## 4 Presentation and Discussion of Findings  
Table 2 summarised the network information for levels 1 and 2 artificial neural network training and testing. Table 2 also presents the summary of the inputs, hidden and output layers. 135 valid samples provided 17 and 18 units excluding the biases for the multilayer perceptron for Levels 1 and 2 of the research frameworks, respectively. The sum of squares error (SSE) is 16.706% and 29.829% respectively, for levels 1 and 2. These SSE values indicate the actual error functions which are realistic for this scale of samples available. The error function for the training is averagely considerable for a predictive model. The SSE is a measure between the discrepancy between the data set and the estimated model [74; 75]. Consequently, a smaller value indicates suitability. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Summary** & **Level 1** & **Level 2** \\ \hline Input Layer & & \\ Valid number of Samples & 135 & 135 \\ Number of Units & 17 & 18 \\ Rescaling Method for Covariates & Standardized & Normalized \\ \hline Hidden Layer & & \\ Number of Hidden Layers (1) & & \\ Number of Units in Hidden (6) Layer 1 & & \\ Activation Function (Hyperbolic tangent) & & \\ \hline Output Layer & & \\ \hline Dependent Variable (1) & & \\ Number of Units (2) & & \\ Activation Function (hyperbolic tangent) & & \\ Error Function (Sum of Squares) & 16.706 & 29.829 \\ Training time (sec) & 2 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Network information for two training and testing sessions excluding bias. The synaptic weights are coefficients showing the connections between the input, hidden and output layers. The training samples inform the strength of the synaptic weights even though there is a partitioning factor. The synaptic parameters in Table 3 are usually indicated by thicker blue or grey lines in the model. This can be in the negative or positive direction. However, the weights in Table 4 do not entirely reflect the complete relationship between the independent or dependent variables or signify stronger association between the variables. The weights in Table 4 were presented to merely indicate the strengths of the synaptic weights. The number of synaptic might not be used to interpret the network result if they are generally large [76]. In this study, the values of the synaptic weights will only lead us to the classification and pseudo-probability. Thus, the values in Table 3 will only be used for a preliminary mode of connecting the neurons. From Table 4, WORKBU; INTERIMV; INCMILE; MONEC; MONMC; and TACT largely associate with H(1:1); H(1:2); H(1:3) and H(1:6). However, H(1:1) and H(1:4) are closely linked to the QS dependent variable. A review of the classification of the predicted dependent variables in Table 5 for levels 1 and 2, signifies the QS dependent variable as 90.90% of the predictions for the post-contract cost controlling techniques. CPM is 67.20%. 80.70% of the neurons are correct for this prediction. In Level 2, 88.30% of the correct predicted outcomes are QS, and the CPM are 34.50%. This implies that 90.90% of QS influence the decisions to choose the most influence post-contract cost controlling techniques in the first model. The first model excludes the profit and loss account. In the next model (level 2) where the profit and loss account is part of the post-contract cost controlling process, the QS has less influence on the choice of post-contract cost controlling techniques. In this