TQM Model of Elements-Deployment Table Developed from Quality ...

y mutual comparison of each Quality Award?
The outline of the composition of the TQM Quality Award Element Comparison
Matrix is described in Para. 5.2, and hereafter, the extraction of factor, the rotation of
the factor by the Principal Factor Analysis, and after it rotates is interpreted, the factor
structure of MBNQA, EQA, JQA, Deming Prize, and ISO 9001 is shown in the figure
based on it, and it explains in Para. 5.3. The study of the analysis results in Para. 5.4
and the conclusion of the research are described in Para. 5.5.
5.2. Struturing of TQM Quality Award Element Comparison Matrix
TQM Quality Award Element Comparison Matrix Ttable (Fig. 4.13 and Fig. 4.14) that
is made in Chaprter 4, TQM Elements Deployment Table that 180 items of their is
deployed as “Case” is in vertical (line, table side of left)[, each TQM Elenent Table of the
criteria of MBNQA, the EQA, the JQA, the Deming Prize and the ISO 9001 are in
holizontal (row, table side on top) as “Variance”, the product of ” Relation degree” and
“Distribution Evaluation Ppoint” in forth level items of each Quality Award TQM
Element Deployment Table such as “Relationship Index” is calculated, weighted, and
the table of total 46 items of 7 iems (MBNQA), 9 items (EQA), 8 items (JQA), 17 items
(Deming Prize), and 5 items (ISO 9001) by first level of major items of four Quality
Awards that is summurlized is shown in Table 5.1. Moreover, they are 17 items as a
whole because the Criteria item divides into three of Foundation item, feature of TQM
item, and Topmanagement item about the Deming Prize.
"TQM Quality Award Element Comparison Matrix" used as multivariate data is
appended as Appendix E.
5.3. Factor Extraction of in Quality Award and ISO 9001
5.3.1 Extraction and Rotation of Factors by Principal Factor Analysises
There was not a case that had to be excluded especially as specific didpersed value,
and either it was tried to extract a common, potential factor by using Factor Analysis
Software (JUSE/ Stat Works) though the data for the comparison prepared in the
process explained by Para 5.2 was examined closely by basic statistic and the
multivariate relation chart, etc. However, the rotation was done by the Normal
Vari-max Method using the Principal Factor Analysis in this thesis. The Principal
Factor Analysis used here does the Eigenvalue resolution by using the one that the
corner element of the Correlation Coefficient Matrix between variables was replaced as
departure matrix by the square of the Multiple Correlation Coefficient between all other
variables of the corresponding variable and requests the factor. The factor at that time
is called Principal Factor, and the situation of the Eigenvalue is seen, the number of
factors is decided, and it rotates. The situation of the Eigenvalue decreased gradually as
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