• The Journal of Asian Finance, Economics and Business
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• 제7권10호
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• pp.161-169
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• 2020

The condition of mining companies that exploit natural resources in their business processes underline this research to emphasize on social and environmental issues. After twelve years of government regulation on CSR practices, this study investigates the factors that influence mining companies in disclosing information about corporate social responsibility based on legitimacy, stakeholders, and agency theory. Thus, independent variables are foreign ownership, company size, leverage, and the board of commissioners. The dependent variable is the corporate social reporting disclosure that is measured using GRI indexing. For sampling, we have used thirty-four Indonesian mining companies listed in IDX during the 2014-2018. out of which only fifty-two companies meet the sample criteria. All data should pass the classical assumption test to get the best estimator. Multiple linear regression is used to test the hypothesis, and the results show that the model is good, and can explain 60% of the dependent variable. Based on F-test, all four variables affect CSR practices simultaneously. The findings of this study suggest that foreign ownership and firm size influences CSR disclosure in a positive direction. However, this study did not support the hypothesis that leverage negatively affects CSR disclosure and board size measures positively affect CSR disclosure.

• 암석학회지
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• 제17권4호
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• pp.231-237
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• 2008

아르헨티나의 대표적인 외국광산기업은 엑스트라타, 바릭 골드, 야마나 골드, 앵글로 골드의 4개 회사들이다. 본문에서는 이들 회사 중 첫 번째로 엑스트라타의 회사연혁, 현재와 미래의 광산프로젝트, 생산량, 재무현황의 개요를 소개하였는데 이는 좋은 협력파트너를 모색하려는 한국 광산기업들에게 심각한 투자실수를 피하기 위해서다.

• 한국정보시스템학회지:정보시스템연구
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• 제14권2호
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• pp.277-292
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• 2005

This paper uses a data mining approach to develop bankruptcy prediction models suitable for traditional (off-line) companies and electronic (on-line) companies. It observes the differences in the composition prediction models between these two types of companies and provides interpretation of bankruptcy classifications. The bankruptcy prediction models revealed the major variables in predicting bankruptcy to be 'cash flow to total assets' and 'gross value-added to net sales' for traditional off-line companies while 'cash flow to liabilities','gross value-added to net sales', and 'current ratio' for electronic companies. The accuracy rates of final prediction models for traditional off-line and electronic companies were found to be $84.7\%\;and\;82.4\%$, respectively. When the model for traditional off-line companies was applied for electronic companies, prediction accuracy dropped significantly in the case of bankruptcy classification (from $70.4\%\;to\;45.2\%$) at the level of a blind guess ($41.30\%$). Therefore, the need for different models for traditional off-line and electronic companies is justified.

• The Journal of Asian Finance, Economics and Business
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• 제8권7호
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• pp.433-446
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• 2021

This study aims to identify the elasticities and casualties of financial performance and determinants of the mining and extractive companies listed in Jordan's stock market over the 2005-2018 period. The conceptual framework is based on the Resource-Based View theory and Arbitrage Pricing theory is used to describe the relationship between the external environment and the financial performance of the companies. Profitability ratio (return on assets) is utilized as a proxy of financial performance measurement. Meantime, the company's characteristics, macroeconomic variables, and non-economic factors are utilized as independent factors. Data sources are panel data set for mining and extractive companies over the above period. Fully Modified Ordinary Least Square (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Pooled Mean Group (PMG) methods are applied. The empirical findings indicated that company size, sales growth, financial leverage, liquidity, and GDP growth were the critical determinants of mining and extractive companies' financial performance in the Amman Stock Exchange. Thus, the findings conclude that company characteristics and GDP growth mainly drive financial performance. Moreover, the findings reveal that a bidirectional causal elasticity exists between GDP and financial leverage and return on assets (ROA). Sound financial performance can be obtained by paying more attention to GDP growth and firms' characteristics.

• 정보기술과데이타베이스저널
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• 제6권1호
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• pp.89-101
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• 1999

Increasing number of companies recognize that the understanding of customers and their markets is indispensable for their survival and business success. The companies are rapidly increasing the amount of investments to develop customer databases which is the basis for the database marketing activities. Database marketing is closely related to data mining. Data mining is the non-trivial extraction of implicit, previously unknown and potentially useful knowledge or patterns from large data. Data mining applied to database marketing can make a great contribution to reinforce the company's competitiveness and sustainable competitive advantages. This paper develops the classification model to select the most responsible customers from the customer databases for telemarketing system and evaluates the performance of the developed model using LIFT measure. The model employs the decision tree algorithm, i.e., CHAID which is one of the well-known data mining techniques. This paper also represents the effective database marketing strategy by applying the data mining technique to a credit card company's telemarketing system.

• 한국정보시스템학회지:정보시스템연구
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• 제29권4호
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• pp.137-151
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• 2020

Purpose Domestic asset management fintech companies are expected to grow by leaps and bounds along with the implementation of the "Data bills." Contrary to the market fever, however, academic research is insufficient. Therefore, we want to analyze user reviews of asset management fintech companies that are expected to grow significantly in the future to derive strengths and complementary points of services that have been provided, and analyze key elements of asset management fintech companies. Design/methodology/approach To analyze large amounts of review text data, this study applied text mining techniques. Bank Salad and Toss, domestic asset management application services, were selected for the study. To get the data, app reviews were crawled in the online app store and preprocessed using natural language processing techniques. Topic Modeling and Aspect-Sentiment Analysis were used as analysis methods. Findings According to the analysis results, this study was able to derive the elements that asset management fintech companies should have. As a result of Topic Modeling, 7 topics were derived from Bank Salad and Toss respectively. As a result, topics related to function and usage and topics on stability and marketing were extracted. Sentiment Analysis showed that users responded positively to function-related topics, but negatively to usage-related topics and stability topics. Through this, we were able to extract the key elements needed for asset management fintech companies.

• 한국CDE학회논문집
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• 제14권6호
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• pp.382-389
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• 2009

Recently, knowledge management has been required in companies as a tool of competitiveness. Companies have constructed Enterprise Resource Planning(ERP) system in order to manage huge knowledge. But, it is not easy to formalize knowledge in organization. We focused on data mining system by genetic programming(GP). Data mining system by genetic programming can be useful tools to derive and extract the necessary information and knowledge from the huge accumulated data. However when we don't have enough amounts of data to perform the learning process of genetic programming, we have to reduce input parameter(s) or increase number of learning or training data. In this study, an enhanced data mining method combining Genetic Programming with Self organizing map, that reduces the number of input parameters, is suggested. Experiment results through a prototype implementation are also discussed.

• 한국정보통신학회논문지
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• 제15권1호
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• pp.133-140
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• 2011

특정 제품이나 서비스에 대한 네티즌의 의견들은 고객들의 구매 행위에서의 참고대상일 뿐만 아니라 기업 입장에서도 마케팅이나 경영전략을 수립하기 위한 중요한 자료가 될 수 있기 때문에 온라인 고객리뷰를 분석하는 것은 매우 중요하다. 본 논문에서는 비정형(unformatted) 데이터형인 자연어(natural language) 형태로 웹상에 게시된 고객 의견들을 분석할 수 있는 새로운 오피년마이닝 기법을 제안한다. 기존 데이터마이닝 기법 중의 하나인 연관규칙탐사 기법을 수정하여 오피년마이닝 과정에 보다 효율적이고 효과적으로 적용하기 위한 방안을 고찰하고 이를 기반으로 실제 시스템을 설계하고 구현하였다.

• The Journal of Asian Finance, Economics and Business
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• 제7권10호
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• pp.803-810
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• 2020

This objective of this study is to enrich the literature by the debt ratio and enterprise performance of Joint stock companies of Vietnam National Coal and Mineral Industries Holding Corporation Limited (Vinacomin). The debt ratio is an important index of capital structure, and it influences and decides the enterprise performance. Therefore, the determination of reasonable debt ratio level is beneficial to the stable operation of Vinacomin's enterprises. Based on the research conclusion about the effect on capital structure of debt ratio from domestic and foreign scholar, collecting data from 2014-2018 of Vinacomin's enterprises as a research sample, the article conducts research on the relationship between debt ratio and business performance of Vinacomin, as measured by return on total Assets. In addition, the study uses free cash flow, company size, growth opportunity, investment opportunities, operating costs to sales ratio as control variables.The study shows the debt ratio of Joint stock companies of Vietnam National Coal and Mineral Industries Holding Corporation Limited has a negative effect on the enterprise performance. Furthermore, the research results of the article are references for Vinacomin' enterprises in the course of production and business activities, determining a reasonable debt ratio, and improving the operational performance of enterprises.

• 유통과학연구
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• 제11권3호
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• pp.5-11
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• 2013

Purpose - Going concern is one of fundamental concepts in accounting and auditing and sometimes the assessment of a company's going concern status that is a tough process. Various going concern prediction models' based on statistical and data mining methods help auditors and stakeholders suggested in the previous literature. Research design - This paper employs a data mining approach to prediction of going concern status of Iranian firms listed in Tehran Stock Exchange using Particle Swarm Optimization. To reach this goal, at the first step, we used the stepwise discriminant analysis it is selected the final variables from among of 42 variables and in the second stage; we applied a grid-search technique using 10-fold cross-validation to find out the optimal model. Results - The empirical tests show that the particle swarm optimization (PSO) model reached 99.92% and 99.28% accuracy rates for training and holdout data. Conclusions - The authors conclude that PSO model is applicable for prediction going concern of Iranian listed companies.