands of learners. Based on the relationships, we can expect a rich diversity of contexts and aspects of included classical literature. In addition, an extension of the width and scope of included classical literature is anticipated. The reduction of workload, the advent of the concept of capability and the dissolution of traditional literature concepts are the changes of external environment, which is continuously requiring renewed investigation into classical literature beyond simple appropriateness.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.17-35
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• 2015

Demand forecasting is the activity of estimating the quantity of a product or service that consumers will purchase for a certain period of time. Developing precise forecasting models are considered important since corporates can make strategic decisions on new markets based on future demand estimated by the models. Many studies have developed market growth curve models, such as Bass, Logistic, Gompertz models, which estimate future demand when a market is in its early stage. Among the models, Bass model, which explains the demand from two types of adopters, innovators and imitators, has been widely used in forecasting. Such models require sufficient demand observations to ensure qualified results. In the beginning of a new market, however, observations are not sufficient for the models to precisely estimate the market's future demand. For this reason, as an alternative, demands guessed from those of most adjacent markets are often used as references in such cases. Reference markets can be those whose products are developed with the same categorical technologies. A market's demand may be expected to have the similar pattern with that of a reference market in case the adoption pattern of a product in the market is determined mainly by the technology related to the product. However, such processes may not always ensure pleasing results because the similarity between markets depends on intuition and/or experience. There are two major drawbacks that human experts cannot effectively handle in this approach. One is the abundance of candidate reference markets to consider, and the other is the difficulty in calculating the similarity between markets. First, there can be too many markets to consider in selecting reference markets. Mostly, markets in the same category in an industrial hierarchy can be reference markets because they are usually based on the similar technologies. However, markets can be classified into different categories even if they are based on the same generic technologies. Therefore, markets in other categories also need to be considered as potential candidates. Next, even domain experts cannot consistently calculate the similarity between markets with their own qualitative standards. The inconsistency implies missing adjacent reference markets, which may lead to the imprecise estimation of future demand. Even though there are no missing reference markets, the new market's parameters can be hardly estimated from the reference markets without quantitative standards. For this reason, this study proposes a case-based expert system that helps experts overcome the drawbacks in discovering referential markets. First, this study proposes the use of Euclidean distance measure to calculate the similarity between markets. Based on their similarities, markets are grouped into clusters. Then, missing markets with the characteristics of the cluster are searched for. Potential candidate reference markets are extracted and recommended to users. After the iteration of these steps, definite reference markets are determined according to the user's selection among those candidates. Then, finally, the new market's parameters are estimated from the reference markets. For this procedure, two techniques are used in the model. One is clustering data mining technique, and the other content-based filtering of recommender systems. The proposed system implemented with those techniques can determine the most adjacent markets based on whether a user accepts candidate markets. Experiments were conducted to validate the usefulness of the system with five ICT experts involved. In the experiments, the experts were given the list of 16 ICT markets whose parameters to be estimated. For each of the markets, the experts estimated its parameters of growth curve models with intuition at first, and then with the system. The comparison of the experiments results show that the estimated parameters are closer when they use the system in comparison with the results when they guessed them without the system.

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

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

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.