• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.131-145
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• 2010

The framework for making a coordinated decision for large-scale facilities has become an important issue in supply chain(SC) management research. The competitive business environment requires companies to continuously search for the ways to achieve high efficiency and lower operational costs. In the areas of production/distribution planning, many researchers and practitioners have developedand evaluated the deterministic models to coordinate important and interrelated logistic decisions such as capacity management, inventory allocation, and vehicle routing. They initially have investigated the various process of SC separately and later become more interested in such problems encompassing the whole SC system. The accurate quotation of ATP(Available-To-Promise) plays a very important role in enhancing customer satisfaction and fill rate maximization. The complexity for intelligent manufacturing system, which includes all the linkages among procurement, production, and distribution, makes the accurate quotation of ATP be a quite difficult job. In addition to, many researchers assumed ATP model with integer time. However, in industry practices, integer times are very rare and the model developed using integer times is therefore approximating the real system. Various alternative models for an ATP system with time lags have been developed and evaluated. In most cases, these models have assumed that the time lags are integer multiples of a unit time grid. However, integer time lags are very rare in practices, and therefore models developed using integer time lags only approximate real systems. The differences occurring by this approximation frequently result in significant accuracy degradations. To introduce the ATP model with time lags, we first introduce the dynamic production function. Hackman and Leachman's dynamic production function in initiated research directly related to the topic of this paper. They propose a modeling framework for a system with non-integer time lags and show how to apply the framework to a variety of systems including continues time series, manufacturing resource planning and critical path method. Their formulation requires no additional variables or constraints and is capable of representing real world systems more accurately. Previously, to cope with non-integer time lags, they usually model a concerned system either by rounding lags to the nearest integers or by subdividing the time grid to make the lags become integer multiples of the grid. But each approach has a critical weakness: the first approach underestimates, potentially leading to infeasibilities or overestimates lead times, potentially resulting in excessive work-inprocesses. The second approach drastically inflates the problem size. We consider an optimized ATP system with non-integer time lag in supply chain management. We focus on a worldwide headquarter, distribution centers, and manufacturing facilities are globally networked. We develop a mixed integer programming(MIP) model for ATP process, which has the definition of required data flow. The illustrative ATP module shows the proposed system is largely affected inSCM. The system we are concerned is composed of a multiple production facility with multiple products, multiple distribution centers and multiple customers. For the system, we consider an ATP scheduling and capacity allocationproblem. In this study, we proposed the model for the ATP system in SCM using the dynamic production function considering the non-integer time lags. The model is developed under the framework suitable for the non-integer lags and, therefore, is more accurate than the models we usually encounter. We developed intelligent ATP System for this model using genetic algorithm. We focus on a capacitated production planning and capacity allocation problem, develop a mixed integer programming model, and propose an efficient heuristic procedure using an evolutionary system to solve it efficiently. This method makes it possible for the population to reach the approximate solution easily. Moreover, we designed and utilized a representation scheme that allows the proposed models to represent real variables. The proposed regeneration procedures, which evaluate each infeasible chromosome, makes the solutions converge to the optimum quickly.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.1
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• pp.301-311
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• 2013

This study aims to analyze characteristics, health behaviors and health management level related to private health examination recipients in one general hospital. To achieve this, we analyzed 150,501 cases of private health examination data for 11 years from 2001 to 2011 for 20,696 participants in 2011 in a Dae-Jeon general hospital health examination center. The cluster analysis for classify private health examination group is used z-score standardization of K-means clustering method. The logistic regression analysis, decision tree and neural network analysis are used to periodic/non-periodic private health examination classification model. 1,000 people were selected as a customer management business group that has high probability to be non-periodic private health examination patients in new private health examination. According to results of this study, private health examination group was categorized by new, periodic and non-periodic group. New participants in private health examination were more 30~39 years old person than other age groups and more patients suspected of having renal disease. Periodic participants in private health examination were more male participants and more patients suspected of having hyperlipidemia. Non-periodic participants in private health examination were more smoking and sitting person and more patients suspected of having anemia and diabetes mellitus. As a result of decision tree, variables related to non-periodic participants in private health examination were sex, age, residence, exercise, anemia, hyperlipidemia, diabetes mellitus, obesity and liver disease. In particular, 71.4% of non-periodic participants were female, non-anemic, non-exercise, and suspicious obesity person. To operation of customized customer management business for private health examination will contribute to efficiency in health examination center.

• Korean Journal of Social Welfare Studies
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• v.44 no.3
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• pp.85-112
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• 2013

This study aims to understand the developmental trajectories of life satisfaction among retirees and to examine what factors differentiate different trajectory classes. This study used three waves of longitudinal data from Korean Retirement and Income Study and data collected every two years(2005, 2007, and 2009). Subjects were respondents aged 50-69 who identified to be retired between wave 1 and wave 2. Finally, this study used 243 respondents for final data analysis. Life satisfaction was measured by seven items. The latent class growth model and multiple logistic regression model were used for data analysis. This study identified three distinct trajectory classes: high stable class(47.7%), high at the early stage but decreased class(42.8%), and low at the early stage and then decreased class(9.5%). This study founded that approximately 50% of the retirees experienced the decline of life satisfaction after retirement and about 10% of the sample was the most vulnerable group. This study analyzed what factors make different among the distinct trajectory groups. As a results, retirees who experienced the improvement in health change were more likely to be in 'high stable class' compared to 'hight at the early stage but decreased class'. In addition, retirees who were less educated, maintained the same health status rather than the improvement, worked as a temporary or a day laborer, and had less household income were more likely to belong to 'low at the early stage and then decreased class' relative to 'high stable class'. This study suggests that there are distinct three trajectories on life satisfaction among the retirees and finds out factors differentiating between trajectory groups. Based on these findings, the study discusses the implications for social work practice and further study.

• 한국노년학
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• v.39 no.3
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• pp.467-484
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• 2019

The purpose of this study is to explore latent classes of longitudinal medical expenses of older people and to analyze its predictors and its effects on subjective health. Among participants of the Korean Health Panel, the sample of this study includes 1,119 people who is 65-year-old or older and reported their medical expenses for nine consecutive years. The analyses were conducted in three steps. First, Growth Mixture Model (GMM) was applied to find distinct subgroups showing similar patterns in medical expenses. The results showed four groups which were classified as high medical expenditure maintenance group, medical expenditure increase group, low medical expenditure maintenance group, and medical expenditure reduction group. Second, the multinominal logistic regression found that the presence of spouse, economic participation, the number of chronic diseases, and the type of health insurance were significant predictors of latent classes in medical expenses. In particular, the greater the number of chronic diseases, the higher the likelihood of belonging to the high medical expenditure maintenance group. In addition, medical benefit recipients are more likely to belong to the low medical cost maintenance and medical cost reduction groups. Third, multiple regression analysis revealed that the older people in the groups with low or reducing expenses reported better subjective health than people with higher expenses. This study has its meanings in exploring the heterogeneity in longitudinal medical expenses among older people and its predictors and its associations with health outcome. The results of this research provide background information in establishing public health policy for older people.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.111-124
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• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• Journal of Preventive Medicine and Public Health
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• v.32 no.1
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• pp.48-59
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• 1999

Objectives. This study was conducted to evaluate the association between upper extremity musculoskeletal symptoms and Rapid Upper Limb Assessment(RULA) in vehicle assembly line workers. The goal of this study is to show the feasibility of RULA as a checklist for work related musculoskeletal symptoms (WMSDs) in Korean workers. Methods. The total number of 199 people from the department of assembly and 115 people from the department of Quality Control(QC) in automotive plant were subjects for this cross sectional study. A standard symptom questionnaire survey has been used for the individual characteristics, work history, musculosketal symptoms and non-occupational covariates. The data were obtained by applying one-on-one interview for the all subjects. RULA has been applied for ergonomic work posture analysis and the primary ergonomic risk sure was computed by RULA method. Association between upper extremity musculoskeletal symptoms and RULA were assessed by multiple logistic regression analysis. Results. A total of 314 workers was examined. The prevalence of musculoskeletal symptoms by NIOSH case definition was 62.4%. The distribution of musculoskeletal symptoms by the part of the body turned out to be following; back:41.4%, neck: 32.8%, shoulder: 26.4%, arm: 10.5% and hand:29.3%. The relationship of the individual RULA scores were statistically significant for the prevalence of musculoskeletal symptoms. As the result of the multiple logistic regressioin analysis, grand final score (OR=2.250 95% CI: 1.402-3.612) was associated with musculoskeletal symptoms in any part of the body.; upper arm score(OR=1.786 95% CI: 1.036-3.079) and posture score A(OR=1.634 95% CI: 1.016-2.626) in neck; muscel use score(OR=3.076 95% CI:1.782-5.310) and posture score A(OR=1.798 95% CI: 1.072-3.017) in shoulder; upper arm score(OR=1.715 95% CI: 1.083-2.715) and muscel use score(OR=2.057 95% CI:1.303-3.248) in neck & shoulder; muscle use score(OR=10.662 95% CI: 3.180-35.742) in arm; writst/wist score(OR=2.068 95% CI: 1.130-3.786) and muscle use score(OR=2.215 95% CI: 1.284-3.819) in hand & wrist.; muscle use score of trunk (OR=2.601 95% CI: 1.147-5.901) in back. Conclusions. Musculoskeletal symptoms of the extremities were strongly associated with individual RULA body score. These results show that RULA can be used as a useful assessment tool for the evaluation of musculoskeletal loading which is known to contribute to work-related musculoskeletal disorders. RULA also can be used as a screening tool or incorporated into a wider ergonomic assessment of epidemiological, physical, mental, environmental and organizational factors. As shown in this study, complement of the analysis system for the other risk factors and characterizing between the upper limb and back part will be needed for future work.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.

• Journal of agricultural medicine and community health
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• v.24 no.2
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• pp.269-289
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• 1999

This study was conducted to examine the relationship among health concern, health practice and ADL of elderly staying at home in a rural community and their affecting factors. Data were collected through direct interviews made with 480 old people aged more than sixty-five from November 15, 1998 to December 20, 1998. Out of 189 male and 291 female, the high-level group that showed high health concern accounted for 44.4%, the medium-level group for 13.1%, and the low-level group for 42.5%, in the health practice, the high-level group accounted for 3.8%, the medium-level group for 18.8%, and the low-level group for 77.5%. In the self-rated health status, the high-level group accounted for 29.0%, the medium-level group for 31.0%, and the low-level group for 40.0%, and in the ADL, the high ADL group accounted for 91.5%, and the low-level ADL group for 8.5%. The result of the chi-square test showed that for male, there was a significant relation between the health concern and the health practice index score. In the relation between the health practice index score and the self-rated health status, there was significant positive relationship between health practice index and self-rated health status, and in the relation between the health practice Index score and the ADL, old people with higher health practices showed good ADL(but not significant). Old people with good ADL also showed good self-rated health status. In the multiple regression analysis where the health practice was used as a dependent variable, the health concern was added to the sociodemographic variables as an independent variables, a formula was formed for male old people only and ones with high concern in health showed good health practice. In the multiple logistic regression analysis where the sociodemographic variables to which the health practices was added were used as an independent variable and the ADL as a dependent variable, the ADL appeared to be not good if for male old people the living costs were born by their sons and daughters and as for female old people their ages increased, but it was good if old people had sources of health information such as hospitals or health centers. The self-rated health status was worse, for male old people, if they had short living costs or diseases and for female old people, if they had spouses, living costs born by their sons and daughters or diseases, but it was better, for male old people, if they had periodical gatherings or carried out health practices a lot, and for female old people, if they had sources of health information such as hospitals or health centers or carried out health practices a lot. In view of the results stated above, the higher the old people had health concern, the more they carried out health practices, and the more they carried out health practices, the better they had ADL and self-rated health status that served as the level of health. Further, the better ADL, the better self-rated health status.

• Journal of Distribution Research
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• v.15 no.3
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• pp.1-34
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• 2010

Introduction The purpose of this research is to develop overall model which involves the effect of ongoing support services by franchisor on franchisee's relationship quality(trust, satisfaction, and commitment) and business performance(financial and non-financial performance), and to investigate the relationships among trust, satisfaction, commitment, financial and non-financial performance. This study also suggests franchise business or franchise system should be based on long-term orientation between franchisor and franchisee rather than short-term orientation, or transactional relationship, and proposes the most effective way of providing on-going support services by franchisor with franchisee thru symbiotic relationship among franchisor and franchisee Research Model and Hypothesis The research model as Figure 1 shows the variables on-going support services which affect the relationship quality between franchisor and franchisee such as trust, satisfaction, and commitment, and also analyze the effects of relationship quality on business performance including financial and non-financial performance We established 12 hypotheses to test as follows; Relationship between on-going support services and trust H1: On-going support services factors (product category & price, logistics service, promotion, information providing & problem solving capability, supervisor's support, and education & training support) have positive effect on franchisee's trust. Relationship between on-going support services and satisfaction H2: On-going support services factors (product category & price, logistics service, promotion, information providing & problem solving capability, supervisor's support, and education & training support) have positive effect on franchisee's satisfaction. Relationship between on-going support services and commitment H3: On-going support services factors (product category & price, logistics service, promotion, information providing & problem solving capability, supervisor's support, and education & training support) have positive effect on franchisee's commitment. Relationship among relationship quality: trust, satisfaction, and commitment H4: Franchisee's trust has positive effect on franchisee's satisfaction. H5: Franchisee's trust has positive effect on franchisee's commitment. H6: Franchisee's satisfaction has positive effect on franchisee's commitment. Relationship between relationship quality and business performance H7: Franchisee's trust has positive effect on franchisee's financial performance. H8: Franchisee's trust has positive effect on franchisee's non-financial performance. H9: Franchisee's satisfaction has positive effect on franchisee's financial performance. H10: Franchisee's satisfaction has positive effect on franchisee's non-financial performance. H11: Franchisee's commitment has positive effect on franchisee's financial performance. H12: Franchisee's commitment has positive effect on franchisee's non-financial performance. Method The on-going support services were defined as an organized system of continuous supporting services by franchisor for the purpose of satisfying the expectation of franchisee based on long-term orientation and classified into six constructs such as product category & price, logistics service, promotion, providing information & problem solving capability, supervisor's support, and education & training support. The six constructs were measured agreement using a 7-point Likert-type scale (1 = strongly disagree to 7 = strongly agree)as follows. The product category & price was measured by four items: menu variety, price of food material provided by franchisor, and support for developing new menu. The logistics service was measured by six items: distribution system of franchisor, return policy for provided food materials, timeliness, inventory control level of franchisor, accuracy of order, and flexibility of emergency order. The promotion was measured by five items: differentiated promotion activities, brand image of franchisor, promotion effect such as customer increase, long-term plan of promotion, and micro-marketing concept in promotion. The providing information & problem solving capability was measured by information providing of new products, information of competitors, information of cost reduction, and efforts for solving problems in franchisee's operations. The supervisor's support was measured by supervisor operations, frequency of visiting franchisee, support by data analysis, processing the suggestions by franchisee, diagnosis and solutions for the franchisee's operations, and support for increasing sales in franchisee. Finally, the of education & training support was measured by recipe training by specialist, service training for store people, systemized training program, and tax & human resources support services. Analysis and results The data were analyzed using Amos. Figure 2 and Table 1 present the result of the structural equation model. Implications The results of this research are as follows: Firstly, the factors of product category, information providing and problem solving capacity influence only franchisee's satisfaction and commitment. Secondly, logistic services and supervising factors influence only trust and satisfaction. Thirdly, continuing education and training factors influence only franchisee's trust and commitment. Fourthly, sales promotion factor influences all the relationship quality representing trust, satisfaction, and commitment. Fifthly, regarding relationship among relationship quality, trust positively influences satisfaction, however, does not directly influence commitment, but satisfaction positively affects commitment. Therefore, satisfaction plays a mediating role between trust and commitment. Sixthly, trust positively influence only financial performance, and satisfaction and commitment influence positively both financial and non-financial performance.