• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.119-138
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• 2017

Recently, SNS has become an important channel for marketing as well as personal communication. However, cybercrime has also evolved with the development of information and communication technology, and illegal advertising is distributed to SNS in large quantity. As a result, personal information is lost and even monetary damages occur more frequently. In this study, we propose a method to analyze which sentences and documents, which have been sent to the SNS, are related to financial fraud. First of all, as a conceptual framework, we developed a matrix of conceptual characteristics of cybercriminality on SNS and emergency management. We also suggested emergency management process which consists of Pre-Cybercriminality (e.g. risk identification) and Post-Cybercriminality steps. Among those we focused on risk identification in this paper. The main process consists of data collection, preprocessing and analysis. First, we selected two words 'daechul(loan)' and 'sachae(private loan)' as seed words and collected data with this word from SNS such as twitter. The collected data are given to the two researchers to decide whether they are related to the cybercriminality, particularly financial fraud, or not. Then we selected some of them as keywords if the vocabularies are related to the nominals and symbols. With the selected keywords, we searched and collected data from web materials such as twitter, news, blog, and more than 820,000 articles collected. The collected articles were refined through preprocessing and made into learning data. The preprocessing process is divided into performing morphological analysis step, removing stop words step, and selecting valid part-of-speech step. In the morphological analysis step, a complex sentence is transformed into some morpheme units to enable mechanical analysis. In the removing stop words step, non-lexical elements such as numbers, punctuation marks, and double spaces are removed from the text. In the step of selecting valid part-of-speech, only two kinds of nouns and symbols are considered. Since nouns could refer to things, the intent of message is expressed better than the other part-of-speech. Moreover, the more illegal the text is, the more frequently symbols are used. The selected data is given 'legal' or 'illegal'. To make the selected data as learning data through the preprocessing process, it is necessary to classify whether each data is legitimate or not. The processed data is then converted into Corpus type and Document-Term Matrix. Finally, the two types of 'legal' and 'illegal' files were mixed and randomly divided into learning data set and test data set. In this study, we set the learning data as 70% and the test data as 30%. SVM was used as the discrimination algorithm. Since SVM requires gamma and cost values as the main parameters, we set gamma as 0.5 and cost as 10, based on the optimal value function. The cost is set higher than general cases. To show the feasibility of the idea proposed in this paper, we compared the proposed method with MLE (Maximum Likelihood Estimation), Term Frequency, and Collective Intelligence method. Overall accuracy and was used as the metric. As a result, the overall accuracy of the proposed method was 92.41% of illegal loan advertisement and 77.75% of illegal visit sales, which is apparently superior to that of the Term Frequency, MLE, etc. Hence, the result suggests that the proposed method is valid and usable practically. In this paper, we propose a framework for crisis management caused by abnormalities of unstructured data sources such as SNS. We hope this study will contribute to the academia by identifying what to consider when applying the SVM-like discrimination algorithm to text analysis. Moreover, the study will also contribute to the practitioners in the field of brand management and opinion mining.

• Korea Science and Art Forum
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• v.31
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• pp.21-32
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• 2017

Local festivals form the regional cultures and atmosphere of communication; they increase the demand of domestic tourism businesses and thus, have an important role in ripple effects (e.g. regional image improvement, tourist influx, job creation, regional contents development, and local product sales) and economic revitalization. IoT (Internet of Thing) technologies have been developed especially, beacon-one of the IoT services has been applied as plenty of types and forms both domestically and internationally. However, notwithstanding expansion of current digital mobile technologies, it still remains as difficult for the individual to track the information about all the local festivals and to fulfill the tourists' needs of enjoying festivals given the weak strategic approaches and advertisement activities. Furthermore, current festival-related mobile applications don't function well as delivering information and have numerous contents issues (e.g. ways of information delivery within the festival places, independent application usage for each festival, one time usage due to one time event). This research, based on the background mentioned above, aims to develop the local festival mobile application and data analysis system applying beacon technology. First of all, three algorithms were developed, namely, 'festival crowding algorithm', 'visitor stats algorithm', and 'customized information algorithm', and then beta test was followed with the developed application and data analysis system. As a result, they could form the database of visitors' types and behaviors, and provide functions and services, such as personalized information, waiting time for festival contents, and 'hot place' function. Besides, in Google Play store, they also got the titles given with more than 13,000 downloads within first three months and as the most exposed application related with festivals; and, thus, got credited with their marketability and excellence. This research follows this order: chapter 2 shows the literature review of local festival related with technology development, beacon service, and festival application. In Chapter 3, design plans and conditions are described of developing local festival mobile application and data analysis system with beacon. Chapter 4 evaluates the results of the beta performance test to verify applicability of the developed application and data analysis system, and lastly, chapter 5 explains the conclusion and suggests the future research.

• Asia Marketing Journal
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• v.13 no.4
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• pp.105-137
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• 2012

After relationship marketing emerged as a new approach in the marketing field in the 1980s, it has been widely studied in the United States, Europe and Asia. Rapid environmental changes and global competition has made it inevitable for companies to consider their relationships with the environment more closely. Under these circumstances, relationship marketing has held a position as a pivotal paradigm in the field of strategy as well as in marketing. In addition, relationship marketing has overcome the limitations of a traditional marketing research while providing richer implications in company's marketing activities. The paradigm shift to relationship marketing has brought fundamental changes in a marketing point of view. First, in philosophical aspects, unlike past research which focused solely on customer satisfaction, organizational relationship parameters which focuses on trust and commitment has become key elements of successful relationship marketing while shifts in thoughts naturally take place from adaptive marketing to strategic marketing. Second, in structural aspects, the relational mechanism of governance such as network structure with a variety of relational partners has emerged as a new marketing organization from the previous simple structure focusing on the micro-economic, marketbased trading between seller and customer. Third, in behavioral aspects, it proposed the strategic course of the action of gaining an advantage over the competition on the individual firm level by focusing on building long-term relationships and considering partnership with the components in the entire marketing system, rather than with one-time transaction-centric action between a seller and a customer. Fourth, in the aspects of marketing performance, marketing performance was sought through the long-term and cooperative relationship with various stakeholders, including customers in the marketing system, focusing on the overall competitive advantage based on relationship rather than individual performance of individual companies' marketing activities, such as market share and customer satisfaction. However, studies of relationship marketing were mostly centered in interorganizational relationships focusing on the relational structure and properties of commercial sector in the marketing system. Paradoxically, the circumstance of the consumer's side that must be considered is evolving again in relationship marketing. In structural aspects, a community, as the new relationship governance structure in the digital environment, and in behavioral aspects, the changing role of consumer participation demanding big changes in the digital environment engaged in the marketing system. The possibility of building a relationship marketing community for common value creation is presented in terms of organization of consumers with the focus on changing marketing environment and marketing system according to the new realities of the 21st century- the popularity of digital environments and the diffusion of customer participation. Therefore, future research of relationship marketing must seek for a truly integrated model including all of the existing structure and properties of the research oriented relationship from both the commercial and consumer sector.

• 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.