Abstract
Crowdfunding has emerged as a critical financial solution that democratizes capital access, driving innovation and economic development. The integration of Artificial Intelligence (AI) into crowdfunding management offers numerous strategic benefits, such as predicting success rates or detecting fraud. Previous research efforts have showcased the potential of leveraging AI for deriving data-driven insights into crowdfunding, especially with regards to reward-based
crowdfunding on platforms such as Kickstarter. Efforts for understanding the dynamics of crowdfunding campaigns have focussed on how these are designed, what determines campaign support or what determines their success). These questions are increasingly answered using Machine Learning (ML) methods. While such works constitute an invaluable knowledge base and
demonstrate that AI is instrumental for illuminating crowdfunding non-linear dynamics, their academic nature, often non-replicable, impedes their translation into actionable tool sets for the practice of crowdfunding optimization and, at the same time, makes it difficult for academics to
reproduce the results obtained in the papers. The goal of our research is to share a hands-on roadmap to study and understand crowdfunding data using AI. More importantly, we do this by discussing the reasoning processes utilized by experienced data scientists, who have developed critical thinking for AI. We here state that quality education in AI for crowdfunding is the basis for a sustainable implementation of the next generation of data-driven crowdfunding professionals, and this piece contributes to bridging the current knowledge gap. From data collection through preprocessing, exploratory data analysis (EDA), and visualization, to the generation of data-driven insights using AI modeling, we here cover AI for crowdfunding as a holistic skillset. We provide careful reasoning on the data scientist’s careful AI mindset, emphasizing the value of critical thinking and providing access to hands-on-doing learning materials vital for informing nextgeneration crowdfunding professionals.
crowdfunding on platforms such as Kickstarter. Efforts for understanding the dynamics of crowdfunding campaigns have focussed on how these are designed, what determines campaign support or what determines their success). These questions are increasingly answered using Machine Learning (ML) methods. While such works constitute an invaluable knowledge base and
demonstrate that AI is instrumental for illuminating crowdfunding non-linear dynamics, their academic nature, often non-replicable, impedes their translation into actionable tool sets for the practice of crowdfunding optimization and, at the same time, makes it difficult for academics to
reproduce the results obtained in the papers. The goal of our research is to share a hands-on roadmap to study and understand crowdfunding data using AI. More importantly, we do this by discussing the reasoning processes utilized by experienced data scientists, who have developed critical thinking for AI. We here state that quality education in AI for crowdfunding is the basis for a sustainable implementation of the next generation of data-driven crowdfunding professionals, and this piece contributes to bridging the current knowledge gap. From data collection through preprocessing, exploratory data analysis (EDA), and visualization, to the generation of data-driven insights using AI modeling, we here cover AI for crowdfunding as a holistic skillset. We provide careful reasoning on the data scientist’s careful AI mindset, emphasizing the value of critical thinking and providing access to hands-on-doing learning materials vital for informing nextgeneration crowdfunding professionals.
| Original language | English |
|---|---|
| Publisher | Social Science Research Network (SSRN) |
| Pages | 1 |
| Number of pages | 19 |
| Publication status | Published - 2024 |
Keywords
- crowdfunding
- AI
- machine learning