2014
DOI: 10.1007/978-3-319-05579-4_41
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Emerging Dynamics in Crowdfunding Campaigns

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Cited by 16 publications
(16 citation statements)
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“…They divided the campaign duration into phases of 5% of length of the respective campaign duration. Rao et al [51] then analyzed phases of the campaign duration that are more or less predictive by creating models trained by predictors with time-series data before the respective phase.…”
Section: Related Workmentioning
confidence: 99%
“…They divided the campaign duration into phases of 5% of length of the respective campaign duration. Rao et al [51] then analyzed phases of the campaign duration that are more or less predictive by creating models trained by predictors with time-series data before the respective phase.…”
Section: Related Workmentioning
confidence: 99%
“…Until few years ago, the pages of newspapers usually had the function that the supporters of projects ask now to the internet, specifically to web platforms (Mourao [1]). Several papers discussed in international scientific meetings, like International Symposium on Distributed Computing and Artificial Intelligence (DCAI), have also addressed this issue (Miglieta and Parisi [17] or Rao et al [18]). The common structure of crowdfunding platforms is simple for explaining -a supporter promotes a project using a web-platform, he/she uses an attractive message that clarifies the ends for the money that will be sent; then, the investors send money using digital channels and investors get a reward if certain objectives are reached (the most common objective is the minimum funding for making the project works).…”
Section: Introductionmentioning
confidence: 98%
“…In any case, our data do not indicate any significant influence of the previous pledges on the current pledges, as it occurs in wealth evolution (Solomon and Richmond, 2001) or citations dynamics, which are governed by the multiplicative or selfexciting processes (Golosovsky and Solomon, 2013). This observation limits the predictive power of the time-pattern fitting techniques used in the past (Etter et al, 2013;Chung and Lee, 2015;Chen et al, 2013;Rao et al, 2014) for prediction success of the Kickstarter campaigns. By contrast, the mere detection of the "backers of type II" in daily pledge distributions is an efficient and very early predictor of success, as we explain below.…”
mentioning
confidence: 53%
“…This predictor achieves 67% accuracy at the launch phase, and approximately 90% accuracy when the campaign proceeds to the 40% stage. Rao et al (2014) used decisiontree models to investigate the extent to which simple inflows and first-order derivatives can predict campaign success. Basing on the initial 15% of money inflows they could predict success with 84% accuracy.…”
Section: Background Information On Kickstartermentioning
confidence: 99%