Crowdfunding for Design Innovation: Prediction Model with Critical Factors

• URL: http://arxiv.org/abs/2007.01404v1
• Date: Thu, 2 Jul 2020 21:44:40 GMT
• Title: Crowdfunding for Design Innovation: Prediction Model with Critical Factors
• Authors: Chaoyang Song, Jianxi Luo, Katja H\"oltt\"a-Otto, Warren Seering, Kevin Otto
• Abstract summary: This paper presents a data-driven methodology to build a prediction model with critical factors for crowdfunding success. We demonstrate the methodology via deriving prediction models and identifying essential factors from 3D printer and smartwatch campaign data.
• Score: 2.789896685059062
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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