• The Journal of Asian Finance, Economics and Business
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• v.7 no.8
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• pp.645-651
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• 2020

The concept of social entrepreneurship (SE) is gaining attention in developing economies for the purpose of greater societal welfare maximization. Still, findings in the field of SE studies have been riddled with conflicting results and counterstatement. Also, the determinants of developing SE are not robustly investigated in developing economies like Bangladesh. This context has mobilized the authors of this current study to focus on determining student's intention to pursue SE as their career choice. Hence, the study aims to examine the predictive determinants of social entrepreneurial intentions (SEI) among Bangladeshi students. The study has investigated the influence of entrepreneurial self-efficacy, social support, prior experience, and educational support on SEI. The survey was conducted from a public university of Bangladesh, and 231 students participated in the study. Questionnaire items under each construct variable have been adopted from pre-tested research studies. Five-point Likert scale questionnaire was applied to measure the variables. SPSS version 23.0 has been used for statistical analysis through which correlation and multiple regression analysis were conducted to measure the impact of the independent variables on SEI. Results exhibited that self-efficacy, social support, and educational support positively and significantly predicted SEI, while prior experience does not influence SEI.

• Journal of the Korean Data and Information Science Society
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• v.10 no.2
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• pp.369-385
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• 1999

For the growth curve model with arbitrary covariance structure, known as unstructured covariance matrix, the problems of detecting outliers are discussed in this paper. In order to detect outliers in the growth curve model, the likelihood ratio testing statistics in mean shift model is established and its distribution is derived. After we detected outliers in growth curve model, we test homo and/or hetero-geneous covariance matrices using PSR Quasi-Bayes Criterion. For illustration, one numerical example is discussed, which compares between before and after outlier deleting.

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.377-387
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• 1998

A Markov Chain Monte Carlo method with data augmentation is developed to compute the features of the posterior distribution. For each observed failure epoch, we introduced latent variables that indicates with component of the Superposition model. This data augmentation approach facilitates specification of the transitional measure in the Markov Chain. Metropolis algorithms along with Gibbs steps are proposed to preform the Bayesian inference of such models. for model determination, we explored the Pre-quential conditional predictive Ordinate(PCPO) criterion that selects the best model with the largest posterior likelihood among models using all possible subsets of the component intensity functions. To relax the monotonic intensity function assumptions, we consider in this paper Superposition of Musa-Okumoto and Erlang(2) models. A numerical example with simulated dataset is given.

• Journal of Environmental Science International
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• v.27 no.11
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• pp.1141-1154
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• 2018

Numerical experiments were carried out to investigate the effect of data assimilation of observational data on weather and PM (particulate matter) prediction. Observational data applied to numerical experiment are aircraft observation, satellite observation, upper level observation, and AWS (automatic weather system) data. In the case of grid nudging, the prediction performance of the meteorological field is largely improved compared with the case without data assimilations because the overall pressure distribution can be changed. So grid nudging effect can be significant when synoptic weather pattern strongly affects Korean Peninsula. Predictability of meteorological factors can be expected to improve through a number of observational data assimilation, but data assimilation by single data often occurred to be less predictive than without data assimilation. Variation of air pressure due to observation nudging with high prediction efficiency can improve prediction accuracy of whole model domain. However, in areas with complex terrain such as the eastern part of the Korean peninsula, the improvement due to grid nudging were only limited. In such cases, it would be more effective to aggregate assimilated data.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.443-450
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• 2021

The paper investigates the factors that affect the decision of young Chinese consumers to buy high-end cosmetics. The study is based on the responses obtained by questionnaires from 400 respondents in Guangzhou, China. The information was collected and classified on the basis of gender, occupation, age and education in order to understand the main characteristics of the sample in a better way. The purposive, convenient and quota sampling techniques of non-probability sampling method were used. Besides this, the predictive test was carried out with 30 respondents to ensure the reliability and validity of the questionnaires. The data was put to descriptive statistical analysis and multiple regression analysis in order to verify the hypotheses. The data revealed that, while brand awareness does not affect the consumer attitude about the high-end cosmetics, other factors like product involvement, perceived quality, subjective norm, and word-of-mouth have significant effect on consumer's attitude and consumers' intention about high-end cosmetics. The findings of the study show that subjective norm, perceived value, word-of-mouth, and consumer attitude of cosmetic products highly affect consumers purchase intention of high-end cosmetic products. The research paper helps to form concrete and effective marketing strategy based on various aspects of consumer behavior for high-end cosmetics in China.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.34 no.11
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• pp.73-79
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• 2018

In this study, for the prediction of energy consumption in the ventilator, one of the components of the air conditioning system, the predicted results were analyzed and accurate by the change in the number of neurons and inputs. The input variables of the prediction model for the energy volume of the fan were the supply air flow rate, the exhaust air flow rate, and the output value was the energy consumption of the fan. A predictive model has been developed to study with the Levenbarg-Marquardt algorithm through 8760 sets of one-minute resolution. Comparison of actual energy use and forecast results showed a margin of error of less than 1% in all cases and utilization time of less than 3% with very high predictability. MBE was distributed with a learning period of 1.7% to 2.95% and a service period of 2.26% to 4.48% respectively, and the distribution rate of ${\pm}10%$ indicated by ASHRAE Guidelines 14 was high.8.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.263-276
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• 2021

This study examines the effect of international capital flows on corporate capital structures in Vietnam by analyzing panel data from all non-financial listed firms from 2005 to 2014 using pooled ordinary least square (OLS) with a variance estimator. The analysis includes a comparison of the signs and significance of the variable coefficients from the perking order and static trade-off theories to the empirical results to determine the optimum approach to the corporate capital structure given Vietnam's high-inflation environment. The results indicate that international capital flows have a positive relation to the debt ratio in the long term, and the relationship is more robust for 2005-2009 than for 2010-2014. Corporate capital structures adjusted to changes in the business environment in different sub-periods (2005-2009 and 2010-2014). When the economic environment became more favorable, the pecking order theory's predictive power increased, and that of trade-off theory lessened. Manufacturing and non-manufacturing firms required different capital structure decisions to fuel their operations and grow under foreign competition. The analysis demonstrates that firms should intensify their use of long-term debt relative to the availability of capital, which is an implication not only for firms in particular but also for industrial innovation overall.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.403-411
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• 2021

This study discusses the influence of economic factors on the clothing exports from China and 15 South and Southeast Asian countries to the United States. A basic gravity trade model with three predictors, including the GDP value produced by exporting and importing countries and their geographical distance was established to explain the bilateral trade patterns. The conventional approach of multiple regression and the novel approach of Artificial Neural Networks (ANNs) were developed based on the value of clothing exports from 2012 to 2018 and applied to the trade pattern prediction of 2019. The results showed that ANNs can achieve a more accurate prediction in bilateral trade patterns than the commonly-used econometric analysis of the basic gravity trade model. Future studies can examine the predictive power of ANNs on an extended gravity model of trade that includes explanatory variables in social and environmental areas, such as policy, initiative, agreement, and infrastructure for trade facilitation, which are crucial for policymaking and managerial consideration. More research should be conducted for the examination of the balance between developing countries' economic growth and their social and environmental sustainability and for the application of more advanced machine-learning algorithms of global trade flow examination.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.15-22
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• 2021

This study aims to obtain a stock investment strategy model based on the industrial sector in Indonesia Stock Exchange (IDX). This study uses IDX data for the period of January 1996 to December 2016. This study uses the Markov Regime Switching Model to identify trends in market conditions that occur in industrial sectors on IDX. Furthermore, by using the Logit Regression Model, we can see the influence of economic factors in determining trends in market conditions sectorally and the probability of trends in market conditions. This probability can be the basis for determining stock investment decisions in certain sectors. The results showed descriptively that the stocks of the consumer goods industry sector had the highest average return and the lowest standard deviation. The trend in sectoral stock market conditions that occur in IDX can be divided into two conditions, namely bullish condition (high returns and low volatility) and bearish condition (low returns and high volatility). Differences in the conditions are mainly due to differences in volatility. The use of a Logit Regression Model to produce probability of market conditions and to estimate the influence of economic factors in determining stock market conditions produces models that have varying predictive abilities.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.6
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• pp.81-93
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• 2022

This research aims to construct a model that combines brand love, brand loyalty, brand image, customer satisfaction, and service quality into a single model, with brand loyalty coming foremost, and test its predictive power in building brand love. Moreover, mediating effect of customer satisfaction and brand image on service quality and brand loyalty affecting brand love was checked. The study adopted an alliance context using an existing SERVQUAL model, a bi-dimensional aspect of brand loyalty and parasocial love relationship theory, to identify brand love as a construct or outcome in the consumer-brand relationship. Using a quantitative approach, survey questionnaires were distributed by unrestricted random sampling among 507 Saudia Airlines customers. Data were analyzed using structural equation modeling with SmartPLS 3.0. The results revealed significant relationships between four variables except for the brand image. It was found that brand image had no mediating effect on the relationship between service quality and customer loyalty. The outcome of this study highlights the importance of airline alliances for service quality, which leads to positive customer satisfaction, brand image, and customer loyalty. A unique contribution of the study is that it revealed that brand loyalty is also an antecedent of brand love.