• Asia-Pacific Journal of Business
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• v.13 no.3
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• pp.311-328
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• 2022

Purpose - The purpose of this study was to examine the relationship between firm's non financial performance and its shareholder return by analyzing PCA while focusing on the classifying all the variables into three categories such as financial, characteristics, and non financial factors of the firms. Design/methodology/approach - By exploring the pattern of self tender from the 801 firms in KOSPI while focusing on the objective of stock disposal, this paper analyzes the change of shareholder return of the firm. Findings - First, the higher major ownership, the lower self tender gets, whereby the higher ownership by foreigners, the ratio of self tender is higher. Secondly, cash dividend has not significant impact on the disposal of self stock, and the high ratio of ownership by foreigners leads to the high probability of retirement rather than the general disposal. In contrast, the major ownership has a negative impact on the general disposal as well as retirement. Thirdly, the score of non financial factors such as Environment(E), Social responsibility(S), and Governance(G) shows the high value in case of the firms with self tender. More specifically, the firms with retirement has the highest value of ESG while it has the lowest value in case of the firms with general disposal. Research implications or Originality - The retirement which means the active shareholder return is strongly affected by the non financial factors. Specifically, the probability of retirement increases in case of the firms with retirement, and even such a tendency is found to the case of the firms with general disposal.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.602-609
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• 2021

Under accrual basic accounting, financial statements may be less reliable compared to cash basis accounting. The purpose of this study is to conduct an empirical analysis to determine the possibility of profit adjustment through the increase and decrease of deferred tax accounts. For our empirical analysis, a dummy variable of '1' was used as a dependent variable when the deferred tax net assets increased from the previous year and '0' when the deferred tax net assets decreased. Meanwhile, the variables of interest were discretionary accruals and ROA variation compared to the previous year. Logistic regression analysis was performed to establish the relevance between variables. Results found larger discretionary accruals related to lower net deferred tax assets compared to the previous year. In addition, there was a correlation between ROA and net deferred tax assets only if the ROA increased and net profit was greater than '0'. Study results will enable deferred tax information to be used in investment decision-making, and supervisory institutions can establish policies to prevent profit adjustments and enhance reporting standards.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.7 no.3
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• pp.1-16
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• 2012

Under the international business circumstance, global supply chain management is considered a vital strategic challenge to small and medium-sized enterprises(SMEs) suffering from deficient resources and capabilities to exploit overseas markets comparing with large corporations. That is because they can expand their business domains into overseas markets by establishing strategic alliances with global supply chain partners. Although a wide range of previous researches have emphasized the cooperative networks in the chain, most are ignoring the importance of developing relational characteristics such as trust and reciprocity with the partners. Besides, verifying the relational factors influencing firms' export performances, some studies proposed different and inconsistent factors. According to the social capital theory, which is the social quality and networks facilitating close cooperation of inter-individual and inter-organization, provides the integrated view to identify the relational characteristics in the aspects of network, trust and reciprocal norm. Meanwhile, a number of researchers shows that global entrepreneurship is the internal and intangible resource necessary to promote SMEs' internationalization. Upon closer examination, however, they cannot explain clearly its influencing mechanism in the inter-firm cooperative relationships. This study is to verify the effect of social capital accumulated within global supply chain on SMEs' qualitative and quantitative export performance. In addition, we shed new light on global entrepreneurship expected to be concerned with the formation of social capital and the enhancement of export performances. For this purpose, the questionnaires, developed through literature review, were collected from 192 Korean SMEs affiliated in Korean Medium Industries Association and Global Chief Executive Officer's Club focusing on their memberships' international business. As a result of multi-regression analysis, the social capital - network, trust and reciprocal norm shared with global supply chain partner - as well as global entrepreneurship - innovativeness, proactiveness and risk-taking - have positive effect on SMEs' export performances. Also global entrepreneurship affects positively social capital which has mediating effect partially in the relationship between global entrepreneurship and performances. These results means that there is a structural process - global entrepreneurship(input), social capital(output), and export performances(outcome). In other words, a firm should consistently invest in and develop the social capital with global supply chain partners in order to achieve common goals, establish strategic collaborations and obtain long-term export performances. Furthermore, it is required to foster the global entrepreneurship in an organization so as to build up the social capital. More detailed practical issues and discussion are made in the conclusion.

• Journal of Technology Innovation
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• v.18 no.2
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• pp.253-288
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• 2010

Production innovation capabilities are critical to the survival and growth of firms. This paper investigates industrial dynamics and dynamic capabilities of firms by looking at how an industry evolution process influences firms' product innovation strategy and how dynamic capabilities affect firms' product innovation process. Korea Wireless Internet industry shows a full cycle of industry evolution process including introduction phase, growth phase, maturity phase, and decline phase using by dynamic technological and market changes. 7 listed companies in Korea Wireless Internet industry were selected. We have conducted multiple case studies based upon in depth interviews. Empirical results show that different phases of industry evolution influence firms' strategy of product innovation. Dynamic capabilities are also appears to be very important to the survival and growth of a firm.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.2
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• pp.1-13
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• 2019

As a way to find new and innovative technologies, many companies have invested in and acquired skilled startups. Because startups are usually small in size and have a small history of past business experience, there are many risks involved in acquiring them as they have limited technical skills and business feasibility verification methods. Thus, venture capital plays an important role in discovering and investing competitive startups. While Independent Venture Capital generally values financial returns, Corporate Venture Capital, which plays investment roles in the firm, values business synergies with the parent company from a strategic perspective. In an industry sector where development of technology is rapid and whether new technology is held determines a company's competitiveness, existing companies incorporate startups with innovative technologies into their investment portfolios, collaborate together, and take over for comprehensive cooperation. In addition, new investments and acquisitions are carried out through the management of portfolio companies to obtain and utilize industry information. In this paper, major U.S. companies listed in the U.S. verified their investment activities through corporate venture capital and their impact on parent companies and startups through regression, while the parent company's acquisition performance was analyzed through an event study based on a stock price analysis. The criteria for startup were defined as companies with less than 12 years of experience, and the analysis showed that the parent companies with corporate venture capital with a larger number of investments actively take over startups. In addition, increasing corporate venture capital's financial investment activities shows a negative impact on the parent companies' acquisition activities, and the acquisition performance increased when the parent companies took over startups in its portfolio.

• Management & Information Systems Review
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• v.37 no.2
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• pp.259-281
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• 2018

Advocates of mandatory IFRS adoption claim that IFRS increase financial statement comparability, which in turn leads to greater cross-border investment(Securities and Exchange Commision, 2008). The notion is that improved financial statement comparability reduces the information acquisition costs of global investors and thereby increase their investment in foreign firms. The purpose of this study is to examine this assertion by examining whether the K-IFRS adoption rusults in improved comparability that leads to increased investment by foreign investment. We also examined whether the relation between comparability and foreign investment has strengthen after adoption of K-IFRS. To achieve the purpose of our study, we measure Korean firms comparability using stock price model, stock return model and cash flow from operation model by Barth et al.(2012). We use both foreign ownership in the end of year and average during the year for dependent variables were to reduce bias. We test our hypothesis using 1,817 firm-year observation of KOSPI firms during the period of our analysis, 2011-2015. Consistent with our hypothesis, we find K-IFRS adoption results in a greater increase in foreign investment in firms with high comparability firms. This result indicate that the adoption of K-IFRS intends to achieve the international accounting convergence as stated in the roadmap and to reduce the Korea Discount.

• Journal of dental hygiene science
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• v.15 no.5
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• pp.585-593
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• 2015

This study, the value of dental hygiene process and business performance among the dental clinics located in Gyeonggi province by comparing and analyzing the financial and non-financial results specifically in the department that provides and did not provide dental hygiene process (ADPIE). The collected data treated with percentage and t-test in utilization of IBM SPSS Statistics ver. 20.0. In terms of the medical cost per patient, the Department A (DA) that applied the dental hygiene process were 216,664 Korean Won (KRW) in 2013 and 324,810 KRW in 2014 whereas Department B (DB) which did not apply the dental hygiene process resulted in 184,655 KRW in 2013 and 225,698 KRW in 2014 (p<0.01). Regarding the number of daily patients, the DA showed increase of 8.08 (p=0.01) while DB showed increase of 2.42 patients (p>0.05). The medical consent rate was 89.17% in DA and 60.09% in DB in 2013 while showing 89.68% and 66.98% respectively in 2014 (p<0.001). The patients' revisit rate was 87.48% in DA and 44.92% in DB in 2013 and that of the DA and DB was 85.89% and 45.55% respectively in 2014 (p<0.001). The rate of regular check-up was 16.01% in DA and 2.53% in DB in 2013 and the same rate in 2014 showed 19.03% and 6.84% respectively in 2014 (p <0.001). The rate of referred patients was 38.46% and 29.98% respectively in DA and DB in 2013 whereas DA showed 47.59% and DB showed 30.77% in 2014 (p<0.05). According to the results, the medical system with dental hygiene process is verified to be a premium medical program that can improve satisfaction as well as management effectiveness in dental service.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.241-254
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• 2011

Financial time-series forecasting is one of the most important issues because it is essential for the risk management of financial institutions. Therefore, researchers have tried to forecast financial time-series using various data mining techniques such as regression, artificial neural networks, decision trees, k-nearest neighbor etc. Recently, support vector machines (SVMs) are popularly applied to this research area because they have advantages that they don't require huge training data and have low possibility of overfitting. However, a user must determine several design factors by heuristics in order to use SVM. For example, the selection of appropriate kernel function and its parameters and proper feature subset selection are major design factors of SVM. Other than these factors, the proper selection of instance subset may also improve the forecasting performance of SVM by eliminating irrelevant and distorting training instances. Nonetheless, there have been few studies that have applied instance selection to SVM, especially in the domain of stock market prediction. Instance selection tries to choose proper instance subsets from original training data. It may be considered as a method of knowledge refinement and it maintains the instance-base. This study proposes the novel instance selection algorithm for SVMs. The proposed technique in this study uses genetic algorithm (GA) to optimize instance selection process with parameter optimization simultaneously. We call the model as ISVM (SVM with Instance selection) in this study. Experiments on stock market data are implemented using ISVM. In this study, the GA searches for optimal or near-optimal values of kernel parameters and relevant instances for SVMs. This study needs two sets of parameters in chromosomes in GA setting : The codes for kernel parameters and for instance selection. For the controlling parameters of the GA search, the population size is set at 50 organisms and the value of the crossover rate is set at 0.7 while the mutation rate is 0.1. As the stopping condition, 50 generations are permitted. The application data used in this study consists of technical indicators and the direction of change in the daily Korea stock price index (KOSPI). The total number of samples is 2218 trading days. We separate the whole data into three subsets as training, test, hold-out data set. The number of data in each subset is 1056, 581, 581 respectively. This study compares ISVM to several comparative models including logistic regression (logit), backpropagation neural networks (ANN), nearest neighbor (1-NN), conventional SVM (SVM) and SVM with the optimized parameters (PSVM). In especial, PSVM uses optimized kernel parameters by the genetic algorithm. The experimental results show that ISVM outperforms 1-NN by 15.32%, ANN by 6.89%, Logit and SVM by 5.34%, and PSVM by 4.82% for the holdout data. For ISVM, only 556 data from 1056 original training data are used to produce the result. In addition, the two-sample test for proportions is used to examine whether ISVM significantly outperforms other comparative models. The results indicate that ISVM outperforms ANN and 1-NN at the 1% statistical significance level. In addition, ISVM performs better than Logit, SVM and PSVM at the 5% statistical significance level.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.59-77
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• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.

• The KIPS Transactions:PartD
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• v.12D no.3 s.99
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• pp.417-428
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• 2005

Today, IT organizations perform projects with vision related to marketing and financial profit. The objective of realizing the vision is to improve the project performing ability in terms of QCD. Organizations have made a lot of efforts to achieve this objective through process improvement. Large companies such as IBM, Ford, and GE have made over $80\%$ of success through business process re-engineering using information technology instead of business improvement effect by computers. It is important to collect, analyze and manage the data on performed projects to achieve the objective, but quantitative measurement is difficult as software is invisible and the effect and efficiency caused by process change are not visibly identified. Therefore, it is not easy to extract the strategy of improvement. This paper measures and analyzes the project performance, focusing on organizations' external effectiveness and internal efficiency (Qualify, Delivery, Cycle time, and Waste). Based on the measured project performance scores, an OT (Opportunity Tree) model was designed for optimizing the project performance. The process of design is as follows. First, meta data are derived from projects and analyzed by quantitative GQM(Goal-Question-Metric) questionnaire. Then, the project performance model is designed with the data obtained from the quantitative GQM questionnaire and organization's performance score for each area is calculated. The value is revised by integrating the measured scores by area vision weights from all stakeholders (CEO, middle-class managers, developer, investor, and custom). Through this, routes for improvement are presented and an optimized improvement method is suggested. Existing methods to improve software process have been highly effective in division of processes' but somewhat unsatisfactory in structural function to develop and systemically manage strategies by applying the processes to Projects. The proposed OT model provides a solution to this problem. The OT model is useful to provide an optimal improvement method in line with organization's goals and can reduce risks which may occur in the course of improving process if it is applied with proposed methods. In addition, satisfaction about the improvement strategy can be improved by obtaining input about vision weight from all stakeholders through the qualitative questionnaire and by reflecting it to the calculation. The OT is also useful to optimize the expansion of market and financial performance by controlling the ability of Quality, Delivery, Cycle time, and Waste.