• Management & Information Systems Review
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• v.37 no.1
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• pp.19-40
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• 2018

The purpose of this study is to analyze the effects of a firm's dynamic capabilities measured by sensing, seizing, transforming, coordinating, and learning capabilities on its competitive capabilities, such as product quality, process flexibility, delivery speed, and low cost. The relationship among dynamic capabilities, competitive capabilities, and export firm performance is set up as a research model based on empirical studies related to the existing dynamic capability perspective and competitive capabilities. To test this research model, this study collected 102 samples of data using a questionnaire survey on both manufacturing and exporting firms. The partial least squares method is used and the following results are derived from an empirical analysis. First, dynamic capabilities have a positive effect on competitive capabilities, such as product quality, process flexibility, delivery speed, and low cost. Second, product quality and process flexibility have a positive effect on export firm performance. Third, unlike previous research results, this study finds that the competitive capabilities of a firm in the areas of delivery speed and low cost do not significantly affect its performance. These findings provide meaningful implications for export venture firms that need to acquire and maintain competitive advantage in a rapidly changing environment.

• Journal of Digital Convergence
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• v.13 no.11
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• pp.119-132
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• 2015

This study empirically examined which factors facilitate or disturb the learning and practical knowledge transfer in consulting and which factors have most powerful influence on the learning and transfer of consulting knowledge. Analysing 160 data collected from FTA origin managers in export companies, the study findings show the ambiguity(-), complexity(+), consulting competences(+), intervention design and delivery(+), self-efficacy(+) and government subsidies(+) significantly affected on Client's learning, while consultant's expertise(+), consulting involvement(+), transfer culture(+) significantly affected on consulting knowledge transfer, respectively. It showed that consulting competence and causal ambiguity have an greater influence on learning while consultant's expertise has a greater influence on consulting knowledge transfer, respectively. The findings implicate that consulting success depends on rather consultant's factors(consultant's expertise and consulting competence) than client's input factors. To succeed in consulting project, it is important that the consultants effectively develop and apply consulting methods & tools as shared interfaces between consultant and client.

• International Area Studies Review
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• v.20 no.1
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• pp.209-234
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• 2016

This study examines the effects of exporter agglomeration on purely local firms' decision to undertake internationalization and the resultant performance enhancement from internationalization using propensity score matching and difference-in-differences approach. We find that the likelihood of starting to export is higher when purely local firms are located in a region with a higher level of exporter agglomeration, as positive externalities allow them to overcome insufficient internal resources and reduce the large initial foreign market entry costs. We also find that newly exporting firms are more likely to experience greater performance enhancement from exporting when they are locate in a region with a lower level of exporter agglomeration.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.176-182
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• 2023

With about 80% of the global economy expected to shift to the global market by 2030, exports of reverse direct purchase products, in which foreign consumers purchase products from online shopping malls in Korea, are growing 55% annually. As of 2021, sales of reverse direct purchases in South Korea increased 50.6% from the previous year, surpassing 40 million. In order for domestic SMEs(Small and medium sized enterprises) to enter overseas markets, it is important to come up with export strategies based on various market analysis information, but for domestic small and medium-sized sellers, entry barriers are high, such as lack of information on overseas markets and difficulty in selecting local preferred products and determining competitive sales prices. This study develops an AI-based product recommendation and sales price estimation model to collect and analyze global shopping malls and product trends to provide marketing information that presents promising and appropriate product sales prices to small and medium-sized sellers who have difficulty collecting global market information. The product recommendation model is based on the LTR (Learning To Rank) methodology. As a result of comparing performance with nDCG, the Pair-wise-based XGBoost-LambdaMART Model was measured to be excellent. The sales price estimation model uses a regression algorithm. According to the R-Squared value, the Light Gradient Boosting Machine performs best in this model.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.10
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• pp.427-436
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• 2022

Korea Trade-Investment Promotion Agency (KOTRA) annually publishes the trade data in South Korea under the guidance of the Ministry of Trade, Industry and Energy in South Korea. The trade data usually contains Gross domestic product (GDP), a custom tariff, business score, and the price of export items in previous and this year, with regards to the trading items and the countries. However, it is challenging to figure out the meaningful insight so as to predict the future price on trading items every year due to the significantly large amount of data accumulated over the several years under the limited human/computing resources. Within this context, this paper proposes a multi layer perception that can predict the future price of potential trading items in the next year by training large amounts of past year's data with a low computational and human cost.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.73-95
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• 2021

This study uses Node2vec graph embedding method and Light GBM link prediction to explore undeveloped export candidate countries in Korea's food and beverage industry. Node2vec is the method that improves the limit of the structural equivalence representation of the network, which is known to be relatively weak compared to the existing link prediction method based on the number of common neighbors of the network. Therefore, the method is known to show excellent performance in both community detection and structural equivalence of the network. The vector value obtained by embedding the network in this way operates under the condition of a constant length from an arbitrarily designated starting point node. Therefore, it has the advantage that it is easy to apply the sequence of nodes as an input value to the model for downstream tasks such as Logistic Regression, Support Vector Machine, and Random Forest. Based on these features of the Node2vec graph embedding method, this study applied the above method to the international trade information of the Korean food and beverage industry. Through this, we intend to contribute to creating the effect of extensive margin diversification in Korea in the global value chain relationship of the industry. The optimal predictive model derived from the results of this study recorded a precision of 0.95 and a recall of 0.79, and an F1 score of 0.86, showing excellent performance. This performance was shown to be superior to that of the binary classifier based on Logistic Regression set as the baseline model. In the baseline model, a precision of 0.95 and a recall of 0.73 were recorded, and an F1 score of 0.83 was recorded. In addition, the light GBM-based optimal prediction model derived from this study showed superior performance than the link prediction model of previous studies, which is set as a benchmarking model in this study. The predictive model of the previous study recorded only a recall rate of 0.75, but the proposed model of this study showed better performance which recall rate is 0.79. The difference in the performance of the prediction results between benchmarking model and this study model is due to the model learning strategy. In this study, groups were classified by the trade value scale, and prediction models were trained differently for these groups. Specific methods are (1) a method of randomly masking and learning a model for all trades without setting specific conditions for trade value, (2) arbitrarily masking a part of the trades with an average trade value or higher and using the model method, and (3) a method of arbitrarily masking some of the trades with the top 25% or higher trade value and learning the model. As a result of the experiment, it was confirmed that the performance of the model trained by randomly masking some of the trades with the above-average trade value in this method was the best and appeared stably. It was found that most of the results of potential export candidates for Korea derived through the above model appeared appropriate through additional investigation. Combining the above, this study could suggest the practical utility of the link prediction method applying Node2vec and Light GBM. In addition, useful implications could be derived for weight update strategies that can perform better link prediction while training the model. On the other hand, this study also has policy utility because it is applied to trade transactions that have not been performed much in the research related to link prediction based on graph embedding. The results of this study support a rapid response to changes in the global value chain such as the recent US-China trade conflict or Japan's export regulations, and I think that it has sufficient usefulness as a tool for policy decision-making.

• The Journal of Korean Association of Computer Education
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• v.5 no.2
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• pp.39-48
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• 2002

It is general that the ICAI(Intelligent Computer Assisted Instruction) consists of 4 modules. Export module, Teacher module, Student module and Interface module. In each module construction, there should be some rules to control strategies efficiently and systematically that are related to the texts and assessment instruments, assessment results and evaluation, feedback, etc. It is necessary to use a method to classify the curriculum into sections with units and to represent the identified relationships between them. These relationships are available to all the process of learning, assessment, evaluation and feedback. In this paper, we propose the method to represent these units and relationships as a graph. In addition, we implement an internet-based courseware authoring tool to support the environment in which several expert can construct concurrently the courseware with cooperation between them.

• Journal of Internet Computing and Services
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• v.3 no.4
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• pp.27-36
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• 2002

The purpose of this study is to develop editor applied to vector image for the distance learning system(FVU), which enables teachers effectively to construct self-page on the screen, to reduce the size of file for teaching, and to correct many different kinds of event which was already made in the previous, The design of the editor is used UML and the editor is named VUEditor. The first page which is needed in class can be constructed by using VUEditor. The contents using VUEditor ore exported into VUAuthor through Vector-transformation. Through this procedure, the size of image file comes to be reduced, it has a low bond width. In conclusion, this VUEditor enables user to construct the first page. even without using such applied program as Image Tool and Power Point, to solve the problem of network traffic for reducing size of the file.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.4
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• pp.163-172
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• 2017

In recent days, the Non-Linear Editing is mainly used in the field of broadcasting. In comparison to conventional editing, Non-Linear Editing can immediately access the image of the desired position and facilitate the insertion and deletion of video frame. Furthermore, it directly apply a title and transition effect to video frame. Moreover, it has an advantage of preview and easy modification in title effect, transition and editing prior to export. However, students who learn Non-Linear Editing first time are not easy to learn it. In this paper, we propose a new learning model based on Reciprocal Peer Teaching (RPT), which helps NLE beginners to understand Non-Linear editing more clearly. We divide the students into two groups i.e. control group and experimental group. The control group students do not apply proposed method while experimental group performs evaluation over our model. Furthermore, we carry out the experiments, which include the overall average of the two groups, academic achievement of students with low grades, standard deviation, T-test and satisfaction surveys. The experimental group shows the superiority in performed experiments and higher satisfaction ratings than the control group.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.83-102
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• 2021

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.