• Journal of Distribution Science
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• v.13 no.8
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• pp.41-48
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• 2015

Purpose - This study's objective is to confirm the effects of the perceived relationship value, alternative attractiveness, and investment size on Korean food service franchisees' commitment, using an investment model. Among the three factors, the study examines which factors enhance or weaken the commitment in the franchising investment model. Research design, data, methodology - The data were collected from 495 franchisees and analyzed by a SEM (Structure Equation Model) using path analysis by SPSS 18.0 and AMOS 18.0. Results - 1) The perceived relationship value has a positive effect on franchisee commitment. 2) The alternative attractiveness has a negative effect on franchisee commitment. 3) The investment size has a positive effect on franchisee commitment. Conclusions - The findings show that the investment model can be adapted to franchising and confirms previous investment model study results. We can assume that the higher the perceived relationship value and the bigger the investment, the stronger the commitment, and the greater the alternative attractiveness, the weaker the commitment. These results offer managerial implications for a franchisor wanting to strengthen franchisee commitment.

• Journal of Information Technology Applications and Management
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• v.28 no.4
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• pp.13-20
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• 2021

In this paper, I was applied the life distribution following linear failure rate distribution, Lindley distribution and Burr-Hatke exponential distribution extensively used in the arena of software reliability and were associated the reliability possessions of the software using the nonhomogeneous Poisson process with finite failure. Furthermore, the average value functions of the life distribution are non-increasing form. Case of the linear failure rate distribution (exponential distribution) than other models, the smaller the estimated value estimation error in comparison with the true value. In terms of accuracy, since Burr-Hatke exponential distribution and exponential distribution model in the linear failure rate distribution have small mean square error values, Burr-Hatke exponential distribution and exponential distribution models were stared as the well-organized model. Also, the linear failure rate distribution (exponential distribution) and Burr-Hatke exponential distribution model, which can be viewed as an effectual model in terms of goodness-of-fit because the larger assessed value of the coefficient of determination than other models. Through this study, software workers can use the design of mean square error, mean value function as a elementary recommendation for discovering software failures.

• Journal of Power Electronics
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• v.19 no.1
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• pp.288-295
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• 2019

Rectifiers are widely used in industrial applications. Although detailed models of rectifiers are usually used to evaluate their performance, they are complex and time-consuming. Therefore, the Average Value Model (AVM) has been introduced to meet the demand for a simple and accurate model. This type of rectifier modeling can be used to simplify the simulations of large systems. The AVM of diode rectifiers has been an area of interest for many electrical engineers. However, healthy diode rectifiers are only considered for average value modeling. By contrast, faults occur frequently on diodes, which eventually cause the diodes to open-circuit. Therefore, it is essential to model bridge rectifiers under this faulty condition. Indeed, conventional AVMs are not appropriate or accurate for faulty rectifiers. In addition, they are significantly different in modeling. In this paper, a novel application of the parametric average value of a three-phase line-commutated rectifier is proposed in which one diode of the rectifier is considered open-circuited. In order to evaluate the proposed AVM, it is compared with experimental and simulation results for the application of a brushless synchronous generator field. The results clearly demonstrate the accuracy of the proposed model.

• Asia Pacific Journal of Business Review
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• v.6 no.1
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• pp.21-48
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• 2021

Promoting low carbon transportation adoption is important for energy saving. Some prior studies have discussed on environmental values affect low carbon transportation commuting is inconclusive. This study has constructed the environmental values, utility value, and social influence-based low-carbon transportation adoption model through the theory of the technology acceptance model and VBN model and the IS success model. Through the SEM model and stepwise regression analysis, we have found that environmental values positively affect utility value, and utility value also positively affects the behavior adoption of low carbon transportation. The utility value as mediating effect in the relationship between environmental values and low carbon transportation commuting behavior. Besides, we also have found that social influence positively impacts the behavior adoption of low carbon transportation. It better enhances the level of household residents' environmental values and utility values, and social influence for promoting the adoption of low carbon transportation. This present research provides theoretical guidance and suggestions for promoting the development of low-carbon transportation innovation.

• Journal of the Korean Society for Railway
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• v.10 no.2 s.39
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• pp.137-145
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• 2007

In traditional financial theory, the discount cash flow model(DCF or NPV) operates as the basic framework for most analyses. In doing valuation analysis, the conventional view is that the net present value(NPV) of a project is the measure of the present value of expected net cash flows. Thus, investing in a positive(negative) NPV project will increase(decrease) firm value. Recently, this framework has come under some fire for failing to consider the options of the managerial flexibilities. Real option valuation(ROV) considers the managerial flexibility to make ongoing decisions regarding the implementation of investment projects and the deployment of real assets. The appeal of the framework is natural given the high degree of uncertainty that firms face in their technology investment decisions. This paper suggests an algorithm for estimating volatility of logarithmic cash flow returns of real assets based on the Black-Sholes option pricing model, the binomial option pricing model, and the Monte Carlo simulation. This paper uses those models to obtain point estimates of real option value with the G7- HSR350X(high-speed train).

• Journal of Environmental Science International
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• v.27 no.3
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• pp.167-177
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• 2018

The removal characteristics of As and Se ions from aqueous solution by hexadecyl trimethyl ammonium bromide (HTMAB) modified anthracite (HTMAB-AT) were investigated under various conditions of contact time, pH and temperature. When the pH is 6, the zeta potential value of anthracite (AT) is -24 mV and on the other hand, the zeta potential value of the HTMAB-AT is +44 mV. It can be seen that the overall increase of about 60 mV. Increasing the (+) potential value indicates that the surface of the adsorbent had a stronger positive charge, so adsorption for the anion metal was increased. The isotherm data was well described by Langmuir and Temkin isotherm model. The maximum adsorption capacity was found to be 7.81 and 6.89 mg/g for As and Se ions from the Langmuir isotherm model at 298 K, respectively. The kinetic data was tested using pseudo first and pseudo second order models. The results indicated that adsorption fitted well with the pseudo second order kinetic model. The mechanism of the adsorption process showed that adsorption was dependent on intra particle diffusion model according to two step diffusion. The thermodynamic parameters(${\Delta}G^{\circ}$, ${\Delta}H^{\circ}$, and ${\Delta}S^{\circ}$) were also determined using the equilibrium constant value obtained at different temperatures. The thermodynamic parameters indicated that the adsorption process was physisorption, and also an endothermic and spontaneous process.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.3
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• pp.167-175
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• 2020

The study aims to examine the relationships between service quality, experience value, relationship quality and behavior intentions. Validated measurements were identified from a literature review. The total of 309 valid respondents were used in this research. The measurement model and the conceptual model depicting hypothesized relationships were evaluated based on the 309 responses using confirmatory factor analysis and a structural equation modeling, accordingly. In addition, this study systematized the concepts, defined and tested the component scales of the relationship model between service quality, experience value, relationship quality and behavior intentions from the collected data, which helps to adequately identify the relationship between the factors in the model as well as the impact. The findings confirm that service quality influences experience values, relationship quality and purchase intention. Service quality, experience value, relationship quality and behavior intentions altogether are not well understood in current literature despite the important implication for managers, academicians and consumers alike. Contributes to a better fit between relationship marketing models and managerial practice in business markets. This study provides managerial implications regarding service quality and experience value so that firms and marketers can consult and apply. Managers should not only focus on the improvement of service quality but overall strategic planning.

• Journal of Integrative Natural Science
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• v.7 no.1
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• pp.67-73
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• 2014

We consider the mean value function for NHPP software reliability model and time series regression model in software failure data. We estimate parameters for the proposed models from two data sets. The values of SSE and MSE is presented from two data sets. We compare the predicted number of faults with the actual two data sets using the mean value function and regression curve.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.125-140
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• 2013

We call a data set in which the number of records belonging to a certain class far outnumbers the number of records belonging to the other class, 'imbalanced data set'. Most of the classification techniques perform poorly on imbalanced data sets. When we evaluate the performance of a certain classification technique, we need to measure not only 'accuracy' but also 'sensitivity' and 'specificity'. In a customer churn prediction problem, 'retention' records account for the majority class, and 'churn' records account for the minority class. Sensitivity measures the proportion of actual retentions which are correctly identified as such. Specificity measures the proportion of churns which are correctly identified as such. The poor performance of the classification techniques on imbalanced data sets is due to the low value of specificity. Many previous researches on imbalanced data sets employed 'oversampling' technique where members of the minority class are sampled more than those of the majority class in order to make a relatively balanced data set. When a classification model is constructed using this oversampled balanced data set, specificity can be improved but sensitivity will be decreased. In this research, we developed a hybrid model of support vector machine (SVM), artificial neural network (ANN) and decision tree, that improves specificity while maintaining sensitivity. We named this hybrid model 'hybrid SVM model.' The process of construction and prediction of our hybrid SVM model is as follows. By oversampling from the original imbalanced data set, a balanced data set is prepared. SVM_I model and ANN_I model are constructed using the imbalanced data set, and SVM_B model is constructed using the balanced data set. SVM_I model is superior in sensitivity and SVM_B model is superior in specificity. For a record on which both SVM_I model and SVM_B model make the same prediction, that prediction becomes the final solution. If they make different prediction, the final solution is determined by the discrimination rules obtained by ANN and decision tree. For a record on which SVM_I model and SVM_B model make different predictions, a decision tree model is constructed using ANN_I output value as input and actual retention or churn as target. We obtained the following two discrimination rules: 'IF ANN_I output value <0.285, THEN Final Solution = Retention' and 'IF ANN_I output value ${\geq}0.285$, THEN Final Solution = Churn.' The threshold 0.285 is the value optimized for the data used in this research. The result we present in this research is the structure or framework of our hybrid SVM model, not a specific threshold value such as 0.285. Therefore, the threshold value in the above discrimination rules can be changed to any value depending on the data. In order to evaluate the performance of our hybrid SVM model, we used the 'churn data set' in UCI Machine Learning Repository, that consists of 85% retention customers and 15% churn customers. Accuracy of the hybrid SVM model is 91.08% that is better than that of SVM_I model or SVM_B model. The points worth noticing here are its sensitivity, 95.02%, and specificity, 69.24%. The sensitivity of SVM_I model is 94.65%, and the specificity of SVM_B model is 67.00%. Therefore the hybrid SVM model developed in this research improves the specificity of SVM_B model while maintaining the sensitivity of SVM_I model.

• Journal of Power System Engineering
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• v.13 no.4
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• pp.68-73
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• 2009

The mathematical model has a different response character with the real system because this mathematical model has the modeling errors and the imprecise value of system's parameters. Therefore to find the value of system parameters as possible as near by real value in the model is necessary to design the controlled system. This study concern about the identification method to estimate the parameter for the magnetic bearing system with RCGA(Real Coded Genetic Algorithm). Firstly, we will get the mathematical model from the current amplifier circuit and the magnetic bearing system. Secondly we will get the step response data in this circuit and system. Finally, we will estimate the unknown parameter's value from the data.