Crowdfunding platforms have become a popular alternative for financing projects. However, campaigners in the platform witness several challenges that affect their success. This study aims to: (a) generate and explore profiles among the non-successful, successful, and very successful campaigns; (b) to interpret investment behaviour of the backers from the profiles. We employed a fresh approach of tree-based comparative analysis (TBCA) as an exploratory tool that augments qualitative comparative analysis (QCA) with data mining evaluation. Campaign profiles were drawn from four groups of 17 predictors, namely: “campaign strategy,” “presentation strategy,” “campaigners” track records, and “temporal data.” We analyzed 647 campaigns in Kickstarter and the analysis was divided into two timelines: before and on the first day of campaigns. The emerging profiles offer practical insights for campaigners to develop a coherent set of campaign decisions and to anticipate the possible outcome. The profiles also indicate that prior to the campaign, investment behaviour is mainly explained by “signal theory.” Interestingly, for the first day of a campaign, the funding ratio performance has the most significant impact on very successful projects — that is, consistent with ”herding behaviour.”
Development of a Geoplay Learning Mechanics and Modality Principle as a Presentation Strategy
