  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
paper 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 step-wise
regression to predict the amount of crowdfunding. We demonstrate the
methodology via deriving prediction models and identifying essential factors
from 3D 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.

Computers and Society