Journal of the Korean Society for Library and Information Science
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v.56
no.3
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pp.5-26
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2022
The purpose of this study is to analyze high school students' understanding and use of the recommended books lists. The survey distributed to high school students in seven high schools located in Seoul, and 311 students responded. Using SPSS 24, the data was analyzed by frequency, binary logistic model, and one-way ANOVA. Results show the followings. First, the meaningful factors affecting students' use of recommended books lists are gender, grade levels, and the degree to which students think recommended books lists include the books that are suitable and interesting. Particularly, the degree to which students think recommended books lists include the suitable books for them is the strong factor affecting students' use of the recommended books lists. Second, male students are less likely to use recommended books lists than female students. Male students consistently are less likely to use the recommended books lists made by school librarians, subject teachers, and reading experts and/or organizations. Third, teacher-librarians believed that the recommended books lists would help students who do not enjoy reading and have difficulties in reading. However, the study finds that students who enjoy reading and read well are more willing to use the recommended books lists made by school librarians, subjects teachers, and reading experts and/or organizations than those who do not. Fourth, students are most willing to use the recommended books lists for college preparation. The findings suggest the further research topics in designing the recommended books lists suitable for high school students and in scaffolding the high school students' use of book information reflected in recommended books lists.
This experiment was conducted to determine the maximum dietary energy levels on growth performance and carcass characteristics of White Pekin duck. the Six dietary treatments were formulated based on their apparent metabolizable energy (AME) concentrations from 2,700 to 3,200 kcal/kg with a 100 kcal/kg gap to evaluate the accurate dietary AME requirement to address current knowledge and further issues for fulfilling the genetic potential of meat-type white Pekin ducklings. A total of 432 one-day-old male White Pekin ducklings were randomly allocated into one of six dietary treatments with six replicates (12 birds per pen). The diets were formulated as corn-soybean meal-based diets to meet or exceed the Nutrient Requirement of Poultry specification for meat-type ducks. Growth performance indices (i.e. average daily gain [ADG], average daily feed intake, feed conversion ratio) were measured weekly. Medium body weight (BW) ducklings from each pen were sacrificed to analyze the carcass traits and abdominal fat content on day 21. Obtained data were analyzed to estimate significant effect using the one-way ANOVA of IBM SPSS Statistics (Version, 25). If the p-value of the results were significant, differences in means among treatments were separated by Tukey's post hoc test. Significant differences were then analyzed with a linear and quadratic broken model to estimate the accurate concentration of AME. Ducklings fed higher dietary AME diets increased (p < 0.05) BW, ADG. Ducklings fed higher AME than 2,900 kcal/kg diets increased abdominal fat accumulation and leg meat portion. The estimated requirement by linear plateau method showed from 3,000.00 kcal/kg to 3,173.03 kcal/kg whereas the requirement by quadratic plateau method indicated from 3,100.00 kcal/kg to 3,306.26 kcal/kg. Collectively, estimated dietary requirements exhibit diverse results based on the measured traits and analysis methods. All the estimated requirements in this experiment present higher than previous research, the maximum requirement for the next diet formulation should be selected by the purpose of the diet.
The purpose of this study is to look into how elderly people's health promoting behaviors influence their successful aging, to realize how their perception of successful aging and their life satisfaction have the mediating effect on the path from health promotion behaviors to successful aging, and to find the significant influence of successful aging perception and life satisfaction on successful aging. This researcher conducted a questionnaire survey with elderly people using a senior welfare center in Gyeonggio-do, and analyzed 250 copies that. For data analysis, SPSS Win 25 was applied to conduct frequency analysis, descriptive statistics, t-test, one-way ANOVA, and correlation analysis. Mediating effect analysis was made to verify the causal relations between health promoting behaviors and successful aging, and to validate the mediating effect of successful aging perception and life satisfaction on the causal relations. As a result, elderly people's health promoting behaviors influenced their perception of successful aging, their life satisfaction, and their successful aging. Their perception of successful aging had the mediating effect on health promotional behaviors and successful aging, but life satisfaction did not so. This study has the following implications: it is necessary to train persons specializing in support for health promoting, to develop an efficient health promotional model and program, and to provide an opportunity of education. It is necessary to come up with a support policy in consideration of tangible or intangible factors. It is necessary to establish a policy in line with economic levels and health conditions of elderly people.
The objective of this research is to examine the mediating roles of nursing professional values and occupational stress in the relationship between self-leadership and patient-centered nursing among nurses employed at COVID-19 designated hospitals. This study were 160 nurses at a COVID-19 designated hospitals, and the data were collected from January 10 to February 30, 2022. The collected data were analyzed by independent t-test, one-way ANOVA, correlation analysis, multiple linear regression analysis, and SPSS PROCESS Macro model No 4 bootstrapping method. The average score for self-leadership was 61.3±8.55, nursing professional values was 95.5±11.66, occupational stress was 51.3±4.76, and patient-centered nursing was 59.3±7.63. The mediating effect of nursing professional values and occupational stress was confirmed in the influence relationship between self-leadership and patient-centered nursing of nurses at COVID-19 designated hospitals. This result suggests that the content related to improve nursing professional values and reduce occupational stress should be considered when applying the patient-centered nursing enhancement program.
This was empirical research aimed at determining the effects of emotional labor, Resilience and service environment, on the performance of Long-term Care Hospital Employee. The participants were 180 employees working in long-term care hospitals in Gyeonggi-do. The collected data were analyzed using the SPSS statics19.0 program. The study were analyzed by frequency analysis and descriptive statistics, ANOVA, Scheffe? test, Pearson correlation coefficients, and stepwise regression. As a result of the study, age, marriage status, career, and position affected performance among the general characteristics.tion coefficients, and stepwise regression. As a result of the study, the average performance was 91.25 (±12.46) points, emotional labor was 41.25 (±4.21) points, Resilience was 52.89 (±6.37), and the service environment was 78.93(±15.3) points. The performance showed a positive correlation with emotional labor(r=.326, p<.001) service environment (r=.384, p=.005) and Resilience (r=.417, p<.001) of Long-term Care Hospital Employee. Service environment was the biggest factor affecting performance, and the second was resilience. The explanatory power of this regression model was 48.2% and was statistically significant (F=58.249, p<.001).
Journal of The Korean Society of Integrative Medicine
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v.12
no.1
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pp.27-39
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2024
Purpose : Smoking is a major factor in chronic obstructive pulmonary disease (COPD), but the effect of electrical cigarette smoking on COPD development is still uncertain. This study aimed to compare the functions of airways and lungs exposed to combustible cigarettes and electrical cigarettes based on the pulmonary function test (PFT) results from the Korean National Health and Nutrition Examination Survey (NHANES). Methods : This study used data from 8,942 participants with PFT results out of 47,309 total subjects from the 6th to 8th Korean NHANES (2014-2015, 2016-2018, and 2019, respectively). Individuals with diseases such as cancer, ex-smokers, and dual tobacco users were excluded. The PFT results were analyzed according to the COPD diagnostic criteria. After adjusting for confounding variables, a complex sample generalized linear model ANOVA test was performed to investigate the association between PFT results and combustible smoker or electrical cigarette user groups. Results : In an analysis based on the obstructive ventilatory disorders (forced expiratory volume in 1 second[FEV1]/forced vital capacity[FVC]<.7), combustible cigarette smokers showed a 3.46 times higher risk of COPD compared to non-smokers, while electrical cigarette smokers exhibited no significant difference in terms of COPD-related risks compared to non-smokers. FEV1 showed a negative relation with combustible cigarette smokers as reported elsewhere (B=-.07, p<.001). FEV1/FVC was negatively related to both combustible cigarette smokers (B=-.03, p<.001) and electrical cigarette smokers (B=-.02, p<.001). Conclusion : FEV1/FVC decreases were observed in the long-term exposure to both combustible and electrical cigarettes. The lower FEV1 in the combustible cigarette group implies the worsening of the severity of COPD, suggesting more damage to the airways and lungs in the short term. Therefore, the temporary electrical cigarettes use for the transition period in order to smoking cessation potentially aids to reduce the harmful effect of combustible cigarettes in COPD development.
This study was a descriptive research study conducted to determine how nursing students' good death awareness and nursing attitudes toward dying patients affect their empathy. The subjects of the study were 155 nursing students, and data were collected using an online survey method. Data analysis was performed using descriptive statistics, independent t-test, one-way ANOVA, and multiple regression using the IBM SPSS Statistics 26. Higher attitude toward care of dying (B=.312) had a statistically significant positive effect on empathy capacity (p<.010). The variables that affected nursing students' empathy capacity were end-of-life experiences of relatives (𝛽=.226) and attitude toward care of dying (𝛽=.220). The regression model was statistically significant (F=6.968, p<.001), explained 10.4% of empathy. This study is expected to be used as basic data for the development of programs to strengthen the empathy capacity of nursing students in the future.
Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.
1. Introduction: Contrast to the offline purchasing environment, online store cannot offer the sense of touch or direct visual information of its product to the consumers. So the builder of the online shopping mall should provide more concrete and detailed product information(Kim 2008), and Alba (1997) also predicted that the quality of the offered information is determined by the post-purchase consumer satisfaction. In practice, many fashion and apparel online shopping malls offer the picture information with the product on the real person model to enhance the usefulness of product information. On the other virtual product experience has been suggested to the ways of overcoming the online consumers' limited perceptual capability (Jiang & Benbasat 2005). However, the adoption and the facilitation of the virtual reality tools requires high investment and technical specialty compared to the text/picture product information offerings (Shaffer 2006). This could make the entry barrier to the online shopping to the small retailers and sometimes it could be demanding high level of consumers' perceptual efforts. So the expensive technological solution could affects negatively to the consumer decision making processes. Nevertheless, most of the previous research on the online product information provision suggests the VR be the more effective tools. 2. Research Model and Hypothesis: Presented in
, research model suggests VR effect could be moderated by the product types by the usage situations. Product types could be defined as the portable product and installed product, and the information offering type as still picture of the product, picture of the product with the real-person model and VR. 3. Methods and Results: 3.1. Experimental design and measured variables We designed the 2(product types) X 3(product information types) experimental setting and measured dependent variables such as information usefulness, attitude toward the shopping mall, overall product quality, purchase intention and the revisiting intention. In the case of information usefulness and attitude toward the shopping mall were measured by multi-item scale. As a result of reliability test, Cronbach's Alpha value of each variable shows more than 0.6. Thus, we ensured that the internal consistency of items. 3.2. Manipulation check The main concern of this study is to verify the moderate effect by the product type of usage situation.
indicates that our experimental manipulation of the moderate effect of the product type was successful. 3.3. Results As
indicates, there was a significant main effect on the only one dependent variable(attitude toward the shopping mall) by the information types. As predicted, VR has highest mean value compared to other information types. Thus, H1 was partially supported. However, main effect by the product types was not found. To evaluate H2 and H3, a two-way ANOVA was conducted. As
indicates, there exist the interaction effects on the three dependent variables(information usefulness, overall product quality and purchase intention) by the information types and the product types. As predicted, picture of the product with the real-person model has highest mean among the information types in the case of portable product. On the other hand, VR has highest mean among the information types in the case of installed product. Thus, H2 and H3 was supported. 4. Implications: The present study found the moderate effect by the product type of usage situation. Based on the findings the following managerial implications are asserted. First, it was found that information types are affect only the attitude toward the shopping mall. The meaning of this finding is that VR effects are not enough to understand the product itself. Therefore, we must consider when and how to use this VR tools. Second, it was found that there exist the interaction effects on the information usefulness, overall product quality and purchase intention. This finding suggests that consideration of usage situation helps consumer's understanding of product and promotes their purchase intention. In conclusion, not only product attributes but also product usage situations must be fully considered by the online retailers when they want to meet the needs of consumers.
Analysis of future business or investment opportunities, such as business feasibility analysis and company or technology valuation, necessitate objective estimation on the relevant market and expected sales. While there are various ways to classify the estimation methods of these new sales or market size, they can be broadly divided into top-down and bottom-up approaches by benchmark references. Both methods, however, require a lot of resources and time. Therefore, we propose a data-based intelligent demand forecasting system to support evaluation of new business. This study focuses on analogical forecasting, one of the traditional quantitative forecasting methods, to develop sales forecasting intelligence systems for new businesses. Instead of simply estimating sales for a few years, we hereby propose a method of estimating the sales of new businesses by using the initial sales and the sales growth rate of similar companies. To demonstrate the appropriateness of this method, it is examined whether the sales performance of recently established companies in the same industry category in Korea can be utilized as a reference variable for the analogical forecasting. In this study, we examined whether the phenomenon of "mean reversion" was observed in the sales of start-up companies in order to identify errors in estimating sales of new businesses based on industry sales growth rate and whether the differences in business environment resulting from the different timing of business launch affects growth rate. We also conducted analyses of variance (ANOVA) and latent growth model (LGM) to identify differences in sales growth rates by industry category. Based on the results, we proposed industry-specific range and linear forecasting models. This study analyzed the sales of only 150,000 start-up companies in Korea in the last 10 years, and identified that the average growth rate of start-ups in Korea is higher than the industry average in the first few years, but it shortly shows the phenomenon of mean-reversion. In addition, although the start-up founding juncture affects the sales growth rate, it is not high significantly and the sales growth rate can be different according to the industry classification. Utilizing both this phenomenon and the performance of start-up companies in relevant industries, we have proposed two models of new business sales based on the sales growth rate. The method proposed in this study makes it possible to objectively and quickly estimate the sales of new business by industry, and it is expected to provide reference information to judge whether sales estimated by other methods (top-down/bottom-up approach) pass the bounds from ordinary cases in relevant industry. In particular, the results of this study can be practically used as useful reference information for business feasibility analysis or technical valuation for entering new business. When using the existing top-down method, it can be used to set the range of market size or market share. As well, when using the bottom-up method, the estimation period may be set in accordance of the mean reverting period information for the growth rate. The two models proposed in this study will enable rapid and objective sales estimation of new businesses, and are expected to improve the efficiency of business feasibility analysis and technology valuation process by developing intelligent information system. In academic perspectives, it is a very important discovery that the phenomenon of 'mean reversion' is found among start-up companies out of general small-and-medium enterprises (SMEs) as well as stable companies such as listed companies. In particular, there exists the significance of this study in that over the large-scale data the mean reverting phenomenon of the start-up firms' sales growth rate is different from that of the listed companies, and that there is a difference in each industry. If a linear model, which is useful for estimating the sales of a specific company, is highly likely to be utilized in practical aspects, it can be explained that the range model, which can be used for the estimation method of the sales of the unspecified firms, is highly likely to be used in political aspects. It implies that when analyzing the business activities and performance of a specific industry group or enterprise group there is political usability in that the range model enables to provide references and compare them by data based start-up sales forecasting system.
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