## 4 Results
### QUEST and CHAID Decision Trees  
The QUEST classification decision tree was used to attempt to predict the entrepreneur intentions. The tree had a yes/no structure. Cross-validation was also applied. The main predictors noted were:  
* Social media marketing (SMM);
* Email marketing (EMAIL);
* Customer relationship management (CRM);
* Online advertising (OA);
* Data management and analytics (DMA). In Figure 2, the QUEST classification decision tree is presented. The rules explaining the QUEST algorithm are as follows:  
N1: Social Media Engagement  
* If the business regularly updates its social media with relevant content, then the probability of considering entrepreneurship is high:
* Class yes = 75.26% (those who said yes to entrepreneurship);
* Class no = 24.74% (those who said no to entrepreneurship). N2: No Social Media Updates
* If the business does not update its social media, then the likelihood of considering entrepreneurship drops slightly:
* Class yes = 70.00%;
* Class no = 30.00%. N3: Content Aligned with Audience Interests
* If the social media content is aligned with the target audience's interests and needs, the likelihood of entrepreneurship remains high:
* Class yes = 75.34%;
* Class no = 24.66%. N4: Content Not Aligned
* If the content is not aligned with the audience but the business still updates its social media, the likelihood of entrepreneurship is still strong:
* Class yes = 75.00%;
* Class no = 25.00%. N5: CRM Strategies Focused on Retention and Acquisition
* For businesses without regular social media updates, if they have strong CRM strategies focused on both customer retention and acquisition, the likelihood of entrepreneurly remains high:
* Class yes = 78.57%;
* Class no = 21.43%. * N6: No Strong CRM Strategies
* If the business lacks CRM strategies focused on retention and acquisition, the likelihood of entrepreneurly decreases:
* Class yes = 50.00%;
* Class no = 50.00%. * N7: Creative Online Advertising
* If the business's online advertising campaigns are creative and attention-grabbing, there is a high likelihood of considering entrepreneurly:
* Class yes = 78.95%;
* Class no = 21.05%. * N8: No Creative Advertising
* If the online advertising is not creative, the likelihood of entrepreneurly is slightly lower but still significant:
* Class yes = 77.76%;
* Class no = 22.24%. * N9: Use of Analytics in Marketing
* For businesses with creative advertising, if they effectively use analytics to improve their marketing strategies, the likelihood of entrepreneurly remains high:
* Class yes = 75.00%;
* Class no = 25.00%. N10: No Use of Analytics
* If businesses with creative advertising do not use analytics, the likelihood of considering entrepreneurly is still relatively strong but slightly higher:
* Class yes = 85.71%;
* Class no = 14.29%. The CHAID decision tree helps to determine how different Internet marketing factors are associated with the entrepreneurly intentions, showing how each factor contributes to predicting different levels of intention. The nodes of the decision tree are as follows:  
Root Node (EMAIL)  
Email Marketing (EMAIL): This is the most important factor influencing entrepreneurly intentions. Lower EMAIL usage: For people or projects with low engagement in email marketing, the tree leads to outcomes of lower to medium entrepreneurly intentions in many cases. Higher EMAIL usage: Projects or people with higher email marketing usage tend to have higher entrepreneurly intentions. The branches lead to a mix of medium, high, and very high entrepreneurly intentions. Left Branch of Node 1: Lower EMAIL Engagement  
Data Management and Analytics (DMA): For those with lower email engagement, data management becomes the next important factor. Lower DMA usage: If there is low use of data management and analytics, it generally leads to lower entrepreneurly intentions, especially when combined with other factors like low content marketing and low customer relationship management. Figure 2: QUEST decision tree algorithm for predicting the entrepreneurship intentions.