• Journal of Technology Innovation
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• v.27 no.4
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• pp.1-19
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• 2019

This study presents a new method to evaluate patents by public R&D program using patent citation network analysis. I used forward citation, degree centrality, betweenness centrality and page rank as the dependent variables which represents the quality of patents. I used primary independent variable as a dummy of public R&D program and controlled patents characteristics, applicant characteristics, technological characteristics and year effect. The empirical result shows that the patents of public R&D program is superior to other patents in regard to the number of forward citation, the degree centrality, the betweenness centrality and the page rank. This empirical result implies that patents of public R&D program directly and effectively connects technologies. Also patents from public R&D program connects important technologies.

• Journal of The Korean Society of Grassland and Forage Science
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• v.36 no.3
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• pp.205-214
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• 2016

The objective of this study was to construct a forage rye (FR) dry matter yield (DMY) estimation model based on climate data by locations in South Korea. The data set (n = 549) during 29 years were used. Six optimal climatic variables were selected through stepwise multiple regression analysis with DMY as the response variable. Subsequently, via general linear model, the final model including the six climatic variables and cultivated locations as dummy variables was constructed as follows: DMY = 104.166SGD + 1.454AAT + 147.863MTJ + 59.183PAT150 - 4.693SRF + 45.106SRD - 5230.001 + Location, where SGD was spring growing days, AAT was autumnal accumulated temperature, MTJ was mean temperature in January, PAT150 was period to accumulated temperature 150, SRF was spring rainfall, and SRD was spring rainfall days. The model constructed in this research could explain 24.4 % of the variations in DMY of FR. The homoscedasticity and the assumption that the mean of the residuals were equal to zero was satisfied. The goodness-of-fit of the model was proper based on most scatters of the predicted DMY values fell within the 95% confidence interval.

• Journal of The Korean Society of Grassland and Forage Science
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• v.36 no.3
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• pp.223-236
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• 2016

The objective of this study was to construct Italian ryegrass (IRG) dry matter yield (DMY) estimation models in South Korea based on climatic data by locations. Obviously, the climatic environment of Jeju Island has great differences with Korean Peninsula. Meanwhile, many data points were from Jeju Island in the prepared data set. Statistically significant differences in both DMY values and climatic variables were observed between south areas of Korean Peninsula and Jeju Island. Therefore, the estimation models were constructed separately for south areas of Korean Peninsula and Jeju Island separately. For south areas of Korean Peninsula, a data set with a sample size of 933 during 26 years was used. Four optimal climatic variables were selected through a stepwise approach of multiple regression analysis with DMY as the response variable. Subsequently, via general linear model, the final model including the selected four climatic variables and cultivated locations as dummy variables was constructed. The model could explain 37.7% of the variations in DMY of IRG in south areas of Korean Peninsula. For Jeju Island, a data set containing 130 data points during 17 years were used in the modeling construction via the stepwise approach of multiple regression analysis. The model constructed in this research could explain 51.0% of the variations in DMY of IRG. For the two models, homoscedasticity and the assumption that the mean of the residuals were equal to zero were satisfied. Meanwhile, the fitness of both models was good based on most scatters of predicted DMY values fell within the 95% confidence interval.

• The Journal of Industrial Distribution & Business
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• v.10 no.6
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• pp.51-58
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• 2019

Purpose - Prior studies reported that the opacity of information caused stock price crash. If managers fail to disclose unfavorable information about the firm over a long period of time, the stock price is overvalued compared to its original value. If the accumulated information reaches a critical point and spreads quickly to the market, the stock price plunges. Information management by management's disclosure policy can cause information uncertainty, which will lead to a plunge in stock prices in the future. Thus, this study aims at examining the impact of disclosure quality on crash risk by focusing on the unfaithful disclosure firms. Research design, data, and methodology - This study covers firms listed on KOSPI and KOSDAQ from 2004 to 2013. Firms excluded from the sample are non-December firms, capital-eroding firms, and financial firms. The financial data used in the research was extracted from the KIS-Value and TS2000 database. Unfaithful disclosure firm designation data was collected from the Korea Exchange's electronic disclosure system (kind.krx.co.kr). Stock crash is measured as a dummy variable that equals one if a firm experiences at least one crash week over the fiscal year, and zero otherwise. Results - Empirical results as to the relation between unfaithful disclosure corporation designation and stock price crashes are as follows: There was a significant positive association between unfaithful disclosure corporation designation and stock price crash. This result supports the hypothesis that firms that have previously exhibited unfaithful disclosure behavior are more likely to suffer stock price plunges due to information asymmetry. Second, stock price crashes due to unfaithful disclosures are more likely to occur in Chaebol firms. Conclusions - While previous studies used estimates as a proxy for information opacity, this study used an objective measure such as unfaithful disclosure corporation designation. The designation by Korea Exchange is an objective evidence that the firm attempted to conceal and distort information in the previous year. The results of this study suggest that capital market investors need to investigate firms' disclosure behaviors.

• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.701-710
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• 2016

The purpose of this paper is to build a model of packet amount of Naver mobile portal. We collected 2004 cases by measuring the sixth per access from September, 2012 to October, 2015. We use regression model with autoregressive errors, in which predictors incorporated into the model were replication, date, time, week, month. It has been found the model which errors follow AR(36), based on AIC and adjusted $R^2$. We found some characteristics from our model as follows. In addition to model building, we also have discussed some meaningful features yielded from the selected model in this paper. Considering the importance of this topic, continuous researches are needed.

• Journal of the Korea Convergence Society
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• v.8 no.5
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• pp.213-221
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• 2017

This study empirically investigates the relation between information quality measured by accruals quality and the KOSPI200 index rebalancing. The accruals quality is used for the proxy of information quality and is estimated by employing the Francis et al. (2005) model. The result shows that there is a statistically significant difference between additions group and deletions group. The average information quality of deletions group is substantially lower than that of additions group. In addition, the regression analysis shows that the relationship between accruals quality and a dummy variable for changes in the KOSPI200 index composition is negative and statistically significant. This result implies that additions to the KOSPI200 stock index improves information quality and relieves the information risk of firm which results in the amelioration of information asymmetry. On the other hand, deletions from the KOSPI200 index result in the deterioration of information quality. These results are consistent with Merton (1987).

• Journal of The Korean Society of Integrative Medicine
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• v.8 no.1
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• pp.193-202
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• 2020

Purpose : The objective of this study was to provide information about the need and perception of continuing education(CE) according to gender in physical therapists. This study also aimed to provide basic data for the improvement of quality of the CE program with physical therapists. Methods : The study analyzed basic data from Korea Physical Therapy Association regarding the 350 physical therapists in Gwangju metropolitan city (Male; 102, female; 248). The questionnaire consisted of 3 categories: general characteristics, need of CE, and perception. For need of CE and perception, it assessed using a 5-point Likert scale. Data were analyzed using frequency analysis, and simple regression analysis using dummy variable. Results : The need of CE showed a weak positive correlation in the only the pediatric physical therapy according to gender in physical therapists (R²=.012) (p<.05). Both male and female physical therapists had the highest need in the musculoskeletal system, followed by nervous system. The perception of CE were not significantly correlated according to gender in physical therapists (p>.05). Both male and female physical therapists were high on the cyber-CE (Q4) and on the need for CE (Q1, Q2, Q3). Whereas, a lower score was found in category that CE helped in the performance of the work (Q6, Q7, Q8). Conclusion : In this study, there was little or no significant correlation between need and perception of CE according to gender in physical therapists. Both male and female physical therapists recognize the need for CE, whereas they are less satisfied the efforts of CE in the performance of the work. Therefore, improvement of CE through various programs should be made to enhance the perception of CE.

• The Journal of the Korea Contents Association
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• v.16 no.3
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• pp.366-375
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• 2016

This study aims to investigate the impact of total, native-born, and foreign-born employment rates on the increases of GDP and per capita GDP for 24 OECD countries out of 34 countries depending on data availability. The panel data analysis is formed by a fixed-effects model which allows dummy variable in it to permit the intercept term to vary over time-series and cross-sectional units. Empirical evidences obtained by simple and multiple panel regressions reveal that the contribution to increasing of GDP by foreign-born employment is obviously lower than the one by native-born employment. And, native-born labor is substituted by foreign-born labor. It also has to be mentioned that the labor is playing a key role in increasing in national income. And, therefore, labor-related policy should be concerned on decreasing in labor productivity and segmentation of labor market resulted from inflow of foreign labor. It means that labor-related policy has to take care of not only the magnitude, but also the quality of foreign-born labor.

• Journal of Labour Economics
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• v.40 no.3
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• pp.1-28
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• 2017

Using Korea Labor and Income Panel Surveys data, this paper estimates the effect of schooling level on income over time and the effect of children's education level on intergenerational income persistence. The results show that the impact of education level on income decreased over time. Also, intergenerational income persistence, measured as a dummy variable that has value one if children's income percentile group is same as the father's, increased with children's educational attainment only when the father is in upper income percentile groups. These findings indicate that education fails to play a significant role of the economic ladder and does not much help in raising intergenerational income mobility. Rather, education may possibly function as a means of intergenerational transmission of wealth through parental investment in their children's private education.

• Journal of Korean Society of Transportation
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• v.25 no.4
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• pp.109-121
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• 2007

In this study, an imputation model using circular probability distribution was developed in order to overcome problems of missing data from a traffic survey. The existing ad-hoc or heuristic, model-based and algorithm-based imputation techniques were reviewed through previous studies, and then their limitations for imputing missing traffic volume data were revealed. The statistical computing language 'R' was employed for model construction, and a mixture of von Mises probability distribution, which is classified as symmetric, and unimodal circular probability were finally fitted on the basis of traffic volume data at survey stations in urban and rural areas, respectively. The circular probability distribution model largely proved to outperform a dummy variable regression model in regards to various evaluation conditions. It turned out that circular probability distribution models depict circularity of hourly volumes well and are very cost-effective and robust to changes in missing mechanisms.