• Proceedings of the Korean DIstribution Association Conference
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• 2005.05a
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• pp.383-405
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• 2005

The traditional market is foremost a regionally positioned place, wherein the market directly represents regional and cultural centered traits while it plays an important role in the circulation of facilities through reciprocal, informative and cultural exchanges while sewing to form local communities. The traditional market in Korea is one of representative retail businesses and premodern marketing techniques by family owned business of less than five members such as product management, purchase method, and marketing patterns etc. Since the 1990s, the appearance of new circulation-type businesses and large discount convenience stores escalated the loss of traditional competitiveness, increased the living standard of customers, changed purchasing patterns, and expanded the ubiquity of the Internet. All of these changes in external circulation circumstances have led the traditional markets to lose their place in the economy. The traditional market should revive on a regional site basis through the formation of a community of regional neighbors and through knowledge-sharing that leads to the creation of wealth. For the purpose of creating a wealth in a place, the following components are necessary: 1) a facility suitable for the spatial place of the present, 2)trust built through exchanges within the changing market environment, which would simultaneously satisfy customer's desires, 3) international bench marking on cases such as regionally centered TCM (England), BID (USA), and TMO (Japan) so that the market unit of store placement transfers from a spot policy to a line policy, 4)conversion of communicative conception through a surface policy approach centered around a macro-region perspective. The budget of the traditional market funding policy was operational between 2001 and 2004, serving as a counter move to solve the problem of the old traditional market through government intervention in regional economies to promote national economic strength. This national treasury funding project was centered on environmental improvement, research corps, and business modernization through the expenditure of 3,853 hundred million won (Korean currency). However, the effectiveness of this project has yet to be to proven through investigation. Furthermore, in promoting this funding support project, a lack of professionalism among merchants in the market led to constant limitations in comprehensive striving strategies, reduced capabilities in middle-and long-term plan setup, and created reductions in voluntary merchant agreement solutions. The traditional market should go beyond mere physical place and ordinary products creative site strategies employing the communicative approach must accompany these strategies to make the market a new regional and spatial living place. Thus, regarding recent paradigm changes and the introduction of region-based site functions into the traditional market, acquiring a conversion of direction into the newly developed project is essential to reinvestigate the traditional market composed of cultural and economic meanings, for the purpose of the research. Excavating social policy demands through the comparative analysis of domestic and international cases as well as innovative and expert management leadership development for NPO or NGO civil entrepreneurs through advanced case research on present promotion methods is extremely important. Discovering the seeds of the cultural contents industry cored around regional resource usages, commercializing regionally reknowned products, and constructing complex cultural living places for regional networks are especially important. In order to accelerate these solutions, a comprehensive and systemized approach research operated within a mentor academy system is required, as research will reveal distinctive traits of the traditional market in the aging society.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.133-146
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• 2013

While the use of internet is routine nowadays, users receive and share information through a variety of media. Through the use of internet, information delivery media is diversifying from traditional media of one-way communication, such as newspaper, TV, and radio, into media of two-way communication. In contrast of traditional media, blogs enable individuals to directly upload and share news, which can be considered to have a differential speed of information diffusion than news media that convey information unilaterally. Therefore this Study focused on the difference between online news and social media blogs. Moreover, there are variations in the speed of information diffusion because that information closely related to one person boosts communications between individuals. We believe that users' standard of evaluation would change based on the types of information. As well, the speed of information diffusion would change based on the level of proximity. Therefore, the purpose of this study is to examine the differences in information diffusion based on the types of media. And then information is segmentalized and an examination is done to see how information diffusion differentiates based on the types of information. This study used the Bass diffusion model, which has been frequently used because this model has higher explanatory power than other models by explaining diffusion of market through innovation effect and imitation effect. Also this model has been applied a lot in other information diffusion related studies. The Bass diffusion model includes an innovation effect and an imitation effect. Innovation effect measures the early-stage impact, while the imitation effect measures the impact of word of mouth at the later stage. According to Mahajan et al. (2000), Innovation effect is emphasized by usefulness and ease-of-use, as well Imitation effect is emphasized by subjective norm and word-of-mouth. Also, according to Lee et al. (2011), Innovation effect is emphasized by mass communication. According to Moore and Benbasat (1996), Innovation effect is emphasized by relative advantage. Because Imitation effect is adopted by within-group influences and Innovation effects is adopted by product's or service's innovation. Therefore, ours study compared online news and social media blogs to examine the differences between media. We also choose different types of information including entertainment related information "Psy Gentelman", Current affair news "Earthquake in Sichuan, China", and product related information "Galaxy S4" in order to examine the variations on information diffusion. We considered that users' information proximity alters based on the types of information. Hence, we chose the three types of information mentioned above, which have different level of proximity from users' standpoint, in order to examine the flow of information diffusion. The first conclusion of this study is that different media has similar effect on information diffusion, even the types of media of information provider are different. Information diffusion has only been distinguished by a disparity between proximity of information. Second, information diffusions differ based on types of information. From the standpoint of users, product and entertainment related information has high imitation effect because of word of mouth. On the other hand, imitation effect dominates innovation effect on Current affair news. From the results of this study, the flow changes of information diffusion is examined and be applied to practical use. This study has some limitations, and those limitations would be able to provide opportunities and suggestions for future research. Presenting the difference of Information diffusion according to media and proximity has difficulties for generalization of theory due to small sample size. Therefore, if further studies adopt to a request for an increase of sample size and media diversity, difference of the information diffusion according to media type and information proximity could be understood more detailed.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.35-55
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• 2013

Information technology is a critical resource necessary for any company hoping to support and realize its strategic goals, which contribute to growth promotion and sustainable development. The selection of information technology and its strategic use are imperative for the enhanced performance of every aspect of company management, leading a wide range of companies to have invested continuously in information technology. Despite researchers, managers, and policy makers' keen interest in how information technology contributes to organizational performance, there is uncertainty and debate about the result of information technology investment. In other words, researchers and managers cannot easily identify the independent factors that can impact the investment performance of information technology. This is mainly owing to the fact that many factors, ranging from the internal components of a company, strategies, and external customers, are interconnected with the investment performance of information technology. Using an agent-based simulation technique, this research extracts factors expected to affect investment performance on information technology, simplifies the analyses of their relationship with economic modeling, and examines the performance dependent on changes in the factors. In terms of economic modeling, I expand the model that highlights the way in which product quality moderates the relationship between information technology investments and economic performance (Thatcher and Pingry, 2004) by considering the cost of information technology investment and the demand creation resulting from product quality enhancement. For quality enhancement and its consequences for demand creation, I apply the concept of information quality and decision-maker quality (Raghunathan, 1999). This concept implies that the investment on information technology improves the quality of information, which, in turn, improves decision quality and performance, thus enhancing the level of product or service quality. Additionally, I consider the effect of word of mouth among consumers, which creates new demand for a product or service through the information diffusion effect. This demand creation is analyzed with an agent-based simulation model that is widely used for network analyses. Results show that the investment on information technology enhances the quality of a company's product or service, which indirectly affects the economic performance of that company, particularly with regard to factors such as consumer surplus, company profit, and company productivity. Specifically, when a company makes its initial investment in information technology, the resultant increase in the quality of a company's product or service immediately has a positive effect on consumer surplus, but the investment cost has a negative effect on company productivity and profit. As time goes by, the enhancement of the quality of that company's product or service creates new consumer demand through the information diffusion effect. Finally, the new demand positively affects the company's profit and productivity. In terms of the investment strategy for information technology, this study's results also reveal that the selection of information technology needs to be based on analysis of service and the network effect of customers, and demonstrate that information technology implementation should fit into the company's business strategy. Specifically, if a company seeks the short-term enhancement of company performance, it needs to have a one-shot strategy (making a large investment at one time). On the other hand, if a company seeks a long-term sustainable profit structure, it needs to have a split strategy (making several small investments at different times). The findings from this study make several contributions to the literature. In terms of methodology, the study integrates both economic modeling and simulation technique in order to overcome the limitations of each methodology. It also indicates the mediating effect of product quality on the relationship between information technology and the performance of a company. Finally, it analyzes the effect of information technology investment strategies and information diffusion among consumers on the investment performance of information technology.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.

• 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.