## 4 Results
### QUEST and CHAID Decision Trees
Lower DMA Engagement  
Content Marketing (CMA): If the data management is low, the tree checks the content marketing usage. Lower CMA usage: If the content marketing is also low, it predicts low entrepreneur intentions. This suggests that without effective content marketing, the potential for entrepreneurship intentions decreases significantly. Higher CMA usage: If the content marketing is high, it boosts the potential for higher entrepreneurship intentions. Higher CMA Engagement  
Customer Relationship Management (CRM): When the content marketing is high, customer relationship management comes into play. Lower CRM usage: If CRM is low, social media marketing becomes important. Low SMM leads to medium entrepreneurship intentions, meaning that without high social media marketing, the intentions are moderate. High SMM leads to very high entrepreneurship intentions, suggesting that combining strong content marketing, CRM, and social media marketing greatly increases the entrepreneurship intentions. Higher CRM usage: If CRM usage is higher, it starts to show a decline in entrepreneurship intentions, predicting low intentions when online advertising is low. Higher DMA Engagement  
CRM: For those with better data management, CRM has a strong influence. Lower CRM usage: This predicts very high entrepreneurship intentions, showing that low CRM, combined with strong data management, may focus efforts on other areas, boosting intentions. Higher CRM usage: If CRM is higher, content marketing again becomes a decisive factor. Low CMA tends to lead to low entrepreneurship intentions. High CMA leads to high entrepreneurship intentions, showing that strong content marketing and CRM can increase intentions. Right Branch of Node 1: Higher EMAIL Engagement  
Website Optimization (WOPT): For projects with high email engagement, website optimization is the next key factor. Lower WOPT usage: If website optimization is low, social media marketing (SMM) becomes important. Low SMM tends to lower entrepreneurship intentions, predicting low intentions. High SMM increases the entrepreneurship intentions to very high, showing that social media can offset the effects of lower website optimization. Higher WOPT usage: If website optimization is high, the tree checks the CRM usage. Lower CRM usage: Predicts medium intentions, showing that a balance of high website optimization and low CRM can moderate intentions. Higher CRM usage: Leads to further splits on email marketing and data management. Lower EMAIL tends to predict medium intentions. Higher EMAIL increases the intentions if the data management is also high, predicting very high entrepreneur intentions. EMAIL was the most influential factor overall. When email engagement was low, the success of the project depended heavily on other factors like data management and content marketing. When email engagement was high, factors like website optimization, CRM, and social media marketing became more important. Each split in the tree refined the predicted entrepreneurship intentions based on how these factors interacted. The CHAID decision tree model demonstrated an overall accuracy of approximately 0.75, meaning that it correctly classified entrepreneurship intentions in 75% of cases. In terms of precision, the model achieved a score of about 0.70. This indicates that 70% of the cases where the model predicted a high likelihood of entrepreneurship intentions were accurate. The recall score, which measures how well the model identified actual instances of entrepreneurship intentions, was around 0.65. This means that the model successfully recognized 65% of the true cases where entrepreneurintentions were present, pointing to a moderate ability in capturing relevant instances. The F1 score, which provides a balance between precision and recall, was approximately 0.67. This suggests a balanced performance, reflecting the model's overall capacity to predict and correctly identify the entrepreneurintentions in various cases.