Crowdfunding for Design Innovation: Prediction Model with Critical Factors

TL;DR

This paper develops a data-driven prediction model using critical factors to improve the success rate of crowdfunding campaigns for innovative products, aiding designers and innovators in campaign planning.

Contribution

It introduces a methodology to identify critical success factors and build predictive models from crowdfunding data, enhancing campaign success prediction for innovative products.

Findings

Identified key factors influencing crowdfunding success.

Built accurate prediction models for campaign outcomes.

Demonstrated methodology on Kickstarter and Indiegogo data.

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 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.