## 4 Discussion
### Performance Metrics for Evaluating the Platform's Effectiveness
#### 4.3.1 Qualitative and Quantitative Insights from User and Stakeholder Research  
When designing the SPARK-IT platform, we first conducted a user research stage through questionnaires and stakeholder interviews. More specifically, we conducted an online survey through Typeform to gather insights. The survey was designed to adapt to the different personas of our target audience: startup founders or innovators, experts, and startup accelerators. Up until now, we have received 28 responses from individuals from Central and Eastern Europe and Brazil. The distribution among the three respondent categories (experts, innovators, and organizational representatives) is illustrated in Figure 5.  
The questions addressed in the survey can be categorized into the following themes:  
1. **Mentor Selection and Matchmaking** * Mentor information; * Importance of the mentor's professional network; * Validation criteria for mentors (experience, academic background, certifications, peer reviews, and mentee feedback);* Method of mentor-startup matching (self-selection, automated algorithms, and manual review). * **Feedback and Validation*
* Methods of incorporating mentee feedback;
* Role of peer reviews;
* Comfort level with publicly sharing mentorship outcomes;
* Demonstrating mentor effectiveness (professional achievements, endorsements, and peer reviews). * **Mentorship Process and Success Metrics*
* Elements of successful mentorship (continuous feedback, defined goals, communication, and respect);
* Tracking and measuring progress (milestone achievements, mentee feedback, mentor evaluations, project outcomes, and KPIs). * **Mentorship Management*
* Handling unsuccessful mentorships (switching mentors, feedback loops, and follow-ups);
* Scheduling and appointment methods (calendar integration and availability slots). * **Budget and Financial Considerations*
* Budget preferences and flexibility;
* Factors affecting budget decisions (mentor expertise, project nature, and engagement duration). * **Intellectual Property Protection*
* Methods for IP protection (NDAs, confidentiality agreements, secure document sharing, and legal support). * **Resources and Support*
* Elements of a successful mentorship (networking opportunities, communication tools, administrative support, and training materials). Regarding **mentor selection and matchmaking**, users emphasized industry experience (87.5%) and personality fit (75%) as the most important factors. A majority (71.4%) preferred a hybrid approach combining automated algorithms and human review for effective matchmaking. That is the reason behind our approach, where we designed an AI algorithm  
Figure 5: Distribution of respondents by profile type: experts/mentors, founders/innovators, and organization representatives (\(n=28\)). to perform the matchmaking and we present the results to the user so that the final decision is theirs. In the area of **feedback and validation**, respondents significantly valued public mentee ratings (83.3%) and showed high comfort levels with publicly sharing mentorship experiences (71.4%). Participants considered professional achievements (85.7%) and endorsements (78.6%) to best showcase mentor effectiveness. Therefore, we designed the public reputation system from SPARK-IT, and we integrated logic to scrape for achievements and endorsements on platforms that allow it, such as LinkedIn. Concerning the **mentorship process and success metrics**, most respondents identified continuous feedback and defined goals (78.6%) as critical elements for mentorship success. Progress measurement was highly recommended through milestone achievements (82.1%) and mentee feedback (67.9%). This led to the decision to impose financial penalties on the mentor in case the deadline for collaboration is not respected. In terms of **intellectual property protection**, respondents had to choose between six different protection measures.