Backers investment behavior on explicit and implicit factors in reward-based crowdfunding based on ELM theory.

• Main Authors: Rui Hou, Leiming Li, Bingquan Liu
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2020-01-01
• Series: PLoS ONE
• Online Access: https://doi.org/10.1371/journal.pone.0236979

The aim of this study is to identify the dynamic explicit and implicit information factors which displayed on the webpage of platforms that influence backers' investment decision-making behavior. We analyze the connections among these factors by collecting the longitudinal dataset from reward-based crowdfunding platform. Based on ELM model, we establish Fixed Estimation Panel Data Model respectively according to explicit and implicit factors and take Funding Status (crowdfunding results) as the moderating variable to observe the goal gradient effect. Results indicate that most variables in the central route affect backers' investment behavior positively, while most variables in the periphery route have a negative impact on backers' investment behavior. The Funding Status has a significant negative moderating effect on the explicit variables, and has no significant moderating effect on the implicit information variables of the project. In addition, we upgrade the econometric method used by previous scholars, which could improve the accuracy of the FE model. Furthermore, we find strong support for the herding effect in reward-based crowdfunding and the intensity tends to decrease before the funding goal draws near.