• Land and Housing Review
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• v.14 no.1
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• pp.47-66
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• 2023

This study reviews prior studies on the residential environment characteristics, residential satisfaction, residential ownership consciousness and housing movement of MZ generation and analyze the structural equation models using the 2020 Korea Housing Survey data. Using 14 residential characteristics based on three classifications, we explore the effects on residential satisfaction, residential ownership consciousness, and housing movement. The empirical results are summarized as follows. First, based on factor analysis with Varimax of principal component analysis, parking facility items were excluded from the analysis by hindering validity, and as a result, KMO was 0.925 and Bartlett's test result showed a significant probability of less than 0.01. This indicates that the factor analysis model was suitable. Second, the results of the structural equation analysis for the MZ generation show that the surrounding environment, which is a potential variable of the residential environment characteristics, was statistically significant, but the accessibility and convenience were not statistically significant. Third, we find that the higher the satisfaction with the accessibility of commercial facilities, the more significant the sense of housing ownership appears. This suggests that the younger generation such as the MZ generation has a stronger desire for consumption. Fourth, the overall housing satisfaction of the MZ generation was significant for housing movement, but not for housing ownership. Compared to the industrialized generation, the baby boom generation, and the X generation, MZ generation shows distinct factors for housing satisfaction, housing ownership, and housing movement. Therefore, the residential environment characteristics of the residential survey should be improved and supplemented following the trend of the times. In addition, the government and local governments should prioritize actively participating in the housing market that suits the environment and characteristics of the target generation. Finally, our study provides implications regarding the need for housing-related research on how differ in special temporal situations such as COVID-19 in the future.

• Analyses & Alternatives
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• v.6 no.2
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• pp.79-115
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• 2022

This study aims to account for electoral choice in the 2020 presidential election by focusing on social identity which forms the basis for core partisan groups. Two views compete to explain the origins of polarization, policy versus party. One emphasizes policy as more influential in choosing presidential candidates. This follows the tradition of retrospective voting theory in which voters' choice rely on government performance. Incumbent president whose performance proves well are rewarded to be reelected. Policy performance is based on measures around distinctive preferences for government spending. Republican Individuals prefer individual responsibility to government support, while Democratic counterparts support government support. Another perspective put an emphasis on the role partisanship which favors in-party members and disfavors partisan out-groups. Interparty animosity plays the key role in determining electoral behavior. This study relies on the Views of the Electorate Research (VOTER) Survey which provides a panel data of several waves from 2011 to 2020. A comparative evaluation of two views highlights three findings. First, policy matters. Policy preferences of voters are the primary drives of political behavior. Electoral outcomes in 2020 turned out to be the results of policy considerations of voters. 53.7 percent of voters tilted toward individual responsibility voted for Trump, whereas 70.4 percent of those favorable views of government support than individual responsibility voted for Biden. Thus effects of policy correspond to a positive difference of 26.4 percent points. Second, partisanship effects are of similar extent in influencing electoral choice of candidates: Democrats are less likely to vote for Trump by 42.4 percent points, while Republicans are less likely to vote for Biden by 48.7 percent points. Third, animosity of Republicans toward Democrat core groups creates 26.5 percent points of favoring Trump over Biden. Democrat animosity toward Republican core groups creates a positive difference of 13.7 percent points of favoring Biden.

• Journal of Korean Elementary Science Education
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• v.42 no.4
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• pp.523-537
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• 2023

This study encompassed the responses of 284 elementary school teachers, focusing on their teaching experiences, readiness, and needs for science education concerning the risk posed by science and technology. The key findings are summarized as follows. First, a significant portion of teachers lacked prior experience in addressing risks associated with science and technology within their science education practices. Second, a greater number of teachers were aware of the inclusion of risk-related content in the 2022 revised science curriculum's achievement standards than those who were not. Third, in terms of teachers' understanding of risk perception, risk assessment, and risk management, they demonstrated a relatively high level of understanding of risk perception but a lower level of understanding of risk assessment. Fourth, most teachers had not undergone any formal education or training related to risk. Fifth, among the 10 objectives of risk education, teachers displayed the highest competence in teaching "information use" and "action skills," while their lowest competence was observed in "interpreting probabilities" and "evaluating risk assessment." Sixth, a majority of teachers believe that it is important to teach about the risks posed by science and technology in school science classes, with "action skills," "information use," and "decision-making skills" being considered the most important and "action skills," "information use," and "influence of mass media" being regarded as the most urgent. However, teachers anticipated difficulties in addressing risk in school science classes, including a lack of relevant educational materials, a lack of understanding of teaching theories related to risk education, and the relationship between science curriculum content and achievement standards. Seventh, as a result of calculating the educational needs for each of the 10 goals of risk education, "influence of risk perception," "decision-making skills," "action skills," and "evaluate risk assessment" were the priority needs of elementary school teachers.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.