• 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.

• The Journal of Society for e-Business Studies
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• v.25 no.4
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• pp.33-60
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• 2020

Along with the rapid advance in internet technologies, ubiquitous mobile device usage has enabled consumers to access real-time information and increased interaction with others through various social media. Consumers can now get information more easily when making purchase decisions, and these changes are affecting the brand landscape. In a digitally connected world, brand image is not communicated to the consumers one-sidedly. Rather, with consumers' growing influence, it is a result of co-creation where consumers have an active role in building brand image. This explains a reality where people no longer purchase products just because they know the brand or because it is a famous brand. However, there has been little discussion on the matter, and many practitioners still rely on the traditional measures of brand indicators. The goal of this research is to present the limitations of traditional definition and measurement of brand and brand image, and propose a more direct and adequate measure that reflects the nature of a connected world. Inspired by the proverb, "A man is known by the company he keeps," the proposed measurement offers insight to the position of brand (or brand image) through co-purchased product networks. This paper suggests a framework of network analysis that clusters brands of cosmetics by the frequency of other products purchased together. This is done by analyzing product networks of a brand extracted from actual purchase data on Amazon.com. This is a more direct approach, compared to past measures where consumers' intention or cognitive aspects are examined through survey. The practical implication is that our research attempts to close the gap between brand indicators and actual purchase behavior. From a theoretical standpoint, this paper extends the traditional conceptualization of brand image to a network perspective that reflects the nature of a digitally connected society.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.8 no.3
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• pp.113-124
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• 2013

The government strives to create venture ecosystem for realizing creative economy, at the same time, the Mistry of Education is spending huge resources and efforts to spread entrepreneurship education to universities in Korea. Since entrepreneurship education gives motivation to individuals and creates market innovation and these connect to the growth of national economy through increased efficiency, entrepreneurship education is becoming increasingly more important for realizing creative economy. Based on the importance, entrepreneurship education in the universities is now spreading rapidly. However, college students' entrepreneurial intention has still not been improved comparing to spreading entrepreneurship education. To overcome the poor improvement, entrepreneurship education needs to be driven more systematic direction through the study on the effect of students' motivation and environment. In this study, entrepreneurship as a part of careers perspectives, is analyzed on students' career reasons with entrepreneurial intention. For this study, 918 surveys was collected from 7 universities having entrepreneurship courses in Seoul and Gyeonggi regions in 2012 and analyzed 858 surveys in order to prove the hypothesis. The results disclosed the relationship between students' career reasons and entrepreneurial self-efficacy and intention. Motivation factors of self-realization, innovation and role model have positive effect on entrepreneurial self-efficacy following by increased entrepreneurial intention, unlike the common notion financial success and independence factors are not significant with entrepreneurial intention of students. Based on these results having meaningful implication to Korea entrepreneurship education, this study is expected to have contribution to the successful promoting the creative economy realization of our government.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.6
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• pp.131-144
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• 2021

The organizational ambidexterity is an organizational strategy designed to pursue exploration activities to seize new opportunities and exploitation activities to efficiently use resources. Most of these ambidextrous structures have been studied for large corporations with slack resources, and there are still not many studies on the necessity of an ambidextrous structure for startups with relatively low-level resources. However, recently, the startup ecosystem is being advanced globally, and the amount of VC investment is rapidly increasing. This is a time when a lot of venture fund is invested in startups and a startup-friendly environment for rapid growth is created. This is the time to discuss the necessity and applicability of an ambidextrous organizational structure for startups. Therefore, this study conducted a hypothesis test whether the importance and necessity of balance that startups solving market problems with new ideas and utilizing accumulated resources have. To conduct this study, we analyzed 140 startups data gathered from the survey and the moderation effect was also analyzed. As a result of the study, it was verified that the balance of startup exploration and exploitation had a significant effect on startup performance, and the moderating effect of environmental dynamics was found to have a significant effect on the relationship with non-financial performance. Therefore, for startups with insufficient resources, it was concluded that the surplus resources generated in the process of a firm's growth should be effectively utilized and the balance between exploration and exploitation should be balanced from the initial stage of searching for a new business. In other words, it was confirmed that it is important for continuous growth and survival to seek the structure of an ambidextrous organization in order to internalize a mechanism that enables startups to pursue both effectiveness and efficiency in the long term. This study suggests a strategic direction for the growth of startups from the perspective of organizational structure. We expect that this meaningful results on the relationship between the ambidextrous capabilities of startups and performance contribute to the growth of startups in the rapidly growing startup venture environment.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• Journal of Distribution Science
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• v.9 no.2
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• pp.111-122
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• 2011

The purpose of this study is to survey the successful cases of small and medium Business Systematization Cognition by examining their entrepreneurial characteristics and analysing the factors affecting their success. To that end, previous studies on the association types of small businesses were studied. A research model was developed, and research hypotheses for an empirical analysis were established upon it. Suh et al. (2010) insist on the importance of Small Business Systematization in Korea but also show that small business performance is suffering: they are too small to stand alone. That is why association is so crucial for them: they must stand together. Unfortunately, association is difficult, as they have few specific links and little motivation. Even in franchising networks, association tends to be initiated by big franchisers, not small ones. In that sense, association among small businesses is crucial for their long-term survival. With this in mind, this study examines how they think and feel about the issue of 'Industrial Classification', how important Industrial Classification is to their business success, and what kinds of problems it raises in the markets. This study seeks the different cognitions among the association types of small businesses from the perspectives of participation motivation, systematization expectation, policy demand level, and management performance. We assume that different industrial classification types of small businesses will have different cognitions concerning these factors. There are four basic industrial classification types of small businesses: retail sales, restaurant, service, and manufacturing. To date, most of the studies in this area have focused on collecting data on the external environments of small businesses or performing statistical analyses on their status. In this study, we surveyed 4 market areas in Busan, Masan, and Changwon in Korea, where business associations consist of merchants, shop owners, and traders. We surveyed 330 shops and merchants by sending a questionnaire or visiting. Finally, 268 questionnaires were collected and used for the analysis. An ANOVA, T-test, and regression analyses were conducted to test the research hypotheses. The results demonstrate that there are differences in cognition depending upon the industrial classification type. Restaurants generally have a higher cognition concerning job offer problems and a lower cognition concerning their competitiveness. Restaurants also depend more on systematization expectation than do the other industrial classification types. On the policy demand level, restaurants have a higher cognition. This study identifies several factors that are contributing to management performance through differences in cognition that depend upon association type: systematization expectation and policy demand level have positive effects on management performance; participation motivation has a negative effect on management performance. We confirm also that the image factors of different cognitions are linked to an awareness of the value of systematization and that these factors show sequential and continual patterns in the course of generating performances. In conclusion, this study carries significant implications in its classifying of small businesses into the four different associational types (retail sales, restaurant, services, and manufacturing). We believe our study to be the first one to conduct an empirical survey in this subject area. More studies in this area will likely use our research frameworks. The data show that regionally based industrial classification associations such as those in rural cities or less developed areas tend to suffer more problems than those in urban areas. Moreover, restaurants suffer more problems than the norm. Most of the problems raised in this study concern the act of 'associating itself'. Most associations have serious difficulties in associating. On the other hand, the area where they have the least policy demand is that of service types. This study contributes to the argument that associating, rather than financial assistance or management consulting, promotes the start-up and managerial performance of small businesses. This study also has some limitations. The main limitation is the number of questionnaires. We could not survey all the industrial classification types across the country because of budget and time limitations. If we had, we could have produced many more useful results and enhanced the precision of our analysis. The history of systemization is very short and the number of industrial classification associations is relatively low in Korea. We should keep in mind, though, that this is very crucial to systemization entrepreneurs starting their businesses, as it can heavily affect their chances of success. Being strongly associated with each other might be critical to the business success of industrial classification members. Thus, the government needs to put more effort and resources into supporting the drive of industrial classification members to become more strongly associated.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.121-139
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• 2014

Predicting corporate failure has been an important topic in accounting and finance. The costs associated with bankruptcy are high, so the accuracy of bankruptcy prediction is greatly important for financial institutions. Lots of researchers have dealt with the topic associated with bankruptcy prediction in the past three decades. The current research attempts to use ensemble models for improving the performance of bankruptcy prediction. Ensemble classification is to combine individually trained classifiers in order to gain more accurate prediction than individual models. Ensemble techniques are shown to be very useful for improving the generalization ability of the classifier. Bagging is the most commonly used methods for constructing ensemble classifiers. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. Instance selection is to select critical instances while deleting and removing irrelevant and harmful instances from the original set. Instance selection and bagging are quite well known in data mining. However, few studies have dealt with the integration of instance selection and bagging. This study proposes an improved bagging ensemble based on instance selection using genetic algorithms (GA) for improving the performance of SVM. GA is an efficient optimization procedure based on the theory of natural selection and evolution. GA uses the idea of survival of the fittest by progressively accepting better solutions to the problems. GA searches by maintaining a population of solutions from which better solutions are created rather than making incremental changes to a single solution to the problem. The initial solution population is generated randomly and evolves into the next generation by genetic operators such as selection, crossover and mutation. The solutions coded by strings are evaluated by the fitness function. The proposed model consists of two phases: GA based Instance Selection and Instance based Bagging. In the first phase, GA is used to select optimal instance subset that is used as input data of bagging model. In this study, the chromosome is encoded as a form of binary string for the instance subset. In this phase, the population size was set to 100 while maximum number of generations was set to 150. We set the crossover rate and mutation rate to 0.7 and 0.1 respectively. We used the prediction accuracy of model as the fitness function of GA. SVM model is trained on training data set using the selected instance subset. The prediction accuracy of SVM model over test data set is used as fitness value in order to avoid overfitting. In the second phase, we used the optimal instance subset selected in the first phase as input data of bagging model. We used SVM model as base classifier for bagging ensemble. The majority voting scheme was used as a combining method in this study. This study applies the proposed model to the bankruptcy prediction problem using a real data set from Korean companies. The research data used in this study contains 1832 externally non-audited firms which filed for bankruptcy (916 cases) and non-bankruptcy (916 cases). Financial ratios categorized as stability, profitability, growth, activity and cash flow were investigated through literature review and basic statistical methods and we selected 8 financial ratios as the final input variables. We separated the whole data into three subsets as training, test and validation data set. In this study, we compared the proposed model with several comparative models including the simple individual SVM model, the simple bagging model and the instance selection based SVM model. The McNemar tests were used to examine whether the proposed model significantly outperforms the other models. The experimental results show that the proposed model outperforms the other models.

• The Korean Society of Law and Medicine
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• v.15 no.1
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• pp.335-373
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• 2014

According to one Medicare report, in the US, total federal spending on health care expends almost 18 percent of the nation's GDP, about double what most industrialized nations spend on health care. And in 2011, Medicare spending reached close to $554 billion, which amounted to 21 percent of the total spent on U.S. health care in that year. Of that $554 billion, Medicare spent 28 percent, or about $170 billion, on patients' last six months of life. So what are the reasons of this high cost in EOL care and its possible solutions? Much spendings of Medicare on End-of-Life care for the terminally ill/chronically ill in the US has led health economics experts to assess the characteristics of the care. Decades of study shows that EOL care is usually supply-sensitive and poor in cost-effectiveness. The volume of care is sensitively depending on the supply of resources, rather than the severity of illness or preferences of patients. This means at the End-of-Life care, the medical resources are being overused. On the other hand, opposed to the common assumption, "The more care the better utility", the study shows that the outcome is very poor. Actually the patient preference and concerns are quite the opposite from what intense EOL care would bring about. This study analyzes the reasons for the supply-sensitiveness of EOL care. It can be resulted from the common misconception about the intense care and the outcome, physicians' mission for patients, lack of End-of-Life Care Decision which helps the patients choose their own preferred treatment intensity. It also could be resulted from physicians' fear of legal liabilities, and the management strategy since the hospitals are also seeking for financial benefits. This study suggests the possible solutions for over-treatment at the End-of-Life resulting from supply-sensitiveness. Solutions can be sought in two aspects, legal implementation and management strategy. In order to implement advance directive properly, active ethics education for physicians to change their attitude toward EOL care and more conversations about end-of-life care between physicians and patients is crucial, and incentive system for the physicians who actively have the conversations with patients will also help. Also, the general education towards the public is also important in the long run, and easy and official advance directive registry system-such as online registry-has to be built and utilized more widely. Alternative strategies in management are also needed. For example, the new strategic cost management and management education, such as cutting unnecessary costs and resetting values as medical providers have to be considered. In order to effectively resolve the problem in EOL care for the terminally ill/chronically ill and provide better experience to the patients, first of all, the misconception and the wrong conventional wisdom among doctors, patients, and the government have to be overcome. And then there should be improvements in systems and cultures of the EOL care.

• Journal of the Korean Society of Food Science and Nutrition
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• v.38 no.5
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• pp.626-635
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• 2009

The purposes of the study were to identify a comprehensive competency list related to the jobs of nutrition teachers and to compare the competency needs for nutrition teachers and dieticians' competency levels. Job functional areas and a competency list for nutrition teachers were developed based on a review of literature and revised through expert panel reviews. A total of 53 competency statements associated with 11 job functional areas were specified. Questionnaires were distributed to 457 dieticians engaged in school food service and 148 responses were returned (response rate: 32.4%). Excluding incomplete responses, 142 questionnaires were used for data analysis. Approximately half of the respondents were enrolled in an 1-year nutrition teacher certificate program, 25% in a graduate school of nutrition education, and 19% had completed an 1-year nutrition teacher certificate program. The dieticians highly rated requirements of the competencies related to sanitation and employee safety, nutrition counseling, nutrition education, and teaching practices; in contrary, their competency levels for nutrition education, nutrition counseling, and teaching practices were low. Respondents' competency levels were significantly lower than the required competency levels of effective nutrition teachers in all functional areas. A quadratic analysis based on the requirement and self-evaluation of the competencies revealed that priorities of education programs targeting school nutrition teachers or students preparing to be nutrition teachers should be placed on improving competencies related to nutrition education, nutrition counseling, teaching practices, sanitation and employee safety, menu management, and human resource management. These results can be used to develop curriculum materials for basic and continuing professional education for nutrition teachers. It is necessary to review and update competencies regularly to reflect environmental changes in school food service programs.

• Management & Information Systems Review
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• v.38 no.2
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• pp.67-81
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• 2019

In accordance with K-IFRS 1109, financial instruments are classified to amortized cost (AC), fair value through other comprehensive income (FVOCI) and fair value through profit or loss (FVPL). And disposal gains are prohibited to be recirculated for net income when FVOCI financial instruments would be sold in the future, so-called recirculation prohibition. This research investigates whether accumulated other comprehensive income of available-for sale financial assets(AFS) under K-IFRS 1039, could affect reclassified amounts to the FVPL securities from the AFS securities. Also, this study investigates the effects of the reported income on the reclassified FVPL, because CEOs are likely to try earnings management when net income is predicted to be less than target or is low, comparing other firms. As a result of empirical analysis, first, I find that accumulated other comprehensive income of the AFS has a positive impact on the reclassified FVPL. Second, level of reporting income has no significant impact on the reclassified FVPL. Third, interaction effects are significantly positive on the firms which have more other comprehensive income and less level of reported income. Fourth, the effects of the bank and securities are more distinct than those of the manufactures. This study is the first research to investigate earnings management through AFS at the timing of the first adoption of K-IFRS 1109. Empirical results of this study provide evidence of earnings management on the reclassification of FVPL which gives meaningful implications to regulators, academic researchers and auditors.