Crowdfunding for Design Innovation: Prediction Model With Critical Factors
Name
2007.01404.pdf
Description
Accepted version
Size
605.74 KB
Format
Adobe PDF
Checksum (MD5)
f5a30b8ffad154f89626a33da50a7f8c
Author(s)
Song, Chaoyang
Luo, Jianxi
Holtta-Otto, Katja
Seering, Warren
Otto, Kevin
Date Issued
2020
Journal
IEEE Transactions on Engineering Management
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Song, Chaoyang, Luo, Jianxi, Holtta-Otto, Katja, Seering, Warren and Otto, Kevin. 2020. "Crowdfunding for Design Innovation: Prediction Model With Critical Factors." IEEE Transactions on Engineering Management.
Version
Author's final manuscript
Abstract
Online reward-based crowdfunding campaigns have emerged as an innovative approach for validating demands, discovering early adopters, and seeking learning and feedback in the design processes of innovative products. However, crowdfunding campaigns for innovative products are faced with a high degree of uncertainty and suffer meager rates of success to fulfill their values for design. To guide designers and innovators for crowdfunding campaigns, this article presents a data-driven methodology to build a prediction model with critical factors for crowdfunding success, based on public online crowdfunding campaign data. Specifically, the methodology filters 26 candidate factors in the real-win-worth framework and identifies the critical ones via stepwise regression to predict the amount of crowdfunding. We demonstrate the methods via deriving prediction models and identifying essential factors from three-dimensional printer and smartwatch campaign data on Kickstarter and Indiegogo. The critical factors can guide campaign developments, and the prediction model may evaluate crowdfunding potential of innovations in contexts, to increase the chance of crowdfunding success of innovative products.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/TEM.2020.3001764
