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Abstract
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.
Highlights
As an important branch of crowdfunding, reward-based crowdfunding has attracted many scholars’ attention these years
This section focuses on a descriptive statistical analysis of the data set involved in this study, including descriptive analysis of normalized data, distribution figure and the original data analysis
The purpose of this paper is to explore the backers’ investment behavior with dynamic explicit and implicit information factors
Summary
As an important branch of crowdfunding, reward-based crowdfunding has attracted many scholars’ attention these years. An emerging literature on reward-based crowdfunding mainly focus on two aspects, one is the static factors driving a campaign’s success. Such factors include both project-level signals, for instance project funding goal [1], project design [2], product categories [3] and other project preparedness [4, 5] and individual-level signals, such as creator’s gender [6], experience of creator [7] and social capital of creator [8]. The other one is the influence of backers’ decision behavior on dynamic information factors. Kuppuswamy and Bayus [10], leveraging
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