• Journal of Korean Society for Quality Management
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• v.48 no.1
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• pp.125-147
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• 2020

Purpose: This study would measure the users'perceived overall quality level of A automobile Company, which has leading market power in the domestic automobile market and analyze the causal relationships in the quality value process Model to quality capability, customer value and loyalty based on that. Especially, this study would analyze the relative impacts of the users'perceived risks appearing in the quality value process model of the formation of Quality Factors(QF), Customer Value(CV), and Customer Loyalty(CL) and analyze the moderating effect of the causal relationships among the components. Methods: For an analysis of causal relationships connected to QF, CV, and CL of the customers who purchased Auto manufacturer A's automobile users, 179 users who used within 3 years were utilized as samples for the analysis. As for QF, based on the Garvin(1988), the QF of automobiles were redesigned. For a structural equation analysis of the entire research model, the PLS(S(Partial Least Square) model was utilized. Results: As a result of an analysis, R2 of CV and CL was 0.652 for CV and 0.664 for CL, which was a very stable Goodness of fit. As a result of an analysis of the hypotheses of QF and CV, automobile performance, conformance, aesthetics, serviceability, and durability. In addition, it turned out that the perceived risk had a moderating effect on convenience, service availability, and perceived quality. Conclusion: This study found that the perceived quality risk appearing among automobile users had negative effects on the quality value process model to QF, CV and CL. In contrast, there were factors not affecting the users'quality value process in spite of the perceived risk. These factors can suggest important managerial implications in that they can be utilized as Auto manufacturer A's market-dominant strengths.

• ETRI Journal
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• v.28 no.2
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• pp.191-204
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• 2006

Many previous studies of data quality have focused on the realization and evaluation of both data value quality and data service quality. These studies revealed that poor data value quality and poor data service quality were caused by poor data structure. In this study we focus on metadata management, namely, data structure quality and introduce the data quality management maturity model as a preferred maturity model. We empirically show that data quality improves as data management matures.

• Journal of the East Asian Society of Dietary Life
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• v.20 no.4
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• pp.606-618
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• 2010

The study investigated how 'word of mouth' originates in the chain restaurant industry. It has long been acknowledged that 'word of mouth' is a critical factor for the success of a restaurant business due to its targetability and cost effectiveness. A review of the literature revealed four antecedents of 'word of mouth': service quality, perceived value, satisfaction, and relationship quality. Based on the theoretical/empirical relationships between those constructs, a structural model composed of the hypotheses was proposed. The structural model was tested with data collected from 471 chain restaurant patrons. The structural equation modeling analysis revealed that five constructs in the proposed model are interrelated, and during this process, word of mouth is formed in the chain restaurant industry. A positive relationship between service quality and satisfaction (0.265, p<0.05), service quality and perceived value (0.831, p<0.05), service quality and relationship quality (0.465, p< 0.05), and service quality and WOM (0.263, p< 0.05) were found, indicating that service quality is a key prerequisite for word of mouth and other constructs proposed in the model. It was revealed that perceived value doe not have a direct impact on WOM formation (t=1.275, p=0.202), but a positive relationship between perceived value and satisfaction (0.293, p<0.05) and between satisfaction and WOM (0.627, p< 0.05) were found. Therefore, it was concluded that patrons' perceived value influences word of mouth formation, but that impact is mediated by satisfaction. During this process, relationship quality also plays a mediating role in generating word of mouth. Based on data analysis, theoretical/managerial implications are discussed.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.165-172
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• 2017

Machines are physically or chemically degenerated by continuous usage. One of the results of this degeneration is the process mean shift. Under the process mean shift, production cost, failure cost and quality loss function cost are increasing continuously. Therefore a periodic preventive resetting the process is necessary. We suppose that the wear level is observable. In this case, process mean shift problem has similar characteristics to the maintenance policy model. In the previous studies, process mean shift problem has been studied in several fields such as 'Tool wear limit', 'Canning Process' and 'Quality Loss Function' separately or partially integrated form. This paper proposes an integrated cost model which involves production cost by the material, failure cost by the nonconforming items, quality loss function cost by the deviation between the quality characteristics from the target value and resetting the process cost. We expand this process mean shift problem a little more by dealing the process variance as a function, not a constant value. We suggested a multiplier function model to the process variance according to the analysis result with practical data. We adopted two-side specification to our model. The initial process mean is generally set somewhat above the lower specification. The objective function is total integrated costs per unit wear and independent variables are wear limit and initial setting process mean. The optimum is derived from numerical analysis because the integral form of the objective function is not possible. A numerical example is presented.

• Information Systems Review
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• v.6 no.2
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• pp.195-208
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• 2004

In this paper we propose a new software project development management model incorporating quality management to schedule management. Though many efficient techniques such as code review and inspection are used to remove defects, the effect of defect removal time on project schedule hasn't been studied much. However, poor quality management has an important effect upon overall schedule and sometimes software projects fails due to it. Thus, quality management and schedule management should be considered together and we need to reflect the time to maintain software quality into the schedule management. For the proposed model we introduced "Quality Value" representing the needed time to remove software defects. We assume PSP/TSP to gather the needed data for quality value. The proposed model can be used to predict the effect of software defects on schedule in advance and to prevent schedule lag.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.1
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• pp.45-50
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• 2021

Machines and facilities are physically or chemically degenerated by continuous usage. The representative type of the degeneration is the wearing of tools, which results in the process mean shift. According to the increasing wear level, non-conforming products cost and quality loss cost are increasing simultaneously. Therefore, a preventive maintenance is necessary at some point. The problem of determining the maintenance period (or wear limit) which minimizes the total cost is called the 'process mean shift problem'. The total cost includes three items: maintenance cost (or adjustment cost), non-conforming cost due to the non-conforming products, and quality loss cost due to the difference between the process target value and the product characteristic value among the conforming products. In this study, we set the production volume as a decreasing function rather than a constant. Also we treat the process variance as a function to the increasing wear rather than a constant. To the quality loss function, we adopted the Cpm+, which is the left and right asymmetric process capability index based on the process target value. These can more reflect the production site. In this study, we presented a more extensive maintenance model compared to previous studies, by integrating the items mentioned above. The objective equation of this model is the total cost per unit wear. The determining variables are the wear limit and the initial process setting position that minimize the objective equation.

• The Journal of Information Systems
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• v.13 no.1
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• pp.97-119
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• 2004

Recently, many e-business companies started to charge fees to the use of information contents service. However, little is known about how users evaluate and determine to purchase information services. Past technology adoption research has focused primarily on the positive utility gains side, focusing on usefulness and ease of use. Purchase of e-service, however, involves not only the position utilities but also negative utilities. This research uses the service value model(SVM) and explains user's intention of purchasing a new information service. Based on the Perceived Value Framework, this research investigates the impacts of the service quality and the fee charge on the user's perceived service value and further on user's intention of adopting the e-service. One of the most important postulations of this research is that both service quality and the fee charge influence user's intention through affecting the user's perceived service value. This research presences a conceptual model of users' e-service evaluation process. The conceptual framework provides a basis for understanding how perceptions of quality and sacrifice influence value perceptions and purchase intentions. The results of an empirical research suggest that the both service quality and fee charge have influences on the perceived service value. However, they do not directly affect user's intention to purchase the e-service. They affect user's intention to purchase through affecting the perceived service value. In conclusion, this research provides a base to build on for other research studying use intention model of new e-service.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.4
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• pp.203-212
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• 2018

Quality requirements of manufactured products or parts are given in the form of specification limits on the quality characteristics of individual units. If a product is to meet the customer's fitness for use criteria, it should be produced by a process which is stable or repeatable. In other words, it must be capable of operating with little variability around the target value or nominal value of the product's quality characteristic. In order to maintain and improve product quality, we need to apply statistical process control techniques such as histogram, check sheet, Pareto chart, cause and effect diagram, or control charts. Among those techniques, the most important one is control charting. The cumulative sum (CUSUM) control charts have been used in statistical process control (SPC) in industries for monitoring process shifts and supporting online measurement. The objective of this research is to apply Taguchi's quality loss function concept to cost based CUSUM control chart design. In this study, a modified quality loss function was developed to reflect quality loss situation where general quadratic loss curve is not appropriate. This research also provided a methodology for the design of CUSUM charts using Taguchi quality loss function concept based on the minimum cost per hour criterion. The new model differs from previous models in that the model assumes that quality loss is incurred even in the incontrol period. This model was compared with other cost based CUSUM models by Wu and Goel, According to numerical sensitivity analysis, the proposed model results in longer average run length in in-control period compared to the other two models.

• Journal of Korean Institute of Industrial Engineers
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• v.30 no.2
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• pp.84-92
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• 2004

A process control procedure is proposed when 100% inspection is performed in a process with excellent capability. Only the incomplete measurement data is assumed to be available, i.e. the specific measurement value of the quality characteristic is not available for each item but it can be determined to be smaller or larger than any prescribed value. In the suggested model, a signal limit is introduced to determine whether the process under study is in control or not. If the quality characteristic of an incoming item exceeds the upper signal or the lower signal limit, the process is determined to be stopped or not by comparing the number of consecutively accepted items with a predetermined threshold number. The procedure is designed based on the type I and II errors. The performance of the model is evaluated by the expected number of items produced under the in-control and out-of-control states until the process is stopped.

• Journal of Applied Reliability
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• v.16 no.2
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• pp.98-103
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• 2016

Purpose: The purpose of this paper is to get a meaningful information for improving manufacturing quality of the products before they are produced in client's manufacturing process. Methods: A variety of data mining techniques have been being used for wide range of industries from process data in manufacturing factories for quality improvement. One application of those is to get meaningful information from process data in manufacturing factories for quality improvement. In this paper, the failure rate at client's manufacturing process is predicted by using the parameters of the characteristics of the product based on PCA (Principle Component Analysis) and regression analysis. Results: Through a case study, we proposed the predicting methodology and regression model. The proposed model is verified through comparing the failure rates of actual data and the estimated value. Conclusion: This study can provide the guidance for predicting the failure rate on the manufacturing process. And the manufacturers can prevent the defects by confirming the factor which affects the failure rate.