• Asia Marketing Journal
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• v.15 no.4
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• pp.103-129
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• 2014

Differentiated positioning becomes increasingly difficult when brand salience weakens. Also, the daily increase in new media use and information load has led to a social climate that regards advertising stimuli as spamming. For these reasons, the focus of advertisement-related communication is shifting from persuading consumers through the direct delivery of information to an emphasis on appealing to their emotions using matching stimuli to enhance persuasion effects. Recently, both academia and industry have increasingly shown an interest in storytelling methods that can generate positive emotional responses and attitude changes by arousing consumers' narrative processing. The purpose of storytelling is to elicit consumers' emotional experience to meet the objectives of advertisement producers. Therefore, the most important requirement for storytelling in advertising is that it evokes consumers' sympathy for the main character in the advertisement. This does not involve advertisements directly persuading consumers, but rather, consumers themselves finding an answer through the advertisement's story. Thus, consumers have an indirect experience regarding the product features and usage through empathy with the advertisement's main character. In this study, we took the results of a precedent study as the starting point, according to which consumers' emotional response can be altered depending on the storytelling methods adopted for storytelling ads. Previous studies have reported that drama-type and vignette-type storytelling methods have a considerably different impact on the emotional responses of advertising audiences, due to their different structural characteristics. Thus, this study aims to verify that emotional response aroused by different types of advertisement storytelling (drama ads vs. vignette ads) can be controlled by the socio-psychological gender difference of advertising audiences and that the interaction effects between the socio-psychological gender differences of the audience and the gender stereotype of emotions to which advertisements appeal can exert an influence on emotional responses to types of storytelling in advertising. To achieve this, an experiment was conducted employing a between-group design consisting of 2 (storytelling type: drama ads vs. vignette ads) × 2 (socio-psychological gender of the audience: masculinity vs. femininity) × 2 (advertising appeal emotion type: male stereotype emotion vs. female stereotype emotion). The experiment revealed that the femininity group displayed a strong and consistent empathy for drama ads regardless of whether the ads appealed to masculine or feminine emotions, whereas the masculinity group displayed a stronger empathy for drama ads appealing to the emotional types matching its own gender as well as for vignette ads. The theoretical contribution of this study is significant in that it sheds light on the controllability of the audiences' emotional responses to advertisement storytelling depending on their socio-psychological gender and gender stereotype of emotions appealed to through advertising. Specifically, its considerable practical contribution consists in easing unnecessary creative constraints by comprehensively analyzing essential advertising strategic factors such as the target consumers' gender and the objective of the advertisement, in contrast to the oversimplified view of previous studies that considered emotional responses to storytelling ads were determined by the different types of production techniques used. This study revealed that emotional response to advertisement storytelling varies depending on the target gender of and emotion type appealed to by the advertisement. This suggests that an understanding of the targeted gender is necessary prior to producing an advertisement and that in deciding on an advertisement storytelling type, strategic attention should be directed to the advertisement's appeal concept or emotion type. Thus, it is safe to use drama-type storytelling that expresses masculine emotions (ex. fun, happy, encouraged) when the advertisement target, like Bacchus, includes both men and women. For brands and advertisements targeting only women (ex. female clothes), it is more effective to use a drama-type storytelling method that expresses feminine emotions (lovely, romantic, sad). The drama method can be still more effective than the vignette when women are the main target and a masculine concept-based creative is to be produced. However, when male consumers are targeted and the brand concept or advertisement concept is focused on feminine emotions (ex. romantic), vignette ads can more effectively induce empathy than drama ads.

• Journal of Nutrition and Health
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• v.56 no.6
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• pp.602-614
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• 2023

Purpose: The balance between synthesis and degradation of proteins plays a critical role in the maintenance of skeletal muscle mass. Mitochondrial dysfunction has been closely associated with skeletal muscle atrophy caused by aging, cancer, and chemotherapy. Polygalacin D is a saponin derivative isolated from Platycodon grandiflorum (Jacq.) A. DC. This study aimed to investigate the effects of polygalacin D on myoblast differentiation and muscle atrophy in association with mitochondrial function in in vitro and in zebrafish models in vivo. Methods: C2C12 myoblasts were cultured in differentiation media containing different concentrations of polygalacin D, followed by the immunostaining of the myotubes with myosin heavy chain (MHC). The mRNA expression of markers related to myogenesis, muscle atrophy, and mitochondrial function was determined by real-time quantitative reverse transcription polymerase chain reaction. Wild type AB^* zebrafish (Danio rerio) embryos were treated with 5-fluorouracil, leucovorin, and irinotecan (FOLFIRI) with or without polygalacin D, and immunostained to detect slow and fast types of muscle fibers. The Tg(Xla.Eef1a1:mitoEGFP) zebrafish expressing mitochondria-targeted green fluorescent protein was used to monitor mitochondrial morphology. Results: The exposure of C2C12 myotubes to 0.1 ng/mL of polygalacin D increased the formation of MHC-positive multinucleated myotubes (≥ 8 nuclei) compared with the control. Polygalacin D significantly increased the expression of MHC isoforms (Myh1, Myh2, Myh4, and Myh7) involved in myoblast differentiation while it decreased the expression of atrophic markers including muscle RING-finger protein-1 (MuRF1), mothers against decapentaplegic homolog (Smad)2, and Smad3. In addition, polygalacin D promoted peroxisome proliferator-activated receptor-gamma coactivator (Pgc1α) expression and reduced the level of mitochondrial fission regulators such as dynamin-1-like protein (Drp1) and mitochondrial fission 1 (Fis1). In a zebrafish model of FOLFIRI-induced muscle atrophy, polygalacin D improved not only mitochondrial dysfunction but also slow and fast muscle fiber atrophy. Conclusion: These results demonstrated that polygalacin D promotes myogenesis and alleviates chemotherapy-induced muscle atrophy by improving mitochondrial function. Thus, polygalacin D could be useful as nutrition support to prevent and ameliorate muscle wasting and weakness.

• Journal of the Korean Society of Radiology
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• v.81 no.1
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• pp.166-175
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• 2020

Purpose This study aimed to compare the image quality and adverse events between Iopamidol 250 and Ioversol 320 usage during transcatheter arterial chemoembolization (TACE) for hepatocellular carcinoma (HCC). Materials and Methods Medical records and hepatic angiography from 113 patients who underwent TACE with Iopamidol 250 (44 patients) and Ioversol 320 (69 patients) were retrospectively reviewed. Vessel perception on hepatic angiography was graded into three categories by two radiologists for hepatic subsegmental arteries, the right gastroepiploic artery, right gastric artery, and pancreaticoduodenal artery. Imaging concordance was assessed by comparing the number of detected HCCs on hepatic angiography and CT. The adverse events before and after hepatic angiography were evaluated. Results The mean vessel perception scores were 2.92 and 2.94 for Iopamidol 250 and Ioversol 320, respectively. The imaging concordance was 31 (70.5%) and 46 (66.7%) patients for Iopamidol 250 and Ioversol 320, respectively. There were no statistical differences in vessel perception or imaging concordance (p > 0.05). One and six patients experienced nausea for Iopamidol 250 and Ioversol 320, respectively. There was no statistical difference in adverse events (p = 0.24). Conclusion Iopamidol 250 can be used in hepatic angiography for TACE without significant difference in image quality or occurrence of adverse events from Ioversol 320.

• Journal of Ginseng Culture
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• v.6
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• pp.35-50
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• 2024

"Culture refers to the behavioral and lifestyle patterns that a society has shared and transmitted within the community over a long period. Ginseng, frequently encountered in the daily life of Koreans through tools, crafts, folklore, and poetry, holds a deep place in the behavioral and lifestyle patterns of the Korean people. Ginseng, engraved in everyday objects, crafts, and poems, is symbolic in our culture as a representation of longevity and well-being. Ginseng elegantly depicted on ceramics serves as a symbol of longevity along with aesthetic beauty. The common inclusion of ginseng in ritual items in mountain deity beliefs, particularly represented by the 'Bullocho' (不老草) ginseng, reflects a strong belief in the mystical qualities of ginseng associated with longevity and prosperity. The incorporation of ginseng into commonly used everyday tools such as rice cakes, dining tables, decorations, matches, and fans suggests that ginseng was considered a talisman symbolizing health and longevity, kept close as a wish for good fortune. Rice cakes, often presented at ceremonies like ancestral rites, 60th-anniversary celebrations, weddings, and birthdays, had ginseng patterns carved into them as a way for our ancestors to inscribe the spirit and health-symbolizing ginseng onto the food. In family communities, ginseng patterns are frequently found on utensils related to eating, such as chopsticks, spoons, tea cups, and trays. Among the various folklore related to ginseng being passed down, the most prevalent are anecdotes illustrating its efficacy. Ginseng, gifted and exchanged as a symbol of gratitude in letters and poems, goes beyond being a mere medicinal herb to embody friendship and blessings. The symbolism of ginseng, as revealed in everyday objects, artworks, poems, and letters, can be summarized as follows: 1. In folklore and legends, ginseng symbolized filial piety offered to parents. 2. It represented gratitude sent to respected teachers and close friends. 3. Ginseng depicted on daily objects and artworks not only showcased aesthetics but also played a magical role in symbolizing longevity and well-being. Ginseng patterns on items like rice cake molds and dining tables embody the spirit of a caring community, wishing for longevity and prosperity."

• Journal of Distribution Research
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• v.16 no.1
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• pp.141-177
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• 2011

I. Research Objectives: Most of the previous studies on diffusion have concentrated on efficacy of WOM communication with the use of variables at individual level (Iacobucci 1996; Midgley et al. 1992). However, there is a paucity of studies which investigated network's structural properties as antecedents of WOM from the perspective of consumers' sociocultural propensities. Against this research backbone, this study attempted to link the network's structural properties and consumer' WOM behavior on cross-national basis. The major research objective of this study was to examine the relationship between network properties and WOM by comparing Korean and Chinese consumers. Specific objectives of this research are threefold; firstly, it sought to examine whether network properties (i.e., tie strength, centrality, range) affect WOM (WOM intention and quality of WOM). Secondly, it aimed to explore the moderating effects of cutural orientation (uncertainty avoidance and individuality) on the relationship between network properties and WOM. Thirdly, it substantiates the role of innovativeness as antecedents to both network properties and WOM. II. Research Hypotheses: Based on the above research objectives, the study put forth the following research hypotheses to validate. ${\cdot}$ H 1-1 : The Strength of tie between two counterparts within network will positively influence WOM effectivenes ${\cdot}$ H 1-2 : The network centrality will positively influence the WOM effectiveness ${\cdot}$ H 1-3 : The network range will positively influence the WOM effectiveness ${\cdot}$ H 2-1 : The consumer's uncertainty avoidance tendency will moderate the relationship between network properties and WOM effectiveness ${\cdot}$ H 2-2 : The consumer's individualism tendency will moderate the relationship between network properties and WOM effectiveness ${\cdot}$ H 3-1 : The consumer's innovativeness will positively influence the social network properties ${\cdot}$ H 3-2 : The consumer's innovativeness will positively influence WOM effectiveness III. Methodology: Through a pilot study and back-translation, two versions of questionnaire were prepared, one in Korean and the other in Chinese. The chinese data were collected from the chinese students enrolled in language schools in Suwon city in Korea, while Korean data were collected from students taking classes in a major university in Seoul. A total of 277 questionnaire were used for analysis of Korean data and 212 for Chinese data. The reason why Chinese students living in Korea rather than in China were selected was based on two factors: one was to neutralize the differences (ie, retail channel availability) that may arise from living in separate countries and the second was to minimize the difference in communication venues such as internet accessibility and cell phone usability. SPSS 12.0 and AMOS 7.0 were used for analysis. IV. Results: Prior to hypothesis verification, mean differences between the two countries in terms of major constructs were performed with the following result; As for network properties (tie strength, centrality and range), Koreans showed higher scores in all three constructs. For cultural orientation traits, Koreans scored higher only on uncertainty avoidance trait than Chinese. As a result of verifying the first research objective, confirming the relationship between network properties and WOM effectiveness, on Korean side, tie strength(Beta=.116; t=1.785) and centrality (Beta=.499; t=6.776) significantly influenced on WOM intention, and similar finding was obtained for Chinese side, with tie strength (Beta=.246; t=3.544) and centrality (Beta=.247; t=3.538) being significant. However, with regard to WOM argument quality, Korean data yielded only centrality (Beta=.82; t=7.600) having a significant impact on WOM, whereas China showed both tie strength(Beat=.142; t=2.052) and centrality(Beta=.348; t=5.031) being influential. To answer for the second research objective addressing the moderating role of cultural orientation, moderated regression anaylsis was performed and the result showed that uncertainty avoidance moderated between network range and WOM intention for both Korea and China, But for Korea, the uncertainty avoidance moderated between tie strength and WOM quality, while for China it moderated between network range and WOM intention. And innovativeness moderated between tie strength and WOM intention for Korea but it moderated between network range and WOM intention for China. As a result of analysing for third research objective, we found that for Korea, innovativeness positively influenced centrality only (Beta=.546; t=10.808), while for China it influenced both tie strength (Beta=.203; t=2.998) and centrality(Beta=.518; t=8.782). But for both countries alike, the innovativeness influenced positively on WOM (WOM intention and WOM quality). V. Implications: The study yields the two practical implications. Firstly, the result suggests that companies targeting multinational customers need to identify segments which are susceptible to the positive WOM and WOM information based on individual traits such as uncertainty avoidance and individualism and based on that, develop marketing communication strategy. Secondly, the companies need to divide the market on Roger's five innovation stages and based on this information, enforce marketing strategy which utilizes social networking tools such as public media and WOM. For instance, innovator and early adopters, if provided with new product information, will be able to capitalize upon the network advantages and thus add informational value to network operations using SNS or corporate blog.

• Journal of Global Scholars of Marketing Science
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• v.18 no.1
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• pp.59-80
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• 2008

Recently, e-sports are growing with potentiality as a new industry with conspicuous profit model. But studies that dealing with e-sports are not enough. Hence, proposes of this paper are both to establish basic model that is for the design of e-sport marketing strategy and to contribute toward future studies which are related to e-sports. Recently, the researches to explain sports-sponsorship through the identification theory have been discovered. Many researches say that somewhat proper identification is a requirement for most sponsors to improve the their images which is essential to sponsorship activity. Consequently, the research for sponsorship associated with identification in the e-sports, not in the physical sports is the core sector of this study. We extracted the variables from online's major characteristics and existing sport sponsorship researches. First, because e-sports mean the tournaments or leagues in the use of online game, the main event of the game is likely to call it online game. Online media's attributes are distinguished from those of offline. Especially, interactivity, anonymity, and expandibility as a e-sport game attributes are able to be mentioned. So, these inherent online attributes are examined on the relationship with flow. Second, in physical sports games, Fisher(1998) revealed that team similarity and team attractivity were positively related to team identification. Wann(1996) said that the result of former game influenced the evaluation of the next game, then in turn has an effect on the identification of team supporters. Considering these results in the e-sports side, e-sports gamer' attractivity, similarity, and match result seem to be important precedent variables of the identification with a gamer. So, these e-sport gamer attributes are examined on the relationship with both flow and identification with a gamer. Csikszentmihalyi(1988) defined the term flow as feeling status for him to be making current positive experience optimally. Hoffman and Novak(1996) also said that if a user experienced the flow he would visit a website without any reward. Therefore flow might be positively associated with user's identification with a gamer. And, Swanson(2003) disclosed that team identification influenced the positive results of sponsorship, which included attitude toward sponsors, sponsor patronage, and satisfaction with sponsors. That is, identification with a gamer expect to be connected with corporation identification significantly. According to the above, we can design the following research model. All variables used in this study(interactivity, anonymity, expandibility, attractivity, similarity, match result, flow, identification with a gamer, and identification with a sponsor) definitely were defined operationally underlying precedent researches. Sample collection was carried out to the person who has an experience to have enjoyed e-sports during June 2006. Much portion of samples is men because much more men than women enjoy e-sports in general. Two-step approach was used to test the hypotheses. First, confirmatory factor analysis was committed to guarantee the validity and reliability of variables. The results showed that all variables had not only intensive and discriminant validity, but also reliability. Then, research model was examined with fully structural equation using LISREL 8.3 version. The fitness of the suggested model mostly was at the acceptable level. Shortly speaking about the results, first of all, in e-sports game attributes, only interactivity which is called a basic feature in online situation affected flow positively. Secondly, in e-sports gamer's attributes, similarity with a gamer and match result influenced flow positively, but there was no significant effect in the relationship between the attractivity of a gamer and flow. And as expected, similarity had an effect on identification with a gamer significantly. But unexpectedly attractivity and match result did not influence identification with a gamer significantly. Just the same as the fact verified in the many precedent researches, flow greatly influenced identification with a gamer, and identification with a gamer continually had an influence on the identification with a sponsor significantly. There are some implications in these results. If the sponsor of e-sports supports the pro-game player who absolutely should have the superior ability to others and is similar to the user enjoying e-sports, many amateur gamers will feel much of the flow and identification with a pro-gamer, and then after all, feel the identification with a sponsor. Such identification with a sponsor leads people enjoying e-sports to have purchasing intention for products produced by the sponsor and to make a positive word-of-mouth for those products or the sponsor. For the future studies, we recommend a few ideas. Based on the results of this study, it is necessary to find new variables relating to the e-sports, which is not mentioned in this study. For this work to be possible, qualitative research seems to be needed to consider the inherent e-sport attributes. Finally, to generalize the results related to e-sports, a wide range of generations not a specific generation should be researched.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.73-92
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• 2014

An Adaptive Clustering-based Collaborative Filtering Technique was proposed to solve the fundamental problems of collaborative filtering, such as cold-start problems, scalability problems and data sparsity problems. Previous collaborative filtering techniques were carried out according to the recommendations based on the predicted preference of the user to a particular item using a similar item subset and a similar user subset composed based on the preference of users to items. For this reason, if the density of the user preference matrix is low, the reliability of the recommendation system will decrease rapidly. Therefore, the difficulty of creating a similar item subset and similar user subset will be increased. In addition, as the scale of service increases, the time needed to create a similar item subset and similar user subset increases geometrically, and the response time of the recommendation system is then increased. To solve these problems, this paper suggests a collaborative filtering technique that adapts a condition actively to the model and adopts the concepts of a context-based filtering technique. This technique consists of four major methodologies. First, items are made, the users are clustered according their feature vectors, and an inter-cluster preference between each item cluster and user cluster is then assumed. According to this method, the run-time for creating a similar item subset or user subset can be economized, the reliability of a recommendation system can be made higher than that using only the user preference information for creating a similar item subset or similar user subset, and the cold start problem can be partially solved. Second, recommendations are made using the prior composed item and user clusters and inter-cluster preference between each item cluster and user cluster. In this phase, a list of items is made for users by examining the item clusters in the order of the size of the inter-cluster preference of the user cluster, in which the user belongs, and selecting and ranking the items according to the predicted or recorded user preference information. Using this method, the creation of a recommendation model phase bears the highest load of the recommendation system, and it minimizes the load of the recommendation system in run-time. Therefore, the scalability problem and large scale recommendation system can be performed with collaborative filtering, which is highly reliable. Third, the missing user preference information is predicted using the item and user clusters. Using this method, the problem caused by the low density of the user preference matrix can be mitigated. Existing studies on this used an item-based prediction or user-based prediction. In this paper, Hao Ji's idea, which uses both an item-based prediction and user-based prediction, was improved. The reliability of the recommendation service can be improved by combining the predictive values of both techniques by applying the condition of the recommendation model. By predicting the user preference based on the item or user clusters, the time required to predict the user preference can be reduced, and missing user preference in run-time can be predicted. Fourth, the item and user feature vector can be made to learn the following input of the user feedback. This phase applied normalized user feedback to the item and user feature vector. This method can mitigate the problems caused by the use of the concepts of context-based filtering, such as the item and user feature vector based on the user profile and item properties. The problems with using the item and user feature vector are due to the limitation of quantifying the qualitative features of the items and users. Therefore, the elements of the user and item feature vectors are made to match one to one, and if user feedback to a particular item is obtained, it will be applied to the feature vector using the opposite one. Verification of this method was accomplished by comparing the performance with existing hybrid filtering techniques. Two methods were used for verification: MAE(Mean Absolute Error) and response time. Using MAE, this technique was confirmed to improve the reliability of the recommendation system. Using the response time, this technique was found to be suitable for a large scaled recommendation system. This paper suggested an Adaptive Clustering-based Collaborative Filtering Technique with high reliability and low time complexity, but it had some limitations. This technique focused on reducing the time complexity. Hence, an improvement in reliability was not expected. The next topic will be to improve this technique by rule-based filtering.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.149-169
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• 2020

"The Urban Renewal New Deal project", one of the government's major national projects, is about developing underdeveloped areas by investing 50 trillion won in 100 locations on the first year and 500 over the next four years. This project is drawing keen attention from the media and local governments. However, the project model which fails to reflect the original characteristics of the area as it divides project area into five categories: "Our Neighborhood Restoration, Housing Maintenance Support Type, General Neighborhood Type, Central Urban Type, and Economic Base Type," According to keywords for successful urban regeneration in Korea, "resident participation," "regional specialization," "ministerial cooperation" and "public-private cooperation", when local governments propose urban regeneration projects to the government, they can see that it is most important to accurately understand the characteristics of the city and push ahead with the projects in a way that suits the characteristics of the city with the help of local residents and private companies. In addition, considering the gentrification problem, which is one of the side effects of urban regeneration projects, it is important to select and implement urban regeneration types suitable for the characteristics of the area. In order to supplement the limitations of the 'Urban Regeneration New Deal Project' methodology, this study aims to propose a system that recommends urban regeneration types suitable for urban regeneration sites by utilizing various machine learning algorithms, referring to the urban regeneration types of the '2025 Seoul Metropolitan Government Urban Regeneration Strategy Plan' promoted based on regional characteristics. There are four types of urban regeneration in Seoul: "Low-use Low-Level Development, Abandonment, Deteriorated Housing, and Specialization of Historical and Cultural Resources" (Shon and Park, 2017). In order to identify regional characteristics, approximately 100,000 text data were collected for 22 regions where the project was carried out for a total of four types of urban regeneration. Using the collected data, we drew key keywords for each region according to the type of urban regeneration and conducted topic modeling to explore whether there were differences between types. As a result, it was confirmed that a number of topics related to real estate and economy appeared in old residential areas, and in the case of declining and underdeveloped areas, topics reflecting the characteristics of areas where industrial activities were active in the past appeared. In the case of the historical and cultural resource area, since it is an area that contains traces of the past, many keywords related to the government appeared. Therefore, it was possible to confirm political topics and cultural topics resulting from various events. Finally, in the case of low-use and under-developed areas, many topics on real estate and accessibility are emerging, so accessibility is good. It mainly had the characteristics of a region where development is planned or is likely to be developed. Furthermore, a model was implemented that proposes urban regeneration types tailored to regional characteristics for regions other than Seoul. Machine learning technology was used to implement the model, and training data and test data were randomly extracted at an 8:2 ratio and used. In order to compare the performance between various models, the input variables are set in two ways: Count Vector and TF-IDF Vector, and as Classifier, there are 5 types of SVM (Support Vector Machine), Decision Tree, Random Forest, Logistic Regression, and Gradient Boosting. By applying it, performance comparison for a total of 10 models was conducted. The model with the highest performance was the Gradient Boosting method using TF-IDF Vector input data, and the accuracy was 97%. Therefore, the recommendation system proposed in this study is expected to recommend urban regeneration types based on the regional characteristics of new business sites in the process of carrying out urban regeneration projects."

• Journal of Distribution Research
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• v.15 no.4
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• pp.61-85
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• 2010

Introduction: Diffusion is process by which an innovation is communicated through certain channel overtime among the members of a social system(Rogers 1983). Bass(1969) suggested the Bass model describing diffusion process. The Bass model assumes potential adopters of innovation are influenced by mass-media and word-of-mouth from communication with previous adopters. Various expansions of the Bass model have been conducted. Some of them proposed a third factor affecting diffusion. Others proposed multinational diffusion model and it stressed interactive effect on diffusion among several countries. We add a spatial factor in the Bass model as a third communication factor. Because of situation where we can not control the interaction between markets, we need to consider that diffusion within certain market can be influenced by diffusion in contiguous market. The process that certain type of retail extends is a result that particular market can be described by the retail life cycle. Diffusion of retail has pattern following three phases of spatial diffusion: adoption of innovation happens in near the diffusion center first, spreads to the vicinity of the diffusing center and then adoption of innovation is completed in peripheral areas in saturation stage. So we expect spatial effect to be important to describe diffusion of domestic discount store. We define a spatial diffusion model using multinational diffusion model and apply it to the diffusion of discount store. Modeling: In this paper, we define a spatial diffusion model and apply it to the diffusion of discount store. To define a spatial diffusion model, we expand learning model(Kumar and Krishnan 2002) and separate diffusion process in diffusion center(market A) from diffusion process in the vicinity of the diffusing center(market B). The proposed spatial diffusion model is shown in equation (1a) and (1b). Equation (1a) is the diffusion process in diffusion center and equation (1b) is one in the vicinity of the diffusing center. $$\array{{S_{i,t}=(p_i+q_i{\frac{Y_{i,t-1}}{m_i}})(m_i-Y_{i,t-1})\;i{\in}\{1,{\cdots},I\}\;(1a)}\\{S_{j,t}=(p_j+q_j{\frac{Y_{j,t-1}}{m_i}}+{\sum\limits_{i=1}^I}{\gamma}_{ij}{\frac{Y_{i,t-1}}{m_i}})(m_j-Y_{j,t-1})\;i{\in}\{1,{\cdots},I\},\;j{\in}\{I+1,{\cdots},I+J\}\;(1b)}}$$ We rise two research questions. (1) The proposed spatial diffusion model is more effective than the Bass model to describe the diffusion of discount stores. (2) The more similar retail environment of diffusing center with that of the vicinity of the contiguous market is, the larger spatial effect of diffusing center on diffusion of the vicinity of the contiguous market is. To examine above two questions, we adopt the Bass model to estimate diffusion of discount store first. Next spatial diffusion model where spatial factor is added to the Bass model is used to estimate it. Finally by comparing Bass model with spatial diffusion model, we try to find out which model describes diffusion of discount store better. In addition, we investigate the relationship between similarity of retail environment(conceptual distance) and spatial factor impact with correlation analysis. Result and Implication: We suggest spatial diffusion model to describe diffusion of discount stores. To examine the proposed spatial diffusion model, 347 domestic discount stores are used and we divide nation into 5 districts, Seoul-Gyeongin(SG), Busan-Gyeongnam(BG), Daegu-Gyeongbuk(DG), Gwan- gju-Jeonla(GJ), Daejeon-Chungcheong(DC), and the result is shown