## 5 Results
### Structural Equation Modelling  
Although our sample is above the advised threshold of 200 for using SEM [60], our number of variables was too large to use latent variables for our sample size, as indicated by too many degrees of freedom compared to our sample size. The minimum ratio between the degrees of freedom and the sample size is 5:1 [69]. However, others claim that if the ratio falls below 10:1 the results and trustworthiness of the research is already questionable [60]. The SEM analysis was therefore carried out in AMOS using the composite scale developed in SPSS for affective commitment to change and the three components of OLC. The three components of OLC (OLCIRI, OLCIP, and OLCCKS) were treated as observed variables belonging to the unobserved variable OLC in the SEM analysis. The final ratio of the degrees of freedom and the sample size was 7:1, which is an acceptable ratio.  
As stated earlier, the overall fit of the models was measured using a combination of absolute and relative fit indices [60]: relative chi square (CMIN/DF), a goodness of fit index (GFI), a comparative fit index (CFI), a root mean square error of approximation (RMSEA) and the \(p\) of Close Fit (PClose).  
The original model, including the two control variables (gender and year of birth), did not have acceptable fit indices. To reach a good fitting model, several insignificant relations between the variables were removed from the model. The variables BBPP, SROI, and Skills were removed from the model, as they had no significant relationships to any of the variables. For the same reason, the control variable gender was also removed. To further improve the model fit, the modification indices and expected parameter change values were examined. Based on this and theoretical reasoning, error term correlations were added for the different types of SPP [70]. After examining the modification indices and theoretical reasoning, it also became apparent that knowledge and affective commitment to SPP partially mediated the relationship between OLC and GPP.  
The revised and final model is presented in Figure 2; only the significant relations (<0.05) are shown. The model provided an adequate fit to the data (see also Table 2). The model had the following scores on the relative and absolute fit indices: CMIN/DF = 1.143, GFI = 0.987, CFI = 0.992, RMSEA = 0.022, and PClose = 0.814. The final model shows that OLC has a positive and significant direct effect on GPP (\(\beta\) = 0.23 **), IPP (\(\beta\) = 0.24 **) and CIE (\(\beta\) = 0.15 *). Thus, the more public procurers perceive that their organisation has an organisational learning capacity, the more they implement GPP, IPP, and CIE. Interestingly, more OLC does not result in more BPP or SROI.  
The final model also shows that OLC also indirectly increases the implementation of GPP, via knowledge and ACC (affective commitment to SPP). OLC has a positive effect on the perceived degree of knowledge about SPP (\(\beta\) = 0.22 **), which in turn has a positive effect on the degree of affective commitment to SPP (\(\beta\) = 0.30 ***), which then has a positive effect on the implementation of SPP (\(\beta\) = 0.18 **). A bootstrap analysis via AMOS [65] showed that the indirect relationship between OLC and GPP via knowledge and affective commitment to SPP was also significant (\(p\) value of 0.014). The relationship between knowledge and GPP, mediated by ACC, is also significant (\(p\) value = 0.002), as is the relationship between OLC and ACC that is mediated by knowledge (\(p\) value 0.018).