• International Journal of Advanced Culture Technology
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• v.7 no.4
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• pp.86-95
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• 2019

This study verified the research hypothesis by establishing a research model to achieve the purpose of empirical analysis on the relationship between O2O platform quality and personal behavioral value and relationship quality. The main results of this study are as follows. First, information quality (hypothesis 1-1), system quality (hypothesis 1-2), service quality (hypothesis 1-3), perceived product quality (hypothesis 1-4), perceived price (Hypothesis 1-5) was statistically significant, indicating a positive effect on individual behavioral value. Second, as a result of empirical analysis of the relationship between O2O platform quality and relationship quality, hypothesis 2, information quality (hypothesis 2-1), perceived product quality (hypothesis 2-4), and perceived price (hypothesis 2-5) While there was a positive effect on quality, system quality (hypothesis 2-2) and service quality (hypothesis 2-3) were not statistically significant. Third, as a result of verifying the relationship between the relationship quality and personal behavior characteristics of hypothesis 3, as the quality of personal behavior improved as the quality of relationship improved, it was required to continuously improve and strengthen the relationship quality.

• Journal of the Korean Data and Information Science Society
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• v.16 no.2
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• pp.349-357
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• 2005

Principal component analysis(PCA) was carried out to evaluate the water quality with the monitering data collected from 1997 to 2003 along the coastal area of Ulsan, Korea. To enhance evaluation and to complement descriptive power of traditional PCA, optimal scaling was applied to transform the original data into optimally scaled data. Cluster analysis was also applied to classify the monitering stations according to their characteristics of water quality.

• The Journal of Industrial Distribution & Business
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• v.9 no.11
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• pp.25-38
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• 2018

Purpose - This study aimed to identify the sub-dimensions of fashion social media quality (information quality, social quality, service quality, system quality) and investigate how they affect purchase intention through fashion information use behavior (information acceptance, information diffusion). Research design, data, and methodology - Data collection was carried out twice for systematic verification of the research model. In the first data collection, the reliability and validity of research variables were verified through 238 respondents and questionnaires were revised and supplemented based on their responses. In March 2018, the final survey was conducted from 755 respondents the age of 20 to 49. Using SPSS 23.0, descriptive statistics, exploratory factor analysis, correlation analysis were performed. In order to test hypotheses, structural equational modeling technique was employed using AMOS 23.0. Results - First of all, fashion Social media quality consists of four factors including information quality, social quality, service quality and system quality. Second, fashion Social media information quality, social quality, and system quality were shown to have a positive(+) effect on information acceptance behavior, and social quality, service quality and system quality were shown to have a positive(+) effect on information diffusion behavior. It was also determined that the acceptance and diffusion behaviors of fashion information through fashion Social media had positive(+) influence on purchase intention. Conclusions - This study holds academic significance in its identification of the components of fashion Social media quality and for conducting an empirical analysis on the causal relationship between fashion information acceptance and diffusion behaviors, and purchase intention. The results of this study indicate that fashion involvement is the key factors in determining the quality of Social media, the acceptance of information through Social media, and, by extension, the purchase of fashion products. Practitioners in the fashion industry may use the findings of this study in order to build more effective Social media strategy.

• Journal of Korean Society for Quality Management
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• v.30 no.2
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• pp.72-85
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• 2002

Advances in computer and sensor technology have made it possible to obtain superlarge manufacturing process data in real time, letting us extract meaningful information from these superlarge data sets. We propose a systematic data analysis procedure which field engineers can apply easily to manufacture quality products. The procedure consists of data cleaning and data analysis stages. Data cleaning stage is to construct a database suitable for statistical analysis from the original superlarge manufacturing process data. In the data analysis stage, we suggest a graphical easy-to-implement approach to extract practical information from the cleaned database. This study will help manufacturing companies to achieve six sigma quality.

• Journal of the military operations research society of Korea
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• v.27 no.1
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• pp.101-113
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• 2001

Taguchi and VTA(variation Transmission Analysis) methods have been widely used recently as new methods for product design. In this study, Taguchi method using analysis of variance and VTA method using regression analysis are reviewed and compared with each other in terms of parameter design and tolerance design. In analysis of variance, variation of quality characteristics arises from noise factors, therefore the optimal levels of design factors are selected to minimize the effect of noise factors. n regression analysis, variation of quality characteristics arises from variation of each own design factors. As a method to reduce variation of these quality characteristics, sensitivity analysis was performed for each design factors. An example of calculating tolerance interval for the given defect rate in PPM is also introduced. Especially, the new method is suggested to increase the estimation accuracy of variation of quality characteristics through regression analysis.

• Journal of Environmental Health Sciences
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• v.42 no.4
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• pp.280-292
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• 2016

Objectives: The objective of this study was to build COD regression models for the Han River and evaluate water quality. Methods: Water quality data sets for the dry season (as of January) during a four-year period (2012-2015) were collected from the database of the Han River automatic water quality monitoring stations. Statistical techniques, including combined genetic algorithm-multiple linear regression (GA-MLR) were used to build five-descriptor COD models. Multivariate statistical techniques such as principal component analysis (PCA) and cluster analysis (CA) are useful tools for extracting meaningful information. Results: The $r^2$ of the best COD models provided significant high values (> 0.8) between 2012 and 2015. Total organic carbon (TOC) was a surrogate indicator for COD (as COD/TOC) with high reliability ($r^2=0.63$ in 2012, $r^2=0.75$ for 2013, $r^2=0.79$ for 2014 and $r^2=0.85$ for 2015). The ratios of COD/TOC were calculated as 2.08 in 2012, 1.79 in 2013, 1.52 and 1.45 in 2015, indicating that biodegradability in the water body of the Han River was being sustained, thereby further improving water quality. The BOD/COD ratio supported these findings. The cluster analysis revealed higher annual levels of microorganisms and phosphorous at stations along the Hangang-Seoul and Hantangang areas. Nevertheless, the overall water quality over the last four years showed an observable trend toward continuous improvement. These findings also suggest that non-point pollution control strategies should consider the influence of upstreams and downstreams to protect water quality in the Han River. Conclusion: This data analysis procedure provided an efficient and comprehensive tool to interpret complex water quality data matrices. Results from a trend analysis provided much important information about sources and parameters for Han River water quality management.

• Journal of dental hygiene science
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• v.18 no.1
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• pp.32-41
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• 2018

This study investigated the relationship between the organizational culture, organizational support, organizational health, personal health, and quality of work life of dental hygienists and analyzed the factors affecting the quality of work life in order to identify ways to improve their quality of work life. A total of 320 dental hygienists completed a self-administered survey; after excluding data from 21 respondents, 299 responses were included in the analysis. Frequency analyses, t-tests, one-way analysis of variation (ANOVA), and correlation analyses were conducted. A path analysis was also conducted to confirm the causal relationships. The findings are as follows. First, there was a significant difference in several general characteristics of the organizational culture including years in the current job and the number of dental hygienists; organizational support including age and the number of dental hygienists; organizational health including years in the current job and annual salary; and personal health including annual salary. Second, the quality of work life showed a positive correlation with organizational culture, organizational support, personal health, and organizational health in that order. Third, the results of path analysis revealed that organizational culture had a positive effect on organizational support; organizational support and personal health on organizational health; organizational support on personal health; and organizational support and organizational health on quality of work life. In addition, organizational support and organizational health had a direct effect on the quality of work life, while organizational culture, organizational support, and personal health had an indirect effect. These results indicated existence of a relationship among organizational culture, organizational support, organizational health, personal health, and quality of work life. It is necessary to identify ways to improve the quality of work life of dental hygienists.

• Educational Technology International
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• v.24 no.1
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• pp.53-80
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• 2023

This study investigates faculty support for quality assurance in online education, and offers suggestions for its improvement based on feedback from Instructional Design (ID) staff working at a public university in the U.S. Qualitative research using semi-structured interviews was conducted with seven ID staff in order to examine their perceptions regarding faculty support related to quality assurance in online education. The results of the data analysis indicate that four types of faculty support-quality assurance reviews using Quality Matter (QM) standards, templates, individual consultations with ongoing support, and monitoring-were offered for faculty. Faculty support for quality assurance in online education could be improved by developing specific quality assurance standards, recruiting external experts, examining learning effects, developing a quality assurance management system, and sharing documents among ID staff. This study highlights the necessity of quality assurance in online education and provides cases of faculty support in a real higher education setting.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.1
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• pp.70-77
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• 2013

The theory of two-dimensional quality, in particular, the Kano model that is developed by the analogy with the M-H theory, has been applied in various industry fields for more than three decades. Importance-Performance Analysis (IPA) assumes that the degree of physical fulfilment of quality attributes and the satisfaction of that attribute is linear, and therefore, it is applicable to the traditional one-dimensional attribute, not other quality types defined in the Kano's model such as attractive or must-be attribute. To solve this problem, the current study suggests a new importance-satisfaction analysis using a modified IPA in accordance with the three quality types and a diagonal method introduced by Slack (1999) to determine improvement priority. For this, I investigated 19 smartphone's quality attributes and conducted a survey of 334 university students for the results of Kano's model, which adopted from Song and Park (2012)'s study, and the importance/satisfaction of the quality attributes and the results of the priority for improvement of the 19 quality attributes. The results show that the proposed I-S priority model is better than the conventional IPA based on the comparison results of determination coefficient from the regression analysis of the two models.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.37-46
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• 2016

Quality has been a key issue to manufacturers. Many distinguished scholars have defined quality with profound insight. Korean firms struggle to make better products to fulfil requirements and satisfy customers. Korean industries have implemented quality management from Japan in early 70s. Statistical quality control, QCC (Quality Control Circle), and total quality management have also been introduced in succession. Chief executive officers, managers, and field employees have been aware of the importance of quality since then. This quality movement force workers to improve quality. They have to maintain the quality of products and compete with foreign products. Korean industries were able to compete with foreign industries in price. However, Korean firms now have to compete in quality as well as price. ISO (International Organization for Standardization) was established and industries around world have started to implement standardized systems depending on their need. ISO 9000 has continuously been revised and firms around world started to register a ISO 9000 certificate. Today's quality competitiveness gets more deeply involved. KSA (Korean Standard Association) have launched QCAS (Quality Competitiveness Assessment System) since 1997. Up until now recent status of QCAS have been reported but the characteristics of QCAS results have not been analyzed. In this article we examine the QCAS results of 41 firms in 2014. QCAS consisted of 13 subsections : strategy and management system, organization culture and development of human resource, information management, quality system, customer satisfaction, management achievement, TPM, logistics, product development and technology, PL, QCC, SQC/SPC, and reliability. We performed one way ANOVA to discover the difference among the levels of firm size, business type, and quality hall of fame using the total scores of 13 subsections resulted from QCAS. We also analyzed the scores of 13 individual subsections of QCAS to see if there is any differences based on firm size and business type. We interpret the results and implication of analysis and finally draw a conclusion.