• KDI Journal of Economic Policy
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• 제43권4호
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• pp.1-21
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• 2021

This paper aims to investigate whether there is empirical evidence supporting the learning-to-export hypothesis, which has received little attention in the literature. By taking full advantage of plant-product level data from Korea during 1990-1998, we find some evidence for the learning-to-export effect, especially for the innovated product varieties with delayed exporters: their productivity, together with research and development and investment activity, was superior to their matched sample. On the other hand, this learning-to-export effect was not significantly pronounced for industries protected by import tariffs. Thus, our empirical findings suggest that it would be desirable to implement certain policy tools to promote the learning-to-export effect, whereas tariff protection is not justifiable for that purpose.

• 산업경영시스템학회지
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• 제46권2호
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• pp.152-159
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• 2023

Due to COVID-19, changes in consumption trends are taking place in the distribution sector, such as an increase in non-face-to-face consumption and a rapid growth in the online shopping market. However, it is difficult for small and medium-sized export sellers to obtain forecast information on the export market by country, compared to large distributors who can easily build a global sales network. This study is about the prediction of export amount and export volume by country and item for market information analysis of small and medium export sellers. A prediction model was developed using Lasso, XGBoost, and MLP models based on supervised learning and deep learning, and export trends for clothing, cosmetics, and household electronic devices were predicted for Korea's major export countries, the United States, China, and Vietnam. As a result of the prediction, the performance of MAE and RMSE for the Lasso model was excellent, and based on the development results, a market analysis system for small and medium sellers was developed.

• 유통과학연구
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• 제14권2호
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• pp.135-144
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• 2016

Purpose - The purpose of this paper is to empirically investigate the moderating effects of social capital on antecedents factors path of export performance in the materials and components SMEs(Small and Medium-sized Enterprises) of Busan and Kyungnam region. In case of materials and components SMEs, they are always trying to achieve business performance including export sales and market share, but it is difficult for them to increase performance due to the limitation of inner & tangible resources. Therefore intangible asset such as technology capability and its antecedents factors which are technology innovation and learning orientation are getting more important to SMEs. In addition, it is supposed that social capital such as local network including distribution channel in overseas market plays an essential role to enhance export performance. Accordingly, the main goal of this study is to find out the relationship between export performance and antecedents factors and the validity of social capital as a moderating valuable. Research design, data, and methodology - Technology innovation, learning orientation and technology capability have been used as antecedents factors for export performance and social capital such as network diversity and intensity has been used for moderating effects analysis. In order to select these valuables mentioned above, this study examined the existing researches on a basis of Resources Based View, Organizational Learning Theory and Social Capital theory. To achieve the objective of this paper, 7 hypotheses including the moderating effects have been proposed with 6 potential variables measured by 24 questions. The survey was carried out from December 2014 to March 2015 and 137 samples out of total 175 were selected for the analysis. PLS(Partial Least Squares) has been used for the methodology of empirical analysis for both antecedents factors path and moderating effects. Results - Research findings are as follows. First, technology innovation has a significant impact on learning orientation, learning orientation has a positive effect on the technology capability and technology capability also has a significant impact on export performance. Therefore 3 valuables are proved as antecedents factors of export performance. Second, the social capital(both network diversity and intensity) plays a moderating role with learning orientation to technology capability. However, there is no moderating effects between both of social capital and technology capability regarding export performance. Conclusions - According to path analysis results, it is suggested that the materials and components SMEs should raise technology innovation and learning orientation in order to improve technology capability and export performance. Meantime, the moderating effect analysis shows that SMEs should consider local network diversity and intensity along with learning orientation to add up technology capability. But local network diversity and intensity does not work systematically with technology capability for export performance and it means that SMEs should find the appropriate local partners for the purpose of establishing concrete distribution channels based on marketing perspective, not for improving technology capability.

• 무역학회지
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• 제42권5호
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• pp.93-112
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• 2017

본 연구는 수출 중소기업의 FTA 정보학습의 역할을 실증분석하는데 목적을 두었다. 이에 중소기업 수출성과 경로에서 FTA 정보학습의 조절효과를 포함한 통합적 모형을 구축했다. 중소기업의 현지화 전략, 제품혁신 역량, FTA 정보학습의 관계를 수출성과에 연계하여 수출 중소기업 195개를 대상으로 실증분석을 시행했다. 구조방정식모형을 사용하여 경로분석을 시행했으며, 조절효과를 포함한 6개 가설을 검정했다. 분석결과 중소기업의 현지화 전략은 제품혁신 역량에 긍정적 영향을 주었다. 반면 FTA 정보학습은 유의한 결과를 보여주지 못했다. 수출성과 선행요인으로 제품혁신 역량과 FTA 정보학습은 모두 유의한 결과를 보여주었다. 조절효과의 경우 현지화 전략과 FTA 정보학습은 제품혁신 역량에 유의한 조절효과를 나타내지 못했다. 반면 제품혁신 역량과 FTA 정보학습은 중소기업 수출성과에 유의한 영향을 미치는 것으로 나타났다.

• 무역학회지
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• 제46권6호
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• pp.191-209
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• 2021

In this paper, various time series analysis models and machine learning models are presented for long-term prediction of export growth rate, and the prediction performance is compared and reviewed by RMSE and MAE. Export growth rate is one of the major economic indicators to evaluate the economic status. And It is also used to predict economic forecast. The export growth rate may have a negative (-) value as well as a positive (+) value. Therefore, Instead of using the ReLU function, which is often used for time series prediction of deep learning models, the PReLU function, which can have a negative (-) value as an output value, was used as the activation function of deep learning models. The time series prediction performance of each model for three types of data was compared and reviewed. The forecast data of long-term prediction of export growth rate was deduced by three forecast methods such as a fixed forecast method, a recursive forecast method and a rolling forecast method. As a result of the forecast, the traditional time series analysis model, ARDL, showed excellent performance, but as the time period of learning data increases, the performance of machine learning models including LSTM was relatively improved.

• 무역학회지
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• 제46권1호
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• pp.169-187
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• 2021

This paper analyzes the feasibility of using machine learning and deep learning methods to forecast the income and employment using the strategic industries as well as investment, export, and exchange rates. The decision tree, artificial neural network, support vector machine, and deep learning models were used to forecast the income and employment in Busan. The following were the main findings of the comparison of their predictive abilities. First, the decision tree models predict the income and employment well. The forecasting values for the income and employment appeared somewhat differently according to the depth of decision trees and several conditions of strategic industries as well as investment, export, and exchange rates. Second, since the artificial neural network models show that the coefficients are somewhat low and RMSE are somewhat high, these models are not good forecasting the income and employment. Third, the support vector machine models show the high predictive power with the high coefficients of determination and low RMSE. Fourth, the deep neural network models show the higher predictive power with appropriate epochs and batch sizes. Thus, since the machine learning and deep learning models can predict the employment well, we need to adopt the machine learning and deep learning models to forecast the income and employment.

• 무역학회지
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• 제43권2호
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• pp.107-125
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• 2018

본 연구는 수출 중소기업을 대상으로 개방형혁신과 제휴 유형이 수출성과에 영향을 미치는 경로를 분석하였다. 기존 연구에 따라 개방형혁신은 기술흡수형혁신과 기술활용형혁신으로 나누었다. 제휴 유형은 마케팅과 기술 제휴로 나누었다. 수출성과를 포함하여 5개 잠재변인 간에 6개의 가설을 설정하였다. 수출활동을 전개하는 중소기업 202개를 표본으로 실증분석을 시행하였다. 분석결과 기술흡수형혁신은 마케팅 제휴와 기술 제휴에 모두 유의한 영향을 주었다. 반면 기술활용형혁신은 기술 제휴에만 유의한 효과를 보였다. 제휴 유형은 수출성과에 모두 긍정적 영향을 보여주었다. 이로써 한국 중소기업의 수출활동에 개방형 혁신과 유통경로 확보 등에 필요한 마케팅 제휴의 관계를 활성화해야 함을 시사점으로 제시했다.

• 융합경영연구
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• 제8권1호
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• pp.36-49
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• 2020

Purpose - This study investigates factors that affected the utilization of trade support policies and further analyzed how the utilization of trade support policies affected export performance. Research design, data, and methodology - With a sample of 223 small and medium-sized export firms from South Korea, this study examines the determinants of the utilization level of trade support system such as export market orientation, learning orientation, network capability and environmental uncertainty by regression analysis. Results - Export market orientation have a positive effect on the utilization of the trade support system and there is positive relationship between learning orientation and the utilization of trade support system. And network capabilities have had a positive impact on the utilization of the trade support system but there is no relationship between environmental uncertainty and the utilization of trade support system. The utilization of the trade support system had a positive effect on export performance. Conclusions - The internal and external factors of the organization have affected small and medium-sized export firms use of trade support systems. The utilization of trade support system can enhance positive export performance by providing valuable information and resource to external knowledge and also to complementary resources from the external partners.

• Asian Journal of Business Environment
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• 제12권3호
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• pp.1-12
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• 2022

Purpose: This study aims to investigate whether AIS data can be used as a supporting indicator or as an initial signal to describe Indonesia's export-import conditions in real-time. Research design, data, and methodology: This study performs several stages of data selection to obtain indicators from AIS that truly reflect export-import activities in Indonesia. Also, investigate the potential of AIS indicators in producing forecasts of the value and volume of Indonesian export-import using conventional statistical methods and machine learning techniques. Results: The six preprocessing stages defined in this study filtered AIS data from 661.8 million messages to 73.5 million messages. Seven predictors were formed from the selected AIS data. The AIS indicator can be used to provide an initial signal about Indonesia's import-export activities. Each export or import activity has its own predictor. Conventional statistical methods and machine learning techniques have the same ability both in forecasting Indonesia's exports and imports. Conclusions: Big data AIS can be used as a supporting indicator as a signal of the condition of export-import values in Indonesia. The right method of building indicators can make the data valuable for the performance of the forecasting model.

• 무역학회지
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• 제46권2호
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• pp.281-299
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• 2021

This paper analyzes the feasibility of using machine learning methods to forecast the employment. The machine learning methods, such as decision tree, artificial neural network, and ensemble models such as random forest and gradient boosting regression tree were used to forecast the employment in Busan regional economy. The following were the main findings of the comparison of their predictive abilities. First, the forecasting power of machine learning methods can predict the employment well. Second, the forecasting values for the employment by decision tree models appeared somewhat differently according to the depth of decision trees. Third, the predictive power of artificial neural network model, however, does not show the high predictive power. Fourth, the ensemble models such as random forest and gradient boosting regression tree model show the higher predictive power. Thus, since the machine learning method can accurately predict the employment, we need to improve the accuracy of forecasting employment with the use of machine learning methods.