- P-ISSN2287-1608
- E-ISSN2287-1616
- KCI
ISSN : 2287-1608
Vol.13 No.1
Abstract
Enhancing the efficiency of research and development (R&D) is crucial for organizations to remain competitive and generate innovative solutions. Data Envelopment Analysis (DEA) has emerged as a powerful tool for evaluating R&D efficiency. However, traditional DEA models heavily rely on the selection of input and output variables, which can limit their effectiveness. To overcome this dependency and improve the robustness of DEA, this study proposes a novel methodology that integrates machine learning techniques with DEA for determining the most suitable input and output variables. The proposed approach is particularly relevant for specialized R&D fields, such as Radiation Emergency Medicine (REM). REM is a critical domain that deals with the medical and public health consequences of nuclear emergencies. The selection of REM as the focus of this study is motivated by several factors, including the unique challenges posed by the field, the potential for significant societal impact, and the need for efficient resource allocation in emergency situations. By leveraging machine learning algorithms, such as Support Vector Machines (SVM), the proposed methodology aims to identify the most relevant input and output variables for DEA in the context of REM. The integration of machine learning enables the DEA model to capture complex relationships and non-linearities in the data, leading to more accurate and reliable efficiency assessments. The effectiveness of the proposed methodology is demonstrated through a comprehensive evaluation using real-world REM data. The results highlight the superior performance of the machine learning-integrated DEA approach compared to traditional DEA models. This study contributes to the advancement of R&D efficiency assessment in specialized fields and provides valuable insights for decision-makers in REM and other critical domains.
Abstract
Smart farming (SF) receives significant attention not only as a maximizer of agricultural productivity, but also as a strategy to achieve United Nation’s Sustainable Development Goals (SDGs), yet the actual state of its contribution to the environmental SDGs remains uncertain. This paper presents a methodological approach for policy analysis by identifying linkages between South Korean SF policies and Korean Sustainable Goals (K-SDGs) targets addressing six main South Korean agriculture-related environmental issues. Linkage is defined as an explicit measure that acts as a solution to prevent or minimize a specific issue. First, an overview of K-SDGs and six environmental issues (yield productivity, greenhouse gas emission, pest and weeds, water resources, soil quality and biodiversity) reveals that 17 K-SDGs targets address the issues. The analysis reveals significant shortcomings, particularly in the low integration of pesticide use and soil quality concerns into the K-SDGs. Out of a possible 68 linkages between four SF policies and 17 K-SDGs, only 17 were identified, with 10 linking to food production and consumption-related SDGs. This indicates that current smart farming policies put a secondary focus on smart farm technology’s potential to minimize environmental challenges. To bridge the gap between SF and sustainable agriculture, SF policies should incorporate climate-smart agriculture, with a specific focus on reducing greenhouse gas emissions, and promote greater collaboration among policymaking institutions.
Abstract
This paper analyzes the factors influencing the selection of supercomputing resources. Using the results of a survey targeting supercomputing resources in the public sector, a resource selection model was presented through logistic regression and principal component analysis methods. As a result of the analysis, it was confirmed that affiliation, purpose of use, size of research funding, possession of a supercomputer, and whether specialized services are needed have a significant impact on resource selection. In the future, we expect that the results of this study will be used in various ways to manage demand for supercomputing resources.
Abstract
The purpose of this study was to analyze the relationships among science gifted students’ MBTI personality tendencies, Holland’s professional personality types, and academic achievement, with the aim of developing individualized guidance strategies based on these results. To achieve this, we first examined the relationship between MBTI personality tendencies and Holland’s professional personality types, the relationship between MBTI personality tendencies and the academic achievement of science gifted students, and the academic achievements based on the psychological functional type and psychological temperament type of science gifted students. The findings are as follows: Firstly, an analysis of the differences in Holland personality types between the introverted (I) and extroverted (E) student groups revealed significant differences in the enterprising type. Specifically, extroverted students scored higher than introverted students in the enterprising type. Secondly, a comparison of Holland personality type scores between judging (J) and perceiving (P) student groups showed that the judging (J) group scored higher in the realistic type than the perceiving (P) group. Differences in academic achievement were observed in terms of energy direction, information processing, and approach to life among the four MBTI personality tendencies. Finally, differences were found in psychological temperament type, but not in psychological functional type. The sensing perceivers (SP) type showed the highest score, while the sensing judger (SJ) type showed the lowest score in basic academic ability, and this difference was statistically significant
Abstract
This is a case study on a university’s support for entrepreneurs preparing startups. Previous studies have focused on startups within universities, but this study differs in its focus on support for external entrepreneurs. First, university startup support worked in the form of open innovation for those preparing to start a business. In other words, performance varied depending on the degree to which entrepreneurs accepted the support. Second, this study showed that, unlike previous studies, the process of preparing to start a business is nonlinear. Third, startups are largely divided into small and medium-sized businesses and innovative businesses, and a new hybrid business type was identified through university support. This study shows that university support for startups is not limited to the In-Out model, which uses university knowledge and technology, but an Out-In model is also possible. Additionally, startup support can be added as one of the entrepreneurial university’s activities.
Abstract
This study examines the Critical Success Factors (CSFs) for Mass Rapid Transit (MRT) projects in Bangkok through Public-Private Partnership (PPP) scheme. It measures the relative importance and performance of 22 CSF indicators of PPP for Bangkok's MRT projects and assesses the gap between importance and performance to prioritize the areas for improvement. This study revealed that eight indicators showed lower performance compared to their importance. Based on the gap analysis, the improvement priorities were identified in the following order: sharing information, incorporating technical elements, clarifying the legal framework, stable committee composition, the various finance resources, improved utilization of the PPP act, a transparent procurement process, and tax incentives. The findings of this research offer strategic insights for enhancing guidelines and processes to address the specific challenges in the PPP project, thereby improving the framework and policies necessary for the successful completion of public transportation projects in developing countries.