• Management & Information Systems Review
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• v.35 no.4
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• pp.141-153
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• 2016

This study estimates the productivity change of pharmaceutical industry and decompose the change into technical efficiency change and technological change to explore ways to improve the productive efficiency. Unlike most previous studies, this study employs the non-radial and non-oriented slacks-based Malmquist productivity index which can deals directly with the input excesses and output shortfalls. In addition a smoothed bootstrap method is applied to estimate statistical confidence intervals for the Malmquist Index and its components. For the empirical analysis a balanced panel data set is constructed covering ten years over the period from 2005 to 2014. The input variables used in this study are number of workers, tangible assets and major production cost. The gross production is selected as a main output variable. The empirical results suggest that the productivity of pharmaceutical industry has decreased due mainly to decline in the technological progress rather than improvements in technical efficiency. An analysis result shows that the scale efficiency outweighs the pure efficiency in determining the technical efficiency of pharmaceutical industry. Therefore, in order to enhance the competitiveness of domestic pharmaceutical industry, it is important to continue supporting policies to promote the technology innovation capability through efficient R&D investment and industry reform strategy.

• Journal of Korea Trade
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• v.25 no.7
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• pp.92-107
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• 2021

Purpose - This paper analyzes the relationship between trade facilitation and agricultural products exports and estimates the effects of trade facilitation in importing countries on Chinese agricultural products exports, which is of great significance for promoting agricultural trade between China,Japan and Korea and the governments of the three countries to formulate targeted trade facilitation policies. Design/methodology - Based on Wilson (2003) theoretical framework, this paper sets up its own trade facilitation level measurement system by involving four primary indicators and fifteen secondary indicators to evaluate the trade facilitation levels of Japan and Korea from 2011 to 2018 respectively. The paper selected the data on China's agricultural exports at the HS4 level from 2011-2018 and used a fixed-effects model to estimate the effect of changes in trade facilitation levels in trading partner countries on China's agricultural trade. Findings - Our main findings can be summarized as follows: the level of trade facilitation in importing countries has a significantly positive effect on China's agricultural exports. The higher the level of trade facilitation in trading partner countries, the more Chinese agricultural exports trade, i.e. for every 1 percentage point increase in the level of trade facilitation, the volume of exports will increase by 2.299%.The sub-sample test shows that China's main agricultural products exported to Japan and Korea, such as aquatic products, vegetables, fruits and other perishable fresh products, are particularly significantly affected by the level of trade facilitation. Originality/value - First, from the innovation of the research perspective, which is different from the analysis of the existing paper on the overall trade facilitation of all traded commodities. This article is based on the close trade relations between China, Japan and Korea, and the particularity of agricultural products, from the perspective of China's agricultural exports to Japan and Korea, discuss the impact of importing countries-Japan and Korea's trade facilitation levels on China's agricultural exports;Secondly, in this paper, the hierarchical data of the HS4 quartile is used to avoid the information loss of the industry, and to analyse the impact of the importing country's trade facilitation level on the export of different types of agricultural products more scientifically.

• Journal of the Korea Convergence Society
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• v.10 no.10
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• pp.153-160
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• 2019

In this study, The purpose of this study is to analyze how the rating scale and review contents attributes of social network-based services and products affect consumer purchasing patterns. information provided by screening the main factors. These analyzes are closely and quickly integrated between individuals and businesses, and enable to analyze the transaction that the impact of changing consumers on consumption and purchasing through the usefulness and a priori estimates of reviews and ratings at this time when networks and smart technologies are involved in a wide range of consumer activities. For this study, hierarchical analysis (AHP) and delphi (Delphi) methods applied to classify the high end variables into usefulness, technicality and value, Each subvariable was grouped into three factors and analyzed for importance through evaluation weights. As a result, we could analyze the importance of durability, usefulness, technological innovation, and cost and quality of value. Therefore, this study is expected to provide supplementary and additional useful information to consumers and companies participating in economic activities in various ways by simultaneously analyzing the review score and the reliability of posting information provided by verifying the main factors.

• Journal of Korea Trade
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• v.24 no.7
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• pp.1-28
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• 2020

Purpose - This paper empirically investigates the predictors and main determinants of consumers' ratings of mobile applications in the Google Play Store. Using a linear and nonlinear model comparison to identify the function of users' review, in determining application rating across countries, this study estimates the direct effects of users' reviews on the application rating. In addition, extending our modelling into a sentimental analysis, this paper also aims to explore the effects of review polarity and subjectivity on the application rating, followed by an examination of the moderating effect of user reviews on the polarity-rating and subjectivity-rating relationships. Design/methodology - Our empirical model considers nonlinear association as well as linear causality between features and targets. This study employs competing theoretical frameworks - multiple regression, decision-tree and neural network models - to identify the predictors and main determinants of app ratings, using data from the Google Play Store. Using a cross-validation method, our analysis investigates the direct and moderating effects of predictors and main determinants of application ratings in a global app market. Findings - The main findings of this study can be summarized as follows: the number of user's review is positively associated with the ratings of a given app and it positively moderates the polarity-rating relationship. Applying the review polarity measured by a sentimental analysis to the modelling, it was found that the polarity is not significantly associated with the rating. This result best applies to the function of both positive and negative reviews in playing a word-of-mouth role, as well as serving as a channel for communication, leading to product innovation. Originality/value - Applying a proxy measured by binomial figures, previous studies have predominantly focused on positive and negative sentiment in examining the determinants of app ratings, assuming that they are significantly associated. Given the constraints to measurement of sentiment in current research, this paper employs sentimental analysis to measure the real integer for users' polarity and subjectivity. This paper also seeks to compare the suitability of three distinct models - linear regression, decision-tree and neural network models. Although a comparison between methodologies has long been considered important to the empirical approach, it has hitherto been underexplored in studies on the app market.

• Korean Journal of Radiology
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• v.22 no.12
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• pp.2082-2093
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• 2021

Objective: We conducted a systematic review and meta-analysis of the tissue adequacy and complication rates of percutaneous transthoracic needle biopsy (PTNB) for molecular analysis in patients with non-small cell lung cancer (NSCLC). Materials and Methods: We performed a literature search of the OVID-MEDLINE and Embase databases to identify original studies on the tissue adequacy and complication rates of PTNB for molecular analysis in patients with NSCLC published between January 2005 and January 2020. Inverse variance and random-effects models were used to evaluate and acquire meta-analytic estimates of the outcomes. To explore heterogeneity across the studies, univariable and multivariable metaregression analyses were performed. Results: A total of 21 studies with 2232 biopsies (initial biopsy, 8 studies; rebiopsy after therapy, 13 studies) were included. The pooled rates of tissue adequacy and complications were 89.3% (95% confidence interval [CI]: 85.6%-92.6%; I² = 0.81) and 17.3% (95% CI: 12.1%-23.1%; I² = 0.89), respectively. These rates were 93.5% and 22.2% for the initial biopsies and 86.2% and 16.8% for the rebiopsies, respectively. Severe complications, including pneumothorax requiring chest tube placement and massive hemoptysis, occurred in 0.7% of the cases (95% CI: 0%-2.2%; I² = 0.67). Multivariable meta-regression analysis showed that the tissue adequacy rate was not significantly lower in studies on rebiopsies (p = 0.058). The complication rate was significantly higher in studies that preferentially included older adults (p = 0.001). Conclusion: PTNB demonstrated an average tissue adequacy rate of 89.3% for molecular analysis in patients with NSCLC, with a complication rate of 17.3%. PTNB is a generally safe and effective diagnostic procedure for obtaining tissue samples for molecular analysis in NSCLC. Rebiopsy may be performed actively with an acceptable risk of complications if clinically required.

• Korea Trade Review
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• v.42 no.5
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• pp.137-159
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• 2017

This study divides manufacturing in 18 countries including Korea, China, Japan and OECD countries into 11 areas and estimates and compares the technological efficiency of each industry. The traditional view of productivity is to increase production capacity through technological innovation or process innovation, but it is also influenced by the technological efficiency of production process. A Stochastic Frontier Production Model (SFM) is a representative method for estimating the technical efficiency of such production. First, as a result of estimating the production function by setting the output variable as total output or value-added, in both cases, the output increased significantly in all manufacturing sectors as inputs of labor, capital, and intermediate increased. On the other hand, R&D investment has a large impact on output in chemical, electronics, and machinery industries. Next, as a result of estimating the technological efficiency through the production function, when the total output is set as the output variable, the overall average of each sector is 0.8 or more, showing mostly high efficiency. However, when value-added was set, Japan had the highest level in most manufacturing sectors, while other countries were lower than the efficiency of the total output. Comparing the three countries of Korea, China and Japan, Japan showed the highest efficiency in most manufacturing sectors, and Korea was about half or one third of Japan and China was lower than Korea. However, in the food and electronics sectors, China is higher than Korea, indicating that China's production efficiency has greatly improved. As such, Korea is not able to narrow its gap with Japan relatively faster than China's rapid growth. Therefore, various policy supports are needed to promote technology development. In addition, in order to improve manufacturing productivity, it is necessary to shift to an economic structure that can raise technological efficiency as well as technology development.

• Journal of Korea Water Resources Association
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• v.51 no.5
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• pp.439-449
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• 2018

Many potential sources of bias are used in several steps of the radar-rainfall estimation process because the hydrological and meteorological radars measure the rainfall amount indirectly. Previous studies on radar-rainfall uncertainties were performed to reduce the uncertainty of each step by using bias correction methods in the quantitative radar-rainfall estimation process. However, these studies do not provide comprehensive uncertainty for the entire process and the relative ratios of uncertainty between each step. Consequently, in this study, a suitable approach is proposed that can quantify the uncertainties at each step of the quantitative radar-rainfall estimation process and show the uncertainty propagation through the entire process. First, it is proposed that, in the suitable approach, the new concept can present the initial and final uncertainties, variation of the uncertainty as well as the relative ratio of uncertainty at each step. Second, the Maximum Entropy Method (MEM) and Uncertainty Delta Method (UDM) were applied to quantify the uncertainty and analyze the uncertainty propagation for the entire process. Third, for the uncertainty quantification of radar-rainfall estimation at each step, two quality control algorithms, two radar-rainfall estimation relations, and two bias correction methods as post-processing through the radar-rainfall estimation process in 18 rainfall cases in 2012. For the proposed approach, in the MEM results, the final uncertainty (from post-processing bias correction method step: ME = 3.81) was smaller than the initial uncertainty (from quality control step: ME = 4.28) and, in the UDM results, the initial uncertainty (UDM = 5.33) was greater than the final uncertainty (UDM = 4.75). However uncertainty of the radar-rainfall estimation step was greater because of the use of an unsuitable relation. Furthermore, it was also determined in this study that selecting the appropriate method for each stage would gradually reduce the uncertainty at each step. Therefore, the results indicate that this new approach can significantly quantify uncertainty in the radar-rainfall estimation process and contribute to more accurate estimates of radar rainfall.