• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.33-56
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• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.79-96
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• 2012

As the pace of competition dramatically accelerates and the complexity of change grows, a variety of research have been conducted to improve firms' short-term performance and to enhance firms' long-term survival. In particular, researchers and practitioners have paid their attention to identify promising technologies that lead competitive advantage to a firm. Discovery of promising technology depends on how a firm evaluates the value of technologies, thus many evaluating methods have been proposed. Experts' opinion based approaches have been widely accepted to predict the value of technologies. Whereas this approach provides in-depth analysis and ensures validity of analysis results, it is usually cost-and time-ineffective and is limited to qualitative evaluation. Considerable studies attempt to forecast the value of technology by using patent information to overcome the limitation of experts' opinion based approach. Patent based technology evaluation has served as a valuable assessment approach of the technological forecasting because it contains a full and practical description of technology with uniform structure. Furthermore, it provides information that is not divulged in any other sources. Although patent information based approach has contributed to our understanding of prediction of promising technologies, it has some limitations because prediction has been made based on the past patent information, and the interpretations of patent analyses are not consistent. In order to fill this gap, this study proposes a technology forecasting methodology by integrating patent information approach and artificial intelligence method. The methodology consists of three modules : evaluation of technologies promising, implementation of technologies value prediction model, and recommendation of promising technologies. In the first module, technologies promising is evaluated from three different and complementary dimensions; impact, fusion, and diffusion perspectives. The impact of technologies refers to their influence on future technologies development and improvement, and is also clearly associated with their monetary value. The fusion of technologies denotes the extent to which a technology fuses different technologies, and represents the breadth of search underlying the technology. The fusion of technologies can be calculated based on technology or patent, thus this study measures two types of fusion index; fusion index per technology and fusion index per patent. Finally, the diffusion of technologies denotes their degree of applicability across scientific and technological fields. In the same vein, diffusion index per technology and diffusion index per patent are considered respectively. In the second module, technologies value prediction model is implemented using artificial intelligence method. This studies use the values of five indexes (i.e., impact index, fusion index per technology, fusion index per patent, diffusion index per technology and diffusion index per patent) at different time (e.g., t-n, t-n-1, t-n-2, ${\cdots}$) as input variables. The out variables are values of five indexes at time t, which is used for learning. The learning method adopted in this study is backpropagation algorithm. In the third module, this study recommends final promising technologies based on analytic hierarchy process. AHP provides relative importance of each index, leading to final promising index for technology. Applicability of the proposed methodology is tested by using U.S. patents in international patent class G06F (i.e., electronic digital data processing) from 2000 to 2008. The results show that mean absolute error value for prediction produced by the proposed methodology is lower than the value produced by multiple regression analysis in cases of fusion indexes. However, mean absolute error value of the proposed methodology is slightly higher than the value of multiple regression analysis. These unexpected results may be explained, in part, by small number of patents. Since this study only uses patent data in class G06F, number of sample patent data is relatively small, leading to incomplete learning to satisfy complex artificial intelligence structure. In addition, fusion index per technology and impact index are found to be important criteria to predict promising technology. This study attempts to extend the existing knowledge by proposing a new methodology for prediction technology value by integrating patent information analysis and artificial intelligence network. It helps managers who want to technology develop planning and policy maker who want to implement technology policy by providing quantitative prediction methodology. In addition, this study could help other researchers by proving a deeper understanding of the complex technological forecasting field.

• Journal of Global Scholars of Marketing Science
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• v.20 no.2
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• pp.135-148
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• 2010

Arab boycotts of Danish products, Australian boycotts of French products and Chinese consumer aversion toward Japanese products are all examples of how adverse actions at the country level might impact consumers' behavior. The animosity literature has examined how consumers react to the adverse actions of other countries, and how such animosity impacts consumers' attitudes and preferences for products from the transgressing country. For example, Chinese consumers are less likely to buy Japanese products because of Japanese atrocities during World War II and the unjust economic dealings of the Japanese (Klein, Ettenson and Morris 1998). The marketing literature, however, has not examined how consumers react to adverse actions committed by their own country against other countries, and whether such actions affect their attitudes towards purchasing products that originated from the adversely affected country. The social psychology literature argues that consumers will experience a feeling called collective guilt, in response to such adverse actions. Collective guilt stems from the distress experienced by group members when they accept that their group is responsible for actions that have harmed another group (Branscombe, Slugoski, and Kappenn 2004). Examples include Americans feeling guilty about the atrocities committed by the U.S. military at Abu Ghraib prison (Iyer, Schamder and Lickel 2007), and the Dutch about their occupation of Indonesia in the past (Doosje et al. 1998). The primary aim of this study is to examine consumers' perceptions of adverse actions by members of one's own country against another country and whether such perceptions affected their attitudes towards products originating from the country transgressed against. More specifically, one objective of this study is to examine the perceptual antecedents of collective guilt, an emotional reaction to adverse actions performed by members of one's country against another country. Another objective is to examine the impact of collective guilt on consumers' perceptions of, and preference for, products originating from the country transgressed against by the consumers' own country. If collective guilt emerges as a significant predictor, companies originating from countries that have been transgressed against might be able to capitalize on such unfortunate events. This research utilizes the animosity model introduced by Klein, Ettenson and Morris (1998) and later expanded on by Klein (2002). Klein finds that U.S. consumers harbor animosity toward the Japanese. This animosity is experienced in response to events that occurred during World War II (i.e., the bombing of Pearl Harbor) and more recently the perceived economic threat from Japan. Thus this study argues that the events of Word War II (i.e., bombing of Hiroshima and Nagasaki) might lead U.S. consumers to experience collective guilt. A series of three hypotheses were introduced. The first hypothesis deals with the antecedents of collective guilt. Previous research argues that collective guilt is experienced when consumers perceive that the harm following a transgression is illegitimate and that the country from which the transgressors originate should be responsible for the adverse actions. (Wohl, Branscombe, and Klar 2006). Therefore the following hypothesis was offered: H1a. Higher levels of perceived illegitimacy for the harm committed will result in higher levels of collective guilt. H1b. Higher levels of responsibility will be positively associated with higher levels of collective guilt. The second and third hypotheses deal with the impact of collective guilt on the preferences for Japanese products. Klein (2002) found that higher levels of animosity toward Japan resulted in a lower preference for a Japanese product relative to a South Korean product but not a lower preference for a Japanese product relative to a U.S. product. These results therefore indicate that the experience of collective guilt will lead to a higher preference for a Japanese product if consumers are contemplating a choice that inv olves a decision to buy Japanese versus South Korean product but not if the choice involves a decision to buy a Japanese versus a U.S. product. H2. Collective guilt will be positively related to the preference for a Japanese product over a South Korean product, but will not be related to the preference for a Japanese product over a U.S. product. H3. Collective guilt will be positively related to the preference for a Japanese product over a South Korean product, holding constant product judgments and animosity. An experiment was conducted to test the hypotheses. The illegitimacy of the harm and responsibility were manipulated by exposing respondents to a description of adverse events occurring during World War II. Data were collected using an online consumer panel in the United States. Subjects were randomly assigned to either the low levels of responsibility and illegitimacy condition (n=259) or the high levels of responsibility and illigitemacy (n=268) condition. Latent Variable Structural Equation Modeling (LVSEM) was used to test the hypothesized relationships. The first hypothesis is supported as both the illegitimacy of the harm and responsibility assigned to the Americans for the harm committed against the Japanese during WWII have a positive impact on collective guilt. The second hypothesis is also supported as collective guilt is positively related to preference for a Japanese product over a South Korean product but is not related to preference for a Japanese product over a U.S. product. Finally there is support for the third hypothesis, since collective guilt is positively related to the preference for a Japanese product over a South Korean product while controlling for the effect of product judgments about Japanese products and animosity. The results of these studies lead to several conclusions. First, the illegitimacy of harm and responsibility can be manipulated and that they are antecedents of collective guilt. Second, collective guilt has an impact on a consumers' decision when they face a choice set that includes a product from the country that was the target of the adverse action and a product from another foreign country. This impact however disappears from a consumers' decision when they face a choice set that includes a product from the country that was the target of the adverse action and a domestic product. This result suggests that collective guilt might be a viable factor for company originating from the country transgressed against if its competitors are foreign but not if they are local.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.469-484
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• 2021

Rock mass classification results have a great influence on construction schedule and budget as well as tunnel stability in tunnel design. A total of 3,526 tunnels have been constructed in Korea and the associated techniques in tunnel design and construction have been continuously developed, however, not many studies have been performed on how to assess rock mass quality and grade more accurately. Thus, numerous cases show big differences in the results according to inspectors' experience and judgement. Hence, this study aims to suggest a more reliable rock mass classification (RMR) model using machine learning algorithms, which is surging in availability, through the analyses based on various rock and rock mass information collected from boring investigations. For this, 11 learning parameters (depth, rock type, RQD, electrical resistivity, UCS, V_p, V_s, Young's modulus, unit weight, Poisson's ratio, RMR) from 13 local tunnel cases were selected, 337 learning data sets as well as 60 test data sets were prepared, and 6 machine learning algorithms (DT, SVM, ANN, PCA & ANN, RF, XGBoost) were tested for various hyperparameters for each algorithm. The results show that the mean absolute errors in RMR value from five algorithms except Decision Tree were less than 8 and a Support Vector Machine model is the best model. The applicability of the model, established through this study, was confirmed and this prediction model can be applied for more reliable rock mass classification when additional various data is continuously cumulated.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• Industry Promotion Research
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• v.5 no.3
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• pp.145-155
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• 2020

Co-operatives are a deep-rooted organization that was first organized in Britain in the 19th century and spread to Europe and North America in the early 20th century and to the rest of the world from the mid-20th century. Cooperative in Italy are fraternal (friendly societies) separated from religion, and in the early days of socialism and the late 19th century Catholic Italy, but independent of activity. And the Church's social participation, as well as multiple personalities. Therefore, the purpose of this study is to study the laws and institutions of Italian cooperatives. And let's look at how the laws and systems of Italian co-operatives support society and the national economy. Specifically, firstly, based on prior research, the concept of co-operatives and the cooperative movement and social values are considered. Second, review the development process and characteristics of Italian co-operatives and the legal system. Third, I would like to analyze the case of Italian co-operatives. Fourth, suggest implications according to the results of the study. The results of the study suggested the following. First, the attitude such as attachment and sincerity of representatives and staff of village enterprises is very important. Second, all members of the organization should participate in decision making with empathy and attachment to the vision of the village enterprise. Third, it should be highly likely that village enterprises, which can draw capital from outside according to the needs of the organization, will generate higher economic results. Fourth, it is important to establish a model of mind enterprise by presenting factors and success factors in establishing a village enterprise based on cases and theories. In conclusion, Co-operatives should contribute to social contribution rather than economic profit-seeking.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.2
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• pp.72-81
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• 2014

As big data are one of main issues in the recent days, the interests on their technologies are also increasing. Among several technological bases, this study focuses on data visualization and R based on open source. In general, the term of data visualization can be summarized as the web technologies for constructing, manipulating and displaying various types of graphic objects in the interactive mode. R is an operating environment or a language for statistical data analysis from basic to advanced level. In this study, a web application with these technological aspects and components is newly implemented and exemplified with data visualization for geo-based open data provided by public organizations or government agencies. This application model does not need users' data building or proprietary software installation. Futhermore it is designed for users in the geo-spatial application field with less experiences and little knowledges about R. The results of data visualization by this application can support decision making process of web users accessible to this service. It is expected that the more practical and various applications with R-based geo-statistical analysis functions and complex operations linked to big data contribute to expanding the scope and the range of the geo-spatial application.

• Journal of Bio-Environment Control
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• v.5 no.2
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• pp.174-186
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• 1996

This study was carried out in order to understand the plan, design, constructing and actual condition of management of modernized horticultural facilities in Kyungpook Province which had been constructed from 1992 to 1995 funded by Government support. The aim of this study is to provide reference data for success of the forth project. It was performed by making up a question about driving of project and management condition of equipment after constructing. The results obtained from this study are as follows: 1. 73.5% of facilities horticulture farmhouse recognized that the prospect of greenhouse is bright, but 92.5% of the farmhouse also recognised that they need technical consultation on protected horticulture farming. Therefore, technical educations would must be enhanced about foundation of greenhouse and cultivation technique. 2. The holding times of explanatory meetings, cause of understanding to farmhouse, were one or two times in greenhouse construction, and 62.5% of the farmhouse expressed the insufficiency at the explanation and educational data. For this reason, it was judged that the construction contract had been delayed more than 5 months in 49.3% of the farmhouse after the decision of project budget. 3. In constructing after a contract, the rates of construction delay is 53.4% and defect occurrence is 41.1%. The biggest reasons of construction delay was insufficiency of worker and materials supply. Each percentage is 29.1%. And the reason of defect occurrence is badness of machinery equipment(62.9% ). 4. In management of greenhouse, a pipe-constructed plastic film greenhouse changes plastic film every one and three years because of sticking dust on plastic film. It was needed to about in cleaning technique of coverings. Because that used 3-5 years only half of the expected life span. 5. The order of broken rating in the subsidiary equipment is like this lollop ventilator (42.8%), a general control system(33.3%) especially, in the case of a general control system, the rate of all family can control is 52.7%. so, it is time to develop easy control equipment which every one could use as soon as possible. 6. When choose heat generator as decide capacity, the most priority is the mount of heat generator the percent is 45.5% heat generator and as decide model, the private purchase's percent is 77.3%. It is higher than a public bidding heat generator the percent is 22.7% heat generator when it compare with a public bidding. In the case of $CO_2$ generator, using rate is only 19.0%. The using rate is very low, so it needs education how to use depends on the way of the subsidiary equipment. 7. In the case of seedlings, it is asked to use factory-processed seedling effectively. because it's difficult to get security of labors(58.8%), hoped crops (55.9%) access same crops(29.4%) much more and changing of crops depends on market situation. that is the main reason the lack of knowhow.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.636-645
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• 2017

An information system for defense technology information management should assist the user's work and manager's decision-making by managing and timely providing data held by defense-related organizations. This paper proposes a plan for constructing an advance defense technology information service. DTiMS concentrates on the collection and management of defense science technology information but not its distribution. Therefore, it is important that the advanced distribution service model be established on the concept of total life cycle management that utilizes user information, so that it can provide proper information to each user in the defense field who require the information processed by their roles. This study examined the management of information and operation method through advanced case analysis. In addition, the analysis of existing services revealed improvements in the management of an information standard, the trace ability of information and usability, and improved user-interface. The proposed development direction was implemented by deploying the advanced DTiMS. Therefore, it is expected that the proposed methodology will contribute to the weapon system total life cycle, and will support defense technology planning, and R&D decisions.

• Journal of Distribution Science
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• v.13 no.2
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• pp.85-94
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• 2015

Purpose - This study attempts to examine the impacts of consumers' perceived service convenience of retailers on various service performance metrics such as service quality and customer satisfaction. It also tries to investigate differences in the importance of service convenience dimensions on service performance between a department store and a general super market. Research design, data, and methodology - The four hypotheses in this study were proposed and tested. Two hypotheses were on the causal relationships between service convenience dimensions and service performances (service quality and customer satisfaction). The other two hypotheses were on comparisons for the effects of convenience dimensions on service quality and customer satisfaction between department stores and general super markets. To test the hypotheses, three department store chains (Hyundai, Lotte, and Shinsegae department Store) and three general super markets (E-mart, Homeplus, and Lotte mart) were involved. Overall, 510 usable responses were used. The data were analyzed using regression analysis. Results - The results largely support the hypothesized relationships of the proposed model. The results show that access convenience, transaction convenience, benefit convenience, and post-benefit convenience have positive influences on service quality, whereas decision convenience, access convenience, transaction convenience, benefit convenience, and post-benefit convenience have positive effects on customer satisfaction. Furthermore, the results show that there are differences between department stores and general super markets in the effects of benefit convenience and post-benefit convenience on service quality as well as the effects of transaction convenience and post-benefit convenience on customer satisfaction. Conclusions - The concept of service convenience is important in retail environments but little is known about this topic in retail literature. Specially, while service convenience dimensions have different impacts on service performance in distinct retail environments, there has been little investigation or comparison between retail types as regards service convenience. This study is the first to test the differences between distinct retail types (department stores and general super markets) on the service convenience-service performance links. Managerially, the findings of this study suggest that the service convenience management of retailers is an important part of successful service performance management. Because it is most important that both department stores and general super markets enhance benefit convenience to improve service performance, managers of both store types need to invest their resources to reduce consumers' perceived time and effort expenditures to experience the retailer's core benefits. Therefore, the results of this study suggest that retail stores should spend human and financial resources to enhance customer perceptions of service convenience, while also considering what constitutes the service outcome in the consumer's mind. Furthermore, the findings suggest that managers need to use different service convenience management tactics in department stores and general super markets. Specifically, managers in general super markets should pay more attention to benefit convenience and transaction convenience to achieve better service performance whereas managers in department stores should concentrate on post-benefit convenience to create customers' positive evaluation.