• Journal of Distribution Science
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• v.17 no.8
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• pp.57-66
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• 2019

Purpose - The purpose of this study is to elucidate the perception differences between CEOs and employees, and to derive a plan for improving performance by using theory of planned behavior (TPB) to enhance the competitiveness of SMEs exposed to various difficulties until recently. Research design, data and methodology - A total of 238 valid questionnaires were collected for CEOs and members of SMEs. In order to clarify the difference of perception, independent sample t-test and multi-group analysis using AMOS were conducted. Simple regression analysis and structural equation were used to confirm whether the theory of planned behavior was applied at the level of SME. Results - The CEO group is more aware of company's ethical responsibility activities and organizational support than the group of employees, and collectivism contributes more to organizational development than individualism tendency. Also, the theory of planned behavior is applied to the SME level as well. Conclusion - This study suggest that CEOs need to accept the pluralism of their members for the development of SMEs. In addition, it is necessary to form a consensus on ethical responsibility activities that corporations are performing by supporting diverse strategies and members' participation in management decision-making.

• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.331-338
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• 2022

The purpose of this study is to examine the effect of Gratifications Obtained (GO) on behavioral intention targeting Over-the-Top (OTT). The study chooses quality of content, variety of content, leisure activity, entertainment, the convenience of watching, and reasonable rate ratio as factors of GO. The number of subjects for the research is 330 people aged from their twenties to thirties in Daegu and Gyeongbok province. This study employs multi-regression analysis to analyze the impacts of GO on behavioral intention. The analysis shows that quality of content and variety of content influence behavioral intention at a significance level of 0.01, and entertainment and the convenience of watching influence at a significance level of 0.05.

• Journal of Digital Convergence
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• v.16 no.8
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• pp.27-39
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• 2018

This study explored the influencing factors on the depression of immigrant school-adolescents. It made use of family factors, school factors, and multi-cultural factors to verify the influencing variables of depression in school grade. Data were drawn from the National Survey of Multi-cultural families 2015 and analyzed t-test and chi square test and multiple regression analysis by using SPSS Win 21.0. First, school violence experience was the most important factor on the affecting depression in elementary school students. Multi-cultural family identity was the main cause of depression in middle school students, and school study difficulty in high school students. Second, major variables on the affecting depression in all groups were identified as social discrimination experience. These results implied the differentiated support by ages was asked for the stable settlement of adolescents. Experience of social discrimination was a common factor raising the level of depression in all groups. Therefore, we should be done active intervention in school environment.

• Journal of Digital Convergence
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• v.12 no.12
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• pp.139-149
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• 2014

This paper aims to examine the moderate effects of LMX on the relationships between appraisal justice and organization commitment. Additionally, This study is to identify the influences of appraisal justice on the organization commitment in the organization. To accomplish these purposes, the main factors of the appraisal justice such as distributive justice, procedural justice and interactional justice were found from the previous studies. This study used the statistical techniques such as descriptive analysis, reliability analysis, discriminant analysis, factor analysis, correlation analysis, multi regression analysis, and hierarchical regression analysis. The following are the summary of hypothesis test: First, all three justice factors are essential to enhance the level of organizational commitment in appraisal about employee of enterprises. Second, interactional justice among factors of appraisal justice have differential influence on organization commitment by LMX.

• Korean Journal of Soil Science and Fertilizer
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• v.46 no.5
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• pp.333-342
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• 2013

Soil compaction is affected by soil texture, organic matter (OM), strength (ST) and soil moisture, which is difficult to understand the degree and effects of related factors. The purpose of the study is to assess the impact of them on the compaction with bulk density (BD). The analysis was conducted with data collected from national-wide monitoring sites including 105 upland soils, 246 orchard soils, and 408 paddy soils between 2009 and 2012. The distributions of soil physical properties were measured. The correlation and multi linear regression analysis were performed between soil physical properties using SAS. The regression equation of BD(y) includes ST, gravitational water contents (GWC), and OM as variables commonly, having additional factors, clay content and sand content in paddy soil and upland soil for only subsoil (p<0.001). Our results show that the BD could be explained about 40~50% by various physical properties. The regression was mainly determined by ST in orchard and upland soil and by the GWC in paddy soil. To mitigate soil compaction, it is important to maintain the proper level of OM in upland soil and to consider the moisture condition with soil texture in paddy soil when making work plan. Furthermore, it would be recommended the management criteria classified by soil texture for the paddy soils.

• Food Science of Animal Resources
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• v.42 no.4
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• pp.672-688
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• 2022

The objective of this study was to explore the potential of front face fluorescence spectroscopy (FFFS) as rapid, non-destructive and inclusive technique along with multi-variate analysis for predicting meat adulteration. For this purpose (FFFS) was used to discriminate pure minced beef meat and adulterated minced beef meat containing (1%, 2%, 3%, 4%, 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100%) of chicken meat as an adulterant in uncooked beef meat samples. Fixed excitation (290 nm, 322 nm, and 340 nm) and fixed emission (410 nm) wavelengths were used for performing analysis. Fluorescence spectra were acquired from pure and adulterated meat samples to differentiate pure and binary mixtures of meat samples. Principle component analysis, partial least square regression and hierarchical cluster analysis were used as chemometric tools to find out the information from spectral data. These chemometric tools predict adulteration in minced beef meat up to 10% chicken meat but are not good in distinguishing adulteration level from 1% to 5%. The results of this research provide baseline for future work for generating spectral libraries using larger datasets for on-line detection of meat authenticity by using fluorescence spectroscopy.

• MALSORI
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• no.66
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• pp.41-59
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• 2008

Knowledge in linguistic rhythm of the target language plays a major role in foreign language proficiency. This study attempts to discover valid rhythm features that can be utilized in automatic assessment of non-native English pronunciation. Eight previously proposed and two novel rhythm metrics are investigated with 360 English read speech tokens obtained from 27 Korean learners and 9 native speakers. It is found that some of the speech-rate normalized interval measures and above-word level metrics are effective enough to be further applied for automatic scoring as they are significantly correlated with speakers' proficiency levels. It is also shown that metrics need to be dynamically selected depending upon the structure of target sentences. Results from a preliminary auto-scoring experiment through a Multi Regression analysis suggest that appropriate control of unexpected input utterances is also desirable for better performance.

• Journal of the Korean Home Economics Association
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• v.20 no.4
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• pp.125-132
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• 1982

The purpose of this study was to see that how about the variables of homemaker's age, marital periods, children numbers, homemaker's employment, family types, house types, family income, homemaker's education level, and the convenience of kitchen and laundry influence on homemaker's household preferences. On this study, the household tasks were classified into the tasks on Care of clothes, Mal preparation and clean-up, Care of the house, Care of the family members, and Marketing and record keeping. Questionaires were given to randomly selected homemakers in Busan in July, 1982. Data from the 736 respones were analyzed according to Multi regression and T-test. The results were as follows: 1. The variables affecting the homemaker's household preferences were marital periods, family income and the convenience of kitchen and laundry. Longer the marital periods, higher the family income and more feeling the convence were taken higher preferences on household tasks. 2. The variable of age was less significantly related to the homemaker's household preferences than the marital periods. 3. The variable of house types was less significantly related to the homemaker's household preferences than the convenience of kitchen and laundry.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1029-1035
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• 2019

In recent years, artificial intelligence (AI) services have become one of the most essential parts to extend human capabilities in various fields such as face recognition for security, weather prediction, and so on. Various learning algorithms for existing AI services are utilized, such as classification, regression, and deep learning, to increase accuracy and efficiency for humans. Nonetheless, these services face many challenges such as fake news spread on social media, stock selection, and volatility delay in stock prediction systems and inaccurate movie-based recommendation systems. In this paper, various algorithms are presented to mitigate these issues in different systems and services. Convolutional neural network algorithms are used for detecting fake news in Korean language with a Word-Embedded model. It is based on k-clique and data mining and increased accuracy in personalized recommendation-based services stock selection and volatility delay in stock prediction. Other algorithms like multi-level fusion processing address problems of lack of real-time database.

• Wind and Structures
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• v.2 no.1
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• pp.25-40
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• 1999

In this paper, artificial neural networks, a new kind of intelligent method, are employed to model and predict amplitude dependent damping in buildings based on our full-scale measurements of buildings. The modelling method and procedure using neural networks to model the damping are studied. Comparative analysis of different neural network models of damping, which includes multi-layer perception network (MLP), recurrent neural network, and general regression neural network (GRNN), is performed and discussed in detail. The performances of the models are evaluated and discussed by tests and predictions including self-test, "one-lag" prediction and "multi-lag" prediction of the damping values at high amplitude levels. The established models of damping are used to predict the damping in the following three ways : (1) the model is established by part of the data measured from one building and is used to predict the another part of damping values which are always difficult to obtain from field measurements : the values at the high amplitude level. (2) The model is established by the damping data measured from one building and is used to predict the variation curve of damping for another building. And (3) the model is established by the data measured from more than one buildings and is used to predict the variation curve of damping for another building. The prediction results are discussed.