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Popularity versus quality: analyzing and predicting the success of highly rated crowdfunded projects on Amazon

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Abstract

Crowdfunding is a process of raising money (funding) for a project through a venture of large number of people (crowd). The popular online crowdfunding platforms Kickstarter and Indiegogo provide a stage for innovators worldwide to bring ideas to reality. Despite the popularity and success of many projects on the platforms, it is yet to be determined whether successful projects always produce high quality products. Previously, the quality of crowdfunded products (successfully funded projects from crowdfunding website that are available on Amazon) in the market (e.g., Amazon) has not been statistically and scientifically evaluated. There has been no previous study to understand whether a successful project will receive high/low ratings from customers in e-commerce sites like Amazon. To address this problem, we (i) compare crowdfunded products with traditional products in terms of their ratings on Amazon; (ii) analyze negative reviews of crowdfunded products; (iii) analyze characteristics of the successful projects (received \(\ge \) 4 Amazon rating) and unsuccessful projects (received < 4 Amazon rating); and (iv) build machine learning models at three different stages, to predict high or low star ratings for a crowdfunded product. Our experimental results show that, on average, crowdfunded products received lower ratings than traditional products. Our ensemble model effectively identifies which product will receive high star-ratings from customers on Amazon. The dataset and code used in this manuscript are available at https://github.com/vishalshar/popularity_vs_quality_data-code.

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Notes

  1. https://kck.st/29yRJjZ.

  2. https://www.amazon.com/Amazon-Launchpad/b?node=12034488011.

  3. https://github.com/vishalshar/popularity_vs_quality_data-code.

  4. jamstik+ The SmartGuitar: http://kck.st/2s0TLQ0.

  5. Noke: The World’s Smartest Padlock: http://kck.st/1kU8ztT.

  6. The backer data was obtained from Kickstarter projects associated with 375 successful and unsuccessful Launchpad products.

  7. Superbackers are users who have supported more than 25 projects with pledges of at least $10 in the past year.

  8. We also tried a neural network model which performed poorly, so we do not report its results.

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Acknowledgements

This work was supported in part by the National Science Foundation under Award No. DBI-1759965, Collaborative Research: ABI Development: Symbiota2: Enabling greater collaboration and flexibility for mobilizing biodiversity data, and CNS-1755536, CAREER: Tracking, Revealing and Detecting Crowdsourced Manipulation. Opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect those of the National Science Foundation.

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Authors and Affiliations

  1. Department of Computer Science, Utah State University, Logan, UT, USA

    Vishal Sharma & Curtis Dyreson

  2. Department of Computer Science, Worcester Polytechnic Institute, Worcester, USA

    Kyumin Lee

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  1. Vishal Sharma
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  2. Kyumin Lee
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  3. Curtis Dyreson
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Correspondence to Vishal Sharma.

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Sharma, V., Lee, K. & Dyreson, C. Popularity versus quality: analyzing and predicting the success of highly rated crowdfunded projects on Amazon. Computing 103, 1939–1958 (2021). https://doi.org/10.1007/s00607-021-00926-w

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