Pub Date : 2024-02-29DOI: 10.37727/jkdas.2024.26.1.309
Eun-Mi Jun
This research investigates factors impacting the health-related quality of life in elderly women with osteoarthritis. The study surveyed 1,855 participants using National Health and Nutrition Examination Survey data from 2016 to 2020. Statistical analyses, conducted with SPSS/WIN 25, employed T-tests, analysis of variance (ANOVA), cross-analysis, and general linear analysis for descriptive statistics, along with Bonferroni post-hoc tests. Correlations between variables and the quality of life in elderly women with osteoarthritis were determined through correlation coefficients. Additionally, factors influencing the quality of life were analyzed using multiple regression analysis with general linear models. The study revealed significant differences in the quality of life based on demographic such as age, residence, marital status, education level, economic activity, depression, subjective health status, activity limitation, stress perception, smoking, body mass index, exercise, and aerobic activity. Similarly, there were statistically significant differences in the quality of life based on health-related characteristics, including age, residence, marital status, education level, economic activity, depression, subjective health status, activity limitation, stress perception, smoking, body mass index, exercise, and aerobic activity. The average quality of life score for participants was 0.82±0.18, with notable correlations found with age, subjective health status, and stress perception. Factors influencing health-related quality of life included having a spouse, higher household income, engagement in economic activity, lower age, and lower stress levels, as well as higher subjective health status. The model's explanatory power was 36.5%. Based on these findings, the study underscores the necessity for comprehensive and individualized nursing intervention programs.
{"title":"Factors Influencing the Quality of Life in elderly women with osteoarthritis","authors":"Eun-Mi Jun","doi":"10.37727/jkdas.2024.26.1.309","DOIUrl":"https://doi.org/10.37727/jkdas.2024.26.1.309","url":null,"abstract":"This research investigates factors impacting the health-related quality of life in elderly women with osteoarthritis. The study surveyed 1,855 participants using National Health and Nutrition Examination Survey data from 2016 to 2020. Statistical analyses, conducted with SPSS/WIN 25, employed T-tests, analysis of variance (ANOVA), cross-analysis, and general linear analysis for descriptive statistics, along with Bonferroni post-hoc tests. Correlations between variables and the quality of life in elderly women with osteoarthritis were determined through correlation coefficients. Additionally, factors influencing the quality of life were analyzed using multiple regression analysis with general linear models. The study revealed significant differences in the quality of life based on demographic such as age, residence, marital status, education level, economic activity, depression, subjective health status, activity limitation, stress perception, smoking, body mass index, exercise, and aerobic activity. Similarly, there were statistically significant differences in the quality of life based on health-related characteristics, including age, residence, marital status, education level, economic activity, depression, subjective health status, activity limitation, stress perception, smoking, body mass index, exercise, and aerobic activity. The average quality of life score for participants was 0.82±0.18, with notable correlations found with age, subjective health status, and stress perception. Factors influencing health-related quality of life included having a spouse, higher household income, engagement in economic activity, lower age, and lower stress levels, as well as higher subjective health status. The model's explanatory power was 36.5%. Based on these findings, the study underscores the necessity for comprehensive and individualized nursing intervention programs.","PeriodicalId":299325,"journal":{"name":"The Korean Data Analysis Society","volume":"11 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140413252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-29DOI: 10.37727/jkdas.2024.26.1.35
Ji Hun Park, S. Kim, H. Lee, Y. Ko, H. Park
With the rise in aviation demand and the emergence of Urban Air Mobility, developing a safe aviation system in urban areas is becoming increasingly important. This study addresses the challenge of detecting anomalous flight trajectories, which can be influenced by environmental factors. We propose a novel Long Short-Term Memory-Auto Encoder (LSTM-AE) model that processes both environmental and trajectory data but only reconstructs trajectory data in its output. This approach was validated by assessing the average reconstruction error for specific trajectories. Additionally, the model's ability to identify anomalies was confirmed by evaluating the Area under the ROC curve (AUC) for typical anomalous trajectories, such as go-around maneuvers. Our findings indicate that the proposed LSTM-AE model effectively learns trajectory patterns in relation to environmental variables and shows enhanced anomaly detection capabilities compared to traditional AE and LSTM-AE models. These results contribute to the development of advanced models that incorporate a wider range of environmental factors, enhancing safety in urban air travel.
{"title":"Anomaly Trajectory Detection Model Using LSTM Auto Encoder","authors":"Ji Hun Park, S. Kim, H. Lee, Y. Ko, H. Park","doi":"10.37727/jkdas.2024.26.1.35","DOIUrl":"https://doi.org/10.37727/jkdas.2024.26.1.35","url":null,"abstract":"With the rise in aviation demand and the emergence of Urban Air Mobility, developing a safe aviation system in urban areas is becoming increasingly important. This study addresses the challenge of detecting anomalous flight trajectories, which can be influenced by environmental factors. We propose a novel Long Short-Term Memory-Auto Encoder (LSTM-AE) model that processes both environmental and trajectory data but only reconstructs trajectory data in its output. This approach was validated by assessing the average reconstruction error for specific trajectories. Additionally, the model's ability to identify anomalies was confirmed by evaluating the Area under the ROC curve (AUC) for typical anomalous trajectories, such as go-around maneuvers. Our findings indicate that the proposed LSTM-AE model effectively learns trajectory patterns in relation to environmental variables and shows enhanced anomaly detection capabilities compared to traditional AE and LSTM-AE models. These results contribute to the development of advanced models that incorporate a wider range of environmental factors, enhancing safety in urban air travel.","PeriodicalId":299325,"journal":{"name":"The Korean Data Analysis Society","volume":"2015 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140416385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-29DOI: 10.37727/jkdas.2024.26.1.259
JungYoung Jeong
The objective of this paper is to provide a theorectical and empirical frame work to asses the tradeoffs among three fundamental objectives: profitability, growth, and safety in global general insurance indusry (USA, Japan and Korea). Our financial performance analysis relies on panel data of global general insurance industry for the years from 2010 to 2019 by using key financial variables. A quantitative evaluation of three countries genneral insurers' financial growth, profitability and saferty areas is carried out. The financial evaluation is done in the growth, profitability and safety factors including premium and capital increse ratio, underwriting and investment income, leverage and solvency ratio. The results show that there are no key differences among three impact areas: impact of general insurance growth on profitability, profitability on saferty, safety on growth in three countries. Also, the results suggest that the impact of safety on profitability and growth is a positive and significant relationship and emphasize the need to jointly consider growth, profitability, and safety when evaluating general insurers financial performance. Therefore, global general insurance indusry has to strive to strengthen financial soundness through growth and profitability because underwriting profit and growth depend on the level of safety.
{"title":"An Analysis of Global General Insurance Industry' Growth, Profitability and Safety Performance","authors":"JungYoung Jeong","doi":"10.37727/jkdas.2024.26.1.259","DOIUrl":"https://doi.org/10.37727/jkdas.2024.26.1.259","url":null,"abstract":"The objective of this paper is to provide a theorectical and empirical frame work to asses the tradeoffs among three fundamental objectives: profitability, growth, and safety in global general insurance indusry (USA, Japan and Korea). Our financial performance analysis relies on panel data of global general insurance industry for the years from 2010 to 2019 by using key financial variables. A quantitative evaluation of three countries genneral insurers' financial growth, profitability and saferty areas is carried out. The financial evaluation is done in the growth, profitability and safety factors including premium and capital increse ratio, underwriting and investment income, leverage and solvency ratio. The results show that there are no key differences among three impact areas: impact of general insurance growth on profitability, profitability on saferty, safety on growth in three countries. Also, the results suggest that the impact of safety on profitability and growth is a positive and significant relationship and emphasize the need to jointly consider growth, profitability, and safety when evaluating general insurers financial performance. Therefore, global general insurance indusry has to strive to strengthen financial soundness through growth and profitability because underwriting profit and growth depend on the level of safety.","PeriodicalId":299325,"journal":{"name":"The Korean Data Analysis Society","volume":"4 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140410527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-29DOI: 10.37727/jkdas.2024.26.1.79
Jongkyeong Kang, Seunghwan Park, Sungwan Bang
Sufficient dimension reduction, aimed at finding a lower-dimensional subspace in explanatory variables that contains response variable information, typically relies on inverse-based methodologies. These methods are easy to implement but often require linear or constant variance conditions. To address these limitations, techniques for forwardly estimating the central subspace have been developed. In particular, methods utilizing the Reproducing Kernel Hilbert Space have gained attention, but their use in analyzing large datasets is limited due to the characteristics of the kernel space. In this paper, we study a novel forward approach for sufficient dimension reduction in binomial classification of large-scale data. We propose a method that employs a divide-and-conquer technique to split data into subsets, then independently perform dimension reduction on each subset before synthesizing them into a final model. It was shown that when the number of partitions of data was appropriately selected, the loss in prediction accuracy was not significant compared to the existing method, while being efficient in terms of storage space and calculation cost. In addition, simulations in various models showed superior prediction accuracy than other inverse-based techniques. The utility of the proposed method was confirmed through the real data analysis.
{"title":"A Forward Approach for Sufficient Dimension Reduction in Binary Classification for Large-scale Data","authors":"Jongkyeong Kang, Seunghwan Park, Sungwan Bang","doi":"10.37727/jkdas.2024.26.1.79","DOIUrl":"https://doi.org/10.37727/jkdas.2024.26.1.79","url":null,"abstract":"Sufficient dimension reduction, aimed at finding a lower-dimensional subspace in explanatory variables that contains response variable information, typically relies on inverse-based methodologies. These methods are easy to implement but often require linear or constant variance conditions. To address these limitations, techniques for forwardly estimating the central subspace have been developed. In particular, methods utilizing the Reproducing Kernel Hilbert Space have gained attention, but their use in analyzing large datasets is limited due to the characteristics of the kernel space. In this paper, we study a novel forward approach for sufficient dimension reduction in binomial classification of large-scale data. We propose a method that employs a divide-and-conquer technique to split data into subsets, then independently perform dimension reduction on each subset before synthesizing them into a final model. It was shown that when the number of partitions of data was appropriately selected, the loss in prediction accuracy was not significant compared to the existing method, while being efficient in terms of storage space and calculation cost. In addition, simulations in various models showed superior prediction accuracy than other inverse-based techniques. The utility of the proposed method was confirmed through the real data analysis.","PeriodicalId":299325,"journal":{"name":"The Korean Data Analysis Society","volume":"2008 32","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140416573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-29DOI: 10.37727/jkdas.2024.26.1.135
Choong Lyol Lee, Myung Jin Hwang, Junghack Kim, Ji Na Lee, Dong-Chul Lee, Keewhan Kim
This study analyzed government detailed project budgets by combining AI, big data, and expert judgments. Instead of traditional classifications, the study used the 27 social policy agendas from the announced '2023 Core Social Policy Implementation Plan' to categorize government projects. Additionally, the life cycle was used as a classification criterion. Natural language processing(NLP) technology was employed to understand and classify textual data describing detailed projects, successfully classifying government projects and budgets from 2020 to 2023 according to the 27 agendas. Public data from 'NKIS' and 'Open Finance' were utilized in the classification, and KeyBERT was used for NLP. The classification results allowed the identification of annual changes in the number and budget of government projects according to the 27 agendas, as well as the degree of imbalance in detailed projects for each agenda. Furthermore, the classification results by life cycle provided insights into who the detailed projects and budgets are intended for. While NLP played a key role in the results, expert knowledge and judgment were crucial. The research findings suggest evidence for making judgments on efficient budget execution and interagency cooperation. The study also hints at the potential for more in-depth, field-specific research on the 27 social policy issues and life cycle.
{"title":"Budget Analysis in the Social Policy Field Using Text Data and Fiscal Information","authors":"Choong Lyol Lee, Myung Jin Hwang, Junghack Kim, Ji Na Lee, Dong-Chul Lee, Keewhan Kim","doi":"10.37727/jkdas.2024.26.1.135","DOIUrl":"https://doi.org/10.37727/jkdas.2024.26.1.135","url":null,"abstract":"This study analyzed government detailed project budgets by combining AI, big data, and expert judgments. Instead of traditional classifications, the study used the 27 social policy agendas from the announced '2023 Core Social Policy Implementation Plan' to categorize government projects. Additionally, the life cycle was used as a classification criterion. Natural language processing(NLP) technology was employed to understand and classify textual data describing detailed projects, successfully classifying government projects and budgets from 2020 to 2023 according to the 27 agendas. Public data from 'NKIS' and 'Open Finance' were utilized in the classification, and KeyBERT was used for NLP. The classification results allowed the identification of annual changes in the number and budget of government projects according to the 27 agendas, as well as the degree of imbalance in detailed projects for each agenda. Furthermore, the classification results by life cycle provided insights into who the detailed projects and budgets are intended for. While NLP played a key role in the results, expert knowledge and judgment were crucial. The research findings suggest evidence for making judgments on efficient budget execution and interagency cooperation. The study also hints at the potential for more in-depth, field-specific research on the 27 social policy issues and life cycle.","PeriodicalId":299325,"journal":{"name":"The Korean Data Analysis Society","volume":"2009 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140416555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-29DOI: 10.37727/jkdas.2024.26.1.15
L. l
Low self-efficacy in interpersonal relationships, linked to MMO game addiction, worsens the inclination towards addiction as individuals seek social interaction within the game, leading to attentional bias towards game stimuli. This study aimed to investigate if manipulating perceived social self-efficacy levels could reduce attentional bias in MMO game addiction compared to non-addictive gamers. 503 undergraduates participated, including the MMO addiction group (n=60) and the control group (n=60), identified through the Korean version of the Internet Game Disorder Scale. Participants were divided into high and low perceived social self-efficacy conditions through false feedback. Dot probe tasks assessed attentional bias changes before and after manipulated feedback using a “social intelligence test.” The attentional bias score, initially higher in the addiction group, decreased after intervention with increased social self-efficacy. No significant changes were observed in control groups and the addiction group with decreased social self-efficacy. These findings confirm that boosting perceived social self-efficacy in MMO addiction can reduce attentional bias towards game stimuli, suggesting crucial interventions for alleviating addictive behaviors.
{"title":"Mitigating Attentional Bias: The Impact of Perceived Social Self-Efficacy in Individuals with MMO Games Addiction Tendency","authors":"L. l","doi":"10.37727/jkdas.2024.26.1.15","DOIUrl":"https://doi.org/10.37727/jkdas.2024.26.1.15","url":null,"abstract":"Low self-efficacy in interpersonal relationships, linked to MMO game addiction, worsens the inclination towards addiction as individuals seek social interaction within the game, leading to attentional bias towards game stimuli. This study aimed to investigate if manipulating perceived social self-efficacy levels could reduce attentional bias in MMO game addiction compared to non-addictive gamers. 503 undergraduates participated, including the MMO addiction group (n=60) and the control group (n=60), identified through the Korean version of the Internet Game Disorder Scale. Participants were divided into high and low perceived social self-efficacy conditions through false feedback. Dot probe tasks assessed attentional bias changes before and after manipulated feedback using a “social intelligence test.” The attentional bias score, initially higher in the addiction group, decreased after intervention with increased social self-efficacy. No significant changes were observed in control groups and the addiction group with decreased social self-efficacy. These findings confirm that boosting perceived social self-efficacy in MMO addiction can reduce attentional bias towards game stimuli, suggesting crucial interventions for alleviating addictive behaviors.","PeriodicalId":299325,"journal":{"name":"The Korean Data Analysis Society","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140415231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-29DOI: 10.37727/jkdas.2024.26.1.337
Byungyoun Kim, Chayong Kim
This study examined the exchange relationship between professors who influence sports major students and students who are influenced by both theory and practice, and verified the impact of this relationship on learning outcomes. Accordingly, a total of 72 rows (144 people: students) were collected in the form of couple data (type A, B) according to the research plan from May 11 to October 6, 2023, using APIM (actor and partner interdependent model), which can interpret data. A convenience sample (72 people, 72 professors) was extracted. In addition, keywords such as 'student', 'professor', and 'sports major' were set, and the study was conducted after conducting preliminary interviews with the type A group (10 students) and the type B group (10 major professors). Factors consistent with the purpose were set. Research results: First, the self-effect of student exchange relationships on student learning outcomes was significant. And the counterpart effect was significant, showing that the higher the professor exchange relationship, the higher the student learning performance. Second, the self-effect of the professor exchange relationship had a significant effect on professor and learning outcomes. In addition, it can be seen that the higher the counterpart effect, the higher the teaching and learning performance. And it was found that learning outcomes are not determined by individuals, but that individual interaction methods can be found through exchange relationships (LMX) between members.
{"title":"The impact of student-professor exchange relationship (LMX) majoring in sports on learning outcomes: Application of the interdependence model(APIM)","authors":"Byungyoun Kim, Chayong Kim","doi":"10.37727/jkdas.2024.26.1.337","DOIUrl":"https://doi.org/10.37727/jkdas.2024.26.1.337","url":null,"abstract":"This study examined the exchange relationship between professors who influence sports major students and students who are influenced by both theory and practice, and verified the impact of this relationship on learning outcomes. Accordingly, a total of 72 rows (144 people: students) were collected in the form of couple data (type A, B) according to the research plan from May 11 to October 6, 2023, using APIM (actor and partner interdependent model), which can interpret data. A convenience sample (72 people, 72 professors) was extracted. In addition, keywords such as 'student', 'professor', and 'sports major' were set, and the study was conducted after conducting preliminary interviews with the type A group (10 students) and the type B group (10 major professors). Factors consistent with the purpose were set. Research results: First, the self-effect of student exchange relationships on student learning outcomes was significant. And the counterpart effect was significant, showing that the higher the professor exchange relationship, the higher the student learning performance. Second, the self-effect of the professor exchange relationship had a significant effect on professor and learning outcomes. In addition, it can be seen that the higher the counterpart effect, the higher the teaching and learning performance. And it was found that learning outcomes are not determined by individuals, but that individual interaction methods can be found through exchange relationships (LMX) between members.","PeriodicalId":299325,"journal":{"name":"The Korean Data Analysis Society","volume":"4 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140410912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-29DOI: 10.37727/jkdas.2024.26.1.197
Hyung Chul Lee
This paper empirically investigates the effects of investors' sentiment and the cash conversion cycle. In particular, this research investigates whether investors' sentiment is related to the cash conversion cycle and whether the level of uncertainty and competition in each firm affects the relation. Three hypotheses were drawn. To investigate the hypotheses, stock prices and accounting information data of firms listed on KOSDAQ market at Korea Exchange(KRX) were collected, and the hypotheses were examined by panel regressions and Fama-MacBeth regressions. This research finds that investor sentiment has negative effects on the cash conversion cycle. Further, this study finds that the level of uncertainty and the level of competition in the industry increases the negative relation between investors' sentiment and the cash conversion cycle. This work finds uncertainty and competition has a certain role in the relation between investors sentiment and cash management. The findings suggest that an increase in uncertainty may cause cash management to be less effective. In addition, firms in the high-competition industry may have difficulties in working capital management.
{"title":"The relationship between investors sentiment and cash conversion cycle","authors":"Hyung Chul Lee","doi":"10.37727/jkdas.2024.26.1.197","DOIUrl":"https://doi.org/10.37727/jkdas.2024.26.1.197","url":null,"abstract":"This paper empirically investigates the effects of investors' sentiment and the cash conversion cycle. In particular, this research investigates whether investors' sentiment is related to the cash conversion cycle and whether the level of uncertainty and competition in each firm affects the relation. Three hypotheses were drawn. To investigate the hypotheses, stock prices and accounting information data of firms listed on KOSDAQ market at Korea Exchange(KRX) were collected, and the hypotheses were examined by panel regressions and Fama-MacBeth regressions. This research finds that investor sentiment has negative effects on the cash conversion cycle. Further, this study finds that the level of uncertainty and the level of competition in the industry increases the negative relation between investors' sentiment and the cash conversion cycle. This work finds uncertainty and competition has a certain role in the relation between investors sentiment and cash management. The findings suggest that an increase in uncertainty may cause cash management to be less effective. In addition, firms in the high-competition industry may have difficulties in working capital management.","PeriodicalId":299325,"journal":{"name":"The Korean Data Analysis Society","volume":"47 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140411687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-29DOI: 10.37727/jkdas.2024.26.1.1
L. l
Firm characteristics that determine CEO pays are closely interrelated with one another and make the partitioning of variances among correlated multiple predictors difficult. We decompose the interrelated predictors by orthogonalizing each predictor based on Tonidandel, LeBreton’s (2015) relative weight analysis on both normal and crisis period. In the process, we can rank the relative importance of each predictor and investigate its evolution over the economic crisis period. Firm size is the most dominant determinant, occupying over 60% relative weight. Wage discrimination against small company is obvious. ROA contributes 8.7% for the normal period and 10.8% for the crisis, which implies that CEOs’ ability to generate profits in crisis is particularly valued high and companies reward managers accordingly. The prolonged good performance is especially valued higher (13.9%) than the short-term performance. Risk and cash flow volatility occupy 3.6% and 1.8%, respectively, and the use of funds, such as capital expenditure and interest payment triggered by leverage occupy only marginal portions. This suggests that firms may lower CEO pays to reserve cash when they face risks or new investment opportunities, but the amount of extraction may not be high. In crisis, credit information can potentially outweigh the importance of many other typical predictors.
决定首席执行官薪酬的公司特征彼此密切相关，因此很难在相关的多个预测因子之间划分方差。我们根据 Tonidandel、LeBreton（2015 年）对正常时期和危机时期的相对权重分析，对每个预测因子进行正交化处理，从而对相互关联的预测因子进行分解。在此过程中，我们可以对每个预测因子的相对重要性进行排序，并研究其在经济危机时期的演变情况。公司规模是最主要的决定因素，相对权重超过 60%。对小公司的工资歧视显而易见。正常时期的投资回报率为 8.7%，危机时期为 10.8%，这意味着首席执行官在危机中创造利润的能力尤其受到重视，公司也会相应地奖励经理人。长期良好业绩的估值（13.9%）尤其高于短期业绩。风险和现金流波动分别占 3.6%和 1.8%，而资金的使用，如资本支出和由杠杆引发的利息支出只占很小的比例。这说明企业在面临风险或新的投资机会时，可能会降低 CEO 薪酬以储备现金，但提取的金额可能并不高。在危机中，信用信息有可能超过许多其他典型预测因素的重要性。
{"title":"Determinants of Managerial Pay: The Relative Contribution of Compensation Predictors","authors":"L. l","doi":"10.37727/jkdas.2024.26.1.1","DOIUrl":"https://doi.org/10.37727/jkdas.2024.26.1.1","url":null,"abstract":"Firm characteristics that determine CEO pays are closely interrelated with one another and make the partitioning of variances among correlated multiple predictors difficult. We decompose the interrelated predictors by orthogonalizing each predictor based on Tonidandel, LeBreton’s (2015) relative weight analysis on both normal and crisis period. In the process, we can rank the relative importance of each predictor and investigate its evolution over the economic crisis period. Firm size is the most dominant determinant, occupying over 60% relative weight. Wage discrimination against small company is obvious. ROA contributes 8.7% for the normal period and 10.8% for the crisis, which implies that CEOs’ ability to generate profits in crisis is particularly valued high and companies reward managers accordingly. The prolonged good performance is especially valued higher (13.9%) than the short-term performance. Risk and cash flow volatility occupy 3.6% and 1.8%, respectively, and the use of funds, such as capital expenditure and interest payment triggered by leverage occupy only marginal portions. This suggests that firms may lower CEO pays to reserve cash when they face risks or new investment opportunities, but the amount of extraction may not be high. In crisis, credit information can potentially outweigh the importance of many other typical predictors.","PeriodicalId":299325,"journal":{"name":"The Korean Data Analysis Society","volume":"12 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140412008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-29DOI: 10.37727/jkdas.2024.26.1.105
Seunghye Kim, Eunsik Park
Interest in mental disease is increasing. Recently a positive association between elevated temperature and mental disease has been reported. However, there was a limitation in that detailed interaction with age could not be confirmed. In this study, to overcome such limitations, the association between emergency room visits due to mental disease and mean temperature was explored by using the age-specific distributed lag model. The age-specific distributed lag model is a model in which a coefficient of age and lagged temperature are integrated into the existing distributed lag model. Accordingly the dimension of the parameter space was reduced by expressing the increased parameters as a linear combination of prediction process basis functions and the accuracy of parameter estimation was increased using information on the total age. The degree of borrowing information over age was estimated through variogram modeling. From 2014 to 2020, 906,958 patients visited the emergency room due to mental disease in Seoul. The age groups with a positive cumulative association over the lag period of 0-3 days between mean temperature and emergency room visits due to mental disease were 15 to 24, 40 to 59 and 80 to 84. As the effects of climate change become a reality, understanding detailed vulnerabilities will become very important for public health planning and intervention.
{"title":"Age-specific distributed lag model using predictive process: an association between mean temperature and emergency room visits due to mental disease","authors":"Seunghye Kim, Eunsik Park","doi":"10.37727/jkdas.2024.26.1.105","DOIUrl":"https://doi.org/10.37727/jkdas.2024.26.1.105","url":null,"abstract":"Interest in mental disease is increasing. Recently a positive association between elevated temperature and mental disease has been reported. However, there was a limitation in that detailed interaction with age could not be confirmed. In this study, to overcome such limitations, the association between emergency room visits due to mental disease and mean temperature was explored by using the age-specific distributed lag model. The age-specific distributed lag model is a model in which a coefficient of age and lagged temperature are integrated into the existing distributed lag model. Accordingly the dimension of the parameter space was reduced by expressing the increased parameters as a linear combination of prediction process basis functions and the accuracy of parameter estimation was increased using information on the total age. The degree of borrowing information over age was estimated through variogram modeling. From 2014 to 2020, 906,958 patients visited the emergency room due to mental disease in Seoul. The age groups with a positive cumulative association over the lag period of 0-3 days between mean temperature and emergency room visits due to mental disease were 15 to 24, 40 to 59 and 80 to 84. As the effects of climate change become a reality, understanding detailed vulnerabilities will become very important for public health planning and intervention.","PeriodicalId":299325,"journal":{"name":"The Korean Data Analysis Society","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140414773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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