• Journal of Digital Convergence
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• v.20 no.3
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• pp.283-295
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• 2022

The number of citations in a patent is one of the indicators of the qualitative value of a patent. In this study, negative binomial regression model analysis was performed focusing on 47,354 US patents of 14 global top automotive makers in order to examine the major factors affecting the number of patent citations. As a result of the review, it was found that, elapsed years since filing, the number of patent claims, the number of claim letters, the number of inventors, the number of patent family countries, and the number of patent families, as well as IPC diversity, had a positive and significant effect on the number of citations. The results of this study are expected to provide a basic basis for considering the IPC diversity index together in analyzing and evaluating future patents and establishing strategies for creating excellent patents.

• Journal of Korean Society of Transportation
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• v.26 no.3
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• pp.169-178
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• 2008

Level of Service (LOS) is one of ways to evaluate operational conditions. It is very important factor in evaluation especially for the facility of highways. However, some studies proved that ${\upsilon}/c$ ratio and accident rate is appeared like a second function which has a U-form. It means there is a gap between LOS and safety of highway facilities. Therefore, this study presents a method for evaluation of a signalized intersection which is considered both smooth traffic operation (delay) and traffic safety (accident). Firstly, as a result of our research, accident rates and EPDO are decreased when it has a big delay. In that reason, it is necessary to make a new Level of Service included traffic safety. Secondly, this study has developed a negative binominal regression model which is based on the relation between accident patterns and stream. Thirdly, standards of LOS are presented which is originated from calculation between annual delay costs and annual accident cost at each intersection. Lastly, worksheet form is presented as an expression to an estimation step of a signalized intersection with traffic accident prediction model and new LOS.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.2
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• pp.19-32
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• 2020

The purpose of this study was to investigate the influence factors of the university level centering on the entrepreneurial performance of the university students and full-time faculties in the situation of increasing interest in entrepreneurial university. In order to achieve the purpose of the study, a panel data was established from 2015 to 2018 on the basis of the university notification data. The panel data included universities with data on the number of university students and full-time faculty founders for at least two years. Through this, four year data from 154 universities were used for analysis. As an analysis method, frequency analysis and descriptive statistics were conducted to understand the characteristics of the university. Since then, panel negative binomial regression analysis has been conducted in consideration of the longitudinal features and distribution of the data. Also, based on the Hausman test results, the results were interpreted based on random effect model. The results of this study are as follows. First, as a result of the analysis of the entrepreneurial performance and the change trend of the domestic university from 2015 to 2018, the entrepreneurial performance of the university has been steadily increasing in the last four years, and the increase in the number of university student entrepreneurs was relatively higher than the full-time faculties. Second, economic and educational approaches need to be combined to promote university students' start-ups. The university factors that promote the start-up of university students were found to be scholarships, start-up grants, startup lectures, and startup clubs. Third, the openness and regional characteristics of the univeristy can promote the establishment of university students. Fourth, the establishment of a research environment and support for start-ups for full-time faculty members can enhance their start-up performance. The university factors that promote the start-up of full-time faculty were research funds and staffes who support start-up. The conclusions drawn from these findings are as follows. First, overall efforts are needed to develop into an entrepreneurial university. Second, in order to change into an entrepreneurial university, direct support for entrepreneurship is needed. Third, as an entrepreneurial university, it is necessary to find a way to bridge the gap by university according to region and size. Fourth, it is necessary to reinforce the support for linking the research results of universities to start-ups. Fifth, it is necessary to improve the atmosphere for full-time faculty members to be entrepreneur.

• The Journal of Information Systems
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• v.26 no.1
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• pp.1-19
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• 2017

Purpose This study examines the roles of individual- and country-level social capital in entrepreneurial activities from the context of crowdfunding. Design/methodology/approach Two primary sources were used for data collection. From Kickstarter, the largest U.S.-based crowdfunding platform, this study obtained 15,716 crowdfunding projects and individual-level social capital. For country-level social capital, the social capital index from the 2016 Legatum Prosperity Index was utilized. By matching individual- and country-level social capital for each crowdfunding project, this research estimates the role of social capital in entrepreneurial activities at the individual and country level using the Poisson regression and the negative binomial regression. Findings Individual-level social capital measured by the number of Facebook friends, the number of other crowdfunding projects that a crowdfunding project founder invested in, and the word count of the description of a crowdfunding project are positively associated with the number of crowdfunding projects created by founders. The country-level social capital measured by aggregated social capital index is also positively associated with the number of crowdfunding projects created by founders. Both individual- and country-level social capital have a positive impact on entrepreneurial activities in terms of the creation of new crowdfunding projects.

• Journal of Korean Society of Transportation
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• v.32 no.4
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• pp.369-379
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• 2014

The Statistical regression model has been used to construct crash prediction models, despite its limitations in assuming data distribution and functional form. In response to the limitations associated with the statistical regression models, a few studies based on non-parametric methods such as neural networks have been proposed to develop crash prediction models. However, these models have a major limitation in that they work as black boxes, and therefore cannot be directly used to identify the relationships between crash frequency and crash factors. A genetic programming model can find a solution to a problem without any specified assumptions and remove the black box effect. Hence, this paper investigates the application of the genetic programming technique to develope the crash prediction model. The data collected from the Gyeongbu expressway during the past three years (2010-2012), were separated into straight and curve sections. The random forest technique was applied to select the important variables that affect crash occurrence. The genetic programming model was developed based on the variables that were selected by the random forest. To test the goodness of fit of the genetic programming model, the RMSE of each model was compared to that of the negative binomial regression model. The test results indicate that the goodness of fit of the genetic programming models is superior to that of the negative binomial models.

• Knowledge Management Research
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• v.21 no.3
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• pp.67-87
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• 2020

Maintaining active users who repeatedly share high-quality knowledge is critical for the success of online Q&A sites. This study suggests two paths that lead to active users' continuous knowledge sharing: 1) elaborated decision process, represented by continuance intention, and 2) automated cognitive process, represented by past behavior. The direct and moderating effects of continuance intention and past behavior were verified by analyzing subjective intention data and objective behavior data of 333 active users of Naver Knowledge-iN. Using Cox proportional hazards regression and negative binomial regression, the influences of continuance intention and past behavior on two types of continuous knowledge sharing were examined. The results showed that only past behavior was significantly influential on knowledge sharing continuance and as to the frequency of knowledge sharing, both continuance intention and past behavior's influences were significant. It was also confirmed that past behavior negatively moderates continuance intention's effect on the frequency of knowledge sharing. In order to maintain active users' continuous knowledge sharing, it is important to habituate knowledge sharing through repetitive knowledge sharing behavior. And in order to increase the frequency of knowledge sharing, in addition to the habituation, appropriate benefits that can increase the continuance intention should be provided.

• 한국노년학
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• v.38 no.4
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• pp.837-851
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• 2018

This study aimed to investigate the relationship between frailty and health care utilization in a cross-sectional design of a population-based sample of community-dwelling older adults. We used the data of 516 participants who dwell in Daejon, aged between 65 and 84 years old. Using K-frailty index, frailty status were measured and categorized as three groups: robust, prefrail, and frail. Logistic regression analysis was used to examine if frailty affects emergency department(ED) visit or hospitalization. In addition, negative binomial regression was used to examine the association between outpatient visits and frailty. Our results showed that the frail elderly increased the ED visit and the number of outpatient visit significantly after controlling for demographic characteristics, socioeconomic status, the number of chronic diseases, and self-rated health status. Considering that frailty is an important independent factor affecting health care utilization, more attention is required to prevent the frailty in our health care system.

• Health Policy and Management
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• v.34 no.1
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• pp.68-77
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• 2024

Background: Depressive disorders can be categorized into daily depression and clinical depression. The experience of depressive disorder can increase health care utilization due to decreased treatment compliance and somatization. On the other hand, the clinical depression group may also experience social prejudice associated with the illness, which can limit their access to health care utilization. In terms of the significance of health care utilization as a factor in individual and social issues, this study aims to compare the health care utilization of the clinical depression group with that of the non-depressed group and the daily depression group. Methods: The analysis utilized the inverse probability of treatment weighting based on the generalized propensity score. Results: As a result of the analysis, clinical depression and daily depression were higher among women, low-income groups, individuals with low education levels, and so forth. The clinical depression group was also higher among individuals who were not economically active, did not have private health insurance, or had multiple chronic diseases. The number of outpatient department visits in the depression group was significantly higher than in the non-depressed group. In addition, the number of outpatient department visits for the clinical depression group was significantly higher than that for the daily depression group. Outpatient medical expenses were higher in the depression group than in the non-depressed group, and there was no significant difference between the clinical depression group and the daily depression group. Conclusion: Health care utilization was higher in the depression group than the non-depressed group, it was also higher in the clinical depression group than the daily depression group.

• Journal of Korean Society of Transportation
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• v.27 no.3
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• pp.81-90
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• 2009

The safety management of a road network comprises four basic inter-related components：identification of sites(black spot) requiring safety investigation, diagnosis of safety problems, selection of feasible treatments for potential treatment candidates, and prioritization of treatments given limited budgets(Persaud, 2001). Identification process of selecting black spot is very important for efficient investigation of sites. In this study, the accident prediction model for EB method was developed by using accident data and geometric conditions of black spots selected from four-leg signalized intersections in In-cheon City for three years (2004-2006). In addition, by comparing the rank nomination technique using EB method to that by using accident counts, we managed to show the problems which the existing method have and the necessity for developing rational prediction model. As a result, in terms of total number of accidents, both the counts predicted by existing non-linear regression model and that by EB method have high good of fitness, but EB method, considering both the accident counts by sites and total number of accident, has better good of fitness than non-linear poison model. According to the result of the comparison of ranks nominated for treatment between two methods, the rank for treatment of almost sites does not change but SeoHae intersection and a few other intersections have significant changes in their rank. This shows that, with the technique proposed in the study, the RTM problem caused by using real accident counts can be overcome.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.9
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• pp.56-65
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• 2019

This study empirically analyzes the effect of weather on pedestrian volume in an urban space. We used data from the 2009 Seoul Flow Population Survey and constructed a model with the pedestrian volume as a dependent variable and the weather and physical environment as independent variables. We constructed 28 models and compared the results to determine the effects of weather on pedestrian volume by season, land use, and time zone. A negative binomial regression model was used because the dependent variable did not have a normal distribution. The results show that weather affects the volume of walking. Rain reduced walking volume in most models, and snow and thunderstorms reduced the volume in a small number of models. The effects of the weather depended on the season and land use, and the effects of environmental factors depended on the season. The results have various policy implications. First, it is necessary to provide semi-outdoor urban spaces that can cope with snow or rain. Second, it is necessary to have different policies to encourage walking for each season.