• Title/Summary/Keyword: 경로모형

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The Effects of Luck in Belief and Positive Cognitive Bias on Entrepreneurial Self-Efficacy (행운신념이 긍정적 인지편향과 창업효능감에 미치는 영향)

  • Ha, Hwan Ho;Byun, Chung Gyu
    • Asia-Pacific Journal of Business Venturing and Entrepreneurship
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    • v.18 no.5
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    • pp.33-44
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    • 2023
  • Entrepreneurial self-efficacy is an important variable that explains people's attitudes and behaviors toward start-ups. In this study, we focused on individual psychological characteristics variables such as luck in belief and positive cognitive bias that affect entrepreneurial self-efficacy. Among these variables, we paid particular attention to luck in belief. The belief that business success depends on luck is widespread, but scientific verification about it has not been much. The reason for the academic indifference is that luck is a kind of superstition, related to precognition or extrasensory perception, and randomly caused by the external environment. The study of luck began in earnest as a measure to measure luck as an individual characteristic variable such as personality was developed. The purpose of this study is to examine the existing studies on luck in belief and to examine the effect of this luck in belief on positive cognitive bias and entrepreneurial self-efficacy through empirical analysis. For empirical analysis, this study conducted an on-line survey of 400 ordinary people and conducted a structural equation model analysis using AMOS 21.0 to verify the hypothesis. As a result of hypothesis testing, all hypotheses that luck in belief would have a positive effect on positive cognitive bias(self-enhancement bias, illusion of control bias, unrealism optimistic bias) were adopted. The hypothesis that positive cognitive bias(self-enhancement bias, illusion of control bias, unrealistic optimism bias) will have a positive effect on entrepreneurial self-efficacy was also adopted. Additional analysis was conducted to examine the mediating role of positive cognitive bias in the relationship between luck in belief and entrepreneurial self-efficacy, which showed that 'luck in belief→positive cognitive bias →entrepreneurial self-efficacy' were statistically significant. Through this, we confirmed the mediating effect of positive cognitive bias in the relationship between luck in belief and entrepreneurial self-efficacy. In the conclusion, the implications and limitations of the study were presented based on the results of this study.

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Abundance and Occupancy of Forest Mammals at Mijiang Area in the Lower Tumen River (두만강 하류 밀강 지역의 산림성 포유류 풍부도와 점유율)

  • Hai-Long Li;Chang-Yong Choi
    • Korean Journal of Environment and Ecology
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    • v.37 no.6
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    • pp.429-438
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    • 2023
  • The forest in the lower Tumen River serves as an important ecosystem spanning the territories of North Korea, Russia, and China, and it provides habitat and movement corridors for diverse mammals, including the endangered Amur tiger (Panthera tigris) and Amur leopard (Panthera pardus). This study focuses on the Mijiang area, situated as a potential ecological corridor connecting North Korea and China in the lower Tumen River, playing a crucial role in conserving and restoring the biodiversity of the Korean Peninsula. This study aimed to identify mammal species and estimate their relative abundance, occupancy, and distribution based on the 48 camera traps installed in the Mijiang area from May 2019 to May 2021. The results confirmed the presence of 18 mammal species in the Mijiang area, including large carnivores like tigers and leopards. Among the dominant mammals, four species of ungulates showed high occupancy and detection rates, particularly the Roe deer (Capreolus pygargus) and Wild boar (Sus scrofa). The roe deer was distributed across all areas with a predicted high occupancy rate of 0.97, influenced by altitude, urban residential areas, and patch density. Wild boars showed a predicted occupancy rate of 0.73 and were distributed throughout the entire area, with factors such as wetland ratio, grazing intensity, and spatial heterogeneity in aspects of the landscape influencing their occupancy and detection rates. Sika deer (Cervus nippon) exhibited a predicted occupancy rate of 0.48, confined to specific areas, influenced by slope, habitat fragmentation diversity affecting detection rates, and the ratio of open forests impacting occupancy. Water deer (Hydropotes inermis) displayed a very low occupancy rate of 0.06 along the Tumen River Basin, with higher occupancy in lower altitude areas and increased detection in locations with high spatial heterogeneity in aspects. This study confirmed that the Mijiang area serves as a habitat supporting diverse mammals in the lower Tumen River while also playing a crucial role in facilitating animal movement and habitat connectivity. Additionally, the occupancy prediction model developed in this study is expected to contribute to predicting mammal distribution within the disrupted Tumen River basin due to human interference and identifying and protecting potential ecological corridors in this transboundary region.

Habitat characteristics and prediction of potential distribution according to climate change for Macromia daimoji Okumura, 1949 (Odonata: Macromiidae) (노란잔산잠자리(Macromia daimojiOkumura, 1949)의 서식지 특성 및 기후변화에 따른 잠재적 분포 예측)

  • Soon Jik Kwon;Hyeok Yeong Kwon;In Chul Hwang;Chang Su Lee;Tae Geun Kim;Jae Heung Park;Yung Chul Jun
    • Journal of Wetlands Research
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    • v.26 no.1
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    • pp.21-31
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    • 2024
  • Macromia daimoji Okumura, 1949 was designated as an endangered species and also categorized as Class II Endangered wildlife on the International Union for Conservation of Nature (IUCN) Red List in Korea. The spatial distribution of this species ranged within a region delimited by northern latitude from Sacheon-si(35.1°) to Yeoncheon-gun(38.0°) and eastern longitude from Yeoncheon-gun(126.8°) to Yangsan-si(128.9°). They generally prefer microhabitats such as slowly flowing littoral zones of streams, alluvial stream islands and temporarily formed puddles in the sand-based lowland streams. The objectives of this study were to analyze the similarity of benthic macroinvertebrate communities in M. daimoji habitats, to predict the current potential distribution patterns as well as the changes of distribution ranges under global climate change circumstances. Data was collected both from the Global Biodiversity Information Facility (GBIF) and by field surveys from April 2009 to September 2022. We adopted MaxEnt model to predict the current and future potential distribution for M. daimoji using downloaded 19 variables from the WorldClim database. The differences of benthic macroinvertebrate assemblages in the mainstream of Nakdonggang were smaller than those in its tributaries and the other streams, based on the surrounding environments and stream sizes. MaxEnt model presented that potential distribution displayed high inhabiting probability in Nakdonggang and its tributaries. Applying to the future scenarios by Intergovernmental Panel on Climate Change (IPCC), SSP1 scenario was predicted to expand in a wide area and SSP5 scenario in a narrow area, comparing with current potential distribution. M. daimoji is not only directly threatened by physical disturbances (e.g. river development activities) but also vulnerable to rapidly changing climate circumstances. Therefore, it is necessary to monitor the habitat environments and establish conservation strategies for preserving population of M. daimoji.

A Study of Depression in Female Seniors Living Alone: A Comparison Between the Young-old and the Old-old Adults (여성 독거노인의 우울에 관한 연구: 전기와 후기노인의 비교를 중심으로)

  • Jin-Seop Lim;Je-sun Kim
    • Journal of Industrial Convergence
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    • v.22 no.1
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    • pp.149-162
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    • 2024
  • This study is a longitudinal study of female older adults living alone, one of the most vulnerable groups in our society, to determine how their depression changes over time and what factors affect their depression. At the same time, considering that there is a large difference in age among the same older adults, this study divided the female older adults into the young-old and the old-old to see how the predictors of depression in each group differ from each other. The main findings are as follows First, depression among female older adults living alone appears to have a declining pattern over time. In the conditional model, factors affecting the initial level of the depression trajectory among women living alone were found to be associated with lower initial depression values among those living in metropolitan areas rather than non-metropolitan areas, better subjective health, and those who did not exercise. Next, we examined the factors affecting rate of change (slope) in depression among female living alone older adults and found that the higher the age, the larger the metropolitan area, the better the subjective health, the less socializing, and the more socializing, the greater the decrease in depression level. Finally, there were some differences in the pathways affecting the initial value and slope of depression among female older adults living alone between the early and late older adults. Specifically, the higher the initial level of participation in social activities, the greater the change in depression among the late older adults, while there was no significant relationship among the early older adults. In the early older adults, better initial subjective health was associated with a larger change in depression than in the late older adults. Only in the late older adults did those who regularly exercised in the early years have higher initial depression values than those who did not. Based on the results of the above analyses, suggestions were made to reduce depression among female older adults living alone.

Development of an evaluation tool for dietary guideline adherence in the elderly (노인의 식생활지침 실천 평가도구 개발)

  • Young-Suk Lim;Ji Soo Oh;Hye-Young Kim
    • Journal of Nutrition and Health
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    • v.57 no.1
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    • pp.1-15
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    • 2024
  • Purpose: This study aimed to develop a comprehensive tool for assessing dietary guideline adherence among older Korean adults, focusing on the domains of food and nutrient intake, eating habits, and dietary culture. Methods: Candidate items were selected through a literature search and expert advice. The degree of adherence to dietary guidelines was then evaluated through a face-to-face survey conducted on 800 elderly individuals across five nationwide regions. The items for dietary guideline adherence evaluation tool were selected through exploratory factor analysis of the candidate items in each of the three areas of the dietary guidelines, and construct validity was verified by performing confirmatory factor analysis. Using the path coefficient of the structural equation model, weights were assigned to each area and item to calculate the dietary guideline adherence score. A rating system for the evaluation tool was established based on national survey results. Results: A total of twenty-eight items were selected for evaluating dietary guideline adherence among the elderly. Thirteen items related to food intake, seven to eating habits, and eight to dietary culture. The average score for dietary guideline adherence was 56.9 points, with 49.8 points in the food intake area, 63.2 points in the eating habits area, and 58.6 points in the dietary culture area. Statistically significant correlations were found between dietary guideline adherence scores and food literacy (r = 0.679) and nutrition quotient scores (r = 0.750). Conclusion: The developed evaluation tool for dietary guideline adherence among Korean older adults can be used as a simple and effective instrument for comprehensively assessing their food and nutrient intake, dietary habits, and dietary culture.

Animal Infectious Diseases Prevention through Big Data and Deep Learning (빅데이터와 딥러닝을 활용한 동물 감염병 확산 차단)

  • Kim, Sung Hyun;Choi, Joon Ki;Kim, Jae Seok;Jang, Ah Reum;Lee, Jae Ho;Cha, Kyung Jin;Lee, Sang Won
    • Journal of Intelligence and Information Systems
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    • v.24 no.4
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    • pp.137-154
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    • 2018
  • Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.