• Proceedings of the Korean Society for Quality Management Conference
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• 2006.11a
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• pp.73-78
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• 2006

In Modern Industrial Society, Service industry are became more important and larger. It is necessary in Serve industry that Service Quality system(SQS) which is as like Total Quality Management system. In this appaer, we propose Service Quality System Model which is compose Monitoring-Analysis-Validate-Improve-Control(MAVIC). Specially Monitoring-Analysis are measured and analysis by using SERVQUAL model.

• Journal of Distribution Science
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• v.13 no.8
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• pp.91-96
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• 2015

Purpose - This study reviews the delivery of child welfare service quality and examines how the dimensions of the variables of customer satisfaction impact the results from a service distribution perspective. This study differs from existing research since it proposes that a recognized level of child welfare service quality is necessary to achieve customer satisfaction from the perspective of service distribution. Research design, data, and methodology - This study explores child welfare service quality factors that affect customer satisfaction. The study examines and analyzes demographic variables, service quality dimensions, and the causal relationships between child welfare service quality and customer satisfaction. Data from 300 child welfare cases were collected from organizations in Korea in the areas of Busan and Gyeongsangnamdo. The methods of analysis are as follow. First, using descriptive analysis frequency, the percentages were evaluated to assess the demographic variables. Second, Cronbach's α was used to test reliability and to evaluate the internal consistency of the measuring of items. Third, multiple regression analysis was conducted to find out how much the independent variable can affect customer satisfaction. Results - Five factors of child welfare service quality were identified in three categories: process quality (assurance, empathy), results quality (reliability, caring), and physical environment quality (tangibles). There were significant differences among the effects of the child welfare service quality factors on customer satisfaction. A multiple regression analysis was done with process quality (assurance, empathy), results quality (reliability, caring) and physical environment quality (tangibles) to test the hypothesis: assurance (t=2.434, p<0.05), empathy (t=3.677, p<0.001), reliability (t=3.271, p<0.05), caring (t=4.380, p<0.000), and tangibles (t=3.654, p<0.01) had a positive influence on child welfare service quality from a service distribution perspective. Therefore, hypotheses 1, 2, 3, 4, and 5 were supported. In addition, multiple regression analysis on the effects of the variables showed that caring (β=0.273), empathy (β=0.246), tangibles (β=0.265), reliability (β=0.152), and assurance (β=0.131) all had a positive and strong influence on child welfare service quality from a service distribution perspective. Therefore, all child welfare service quality categories (process, results and physical environment quality) were positively statistically significant. Conclusion - In this study, the main findings can be summarized as follows. First, the quality of service of child welfare consists of three dimensions of quality: process quality, results quality, and physical environment quality. The results of the multiple regression analysis also showed that caring and reliability were confirmed as more meaningful variables by the increasing loading factors. Second, the family members involved in child welfare proposed caring as the most important variable among the dimensions of service quality. Third, the results of the hypothesis testing using regression showed that all child welfare service quality factors had a positive impact on customer satisfaction. The results of the study could provide useful information to help increase the effectiveness of delivery strategies for child welfare service quality from a service distribution perspective.

• Journal of Environmental Science International
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• v.13 no.3
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• pp.233-244
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• 2004

This study was conducted to evaluate water quality in the Mankyung River using multivariate analysis. The analysis data which was surveyed from January 1996 to December 2002 in Mankyung river was aquired by the ministry of environment. Twelve water quality parameters were determined on each survey. The results were summarized as follow; Water quality in the Mankyung River could be explained up to 74.90% by four factors which were included in loading of organic matter and nutrients by the tributaries(43.28%), seasonal variation(10.40%), loading of pathogenic bacteria by domestic sewage of Gapcheon (12.41%) and internal metabolism in river(8.81%). The result of cluster analysis by station was classified into three group that has different water quality characteristics. Especially, Iksan river was appeared to considerable water quality characteristics against other station. In monthly cluster analysis, three group was classified by seasonal characteristics. Also, in yearly cluster analysis, three group was classified. It is necessary to control the pollutant loadings by domestic sewage and livestock waste for water quality management of Mankyung river.

• Bulletin of the Korean Chemical Society
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• v.30 no.10
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• pp.2287-2293
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• 2009

Gas Chromatography-Mass Spectrometry (GC-MS) fingerprint analysis, Principle Components Analysis (PCA), and Hierarchical Cluster Analysis (HCA) were introduced for quality assessment of Curcuma longa L. (C. longa). The GC-MS fingerprint method was developed and validated by analyzing 33 batches of samples of C. longa from different geographic locations. 18 chromatographic peaks were selected as characteristic peaks and their relative peak areas (RPA) were calculated for quantitative expression. Two principal components (PCs) were extracted by PCA. C. longa collected from Guizhou and Fujian were separated from other samples by PC1, capturing 71.83% of variance. While, PC2 contributed for their further separation, capturing 11.13% of variance. HCA confirmed the result of PCA analysis. Therefore, GC-MS fingerprint study with chemometric techniques provides a very flexible and reliable method for quality assessment of C. longa.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.33-40
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• 2020

In order to satisfy customers, it is important to identify the quality elements that affect customers' satisfaction. The Kano model has been widely used in identifying multi-dimensional quality attributes in this purpose. However, the model suffers from various shortcomings and limitations, especially those related to survey practices such as the data amount, reply attitude and cost. In this research, a model based on the text sentiment analysis is proposed, which aims to substitute the survey-based data gathering process of Kano models with sentiment analysis. In this model, from the set of opinion text, quality elements for the research are extracted using the morpheme analysis. The opinions' polarity attributes are evaluated using text sentiment analysis, and those polarity text items are transformed into equivalent Kano survey questions. Replies for the transformed survey questions are generated based on the total score of the original data. Then, the question-reply set is analyzed using both the original Kano evaluation method and the satisfaction index method. The proposed research model has been tested using a large amount of data of public IT service project evaluations. The result shows that it can replace the existing practice and it promises advantages in terms of quality and cost of data gathering. The authors hope that the proposed model of this research may serve as a new quality analysis model for a wide range of areas.

• Journal of Korean Society for Quality Management
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• v.49 no.1
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• pp.81-95
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• 2021

Purpose: The purpose of this study was to analysis pattern of nonconforming farmers who is one of the factors of unconformity in residual pesticides. Methods: Pattern analysis of nonconforming farmers were analyzed through convergence of safety data and farmer's DB data. Exploratory data analysis and association rule analysis were used for extracting factors related to unconformity. Results: The results of this study are as follows; regarding the exploratory data analysis, it was found that factors of farmers influencing unconformity in residual pesticides by total 9 factors; sampling time, gender, age, cultivation region, farming career, agricultural start form, type of agriculture, cultivation area, classification of agricultural products. Regarding the association rule analysis, non-conformity association rules were found over the past three years. There was a difference in the pattern of nonconforming farmers depending on the cultivation period. Conclusion: Exploratory data analysis and association rule analysis will be useful tools to establish more efficient and economical safety management plan for agricultural products.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.4
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• pp.289-294
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• 2015

Data analysts explore collections of data to search for valuable information using various techniques and tricks. Garbage in, garbage out is a well-recognized idiom that emphasizes the importance of the quality of data in data analysis. It is therefore crucial to validate the data quality in the early stage of data analysis, and an effective method of evaluating the quality of data is hence required. In this paper, a method to visually characterize the quality of data using the notion of a saliency score is introduced. The saliency score is a measure comprising five indexes that captures certain aspects of data quality. Some experiment results are presented to show the applicability of proposed method.

• Journal of Korean Society for Quality Management
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• v.37 no.1
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• pp.56-68
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• 2009

A measure for service quality of university dormitory is developed to gain a better understanding of the quality issues that impact on students' experiences and to improve service quality. Literature survey, thorough discussion with staff members and a pilot test are utilized to elicit attributes of service quality. Factor analysis is used to group the service quality attributes into dimensions. The resulting measure is consisted of 6 dimensions; competence, attitude, facility, amenity, security and discipline. Importance-performance analysis is utilized to verify which factors to be focused on with high Priority to improve dormitory service.

• Journal of Korean Academy of Nursing
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• v.40 no.6
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• pp.757-764
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• 2010

Purpose: This paper is a report of a concept analysis of 'quality of pediatric nursing care', Methods: Rodgers's evolutionary method of concept analysis was used. Data were collected from published literature related to quality of pediatric nursing care. Results: Quality of pediatric nursing care was identified with three dimensions and seven attributes: 1) nurse's character: technical competence, interpersonal competence, 2) nurse's activities: developmentally appropriate care, attentiveness, entertainment, 3) nurse-parent interaction: nurse-parent partnership, emotional support. Antecedents of quality of pediatric nursing care were 'child and parent's expectation about pediatric nursing care', 'previous caring experience of pediatric nurses'. Consequences of quality of pediatric nursing care were 'meet child and parent's needs' and 'better health outcomes.' Conclusion: 'Quality of pediatric nursing care' is a core concept in pediatric nursing practice. Appropriate instruments to operationalize the concept need to be developed.

• Advances in environmental research
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• v.6 no.1
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• pp.53-65
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• 2017

Assessment of seasonal changes in surface water quality is an important aspect for evaluating temporal variations of river pollution due to natural or anthropogenic inputs of point and non-point sources. In this study, surface water quality data for 15 physico-chemical parameters collected from 7 monitoring stations in a river during the years from 2014 to 2016 were analyzed. The principal component analysis technique was employed to evaluate the seasonal correlations of water quality parameters, while the principal factor analysis technique was used to extract the parameters that are most important in assessing seasonal variations of river water quality. Analysis shows that a parameter that is most important in contributing to water quality variation for one season may not be important for another season except alkalinity, which is always the most important parameters in contributing to water quality variations for all three seasons.