The model included four latent variables that represent shop floor management tools (SDO_1, WSR_2, I5S_3 and VSI_4) and two latent variables which represent improvement implementation (QCC_5 and Teian_6). Each of the latent variables was measured by their associated observed variable(s). In addition, the four shop floor management latent variables (SDO_1, WSR_2, I5S_3 and VSI_4) were hypothesised independent variables. They were regressed onto their respective dependent variables (QCC_5 and Teian_6). Each of the dependent variables was assigned a residual error term (r1 and r2) and each of the observed variables was assigned a measurement error (e1 to e9) respectively. Finally, the four shop floor latent variables (SDO_1, WSR_2, I5S_3 and VSI_4) were depicted to have inter-correlation.

Model identification for model (a)
Once the model was specified, it was vital to identify the degrees of freedom before the the model estimation (see Table 5.12 in Section 5.3.3). As per the opinion of Rigdon ( 1994, p276), the model (a) was over-identified. As depicted in Table 7.2, the model contained 9 observed variables, hence it had 45 (calculated by 9×10/2) observations or distinct sample moments. Taking in to account the AMOS Parameter Summary, the hypothesised model (a) had 28 unfixed parameters (distinct parameters to be estimated). Thus, the hypothesised path model had 17 (calculated by 45-28) degrees of freedom (D.f) (Table 7.3).

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Number of distinct sample moments: 45
Number of distinct parameters to be estimated: 28
Degrees of freedom (45 – 28): 17

Table 7.3 Computation of degrees of freedom (Hypothesised path model (a), AMOS Output)

In the next step statistical power (?) was obtained. As per recommendation of McQuitty (2004) (Ref. Table 5.14 in Section 5.3.3) the sample size achieved was adequate and the statistical power of the hypothesised path model (a) was ?>0.70 (high), as D.f. of the model was 17 and the sample size was 502

Model estimation for model (a)
After completing the model identification, the model estimation procedure was selected.). Tthe ULS, WLS and ADF were discarded as the sample size was less than 1000 (Ref. Section 5.3, Table 5.15). Since the variables violated the normality assumption, as identified in Section 6.2, the ML method, rather than the GLS method, was adopted for the estimation of parameters.

Model testing for model (a)
As discussed above the ML method was implemented to the model. Following Section 5.3.3, the three common testing indices: i) the parameter estimates with statistical significance level and standard errors; ii) the residuals; and iii) the model fit indices; were included to indicate the model fit.

Firstly, as presented below in the Table 7.4, only 5 paths had significant estimates (P15, P26, P35, P36 and P46, p?0.05, or ‘***’ which indicates p0.05). They indicated poor model fit (Wu, 2009).

Label Standardised Path Estimate Path Estimate S.E. C.R. P (Sign.)