Despite the increasing popularity and research focus on the application of artificial intelligence (AI) in entrepreneurship, no comprehensive bibliometric analysis of relevant papers has been... Show More

Despite the increasing popularity and research focus on the application of artificial intelligence (AI) in entrepreneurship, no comprehensive bibliometric analysis of relevant papers has been conducted. Our review examines 127 Scopus-indexed publications from 2007 to October 2023. The inquiry explores thematic progression, geographical production trends, and seminal works shaping the discourse. Notably, while authors from Asia, mainly China, lead in publication volume, influential works from the USA, the UK, and Australia attract significant scholarly attention. The analysis reveals a prominent trend: highly cited papers often lack theoretical frameworks, with literature reviews and conceptual analyses prevailing over empirical investigations. When theoretical foundations are present, diverse theories are often amalgamated. In conclusion, our review highlights numerous avenues for future academic exploration in this domain. Pragmatically, our effort, the first of its kind, consolidates fragmented insights, providing a cohesive understanding of AI's pivotal role in entrepreneurship.

Due to the risky nature of newly creative projects for entrepreneurs, reward-based crowdfunding is currently an alternative fundraising channel for those who need seed funding to finance the... Show More

Due to the risky nature of newly creative projects for entrepreneurs, reward-based crowdfunding is currently an alternative fundraising channel for those who need seed funding to finance the creation of their prototype. The objectives of this research are to explore the success factors, including entrepreneurial, project and campaign factors, in project fundraising under a reward-based crowdfunding platform. We propose to develop a model for predicting the success of crowdfunding projects by machine learning. The datasets have been retrospectively gathered from historical records of campaigns in the Kickstarter website. The study's findings show that the logistic regression and decision tree models, respectively, had accuracy rates of 88.2% and 88.8%. The highest accuracy percentage of 94.1% originates from new testing data that has been externally validated for the technology industry. The practical implication of our research is that entrepreneurs can apply the proposed prediction model to identify the most influential topical features embedded in campaigns.