• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.603-611
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• 2020

Higher education has long been considered as a means of human resource development in a nation. The faculty member plays a significant role in improving the quality of higher education. It is clear that job satisfaction and motivation have effect on the faculty member's performance. The objective of this study is to investigate the levels and factors affecting lecturers' motivation in Vietnamese public universities. In this study, ordinary least squares (OLS) and exploratory factor analysis (EFA) have been utilized to identify the factors affecting work motivation of lecturers at Vietnamese universities. A questionnaire was administered to a sample of 189 lecturers at different public universities in Vietnam. The finding indicates that seven factors including Work characteristics (WC), Wage and welfare (WW), Social recognition (SR), Peer relationships (PR), Training and promotion opportunities (PO), Leader caring (LC) and Teacher-student interaction and student's attitude (IA) have positive effect on lecturers' work motivation. Among these factors, Teacher-student interaction and student's attitude (IA) has the strongest impact with the coefficient of 0.631 and Peer relationships (PR) has the least impact on work motivation with the coefficient of 0.020. The study findings can facilitate the understanding of how to increase work satisfaction at the universities in Vietnam.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.675-685
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• 2020

The main purpose of this study is to investigate the potential effect of ownership concentration on the relationship between board composition and bank performance. The study employs a sample of Saudi banks listed on Saudi stock exchange (TADAUWL) over the period from 2011 to 2018. To test the study hypotheses and control for endogeneity issues, the Ordinary Least Square (OLS) and the Two-Stage Least Squares (2SLS) techniques are used. The empirical results reveal a significant negative moderating effect of ownership concentration on the association between board composition and bank performance, which confirms the study argument and supports hypotheses. The results indicate that board composition in terms of independent board members, executive board members, and non-executive board members in banks with higher ownership concentration have a weaker positive influence on bank performance. For control variables, the results are almost consistent with theoretical perspectives and previous empirical evidence. The results of this study have important implications for regulatory authorities, companies, and market participants in Saudi Arabia and countries with high concentrated ownership to understand how ownership concentration could affect corporate governance and firm performance and to identify appropriate actions to protect board composition from the influence of ownership concentration.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.4
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• pp.73-81
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• 2013

Ordinary least squares (OLS) regression is the primary statistical method in previous studies for vegetation distribution patterns in relation to landform. However, this global regression lacks the ability to uncover some local-specific relationships and spatial autocorrelation in model residuals. This study employed geographically weighted regression (GWR) to examine the spatially varying relationships between NDVI (Normalized Difference Vegetation Index) patterns and changing trends of landform (elevation, slope) and solar intensity (insolation and duration of sunshine) in Mt Geum-gang of North-Korea. Results denoted that GWR was more powerful than OLS in interpreting relationships between NDVI patterns and landform/solar intensity, since GWR was characterized by higher adjusted R2, and reduced spatial autocorrelations in model residuals. Unlike OLS regression, GWR allowed the coefficients of explanatory variables to differ by locality by giving relatively more weight to NDVI patterns which are affected by local landform and solar factors. The strength of the regression relationships in the GWR increased significantly, by showing regression coefficient of higher than 70% (0.744) in the southern ridge of the experimental area. It is anticipated that this research output will serve to increase the scientific and objective vegetation monitoring in relation to landform and solar intensity by overcoming serious constraints suffered from the past non-GWR-based approach.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1457-1469
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• 2017

Private education expenses is one of the key issues in Korea and there have been many discussions about it. Academically, most of previous researches for private education expenses have used multiple regression linear model based on ordinary least squares (OLS) method. However, if the data do not satisfy the basic assumptions of the OLS method such as the normality and homoscedasticity, there is a problem with the reliability of estimations of parameters. In this case, quantile regression model is preferred to OLS model since it does not depend on the assumptions of nonnormality and heteroscedasticity for the data. In the present study, the data from a survey on private education expenses, conducted by Statistics Korea in 2015 has been analyzed for investigation of the impacting factors for private education expenses. Since the data do not satisfy the OLS assumptions, quantile regression model has been employed in Bayesian approach by using gibbs sampling method. The analysis results show that the gender of the student, parent's age, and the time and cost of participating after school are not significant. Household income is positively significant in proportion to the same size for all levels (quantiles) of private education expenses. Spending on private education in Seoul is higher than other regions and the regional difference grows as private education expenditure increases. Total time for private education and student's achievement have positive effect on the lower quantiles than the higher quantiles. Education level of father is positively significant for midium-high quantiles only, but education level of mother is for all but low quantiles. Participating after school is positively significant for the lower quantiles but EBS textbook cost is positively significant for the higher quantiles.

• Journal of Korean Society of Forest Science
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• v.97 no.1
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• pp.45-52
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• 2008

The objectives of this study are to explain a supply plan of domestic softwood log by long-term prospect of MDF production to stably promote industry of MDF. For it, we developed the long supply function as Ordinary Least Squares Method. Between 2005 and 2050, it was estimated that quantity of domestic production of MDF increased from 1,653 thousand $m^3$ to 2,041 thousand $m^3$. In 2050, quantities of domestic softwood log used by raw materials to product MDF of 2,041 thousand $m^3$ were estimated to be used about 1,355 thousand $m^3$. Exampling Pinus rigida used presently by raw materials to product MDF, cutting area of it is estimated to be 10,828 ha per year. And larch is cutted about 9,160 ha per year. This study estimated annual softwood log cutting amount and total afforestation area at 2050 year by 3 scenarios which are 35%, 45% and 55% about use of domestic softwood log for MDF production. If we do a criterion of cutting area, we advantage to plant larch. But the species of trees are use and growth property. We think that the afforestation policy must be performed on the base of those to supply raw materials of MDF. Although government plans hardwood afforestation policy after cutting Pinus rigida, it needs to support and manage certainly afforestation area of softwoods to need to supply raw materials of MDF to stably promote industry of MDF.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.1
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• pp.43-51
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• 2012

The OLS(ordinary least squares) method is widely used in hedonic housing models. One of the assumptions of the OLS is an independent and uniform distribution of the disturbance term. This assumption can be violated when the spatial autocorrelation exists, which in turn leads to undesirable estimate results. An alterative to this, spatial econometric models have been introduced in housing price studies. This paper describes the comparisons between OLS and spatial econometric models using housing transaction prices of Busan, Korea. Owing to the approaches reflecting spatial autocorrelation, the spatial econometric models showed some superiority to the traditional OLS in terms of log likelihood and sigma square(${\sigma}^2$). Among the spatial models, the SAR(Spatial Autoregressive Models) seemed more appropriate than the SAC(General Spatial Models) and the SEM(Spatial Errors Models) for Busan housing markets. We can make sure the spatial effects on housing prices, and the reconstruction plans have strong impacts on the transaction prices. Selecting a suitable spatial model will play an important role in the housing policy of the government.

• Journal of Korea Water Resources Association
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• v.41 no.3
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• pp.325-340
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• 2008

This study employs Bayesian multiple regression analysis using the ordinary least squares method for regional low flow frequency analysis. The parameter estimates using the Bayesian multiple regression analysis were compared to conventional analysis using the t-distribution. In these comparisons, the mean values from the t-distribution and the Bayesian analysis at each return period are not significantly different. However, the difference between upper and lower limits is remarkably reduced using the Bayesian multiple regression. Therefore, from the point of view of uncertainty analysis, Bayesian multiple regression analysis is more attractive than the conventional method based on a t-distribution because the low flow sample size at the site of interest is typically insufficient to perform low flow frequency analysis. Also, we performed low flow prediction, including confidence interval, at two ungauged catchments in the Nakdong River basin using the developed Bayesian multiple regression model. The Bayesian prediction proves effective to infer the low flow characteristic at the ungauged catchment.

• Journal of Electrical Engineering and Technology
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• v.7 no.4
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• pp.636-645
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• 2012

This study introduces an information granular-based fuzzy radial basis function neural networks (FRBFNN) based on multiobjective optimization and weighted least square (WLS). An improved multiobjective space search algorithm (IMSSA) is proposed to optimize the FRBFNN. In the design of FRBFNN, the premise part of the rules is constructed with the aid of Fuzzy C-Means (FCM) clustering while the consequent part of the fuzzy rules is developed by using four types of polynomials, namely constant, linear, quadratic, and modified quadratic. Information granulation realized with C-Means clustering helps determine the initial values of the apex parameters of the membership function of the fuzzy neural network. To enhance the flexibility of neural network, we use the WLS learning to estimate the coefficients of the polynomials. In comparison with ordinary least square commonly used in the design of fuzzy radial basis function neural networks, WLS could come with a different type of the local model in each rule when dealing with the FRBFNN. Since the performance of the FRBFNN model is directly affected by some parameters such as e.g., the fuzzification coefficient used in the FCM, the number of rules and the orders of the polynomials present in the consequent parts of the rules, we carry out both structural as well as parametric optimization of the network. The proposed IMSSA that aims at the simultaneous minimization of complexity and the maximization of accuracy is exploited here to optimize the parameters of the model. Experimental results illustrate that the proposed neural network leads to better performance in comparison with some existing neurofuzzy models encountered in the literature.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.665-675
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• 2020

The aim of this article is to investigate the relationship between insurance and economic growth at aggregate and disaggregate level for the period 1982-2018. Very few studies have been carried out in this field, with contradictory results and using an aggregate data while, according to different authors, an aggregate data might provide spurious results. The author used Ordinary Least Squares Regressions (OLS) and Granger Causality tests to explore the strength and direction of the relationship between insurance and economic growth at an aggregate level. To check the relationship at disaggregate level life insurance, marine insurance, and property insurance are regressed on trade openness and investment, respectively. Non-life insurance at an aggregate level plays a positive and significant role in promoting economic growth, but life insurance has an insignificant impact on the Pakistan economy. On the other hand, non-life insurances at a disaggregated level such as marine insurance negatively affect a vital part of economic growth, i.e., trade. At the same time, property insurance has a significant and positive role in boosting investment. Life, marine, and property insurance Granger cause economic growth, trade, and investment in a single direction. Nevertheless, is a bi-directional relationship between economic growth and non-life insurance.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.6
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• pp.73-84
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• 2020

The paper investigates the effect of the factors on the disclosure of sustainable development information of enterprises. The research sample includes 120 manufacturing companies listed on Vietnam stock market in 2019. This research uses ordinary least squares (OLS) to address econometric issues and to improve the accuracy of the regression coefficients. The empirical results show that five variables have a statistically significant positive effect on disclosure of sustainable development information of manufacturing companies, including firm size (SIZE), independence of board of directors (BOD), foreign ownership (FRO), return on equity (ROE), and financial leverage (LEV). The results indicate that state ownership (STO) has a statistically significant negative effect on disclosure of sustainable development information of manufacturing companies listed on Vietnam stock market. Besides, the research results also show there is a large difference in the disclosure of sustainable development information between listed companies in Vietnam, those of other emerging economies in the region, and the companies in developed markets. Therefore, this paper provides a new insight to managers and related parties on how to improve the firm's sustainability disclosure to bring benefit for the firm itself and the stakeholders by reasonable decisions about the factors that affect disclosure of sustainable development information.