• The Korean Journal of Food And Nutrition
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• v.37 no.3
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• pp.162-170
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• 2024

The domestic swine industry is currently facing a threat due to the recent increase in pork imports. This study aims to determine what factors influence consumers' intention to consume imported pork and suggest measures to support the domestic pork industry. To achieve this, we analyzed data from the Korea Rural Economic Institute's Food Consumption Behavior Survey using a binary logistic regression model. The results revealed that a higher intention to consume imported pork is linked to a higher intention to consume imported rice, purchasing meat online, frequent purchases of HMR, and procuring U.S. beef, especially among urban residents. On the other hand, a lower intention to consume imported pork is associated with a higher awareness of animal welfare certification, frequently dining out, and older age. Based on these findings, we propose the following response measures for the domestic swine industry: implementing educational programs, marketing, and advertising specifically targeting urban residents to improve their perception of domestic agricultural products; enhancing price competitiveness through distribution optimization; and developing policies to promote the use of domestic pork as an ingredient in processed foods.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.213-225
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• 2010

Data mining has empowered the managers who are charge of the tasks in their company to present personalized and differentiated marketing programs to their customers with the rapid growth of information technology. Most studies on customer' response have focused on predicting whether they would respond or not for their marketing promotion as marketing managers have been eager to identify who would respond to their marketing promotion. So many studies utilizing data mining have tried to resolve the binary decision problems such as bankruptcy prediction, network intrusion detection, and fraud detection in credit card usages. The prediction of customer's response has been studied with similar methods mentioned above because the prediction of customer's response is a kind of dichotomous decision problem. In addition, a number of competitive data mining techniques such as neural networks, SVM(support vector machine), decision trees, logit, and genetic algorithms have been applied to the prediction of customer's response for marketing promotion. The marketing managers also have tried to classify their customers with quantitative measures such as recency, frequency, and monetary acquired from their transaction database. The measures mean that their customers came to purchase in recent or old days, how frequent in a period, and how much they spent once. Using segmented customers we proposed an approach that could enable to differentiate customers in the same rating among the segmented customers. Our approach employed support vector regression to forecast the purchase amount of customers for each customer rating. Our study used the sample that included 41,924 customers extracted from DMEF04 Data Set, who purchased at least once in the last two years. We classified customers from first rating to fifth rating based on the purchase amount after giving a marketing promotion. Here, we divided customers into first rating who has a large amount of purchase and fifth rating who are non-respondents for the promotion. Our proposed model forecasted the purchase amount of the customers in the same rating and the marketing managers could make a differentiated and personalized marketing program for each customer even though they were belong to the same rating. In addition, we proposed more efficient learning method by separating the learning samples. We employed two learning methods to compare the performance of proposed learning method with general learning method for SVRs. LMW (Learning Method using Whole data for purchasing customers) is a general learning method for forecasting the purchase amount of customers. And we proposed a method, LMS (Learning Method using Separated data for classification purchasing customers), that makes four different SVR models for each class of customers. To evaluate the performance of models, we calculated MAE (Mean Absolute Error) and MAPE (Mean Absolute Percent Error) for each model to predict the purchase amount of customers. In LMW, the overall performance was 0.670 MAPE and the best performance showed 0.327 MAPE. Generally, the performances of the proposed LMS model were analyzed as more superior compared to the performance of the LMW model. In LMS, we found that the best performance was 0.275 MAPE. The performance of LMS was higher than LMW in each class of customers. After comparing the performance of our proposed method LMS to LMW, our proposed model had more significant performance for forecasting the purchase amount of customers in each class. In addition, our approach will be useful for marketing managers when they need to customers for their promotion. Even if customers were belonging to same class, marketing managers could offer customers a differentiated and personalized marketing promotion.

• Journal of the Ergonomics Society of Korea
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• v.28 no.3
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• pp.103-114
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• 2009

This study investigated perceived discomfort and reasons related to use three information technology products (personal computer, mobile phone and digital television remote controller), and extracted the universal design factors. 240 people (30 females and 30 males for four age groups) participated in the one-to-one interview type of questionnaire, and replied to degree of discomfort at level of items and the reasons of discomfort at level of detailed elements for each product. As a result, almost all age groups answered that using input buttons of mobile phone and remote controller and watching display of mobile phone caused discomfort. Binary logistic regression of the detailed elements showed that response rate of discomfort mostly increased with age, except for specific elements such as shape of mouse and remote controller, and location of function button of mobile phone. Some of the detailed elements had high response rate of discomfort from all age groups. The age groups also showed similar tendency for the elements to select one alternative for the reason of discomfort, but not for sound volume and size of mobile phone and button sensitivity of remote controller. Finally, the universal design factors were extracted for each product based on the results, and divided into common factors and factors classified by the age group. Through this study, we identified using practices of various age groups and their demands for the products. It is expected that extracted detailed elements can be considered as important design factors to design the products universally.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2829-2840
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• 2018

The random variable with an arbitrary value or more is called semi-continuous variable or zero-inflated one in case that its boundary value is more frequently observed than expected. This means the boundary value is likely to be practically observed more than it should be theoretically under certain probability distribution. When the distribution considered is continuous, the variable is defined as semi-continuous and when one of discrete distribution is assumed for the variable, we regard it as zero-inflated. In this study, we introduce the two-part model, which consists of one part for modelling the binary response and the other part for modelling the variable greater than the boundary value. Especially, the zero-inflated regression models are explained by using Poisson distribution and negative binomial distribution. In real data analysis, we employ the zero-inflated regression models to estimate the number of days under extreme heat-wave circumstances during the last 10 years in South Korea. Based on the estimation results, we create prediction maps for the estimated number of days under heat-wave advisory and heat-wave warning by using the universal kriging, which is one of the spatial prediction methods.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.6
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• pp.887-894
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• 2018

An electric vehicle is emerging as an alternative to the response of global climate change and sustainability. However, an Electric vehicle has not been popular due to the constraints such as its price or technical limitations. In order to analyze the effect of purchasing electric vehicles, this study conducted a binary logistic regression model that demonstrates the relation between purchasing and influencing variables. Variables which have high correlation were excluded from the model through the correlation analysis to prevent multicollinearity. Socio-economic variables such as the number of owned vehicles, sex, ages are not significant. On the other hand, Variables related to prices, charging and policy are found to have a significant to effect on the purchase of electric vehicles. In accordance with the model estimated result, it seems to be necessary to improve the charging incentives, or to provide electric car information and to expand opportunities for experience electric vehicles. The result is also expected to be helpful for spreading electric vehicles and formulating policies.

• Journal of Service Research and Studies
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• v.3 no.1
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• pp.9-16
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• 2013

Data analysis is the universal classification techniques, which requires a lot of effort. It can be easily analyzed to understand the results. Decision tree which is developed by Breiman can be the most representative methods. There are two core contents in decision tree. One of the core content is to divide dimensional space of the independent variables repeatedly, Another is pruning using the data for evaluation. In classification problem, the response variables are categorical variables. It should be repeatedly splitting the dimension of the variable space into a multidimensional rectangular non overlapping share. Where the continuous variables, binary, or a scale of sequences, etc. varies. In this paper, we obtain the coefficients of precision, reproducibility and accuracy of the classification tree to classify and evaluate the performance of the new cases, and through experiments to evaluate.

• Journal of Preventive Medicine and Public Health
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• v.54 no.4
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• pp.230-237
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• 2021

Objectives: This study explored changes in individuals' behavior in response to social distancing (SD) levels and the "no gatherings of more than 5 people" (NGM5) rule in Korea during the coronavirus disease 2019 (COVID-19) pandemic. Methods: Using survey data from the COVID-19 Behavior Tracker, exploratory factor analysis extracted 3 preventive factors: maintenance of personal hygiene, avoiding going out, and avoiding meeting people. Each factor was used as a dependent variable. The chisquare test was used to compare differences in distributions between categorical variables, while binary logistic regression was performed to identify factors associated with high compliance with measures to prevent transmission. Results: In men, all 3 factors were significantly associated with lower compliance. Younger age groups were associated with lower compliance with maintenance of personal hygiene and avoiding meeting people. Employment status was significantly associated with avoiding going out and avoiding meeting people. Residence in the capital area was significantly associated with higher compliance with personal hygiene and avoiding venturing out. Increasing SD levels were associated with personal hygiene, avoiding going out, and avoiding meeting people. The NGM5 policy was not significantly associated with compliance. Conclusions: SD levels, gender, age, employment status, and region had explanatory power for compliance with non-pharmaceutical interventions (NPIs). Strengthening social campaigns to inspire voluntary compliance with NPIs, especially focused on men, younger people, full-time workers, and residents of the capital area is recommended. Simultaneously, efforts need to be made to segment SD measures into substrategies with detailed guidance at each level.

• Journal of Climate Change Research
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• v.8 no.2
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• pp.91-97
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• 2017

Korean solar energy companies are currently suffering bankruptcy, receivership, liquidation of operation, lay-off or other similar event and most of the conglomerate are also downsized or discontinued operations in the industry. This study aims to assist Korean solar energy companies in making decision to overcome the current industrial crisis through looking into the Korean companies' growth, encounter with the crisis and strategies to survive. The main research topic in this study is a comparison between respective effect of M&A and restructuring on corporate value to understand such effects on solar energy companies. In this study, we utilized a variety of research methodologies, including dummy regression analysis, binary analysis of variance, analysis of cross addition to T-test was carried out empirical analysis. As a result, it seems that the companies who chose an M&A are facing a better situation in terms of survival and market share despite the ongoing crisis. Through this study, it could be found that, for a technology company, an M&A would be a better option than restructuring to grow and overcome a crisis.

• Restorative Dentistry and Endodontics
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• v.47 no.2
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• pp.20.1-20.14
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• 2022

Objectives: The study investigated the difficulties experienced by endodontics researchers around the world in conducting studies and writing papers. Materials and Methods: A survey consisting of 18 questions on the difficulties experienced by endodontics researchers in performing studies and writing papers was e-mailed to academics in the field of endodontics working at 202 universities. The independent risk factors were analyzed using binary logistic regression at a significance level of 0.05. Results: A total of 581 individuals (10.7%) agreed to participate in the study. Almost half the participants (48.2%) reported that they had received some type of training in conducting studies and writing papers. In response to the question, "Do you get help from a statistician to perform the statistical analyses of your studies?," 77.1% answered "yes." Around 40% of the participants stated that the need to obtain ethical approval negatively affected their desire to conduct studies. The participants' regions had no effect on the reported difficulties associated with writing papers in English or conducting statistical analyses (p > 0.05). Most participants (81.8%) reported difficulties in writing the Discussion section, regardless of their region, academic degrees, or years of experience. Conclusions: The participants stated they experienced difficulties in many areas, such as conducting statistical analyses, finding new ideas, and writing in English. Engaging in a detailed examination of ethics committee rules, expanding biostatistics education, increasing the number of institutions providing research funding, and increasing the number of endodontics journals can increase the enthusiasm of endodontics researchers to publish papers.

• Journal of agricultural medicine and community health
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• v.46 no.3
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• pp.131-143
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• 2021

Objectives: This study aimed 1) to investigate high school students' reliability on COVID-19 responses in schools and private academies and 2) to identify the differences in COVID-19 prevention practice. Methods: This cross-sectional survey collected data from 200 high school respondents, using an anonymous online questionnaire designed by the Yonsei Global Health Center, from July 2 to 17, 2020 in this study. Chi-square tests were conducted to analyze the differences in preventative practices and practice rates between schools and private academies. Binary logistics regression analysis was conducted to identify the factor affecting the reliability of COVID-19 response. Results: These high school students reliabilityed the schools' COVID-19 response more than the private academy. In addition, students who studied only at school did more COVID-19 prevention practices than students who studied both at school and academy. There was a significant difference in avoiding public transportation (p=.028), sitting in one row while having a meal (p=.011) in the practice rates depending on the schools' COVID-19 response. A significant difference in Covering the mouth when coughing and sneezing (p-.041) was also found in the practice rates depending on the private academies' COVID-19 response. Conclusion: The reason why schools were more reliable than private academies was that there are health teachers. Because schools are supervised by the ministry of education, the Ministry of education and local government need to work together to manage and monitor the COVID-19 response in the academies through cooperation between two organizations. In addition, it is necessary to arrange a temporary circulation health teacher who will provide the COVID-19 prevention education at the academies.