• Korean Journal of Social Welfare Studies
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• v.44 no.3
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• pp.85-112
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• 2013

This study aims to understand the developmental trajectories of life satisfaction among retirees and to examine what factors differentiate different trajectory classes. This study used three waves of longitudinal data from Korean Retirement and Income Study and data collected every two years(2005, 2007, and 2009). Subjects were respondents aged 50-69 who identified to be retired between wave 1 and wave 2. Finally, this study used 243 respondents for final data analysis. Life satisfaction was measured by seven items. The latent class growth model and multiple logistic regression model were used for data analysis. This study identified three distinct trajectory classes: high stable class(47.7%), high at the early stage but decreased class(42.8%), and low at the early stage and then decreased class(9.5%). This study founded that approximately 50% of the retirees experienced the decline of life satisfaction after retirement and about 10% of the sample was the most vulnerable group. This study analyzed what factors make different among the distinct trajectory groups. As a results, retirees who experienced the improvement in health change were more likely to be in 'high stable class' compared to 'hight at the early stage but decreased class'. In addition, retirees who were less educated, maintained the same health status rather than the improvement, worked as a temporary or a day laborer, and had less household income were more likely to belong to 'low at the early stage and then decreased class' relative to 'high stable class'. This study suggests that there are distinct three trajectories on life satisfaction among the retirees and finds out factors differentiating between trajectory groups. Based on these findings, the study discusses the implications for social work practice and further study.

• Land and Housing Review
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• v.14 no.4
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• pp.1-16
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• 2023

As population ageing and shrinking accompanied by dramatically expanded individual life expectancy and declining fertility rate is a global phenomenon, ageing becomes its broader perspective of ageing well embedded into sustained health and well-being, and also the fourth industrial revolution speeds up a more robust and inclusive view of smart ageing. While the latest paradigm of SA has gained considerable attention in the midst of sharply surging demand for health and social services and rapidly declining labor force, the definition has been widely and constantly discussed. This research is to constitute a conceptual framework of smart ageing (SA) from systematic literature review and the use of a series of secondary data and Geographical Information Systems(GIS), and to explore its components. The findings indicate that SA is considered to be an innovative approach to ensuring quality of life and protecting dignity, and identifies its constituents. Indeed, the construct of SA elaborates the multidimensional nature of independent living, encompassing three spheres - Aging in Place (AP), Well Aging (WA), and Active Ageing (AA). AP aims at maintaining independence and autonomy, entails safety, comfort, familiarity and emotional attachment, and it values social supports and services. WA assures physical, psycho-social and economic domains of well-being, and it concerns subjective happiness. AA focuses on both social engagement and economic participation. Moreover, the three constructs of SA are underpinned by specific elements (right to housing, income adequacy, health security, social care, and civic engagement) which are interrelated and interconnected.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.6
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• pp.105-114
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• 2022

The purpose of this study is to analyze the influence of the nation's institutional environment on start-up intention of employees and the mediating role of risk-taking propensity in the relationship between these variables. This study classified the institutional environment into institutional profile regulation, institutional profile norms, and institutional profile recognition. The research data were collected through questionnaires for office workers belonging to domestic companies, and 322 copies of questionnaire data were used for hypothesis verification, except for questionnaires that were omitted or unfaithful. The results of this study are as follows. First, institutional profile regulations and norms were positively related to start-up intention of office workers, while institutional profile cognition had no significant effect on the start-up intention. Second, institutional profile regulations and norms were positively related to risk taking, while institutional profile cognition had no significant effect on risk taking. Finally, risk taking was found to partially mediate the relationship between institutional profile regulation and start-up intention, and completely mediate the relationship between institutional profile norms and start-up intention. The theoretical implications of this study are as follows. First, this study makes a theoretical contribution in that it revealed that the country institutional profile regulation and norms are important prerequisites for start-up intention and risk taking. Next, unlike previous studies, this study makes a theoretical contribution by presenting a start-up intention model of office workers consisting of perception of the institutional environment and risk taking, which is the individual characteristic of entrepreneurs. The practical implications of this study are as follows. First, the government and local governments should strengthen regulations on institutional profiles so that start-ups can be activated. Second, the government and local governments should strengthen the norms for institutional profiles so that start-ups can be activated. Finally, the government, local governments, and educational institutions should devise measures to strengthen the risk taking of start-ups.

• Journal of Environmental Impact Assessment
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• v.33 no.4
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• pp.143-154
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• 2024

Heat waves caused by climate change are rapidly increasing health damage to vulnerable groups, and to prevent this, the national, regional, and local governments are establishing climate crisis adaptation policy. A representative climate crisis adaptation policy to reduce heat wave damage is to expand the number of cooling centers. Because it is highly effective in a short period of time, most metropolitan local governments, except Jeonbuk, include the project as an adaptation policy. However, the criteria for selecting a cooling centers are different depending on the budget and non-budget, so the utilization rate and effectiveness of the cooling centers are all different. Therefore, in this study, we developed logistic regression models that can predict and evaluate areas with a high probability of expanding cooling centers in order to implement adaptation policy in local governments. In Incheon Metropolitan City, which consists of various heat wave-vulnerable environments due to the coexistence of the old city and the new city, a logistic model was developed to predict areas where heat waves can be cooling centered by dividing it into Ganghwa·Ongjin-gun and other regions, taking into account socioeconomic and environmental differences. As a result of the study, the statistical model for the Ganghwa·Ogjin-gun region showed that the higher the ground surface temperature and the more and more the number of elderly people over 65 years old, the higher the possibility of location of cooling centers, and the prediction accuracy was about 80.93%. The developed logistic regression model can predict and evaluate areas with a high potential as cooling centers by considering regional environmental and social characteristics, and is expected to be used for priority selection and management when designating additional cooling centers in the future.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.149-169
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• 2020

"The Urban Renewal New Deal project", one of the government's major national projects, is about developing underdeveloped areas by investing 50 trillion won in 100 locations on the first year and 500 over the next four years. This project is drawing keen attention from the media and local governments. However, the project model which fails to reflect the original characteristics of the area as it divides project area into five categories: "Our Neighborhood Restoration, Housing Maintenance Support Type, General Neighborhood Type, Central Urban Type, and Economic Base Type," According to keywords for successful urban regeneration in Korea, "resident participation," "regional specialization," "ministerial cooperation" and "public-private cooperation", when local governments propose urban regeneration projects to the government, they can see that it is most important to accurately understand the characteristics of the city and push ahead with the projects in a way that suits the characteristics of the city with the help of local residents and private companies. In addition, considering the gentrification problem, which is one of the side effects of urban regeneration projects, it is important to select and implement urban regeneration types suitable for the characteristics of the area. In order to supplement the limitations of the 'Urban Regeneration New Deal Project' methodology, this study aims to propose a system that recommends urban regeneration types suitable for urban regeneration sites by utilizing various machine learning algorithms, referring to the urban regeneration types of the '2025 Seoul Metropolitan Government Urban Regeneration Strategy Plan' promoted based on regional characteristics. There are four types of urban regeneration in Seoul: "Low-use Low-Level Development, Abandonment, Deteriorated Housing, and Specialization of Historical and Cultural Resources" (Shon and Park, 2017). In order to identify regional characteristics, approximately 100,000 text data were collected for 22 regions where the project was carried out for a total of four types of urban regeneration. Using the collected data, we drew key keywords for each region according to the type of urban regeneration and conducted topic modeling to explore whether there were differences between types. As a result, it was confirmed that a number of topics related to real estate and economy appeared in old residential areas, and in the case of declining and underdeveloped areas, topics reflecting the characteristics of areas where industrial activities were active in the past appeared. In the case of the historical and cultural resource area, since it is an area that contains traces of the past, many keywords related to the government appeared. Therefore, it was possible to confirm political topics and cultural topics resulting from various events. Finally, in the case of low-use and under-developed areas, many topics on real estate and accessibility are emerging, so accessibility is good. It mainly had the characteristics of a region where development is planned or is likely to be developed. Furthermore, a model was implemented that proposes urban regeneration types tailored to regional characteristics for regions other than Seoul. Machine learning technology was used to implement the model, and training data and test data were randomly extracted at an 8:2 ratio and used. In order to compare the performance between various models, the input variables are set in two ways: Count Vector and TF-IDF Vector, and as Classifier, there are 5 types of SVM (Support Vector Machine), Decision Tree, Random Forest, Logistic Regression, and Gradient Boosting. By applying it, performance comparison for a total of 10 models was conducted. The model with the highest performance was the Gradient Boosting method using TF-IDF Vector input data, and the accuracy was 97%. Therefore, the recommendation system proposed in this study is expected to recommend urban regeneration types based on the regional characteristics of new business sites in the process of carrying out urban regeneration projects."