• Journal of Korean Academy of Nursing
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• v.28 no.1
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• pp.17-25
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• 1998

This study was done to evaluate the quality of clinical nursing care using the variables of structure, process, and outcome and to analyze the relationship between the variables. This study also explored which variables are validating indicators to evaluate the quality of nursing care. The results analyzed by multiple regression showed that, generally structural variables did not contribute to the variance in outcome scores, but process variables of nursing care contributed significantly to the outcome variable of patient satisfaction. A combination of structure and process variables explained outcome variables more than structural variables alone. Also, patient satisfaction and hospital preference were significantly related to each other. Therefore, if nursing quality evaluation relies solely upon on structural variables such as number of available nurses and workload, it would be inaccurate because process variables of nursing care are strongly related to outcome variables and the two categories of structure and process variables helped to strengthen the relationships. Thus, it is important to focus on variables of structure, process, ant outcome together in evaluating nursing care quality.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.3
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• pp.166-171
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• 2005

With development of the database, there are too many data on process variables and the manufacturing process for the traditional statistical process control methods to identify the process variables related with assignable causes. Data mining is useful in this situation and provides variety of approaches for improving the process. In this paper, we applied control charts to monitor the process and if assignable causes are detected, then we applied the SVM technique and the sequence pattern analysis to find out the process variables suspected. These techniques made possible to predict the behavior of process variables. We illustrated our proposed methods with real manufacturing process data.

• Journal of Korean Society for Quality Management
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• v.39 no.3
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• pp.400-411
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• 2011

Process variables are factors in an experiment that are not mixture components but could affect the blending properties of the mixture ingredients. For example, the effectiveness of an etching solution which is measured as an etch rate is not only a function of the proportions of the three acids that are combined to form the mixture, but also depends on the temperature of the solution and the agitation rate. Efficient designs for the mixture components-process variables experiments depend on the mixture components-process variables model which is called a combined model. We often use the product model between the canonical polynomial model for the mixture and process variables model as a combined model. In this paper we propose three starting models for the mixture components-process variables experiments. One of the starting model we are considering is the model which includes product terms up to cubic order interactions between mixture effects and the linear & pure quadratic effect of the process variables from the product model. In this paper, we propose a method for finding robust designs and practical designs with respect to D-, G-, and I-optimality for the various starting combined models and then, we find practically efficient and robust designs for estimating the regression coefficients for those models. We find the prediction capability of those recommended designs in the case of three components and three process variables to be good by checking FDS(Fraction of Design Space) plots.

• Journal of Korean Society for Quality Management
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• v.37 no.4
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• pp.43-51
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• 2009

This study was designed to analyze the relationship of process quality, result quality and management performance in Korean insurance industry. For this study the linkage scheme of service quality concept is used on PZB model and BSC(Balances Score Card) system. In the linkage model, the 5 service qualitry factors used in PZB model are used as the result quality variables, and internal process factor, learning/growth factor in BSC are used the process quality variables affecting the result quality variables. And also customer satisfation factor and financial performance index are used as the management performance variables. In the ivsurance industry, the process quality variables were verified to meaningfully affect the result quality variables, and the result service quality variables were verified to affect the management performance indices. As the result, the process quality and the service quality must be emhanced for the competitiveness of Korean insurance industry.

• Journal of the Korean Society of Costume
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• v.42
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• pp.91-112
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• 1999

This study mainly focuses on the dissatisfaction of the post-purchase behavior regarding the consumer dissatisfaction complaining behavior and repurchase intention In the literature study approach it was hypothe-sized that dissatisfaction variables was consisted of the cognitive process and emotion. And the dissatisfaction variables and the individual vari-ables and situational variables were set up to contribute to form the complaining behavior. In the process of the complaining behavior the first thing the consumers would do when they would seek for redress or not. Consumers who sought redress and actually received the fair treatment from a store practiced positive word-of-mouth. However consumers who received unfair treat-ment practived negative word-of-mouth exit behavior or the third party complaining Behavior. And consumers who did not seek any redress either showed exit behavior practiced negative word-of-mouth or no complaining behavior at all. Therefore. complaining behavior process consists of redress seeking process and non-redress seeking process. For the empirical study the survey method was used and two other researches were previously conducted. First of all the research was designed to comprehend consumer complaining behaviors process Secondly it was conducted for measuring validity and reliability: Cronbach's q. The sqmple was 569 women aged 2-'s to 5-'s living in Seoul and other suburban areas. The various methods were used to analyze the date such as frequency, percentage and multiple regression analysis. Overall the results were verified. 1) The dissatisfaction variables and individual variables influenced situational variables. 2) The complaining behavior intention was affected by dissatisfaction variables indivi-dual variables and situational variables. 3) The repurchase intention occurred more when the justice was performed Consequently the complaining behavior proce-dure was consisted of the redress seeking and non-redress seeking. And dissatisfaction vari-ables and individual variables were verified to influnce situational variables. These complaining behavior variables affected complaining behavior Especially in a case of redress seeking process complaining behavior and repurchase rate were highly affected by the perceived justice from a store.

• Journal of Korean Society for Quality Management
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• v.40 no.3
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• pp.347-358
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• 2012

Purpose: Given the mixture components - process variables experimental data, we propose the strategy to find the proper combined model. Methods: Process variables are factors in an experiment that are not mixture components but could affect the blending properties of the mixture ingredients. For example, the effectiveness of an etching solution which is measured as an etch rate is not only a function of the proportions of the three acids that are combined to form the mixture, but also depends on the temperature of the solution and the agitation rate. Efficient designs for the mixture components - process variables experiments depend on the mixture components - process variables model which is called a combined model. We often use the product model between the canonical polynomial model for the mixture and process variables model as a combined model. Results: First we choose the reasonable starting models among the class of admissible product models and practical combined models suggested by Lim(2011) based on the model selection criteria and then, search for candidate models which are subset models of the starting model by the sequential variables selection method or all possible regressions procedure. Conclusion: Good candidate models are screened by the evaluation of model selection criteria and checking the residual plots for the validity of the model assumption. The strategy to find the proper combined model is illustrated with examples in this paper.

• Journal of the Korea Safety Management & Science
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• v.11 no.1
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• pp.121-130
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• 2009

This paper considers a six sigma project for reducing the cost copper of the cable materials in a electric wire company. The project follows a disciplined process of five macro phases: define, measure, analyze, improve, and control (DMAIC). A process map is used to identify process input variables. Three key process input variables are selected by using an input variables are selected by using an input variable evaluation table: large cable, plating, and a twisted pair. DOE is utilized for finding the optimal process conditions of the three key process input variables. The implementing result of this six sigma project is enable for reducing of the 2.8% copper materials.

• The Mathematical Education
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• v.43 no.2
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• pp.187-197
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• 2004

The present study investigated the effects of school and student background variables on academic achievement through analyzing Korean data from the OECD's Programme for International Student Assessment(PISA) which had been administered in 2000. The regression analysis was used to analyze the data, responses of 2,769 students(1,545 boys and 1,221 girls). Results of regression analysis were that 5.5% of process variables was accounted for by student variables and 0.3% of process variables was accounted for by school variables. So we try to find many other process variables which effect math achievements.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1364-1367
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• 1996

Recently, direct rolling process, called as strip casting process, has been interested in to save production cost by reducing forming processes. In direct rolling process, since a steel strip of thickness 1-5(mm) can be produced directly from molten metal, it can eliminate secondary hot rolling process. On the other hand, since many process variables are existed in this process and relation of these variables is very complex, it is difficult to realize the process design and the quality control. In this paper, as first step to overcome above difficulties, the quantitative relationship of the process variables affected to quality of the strip has been carried out through the numerical analysis. Also, we determined the process variable to monitor the quality in the direct rolling process. As a result, we show that the solidification final point, called as Nip point, was related directly to quality of the strip.

• Journal of Korean Society for Quality Management
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• v.13 no.2
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• pp.66-72
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• 1985

The objective of mixture experiments with process variables is to find experimental blends and conditions that produce the product of highest quality. In this paper, designs for mixture experiments with process variables are presented, where the emphasis is on using only a fraction of the total number of possible design points and the fitting of reduced models for measuring the effects of the mixture components and process variables.