• Journal of Distribution Science
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• v.9 no.2
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• pp.111-122
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• 2011

The purpose of this study is to survey the successful cases of small and medium Business Systematization Cognition by examining their entrepreneurial characteristics and analysing the factors affecting their success. To that end, previous studies on the association types of small businesses were studied. A research model was developed, and research hypotheses for an empirical analysis were established upon it. Suh et al. (2010) insist on the importance of Small Business Systematization in Korea but also show that small business performance is suffering: they are too small to stand alone. That is why association is so crucial for them: they must stand together. Unfortunately, association is difficult, as they have few specific links and little motivation. Even in franchising networks, association tends to be initiated by big franchisers, not small ones. In that sense, association among small businesses is crucial for their long-term survival. With this in mind, this study examines how they think and feel about the issue of 'Industrial Classification', how important Industrial Classification is to their business success, and what kinds of problems it raises in the markets. This study seeks the different cognitions among the association types of small businesses from the perspectives of participation motivation, systematization expectation, policy demand level, and management performance. We assume that different industrial classification types of small businesses will have different cognitions concerning these factors. There are four basic industrial classification types of small businesses: retail sales, restaurant, service, and manufacturing. To date, most of the studies in this area have focused on collecting data on the external environments of small businesses or performing statistical analyses on their status. In this study, we surveyed 4 market areas in Busan, Masan, and Changwon in Korea, where business associations consist of merchants, shop owners, and traders. We surveyed 330 shops and merchants by sending a questionnaire or visiting. Finally, 268 questionnaires were collected and used for the analysis. An ANOVA, T-test, and regression analyses were conducted to test the research hypotheses. The results demonstrate that there are differences in cognition depending upon the industrial classification type. Restaurants generally have a higher cognition concerning job offer problems and a lower cognition concerning their competitiveness. Restaurants also depend more on systematization expectation than do the other industrial classification types. On the policy demand level, restaurants have a higher cognition. This study identifies several factors that are contributing to management performance through differences in cognition that depend upon association type: systematization expectation and policy demand level have positive effects on management performance; participation motivation has a negative effect on management performance. We confirm also that the image factors of different cognitions are linked to an awareness of the value of systematization and that these factors show sequential and continual patterns in the course of generating performances. In conclusion, this study carries significant implications in its classifying of small businesses into the four different associational types (retail sales, restaurant, services, and manufacturing). We believe our study to be the first one to conduct an empirical survey in this subject area. More studies in this area will likely use our research frameworks. The data show that regionally based industrial classification associations such as those in rural cities or less developed areas tend to suffer more problems than those in urban areas. Moreover, restaurants suffer more problems than the norm. Most of the problems raised in this study concern the act of 'associating itself'. Most associations have serious difficulties in associating. On the other hand, the area where they have the least policy demand is that of service types. This study contributes to the argument that associating, rather than financial assistance or management consulting, promotes the start-up and managerial performance of small businesses. This study also has some limitations. The main limitation is the number of questionnaires. We could not survey all the industrial classification types across the country because of budget and time limitations. If we had, we could have produced many more useful results and enhanced the precision of our analysis. The history of systemization is very short and the number of industrial classification associations is relatively low in Korea. We should keep in mind, though, that this is very crucial to systemization entrepreneurs starting their businesses, as it can heavily affect their chances of success. Being strongly associated with each other might be critical to the business success of industrial classification members. Thus, the government needs to put more effort and resources into supporting the drive of industrial classification members to become more strongly associated.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.139-157
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• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.71-89
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• 2016

Sentiment analysis is used for identifying emotions or sentiments embedded in the user generated data such as customer reviews from blogs, social network services, and so on. Various research fields such as computer science and business management can take advantage of this feature to analyze customer-generated opinions. In previous studies, the star rating of a review is regarded as the same as sentiment embedded in the text. However, it does not always correspond to the sentiment polarity. Due to this supposition, previous studies have some limitations in their accuracy. To solve this issue, the present study uses a supervised sentiment classification model to measure a more accurate sentiment polarity. This study aims to propose an advanced sentiment classifier and to discover the correlation between movie reviews and box-office success. The advanced sentiment classifier is based on two supervised machine learning techniques, the Support Vector Machines (SVM) and Feedforward Neural Network (FNN). The sentiment scores of the movie reviews are measured by the sentiment classifier and are analyzed by statistical correlations between movie reviews and box-office success. Movie reviews are collected along with a star-rate. The dataset used in this study consists of 1,258,538 reviews from 175 films gathered from Naver Movie website (movie.naver.com). The results show that the proposed sentiment classifier outperforms Naive Bayes (NB) classifier as its accuracy is about 6% higher than NB. Furthermore, the results indicate that there are positive correlations between the star-rate and the number of audiences, which can be regarded as the box-office success of a movie. The study also shows that there is the mild, positive correlation between the sentiment scores estimated by the classifier and the number of audiences. To verify the applicability of the sentiment scores, an independent sample t-test was conducted. For this, the movies were divided into two groups using the average of sentiment scores. The two groups are significantly different in terms of the star-rated scores.

• Journal of Korean Ophthalmic Optics Society
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• v.15 no.1
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• pp.25-30
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• 2010

Purpose: This study was conducted to estimate the changes of corrected diopter and corrected visual acuity with the change in vertex distance. Also we aimed to provide basic data for refraction test. Methods: Using the trial lens, we measured the corrected diopter and corrected visual acuity after performing binocular balance test. We measured the changes of corrected diopter and corrected visual acuity in change of vertex distance. We analyzed statistical significance and relations between vertex distance and corrected diopter and corrected visual acuity. Results: There was no difference in corrected diopter with the change of vertex distance within -1.00D, but the corrected diopter increased with it over - 1.25D. In particular, the change of diopter was largest when the vertex distance increased 15 mm. At over 11.00D, there was large changes of diopter with the changes of vertex distance at 5 mm, 10 mm and 15 mm. On correlation analysis between the vertex distance and the corrected diopter, there was strong correlation (r=0.999 at 5 mm increase of vertex distance, r=0.982 at 10 mm increase and r=0.957 at 15 mm increase) and also there was significant (p<0.01). At the change of visual acuity in increased of vertex distance, the range of a decrease in visual acuity was large when the changes of vertex distance was largest. On correlation analysis between the vertex distance and the corrected visual acuity, there was strong correlation (r=0.969 at 5 mm increase of vertex distance, r=0.985 at 10 mm increase and r=0.994 at 15 mm increase) and also there was significant (p<0.01). Conclusions: The vertex distance was very important at the refraction test and at wearing spectacle. On correlation analysis between the vertex distance and the corrected diopter, and the corrected visual acuity, there was strong correlation and statistically significant. Therefore, the vertex distance should be kept at the refraction using trial lens, and the best fitting was made not to slipping forward, and so we suggested regular refitting of spectacle and the managing method of spectacle were educated to the spectacle wearers.

• The Korean Journal of Pesticide Science
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• v.20 no.1
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• pp.14-22
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• 2016

A trial of combining and quantifying the effects of food processing on various pesticides was carried out using a meta-analysis. In this study, weighted mean response ratios and confidence intervals about the reduction of pesticide residue levels in fruits and vegetables treated with various food processing techniques were calculated using a statistical tool of meta-analysis. The weighted mean response ratios for tap water washing, peeling, blanching (boiling) and oven drying were 0.52, 0.14, 0.34 and 0.46, respectively. Among the food processing methods, peeling showed the greatest effect on the reduction of pesticide residues. Pearsons's correlation coefficient (r=0.624) between weighted mean response ratios and octanolwater partition coefficients ($logP_{ow}$) for twelve pesticides processed with tap water washing was confirmed as having a positive correlation in the range of significance level of 0.05 (p=0.03). This means that a pesticide having the higher value of $logP_{ow}$ was observed as showing a higher weighted mean response ratio. These results could be used effectively as a reference data for processing factor in risk assessment and as an information for consumers on how to reduce pesticide residues in agricultural products.

• Journal of The Korean Association For Science Education
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• v.38 no.4
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• pp.495-504
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• 2018

This study was conducted to develop items to measure scientific core competency based on statements of scientific core competencies presented in the 2015 revised national science curriculum and to identify the validity and reliability of the newly developed items. Based on the explanations of scientific reasoning, scientific inquiry ability, scientific problem-solving ability, scientific communication ability, participation/lifelong learning in science presented in the 2015 revised national science curriculum, 25 items were developed by five science education experts. To explore the validity and reliability of the developed items, data were collected from 11,348 students in elementary, middle, and high schools nationwide. The content validity, substantive validity, the internal structure validity, and generalization validity proposed by Messick (1995) were examined by various statistical tests. The results of the MNSQ analysis showed that there were no nonconformity in the 25 items. The confirmatory factor analysis using the structural equation modeling revealed that the five-factor model was a suitable model. The differential item functioning analyses by gender and school level revealed that the nonconformity DIF value was found in only two out of 175 cases. The results of the multivariate analysis of variance by gender and school level showed significant differences of test scores between schools and genders, and the interaction effect was also significant. The assessment items of science core competency based on the 2015 revised national science curriculum are valid from a psychometric point of view and can be used in the science education field.

• Journal of Dental Rehabilitation and Applied Science
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• v.27 no.3
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• pp.277-284
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• 2011

This study is based on the data of adults between ages of 19~65 yrs of the National Health and Nutrition Survey 4th in year of 2009, which includes symptoms of temporomandibular disorder within gender and age. Subjects included in this study were 2,738 males and 3,427 females, total of 6,165. All statistical analysis was measured by Window SPSS 17.0K Program (SPSS Inc., Chicago, USA). Prevalence of analysis of gender, age, and symptoms of temporomandibular disorder was measured by descriptive statistics, and in order to find relationship among gender, age, and symptoms of temporomandibular disorder was based on crosstabulation analysis. As results, prevalence of TMJ sound was 10.1%, of TMJ pain was 1.5%, and of TMJ limitation was 2.0%. Among the three symptoms of temporomandibular disorder, subjects who have at least one symptom was 1.2%. Prevalence of TMJ sound, of TMJ pain, and of TMJ limitation in female were 10.7%, 1.8% and 2.2%, respectively, which were greater than in male 9.3%, 1.2% and 1.6% respectively but it was stastically insignificant (p>0.05). Prevalence of TMJ sound, of TMJ pain, and of TMJ limitation in ages between 19~24 yrs were 18.7%, 3.4% and 4.2% respectively, which were higher than any other ages (p<0.05). Also prevalence of having at least one symptom of temporomandibular disorder, at least two, and three all were higher in females but stastically insignificant (p>0.05). On the other hand, prevalence of having at least one symptom of temporomandibular disorder, at least two, and three all were greater in age below 45 yrs and was stastically significant (p<0.05).

• Therapeutic Science for Rehabilitation
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• v.9 no.1
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• pp.69-78
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• 2020

Objective : The purpose of this study was to conduct a comprehensive survey of children's play in parents of disabled and non-disabled children prior to commencing school. This study aimed to further understanding play recognition and to present a specific direction of play necessary to each parent. Methods : A questionnaire based on prior studies was sent to 700 people who had previously agreed to the take part. A total of 596 questionnaires were analyzed. Uncollected and insincere surveys, of which 106 were questionnaires for parents of disabled children, were exclused from analysis. The SPSS Window 23 program was used for data analysis and frequency analysis and the independent sample T test were performed. Results : Disabled children's parents perceived playing with their children as more important than that of non-disabled children's parents(p<0.01). There was no statistical difference between disabled children and non-disabled children's parents, but there were some differences(p=0,053). Both disabled and non-disabled children had more time to participate with their mothers than with their fathers. Mothers with disabilities had more time to play however, fathers with no disabilities had more time. Both disabled and non-disabled children's parents had the most "ordinary" frequency of buying toys and there was very littele difference between the 2 groups. Both disabled and non-disabled children's parents primarily used the internet to acquire play information, and consideration when buying fun was followed by interest inducement, development level and safety. Conclusion : Through this study, it was possible to compare the status of play recognition and participation by parents of children with or without disabilities. Based on this study, parents will be able to find out what they really need to play and will be provided as a basis for future play studies for children.

• Korean Journal of Ecology and Environment
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• v.36 no.3 s.104
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• pp.336-343
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• 2003

Two types of statistical models, simple and multivariate log linear models, were studied for continuity simulation and trend analysis of water qualities in incoming flows to Lake Paldang. Water quality is a function of one independent variable (flow) in the simple log linear model, and of three different variables (flow, time, and seasonal cycle) in multivariate model. The independent variables act as surrogate variables of water quality in both models. The model coefficients were determined by the monthly data. The water qualities included 5-day Biochemical Oxygen Demand ($BOD_5$), Total Nitrogen (TN), and Total Phosphorus (TP) measured from 1995 to 2000 in the South and the North branches of Han River and the Kyoungan Stream. The results indicated that the multivariate model provided better agreements with field measurements than the simple one in a31 attempted cases. Flow dependency, seasonality, and temporal trends of water quality were tested on the determined coefficients of the multivariate model. The test of flow dependency indicated that BOD concentrations decreased as the water flow increased. In TN and TP concentrations, however, there were no discernible flow effects. From the temporal trend analyses, the following results were obtained: 1) no trends on BOD at all three upstreams, 2) increase on TN at the South Branch and the Kyoungan Stream, 3)decrease on TN at the North Branch,4) no trends on TP at the North and the South Branches and 5) increase on TP at the Kyoungan Stream by 3 to 8% per years. The seasonality test showed that there were significant seasonal variations in all three water qualities at three incoming flows.

• Journal of dental hygiene science
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• v.14 no.1
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• pp.59-66
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• 2014

The purpose of this study was to study of utilization of dental high school and according to the pain experienced dental fear and anxiety. This survey was conducted on 370 high school students in Suwon from November 21 to 23, 2011. A total of 352 questionnaires were collected and analyzed. The collected data was analyzed using the statistical package SPSS 15.0 using frequency, mean and standard deviation analysis, t-test, one-way ANOVA, Duncan's test correlation analysis and Stepwise multiple regression analysis. The results state that students feel fear and anxiety were feeling anesthetic needle ($3.19{\pm}1.43$), seeing anesthetic needle ($3.14{\pm}1.44$). We found that students feel more rear and anxiety from caries treatment than scaling. It influence that having dental fear with past dental pain experienced during dental treatment and also hearing dental treatment of pain from their family and friends. We found out that there are some influencing factors on dental fear and anxiety, gender, oral health condition, smoking, pain experienced during dental treatment. We need to care dental fear and anxiety continuously and have prevention program. We have to try understanding students have dental fear and anxiety. So it's better they have good experience visiting dental clinic. We should develop the system and specially treat well while they have dental treatment with anesthesia and some sharp instruments.