• Journal of The Korean Association For Science Education
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• v.39 no.5
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• pp.599-611
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• 2019

The purpose of this study is to analyze the systems thinking level of pre-service teachers using rubrics of evaluating systems thinking. For this purpose, systems thinking level model, which can be applied to education or science education, was selected through literature analysis. Eight pre-service teachers' systems thinking were investigated through the systems thinking analysis tool used in domestic research. The systems thinking presented by the pre-service teachers were transformed into the box type causal map using Sibley et al. (2007). Two researchers analyzed the systems thinking using rubrics of evaluating systems thinking. For data analysis, quantitative analysis was performed through correlation analysis using SPSS. In addition, the qualitative analysis of the box type causal map was conducted and the consistency with the quantitative analysis results was verified. The results indicated that the correlation between the 5-Likert systems thinking measurement instrument and the rubrics score was highly correlated with the Pearson product-moment of .762 (p <.05). In the hierarchical correlation of the systems thinking level, the STH model was analyzed with a very high correlation with the Pearson product-moment of .722~.791, and 4-step model was analyzed .381~.730. The qualitative analysis suggested the concept to be included in the low level of system thinking, the higher the level, the less the concept that is presented properly. In conclusion, the level of systems thinking can be derived as a result of research that there is clearly, a hierarchical part. Based on the results of this study, it is necessary to develop a systems thinking level model applicable to science education and develop and validate items that can measure the level of systems thinking.

• The Journal of Information Systems
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• v.27 no.2
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• pp.197-219
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• 2018

Purpose The purpose of this study is to examine what motivates users to adopt one of the emerging applications for collaborative consumption of sharing economy. Using the self-determination theory, motivation theory and TAM(Technology Acceptance Model) as the theoretical framework, this study illustrates important factors that influence adoption of collaborative consumption service. We develops the ICTs(Information and Communications Technologies) initiatives and motivation model to collaborative consumption. Design/methodology/approach This paper makes use of a quantitative methodology using survey questionnaire that allows for the measurement of the eight constructs(System Availability, Contents Quality, Design & Personalization, Security & Privacy, Emotional & Social Value, Economic Value, Attitude, Adoption & Consumption) contained in the hypothesized theoretical model on the basis of the prior literatures. Data collected from a sample of 227 respondents who have used the collaborative consumption services and provided the foundation for the examination of the proposed relationships in the model. Findings This study has the following implications for the users and providers of CC platforms and services. The ICTs initiatives (System Availability, Contents Quality, Design & Personalization, Security & Privacy) are the influential factors that motivate the emotional and social value to CC. On the other hand, The ICTs initiatives (System Availability, Contents Quality) are not very significant factors of economic value to CC. The empirical analysis result indicate that there are significant causal effect among emotional & social value, economic value, and adoption to CC. This study provides important theoretical implications for innovation adoption research through an empirical examination of the relationship between ICTs initiatives, motivation factors to collaborative consumption in the sharing economy.

• Journal of Korean Academy of Nursing
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• v.49 no.4
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• pp.398-410
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• 2019

Purpose: This study used a system dynamics methodology to identify correlation and nonlinear feedback structures among factors affecting adolescent cyberbullying victims (CV) in Korea and to construct and verify a simulation model. Methods: Factors affecting CV were identified by reviewing a theoretical background in existing literature and referencing various statistical data. Related variables were identified through content validity verification by an expert group, after which a causal loop diagram (CLD) was constructed based on the variables. A stock-flow diagram (SFD) using Vensim Professional 7.3 was used to establish a CV model. Results: Based on the literature review and expert verification, 22 variables associated with CV were identified and the CLD was prepared. Next, a model was developed by converting the CLD to an SFD. The simulation results showed that the variables such as negative emotions, stress levels, high levels of conflict in schools, parental monitoring, and time spent using new media had the strongest effects on CV. The model's validity was verified using equation check, sensitivity analysis for timestep and simulation with 4 CV adolescent. Conclusion: The system dynamics model constructed in this study can be used to develop intervention strategies in schools that are focused on counseling that can prevent cyberbullying and assist in the victims' recovery by formulating a feedback structure and capturing the dynamic changes observed in CV. To prevent cyberbullying, it is necessary to develop more effective strategies such as prevention education, counseling and treatment that considers factors pertaining to the individual, family, school, and media.

• Journal of Korean Academy of Nursing
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• v.49 no.2
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• pp.191-202
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• 2019

Purpose: Patient-centered care is a widely utilized concept in nursing and health care. However, the key components of patient-centered nursing have not yet been reported. Moreover, previous studies on patient-centered care have mostly focused on components of nursing rather than organizational factors. Therefore, a comprehensive understanding of influential factors of patient-centered care is required. Methods: The purpose of this study was to develop a theoretical model based on person-centered care theory, and the relevant literature and to test the developed model with covariance structure analysis in order to determine the causal paths among the variables. Results: The model fit indices for the hypothetical model were suitable for the recommended level (goodness of fit index=.87, standardized root mean residual=.01, root mean square error of approximation=.06, Tucker-Lewis index=.90, comparative fit index=.92, parsimonious normed fit index=.75). In this study, five of the six paths established in the initial hypothetical model were supported. The variables of teamwork, self-leadership, and empathy accounted for 56.4% of hospital nurses' patient-centered care. Among these, empathy was the strongest predictor of patient-centered care. Conclusion: These results suggest that it is necessary to use strategies to improve self-leadership and empathy. In addition to enhancing the personal factors of nurses, nursing organizations should strive for effective multidisciplinary cooperation with active support for patient-centered care and openness to change.

• Journal of Distribution Science
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• v.13 no.11
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• pp.39-46
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• 2015

Purpose - In Korea, there has been a recent trend that shows housing prices have risen rapidly following the International Monetary Fund crisis. The rapid rise in housing prices is spreading recognition of this as a factor in housing price volatility. In addition, this raises the expectations of housing prices in the future. These expectations are based on the assumption that a relationship exists between the current housing prices and expected housing prices in the real estate industry. By performing an empirical analysis on the validity of the claim that an increase in current housing prices can be correlated with expected housing prices, this study examines whether a long-term equilibrium relationship exists between expected housing prices and existing housing prices. If such a relationship exists, the recovery of equilibrium from disequilibrium is analyzed to derive related implications. Research design, data, and methodology - The relationship between current housing prices and expected housing prices was analyzed empirically using the Vector Error Correction Model. This model was applied to the co-integration test, the long-term equilibrium equation among variables, and the causality test. The housing prices used in the analysis were based on the National Housing Price Trend Survey released by Kookmin Bank. Additionally, the Index of Industrial Product and the Consumer Price Index were also used and were obtained from the Bank of Korea ECOS. The monthly data analyzed were from January 1987 to May 2015. Results - First, a long-term equilibrium relationship was established as one co-integration between current housing price distribution and expected housing prices. Second, the sign of the long-term equilibrium relationship variable was consistent with the theoretical sign, with the elasticity of housing price distribution to expected housing price, the industrial production, and the consumer price volatility revealed as 1.600, 0.104,and 0.092, respectively. This implies that the long-term effect of expected housing price volatility on housing price distribution is more significant than that of the industrial production and consumer price volatility. Third, the sign of the coefficient of the error correction term coincided with the theoretical sign. The absolute value of the coefficient of the correction term in the industrial production equation was 0.006, significantly larger than the coefficients for the expected housing price and the consumer price equation. In case of divergence from the long-term equilibrium relationship, the state of equilibrium will be restored through changes in the interest rate. Fourth, housing-price volatility was found to be causal to expected housing price, and was shown to be bi-directionally causal to industrial production. Conclusions - Based on the finding of this study, it is required to relieve the association between current housing price distribution and expected housing price by using property taxes and the loan-to-value policy to stabilize the housing market. Further, the relationship between housing price distribution and expected housing price can be examined and tested using a sophisticated methodology and policy variables.

• Journal of Korea Port Economic Association
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• v.36 no.2
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• pp.109-124
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• 2020

Using panel data on freight rates and ship prices in the dry freighter market from January 2015 to December 2019, this study investigates the characteristics of shipping industry fluctuations. The analysis aims at two aspects of academic contribution. First, this study analyzes the relationship between shipping indicators and ship price based on separate dry-bulk ships, while the previous research considered the overall shipping index and weighted average ship prices. Second, the VAR model for the causality test is extended to a heterogeneous mixed panel model capable of limiting coefficients. There is a peak estimated by removing the cross-correlation problem, which is mainly raised in panel data analysis, using bootstrap estimation and solving the problem of information loss due to differences in non-stationary data. An empirical investigation of the causal relationship between economic fluctuations and ship price shows that the effect on the ship price from the freight is significant at the 1% level. This implies that there is a one-way relationship with demand in the shipping industry rather than a bilateral relationship.

• Korean Journal of Culture and Social Issue
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• v.20 no.4
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• pp.443-467
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• 2014

The purpose of this study was to analysis the causal model between self-directedness, learning flow, career decision and self-efficacy, and career exploration behavior of undergraduate students. A survey was conducted on 604 undergraduate students, and Structural Equation Modeling was used to analyze. The major findings were as follows: First, learning flow and career decision self-efficacy were found to have positive impacts on career exploration behavior. However, self-directedness was found to have no significant direct impacts on career exploration behavior. Second, self-directedness was found to have positive impacts on learning flow and career decision self-efficacy. Finally, learning flow and career decision self-efficacy were found to have perfect mediating effects on the relationships between self-directedness and career exploration behavior. Considering the size of the specific indirect effect, the mediating effects of learning flow was relatively larger than those of career decision self-efficacy. Based on the results, discussions to increase career exploration behavior were made as well as suggestions for future research.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.129-143
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• 2020

The purpose of this study is to explain users' wearable healthcare device adoption using performance expectancy, effort expectancy, facilitating condition, hedonic motivation and price value of UTAUT2, and to identify the causal relationship between intention to use wearable healthcare device and innovativeness. The research model proposed in this study is based on UTAUT2(Extended Unified Theory of Acceptance and Use of Technology). In specific, performance expectancy, effort expectancy, facilitating condition, hedonic motivation and price value of UTAUT2 and innovativeness are adopted in our research model. To validate the research model, we carry out the analysis of the survey data using Smart PLS 3.0 to test the hypotheses. According to the empirical analysis results, this study confirms that Innovativeness have significant effects on the performance expectancy, effort expectancy, Facilitating condition, Hedonic motivation, and price Value of wearable healthcare devices. It also finds that the performance expectancy, effort expectancy, Facilitating condition, hedonic motivation, and price value affects the intention to use wearable healthcare devices.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.2
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• pp.55-60
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• 2010

The purpose of this study is to analyze compact city planning indicators that have influence on public transportation commuting of residents in the 54 medium and small-sized cities. In the study, land use and transportation infrastructure of cities and other socio-demographic variables are used as explanatory variables in a causal model. 96,552 subjects from 54 cities in korea are selected as the final sample, and a statistical analysis is carried out by applying Random Intercept Logit Model. Analysis shows that a high level of density and jobs-housing balance in the city results in more public transportation commuting. And higher access to bus and subway station influence commuting, so subway & bus stop are important factors to increase public transportation commuting

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2001.10a
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• pp.165-196
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• 2001

DNA chip 또는 microarray는 다수의 유전자 또는 유전자 조각을 (보통 수천내지 수만 개)칩상에 고정시켜 놓고 DNA hybridization 반응을 이용하여 유전자들의 발현 양상을 분석할 수 있는 기술이다. 이러한 high-throughput기술은 예전에는 생각하지 못했던 여러가지 분자생물학의 문제에 대한 해답을 제시해 줄 수 있을 뿐 만 아니라, 분자수준에서의 질병 진단, 신약 개발, 환경 오염 문제의 해결 등 그 응용 가능성이 무한하다. 이 기술의 실용적인 적용을 위해서는 DNA chip을 제작하기 위한 하드웨어/웻웨어 기술 외에도 이러한 데이터로부터 최대한 유용하고 새로운 지식을 창출하기 위한 bioinformatics 기술이 핵심이라고 할 수 있다. 유전자 발현 패턴을 데이터마이닝하는 문제는 크게 clustering, classification, dependency analysis로 구분할 수 있으며 이러한 기술은 통계학과인공지능 기계학습에 기반을 두고 있다. 주로 사용된 기법으로는 principal component analysis, hierarchical clustering, k-means, self-organizing maps, decision trees, multilayer perceptron neural networks, association rules 등이다. 본 세미나에서는 이러한 기본적인 기계학습 기술 외에 최근에 연구되고 있는 새로운 학습 기술로서 probabilistic graphical model (PGM)을 소개하고 이를 DNA chip 데이터 분석에 응용하는 연구를 살펴본다. PGM은 인공신경망, 그래프 이론, 확률 이론이 결합되어 형성된 기계학습 모델로서 인간 두뇌의 기억과 학습 기작에 기반을 두고 있으며 다른 기계학습 모델과의 큰 차이점 중의 하나는 generative model이라는 것이다. 즉 일단 모델이 만들어지면 이것으로부터 새로운 데이터를 생성할 수 있는 능력이 있어서, 만들어진 모델을 검증하고 이로부터 새로운 사실을 추론해 낼 수 있어 biological data mining 문제에서와 같이 새로운 지식을 발견하는 exploratory analysis에 적합하다. 또한probabilistic graphical model은 기존의 신경망 모델과는 달리 deterministic한의사결정이 아니라 확률에 기반한 soft inference를 하고 학습된 모델로부터 관련된 요인들간의 인과관계(causal relationship) 또는 상호의존관계(dependency)를 분석하기에 적합한 장점이 있다. 군체적인 PGM 모델의 예로서, Bayesian network, nonnegative matrix factorization (NMF), generative topographic mapping (GTM)의 구조와 학습 및 추론알고리즘을소개하고 이를 DNA칩 데이터 분석 평가 대회인 CAMDA-2000과 CAMDA-2001에서 사용된cancer diagnosis 문제와 gene-drug dependency analysis 문제에 적용한 결과를 살펴본다.