CHAPTER 6: QUANTITATIVE RESULTS
6.9 Structural Model Evaluation and Testing the Hypotheses
6.9.3 Testing the Moderation Effects
Detecting a moderating influence is significant in social science research because it alters the strength of the causal relationship between the predictors and outcomes variables (Kenny, 2018).
In determining the existence of moderation and its strength of moderation (partial or full), specific procedures must be followed before conducting the moderation analysis (Awang, 2012). Thus, the researcher followed the recommended procedures before conducting the moderation analysis for this research. The regression analysis was conducted using AMOS by the maximum likelihood estimation (MLE) to detect moderating effects. The researcher considered the following before conducting the moderation analysis:
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First step: all the variables (predictors, moderate variables and outcomes) were standardised using the descriptive statistics in SPSS (Dunlap & Kemery, 1987) to be able to determine and avoid multicollinearity (Cronbach, 1987; Dunlap & Kemery, 1987).
Second, the interaction effect(s) was created (independent variable x moderating variable) which is the score of the multiplication between the moderate variable and predictors using a transformation analysis (Awang, 2012; Field, 2013; Hair et al., 2014). The moderation effect exists if the interaction variables are significant. A connection between the predictor and outcome when the moderator levels were low, medium and high was shown after the follow-up analyses on the slopes (Field, 2013).
6.9.3.1 Moderator Model Fit Testing
In assessing the moderating effect of training content and training objectives on the relationships between the training outcomes (learning, behaviour and results), the validated SEM model was tested using AMOS v.25. In this research, the proposed theoretical model has two moderators: the training content and the training objectives. The training content and training objectives are hypothesised to moderate the relationship between learning and behaviour, behaviour and results.
The moderating model for training content with regard to the relationship between learning and behaviour yielded an adequate fit as depicted in Table 6.17. The chi square with degree of freedom (X²/df) = 1.351, p = 0.117; goodness of fit index (GFI) = 0.966, adjusted goodness of fit index (AGFI) = 0.937, root mean square error of approximation (RMSEA) = 0.042, normed fit index (NFI) = 0.936, comparative fit index (CFI)= 0.982, adjusted good fit of index (AGFI) = 0.937, parsimony normed fit index (PNFI) = 0.624 and parsimony goodness of fit index (PGFI)=0.515.
The moderating model for training objectives with regard to moderating the relationship between learning and behaviour yielded an adequate fit, chi square with degree of freedom (X²/df) = 1.204, p = 0.198; goodness of fit index (GFI) = 0.964, adjusted goodness of fit index (AGFI) = 0.938, root mean square error of approximation (RMSEA) = 0.032, normed fit index (NFI) = 0.945, comparative fit index (CFI)= 0.990, adjusted good fit of index (AGFI) = 0.938, parsimony normed fit index (PNFI) = 0.672 and parsimony goodness of fit index (PGFI)=0.561. The summary of the model fit results for all the four moderating models is presented in Table 6.19 below.
172 Table 6. 19 Moderating Fit Models
Measure 𝛘𝟐 P df 𝛘𝟐/𝐝𝐟 GFI RMSEA NFI CFI AGFI PNFI PGFI
Moderating effect of training content on the relationship between learning and behaviour Level of
acceptance
>.05 <5 ≥ 0.9 ≤.06 ≥0.9 ≥0.9 ≥0.9 > 0.5 >0.40 Hypothesised
Model
32.427 .117 24 1.351 .966 .042 .936 .982 .937 .624 .515 Moderating effect of training content on the relationship between behaviour and Results
Level of acceptance
>.05 <5 ≥ 0.9 ≤.06 ≥0.9 ≥0.9 ≥0.9 > 0.5 >0.40 Hypothesised
Model
24.000 .119 17 1.412 .971 .045 .945 .983 .939 .574 .459 Moderating effect of training objectives on the relationship between learning and behaviour
Level of acceptance
>.05 <5 ≥ 0.9 ≤.06 ≥0.9 ≥0.9 ≥0.9 > 0.5 >0.40 Hypothesised
Model
38.535 .198 32 1.204 .964 .032 .945 .990 .938 .672 .561 Moderating effect of training objectives on the relationship between behaviour and results
Level of acceptance
>.05 <5 ≥ 0.9 ≤.06 ≥0.9 ≥0.9 ≥0.9 > 0.5 >0.40 Hypothesised
Model
25.908 .076 17 1.524 .969 .051 .947 .981 .934 .575 .458 𝝌𝟐= 𝐂𝐡𝐢 𝐒𝐪𝐮𝐚𝐫𝐞; df=Degree of Freedom, GFI=Goodness of Fit Indices, RMSEA= Root Means Square Error of Approximation, NFI = Normed Fit Index, CFI= Comparative Fit Index, AGFI=Adjusted Good Fit of Index, PNFI= Parsimony Normed Fit Index, PGFI= Parsimony Goodness Fit Index
The moderating model for training content with regard to the relationship between behaviour and results (training outcome) yielded an adequate fit and has exhibited high degree of fit with the data,
173 as depicted in Table 6.19, chi square with degree of freedom (X²/df) = 1.412, p = 0.119; goodness of fit index (GFI) = 0.971, adjusted goodness of fit index (AGFI) = 0.939, root mean square error of approximation (RMSEA) = 0.045, normed fit index (NFI) = 0.945, comparative fit index (CFI)=
0.983, adjusted good fit of index (AGFI) = 0.939, parsimony normed fit index (PNFI) = 0.574 and parsimony goodness of fit index (PGFI)=0.459.
The moderating model for training objectives with regard to the relationship between behaviour and results yielded an adequate fit and has shown a very high degree of fit with the data as presented in Table 6.19, chi-square with degree of freedom (X²/df) = 1.524, p = 0.76; goodness of fit index (GFI) = 0.969, adjusted goodness of fit index (AGFI) = 0.934, root mean square error of approximation (RMSEA) = 0.051, normed fit index (NFI) = 0.947, comparative fit index (CFI)=
0.981, adjusted good fit of index (AGFI) = 0.934, parsimony normed fit index (PNFI) = 0.575 and parsimony goodness of fit index (PGFI)=0.458. The next section looks at how the hypothesis of the moderation models was tested.
6.9.3.2 Moderation Models Hypothesise Testing
In determining the significance of the moderator effect, three tests should be followed: (a) independent and the dependent variables, (b) moderator and the dependent variables, and (c) compound moderator (interaction effect) with the independent and dependent variables (Hair et al., 2014). According to Awang (2012), a significant moderating effect is identified if the following three conditions are met: (a) if the relationship between the interaction effect and the dependent variables is significant, (b) if the relationship between the moderator and dependent variables is not significant, and (c) if the relationship between the independent and dependent variables is not significant (complete moderation) or if the relationship between the independent and dependent variables is significant (partial moderation). To test the moderating effects of training content and training objectives (M) on the relationship between learning (IV) and behaviour (DV), and between behaviour (IV) and results (DV), the three conditions recommended by Awang (2012) were evaluated. The summary of the analysis is presented in Table 6.20.
174 Table 6. 20 Hypothesis of the Moderation Models
Path β S.E. C.R p-value Decision
Hypothesis testing of the moderating effect of training content on the relationship between learning and behaviour
1. Learning→Behaviour
Training Content→Behaviour TC * Learning→Behaviour
.075 .132 -.099
.067 .043 .288
.930 2.170 2.018
.323 .087 .706
Rejected Accepted Rejected
H3a: Training content moderates the relationship between learning and behaviour Rejected Hypothesis testing of the moderating effect of training content on the relationship between behaviour and results
2. Behaviour→Results Training Content→Results TC * Behaviour→Results
.312 .461 .344
.082 .064 .005
.647 .160 .104
.003**
.095 .004**
Accepted Accepted Accepted
H3b: Training content moderates the relationship between behaviour and results Accepted Hypothesis testing of the moderating effect of training objectives on the relationship between learning and behaviour
3. Learning→Behaviour Objectives→Behaviour OB * Learning→Behaviour
.063 .076 .005
.067 .069 .005
.930 1.112 1.034
.323 .266 .812
Rejected Accepted Rejected
H4a: Training objectives moderate the relationship between learning and behaviour Rejected Hypothesis testing of the moderating effect of training objectives on the relationship between behaviour and results
175 4. Behaviour→Results
Objectives→Results OB * Behaviour→Results
.368 .202 .404
.083 .069 .006
.637 1.585 -1.009
.002**
.113 .005**
Accepted Accepted Accepted
H4b: Training objectives moderate the relationship between learning and behaviour Accepted Note: TC= Training Content, OB= Training Objectives, β= Standardised Path Estimates (regression weights), S.E= Standard Error, C.R= Critical Value (t-value), P=Significance of value, *** = Significant at 0.001 levels (two tailed), ** = significant at 0.01 levels (two-tailed) As shown in Table 6.20, is the comparisons between the independent and dependent variables (simple effects), the effects of the moderator on the dependent variables and the interaction effects with independent variables on dependent variables. The results show no moderating significant effects for the three paths: the training content did not moderate the effects on the relationships between learning and behaviour (β= -0.99, t-value = 0.930, p = 0.706), and training objectives did not moderate the relationship between learning and behaviour (β= 0.005, t-value = 1.304, p = 0.812); and training objectives did not moderate the relationship between behaviour and results (β= 0.404, t-value = -1.009, p = 0.005). A significant moderating effect was found for the one path:
the training content moderate the effects on the relationships between behaviour and results (β=
0.344, t-value = 0.104, p = 0.004). The final hypothesised model is illustrated in Figure 8.1 in Chapter 8.