## 5 Discussion
### Assessing the Results of the Feed-Forward Neural Network  
Finally, the modeled feed-forward neural network was used to assess the proposed hypotheses. The results are as follows:  
H1a: Manager/director age positively affects intentions to implement and apply ICTs within an enterprise.  
Accuracy (proportion of true positive predictions over the total number of actual positives): 84.6%.  
Precision (proportion of true positive predictions over the total number of positive predictions): 75.4%.  
Recall (recall of predicting positive intentions): 79.0%.  
F1-score (harmonic mean of precision and recall): 77.2%.  
Based on the results, the proposed hypothesis failed to be rejected.  
H1b: Manager/director gender positively affects intentions to implement and apply ICTs within an enterprise.  
Accuracy: 80.7%; Precision: 83.3%; Recall: 77.6%; F1-score: 80.45%.  
Based on the results, the proposed hypothesis failed to be rejected.  
H1c: Manager/director education positively affects intentions to implement and apply ICTs within an enterprise.  
Accuracy: 83.8%; Precision: 73.8%; Recall: 89.1%; F1-score: 81.45%.  
Based on the results, the proposed hypothesis failed to be rejected.  
H1d: Enterprise size positively affects intentions to implement and apply ICTs within an enterprise.  
Accuracy: 83.8%; Precision: 83.2%; Recall: 79.9%; F1-score: 81.55%.  
Based on the results, the proposed hypothesis failed to be rejected.  
H2: Business performance positively affects intentions to implement and apply ICTs within an enterprise.  
Accuracy: 83.8%; Precision: 83.2%; Recall: 79.9%; F1-score: 81.55%.  
Based on the results, the proposed hypothesis failed to be rejected.  
H\({}_{3}\): Awareness of ICT importance positively affects intentions to implement and apply ICTs within an enterprise.  
Accuracy: 86.2%; Precision: 81.2%; Recall: 79.4%; F1-score: 80.3%.  
Based on the results, the proposed hypothesis failed to be rejected.  
H\({}_{4}\): Intellectual capital positively affects intentions to implement and apply ICTs within an enterprise.  
Accuracy: 87.1%; Precision: 81.4%; Recall: 76,1%; F1-score: 78.75%.  
Based on the results, the proposed hypothesis failed to be rejected.  
H\({}_{5}\): Market assessment positively affects intentions to implement and apply ICTs within an enterprise.  
Accuracy: 85.7%; Precision: 92.1%; Recall: 80.0%; F1-score: 86.1%.  
Based on the results, the proposed hypothesis failed to be rejected.  
It is interesting to note that various statistical methods produced different results with varying levels of detail. It is important for data mining processes to avoid bias and to set up parameters properly. In this study, the QUEST algorithm was chosen over other classifiers, such as CHAID (chi-squared automatic interaction detection) and CART (classification and regression tree), as it was deemed most appropriate for a dataset that includes ordinal, nominal, and quantitative data. Additionally, the support-vector machine (SVM) provided significant insights into the use of machine learning in data mining. Similarly, the feed-forward neural network produced similar results regarding the evaluation of the hypotheses.