• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.297-307
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• 2021

Although there are several studies on the impact of pesticides use, there is no consistent conclusion about such evidence on capturing the socioeconomic independence. The propose of this paper is to investigate the economy-wide impact of pesticide use in Thailand. The research data and methodology in this paper are depended on a social accounting matrix framework incorporating the pesticide-related illness as an additional sector, following Resosudarmo and Thorbecke (1998), to explain the impact of the pesticides on the related agricultural sector, food sector, and the social welfare of different households. Thus, the main characteristics of the Thai economy can be comprehensively described by providing information contained in this framework. In this respect, the several data sets are constructed to include the economic and social structure interdependencies, which are necessary to analyze the policy implications, especially industrial policy. The results were analyzed according to the general equilibrium theory and the Leontief multiplier matrix. It reveals that the food industry and the economy are significantly affected by the pesticides. One of the most interesting findings of this paper suggest that the food sector needs to determine its output to avoid bottleneck situations and create equality across the food production system.

• Journal of Information Technology Applications and Management
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• v.29 no.3
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• pp.43-55
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• 2022

In this study, with the goal of developing a deep learning-based product recommendation model for effective matching of influencers and products, a deep learning model with a collaborative filtering model combined with generalized matrix decomposition(GMF), a collaborative filtering model based on multi-layer perceptron (MLP), and neural collaborative filtering and generalized matrix Factorization (NeuMF), a hybrid model combining GMP and MLP was developed and tested. In particular, we utilize one-class problem free boosting (OCF-B) method to solve the one-class problem that occurs when training is performed only on positive cases using implicit feedback in the deep learning-based collaborative filtering recommendation model. In relation to model selection based on overall experimental results, the MLP model showed highest performance with weighted average precision, weighted average recall, and f1 score were 0.85 in the model (n=3,000, term=15). This study is meaningful in practice as it attempted to commercialize a deep learning-based recommendation system where influencer's promotion data is being accumulated, pactical personalized recommendation service is not yet commercially applied yet.

• East Asian Journal of Business Economics (EAJBE)
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• v.10 no.1
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• pp.23-34
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• 2022

Purpose - Once individuals in an organization don't comprehend with each other's culture and their interaction context differ, there is a bigger danger of poor communication in the organizations. Therefore, people must have a sufficiently comparable interpretation of data, communicative acts and the office itself. For this reason, the purpose of this study is to investigate the relationship between organizational culture and employee communication. Research design, Data, and methodology - The present researcher used categorization matrix development to review data for content. Additionally, the present researcher coded the data to correspond with the identified categories. The role of the categorization matrix is to ensure that the categories hardly represent the concepts and thus validating the findings of the study. Result - The findings from prior resources shows that the role of organizational culture on employee communication satisfaction is a pertinent issue that must be addressed and analyzed to help organizations make the desired profits and productivity. This study provides five solutions between organizational culture and employee communication satisfaction. Conclusion - This study concludes that every employee performs well where they think they are being appreciated and their efforts are rewarded. On the other hand, different organizations have instilled different organizational cultures to ensure they promote the level of satisfaction of their employees in order to yield them an improved employee performance and overall organizational performance.

• International Commerce and Information Review
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• v.9 no.3
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• pp.59-79
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• 2007

This paper is to identify whether the mechanism of the real market as an off-line market developed from the transaction cost theory from previous research is applied to on-line market, such as usiness to Business e-marketplaces. With three factors(parties, product, environments) selected from previous research, four cases picked up from all B2B e-marketplace sites as of April 2007, in the view of Business to Business Matrix which presented by Kaplan & Sawhney(2000), were studied. They were accompanied by interview with CEO and team manager, and they explained their business and revenue model of the company. From the interview, the relation between three factors and usiness to Business e-marketplace was observed by taking sub-factors into consideration; parties -asset specificity, information asymmetry, product factors - standardization, price stability, and environments factors - competition, market uncertainty. The implications of this study are to analyze the relation of the transaction theory in offline and online. Also, this is the first study that analyzed it. In the future, another research based on this research will be studied.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.499-502
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• 2012

빅 데이터 분석의 시대가 도래하면서 대용량 데이터의 특성과 계산 집약적 연산의 특성을 동시에 가지는 문제 해결에 대한 요구가 늘어나고 있다. 대용량 데이터 처리의 경우 각종 분산 파일 시스템과 분산/병렬 컴퓨팅 기술들이 이미 많이 사용되고 있으며, 계산 집약적 연산 처리의 경우에도 GPGPU 활용 기술의 발달로 보편화되는 추세에 있다. 하지만 대용량 데이터와 계산 집약적 연산 이 두 가지 특성을 모두 가지는 문제를 처리하기 위해서는 많은 제약 사항들을 해결해야 하는데, 본 논문에서는 이에 대한 대안으로 분산 컴퓨팅 프레임워크인 Hadoop MapReduce와 Nvidia의 GPU 병렬 컴퓨팅 아키텍처인 CUDA 흘 연동하는 방안을 제시하고, 이를 밀집행렬(dense matrix) 연산에 적용했을 때 얻을 수 있는 성능 개선 효과에 대해 소개하고자 한다.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.885-896
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• 2021

The general role of commercial banks is to provide financial services to the general public and business, ensuring economic and social stability and sustainable growth of the economy. Commercial banks play an important role in mobilizing and channelizing funds for investment activities. This study analyzes the impact of the key financial variables on the net profit of the selected commercial banks in Oman. The study employs times series panel data - cross-sectional analysis of the key financials of five leading commercial banks for a period of 13 years from 2007 to 2019. The results reveal that the correlation matrix of the selected variables has a positive relationship with net profit, assets, deposits, loans, and interest income. However, the findings also shows a negative relationship between net profit and net loans to total deposits ratio. The study found net loans is the main independent variable that influences the profitability of the banks since the key source of revenue comes from the lending operations. The assets, total capital adequacy ratio have a mixed effect on the profitability of commercial banks. The total deposits and capital adequacy ratio have a negative effect on profitability mainly because excessive liquidity will increase the cost of capital and reduce the return on investment. Focusing on lending operations with a sound credit portfolio will improve profitability.

• Journal of Fashion Business
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• v.23 no.3
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• pp.51-66
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• 2019

The aim of this study is to identify research trends of "Journal of Fashion Business" by analyzing the keyword network of the paper published between 2006 and 2017. The papers selected for analysis in the study were 287 fashion design articles and 281 fashion marketing articles published between February 2006 and December 2017 and titles, volumes, publishing years, authors, keywords, and abstracts of each paper were collected for data analysis. The research was carried out through selection, collection of article data, keyword extraction and coding, keywords refinement, formation of network matrix, and analysis and visualization process. First, based on the title of the paper used in the analysis, the fashion design/aesthetics, marketing/social psychology, clothing materials, clothing composition, and other fields were classified. Research analysis used the Netminer 4 (Ver.4.3.2) program. Results indicated showed that the intellectual structure of the "Fashion Business" research paper showed key word changes over time, and the degree centrality and between centrality of the keywords.

• Industrial Engineering and Management Systems
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• v.8 no.3
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• pp.194-200
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• 2009

This study provides a heuristic algorithm to solve the locating problem of repair service centers (RSCs). To enhance the customer service level with more satisfaction and quicker responsiveness, the locating problem of RSCs has become one of the important issues in reverse supply chain management. This problem is formulated as a zero-one mixed integer programming in which an exiting distributor will be considered to be an un-capacitated repair service center for the objective of cost-minimizing. Since logistical costs are highly interrelated with the multinational location of distributors and RSCs, the fixed cost for setting a repair service center, variable cost, transportation cost, and exchange rates are considered in this study. Recognizing the selection of un-capacitated RSCs' locations is a combinatorial optimization problem and is a zero-one mixed integer programming with NP-hard complexity, we provide a heuristic algorithm named as incremental cost matrix procedure (ICMP) to simplify the solving procedure. By using the concise and structural cost matrix, ICMP can efficiently screen the potential location with cost advantage and effectively decide which distributor should be a RSC. Results obtained from the numerical experiments conducted in small scale problem have shown the fact that ICMP is an effective and efficient heuristic algorithm for solving the RSCs locating problem. In the future, using the extended ICMP to solve problems with larger industrial scale or problems with congestion effects caused by the variation of customer demand and the restriction of the RSC capacity is worth a further investigation.

• The Journal of Industrial Distribution & Business
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• v.9 no.1
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• pp.89-97
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• 2018

Purpose - The Regional Comprehensive Economic Partnership (RCEP) among 16 countries including South Korea, the largest free trade agreement in the Asia-Pacific region, will be concluded next year. The participating countries decided to pursue a comprehensive and high -quality agreement, while ensuring flexibility considering development level of each country. In this study, trade structures between nations from 2005 through 2016 were examined to see the impact that this agreement will have on Korea and to come up with effective countermeasures. Research design, data, and methodology - The method of analysis includes the analysis of the trade matrix, which is useful for identifying the dependency of the individual countries on the market in the region and the reciprocal dependency of the member countries on the market, and the index of intensity of trade, which is useful for figuring out the share of trade between the parties in total trade. Results - The results showed that first, the international trade coefficients of Vietnam and Philippines are higher than those of China and Japan. Secondly, the international inducement coefficients between China and Japan were high, and that between Indonesia and Burma were low, indicating that Korea's exports did not have much effect on export increase of these countries. Third, as a result of analyzing Korea's trade intensity, it was found that export intensity and import intensity were greater than 1 in Vietnam and Philippines, which shows that there is a high degree of relational bond with these countries. India and Laos countries still have a low level of relational bond, which indicates that there is room for improvement in economic relations when the agreement is concluded. After the signing of the agreement in the future, more diverse industrial structures should be continuously studied. Conclusions - The analysis of trade matrix, trade structure, trade inducement coefficient and trade intensity between Korea and RCEP participating countries shows that the majority of the countries have the high level of economic relationship with Korea. Korea should drive a harder bargain when negotiating the terms of the RCEP, in comparison with the level of the existing FTA agreement excluding Japan.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.67-84
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• 2010

In terms of business, forecasting is a work of what is expected to happen in the future to make managerial decisions and plans. Therefore, the accurate forecasting is very important for major managerial decision making and is the basis for making various strategies of business. But it is very difficult to make an unbiased and consistent estimate because of uncertainty and complexity in the future business environment. That is why we should use scientific forecasting model to support business decision making, and make an effort to minimize the model's forecasting error which is difference between observation and estimator. Nevertheless, minimizing the error is not an easy task. Case-based reasoning is a problem solving method that utilizes the past similar case to solve the current problem. To build the successful case-based reasoning models, retrieving the case not only the most similar case but also the most relevant case is very important. To retrieve the similar and relevant case from past cases, the measurement of similarities between cases is an important key factor. Especially, if the cases contain symbolic data, it is more difficult to measure the distances. The purpose of this study is to improve the forecasting accuracy of case-based reasoning approach using fuzzy relation and composition. Especially, two methods are adopted to measure the similarity between cases containing symbolic data. One is to deduct the similarity matrix following binary logic(the judgment of sameness between two symbolic data), the other is to deduct the similarity matrix following fuzzy relation and composition. This study is conducted in the following order; data gathering and preprocessing, model building and analysis, validation analysis, conclusion. First, in the progress of data gathering and preprocessing we collect data set including categorical dependent variables. Also, the data set gathered is cross-section data and independent variables of the data set include several qualitative variables expressed symbolic data. The research data consists of many financial ratios and the corresponding bond ratings of Korean companies. The ratings we employ in this study cover all bonds rated by one of the bond rating agencies in Korea. Our total sample includes 1,816 companies whose commercial papers have been rated in the period 1997~2000. Credit grades are defined as outputs and classified into 5 rating categories(A1, A2, A3, B, C) according to credit levels. Second, in the progress of model building and analysis we deduct the similarity matrix following binary logic and fuzzy composition to measure the similarity between cases containing symbolic data. In this process, the used types of fuzzy composition are max-min, max-product, max-average. And then, the analysis is carried out by case-based reasoning approach with the deducted similarity matrix. Third, in the progress of validation analysis we verify the validation of model through McNemar test based on hit ratio. Finally, we draw a conclusion from the study. As a result, the similarity measuring method using fuzzy relation and composition shows good forecasting performance compared to the similarity measuring method using binary logic for similarity measurement between two symbolic data. But the results of the analysis are not statistically significant in forecasting performance among the types of fuzzy composition. The contributions of this study are as follows. We propose another methodology that fuzzy relation and fuzzy composition could be applied for the similarity measurement between two symbolic data. That is the most important factor to build case-based reasoning model.