![]() It is improper to take this variable as the proxy variable of backers’ decision behavior. , they all use the increased backers per day as the dependent variable. ![]() Kuppuswamy and Bayus, and the following researchers such as Chen et al. Although Kuppuswamy provides a common way for the following researchers, we think that there are still some problems in variables selection when construct the panel data model. To our best known, most of the current research on backers’ dynamic information in reward-based crowdfunding are based on the econometric method proposed by Kuppuswamy and Bayus. The following is a brief review of the prior researches of backers' investment decision behavior. Īfter an in-depth thinking in the dynamic decision behavior of backers, we come up with some different understandings, especially in data processing method and statistical approaches, which is the main research point in this paper. Kuppuswamy and Bayus, leveraging data from Kickstarter platforms, find that backer’s support increases as the goal end state approaches according to the Perceived Impact and Goal Gradient Effect. Some scholars focus on the importance of early contributions in crowdfunding as the herding behavior. point out that besides the influence of traditional investment factors, the backer's emotion (sympathy, curiosity, etc.) towards the project or the creator is also one of the factors that will affect the backer's decision behavior. The other one is the influence of backers' decision behavior on dynamic information factors. Such factors include both project-level signals, for instance project funding goal, project design, product categories and other project preparedness and individual-level signals, such as creator’s gender, experience of creator and social capital of creator. An emerging literature on reward-based crowdfunding mainly focus on two aspects, one is the static factors driving a campaign's success. 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.Īs an important branch of crowdfunding, reward-based crowdfunding has attracted many scholars’ attention these years. In addition, we upgrade the econometric method used by previous scholars, which could improve the accuracy of the FE model. 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. 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. 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. We analyze the connections among these factors by collecting the longitudinal dataset from reward-based crowdfunding platform. 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. ![]()
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