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Journal ArticleDOI

Crowdfunding creative ideas: the dynamics of project backers in kickstarter

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TLDR
This article studied the role of social information in the dynamic behavior of project backers and found that additional backer support is negatively related to its past backer support, while the diffusion of responsibility effects diminish as the project funding cycle approaches its closing date.
Abstract
Entrepreneurs are turning to crowdfunding as a way to finance their creative ideas. Crowdfunding involves relatively small contributions of many consumer-investors over a fixed time limit (generally a few weeks). In online crowdfunding communities, potential donors can see the level of support from other project backers as well as its timing before making their own funding decisions, suggesting that social information (i.e., others’ funding decisions) will play an important role in the ultimate success of a project. Two years of publicly available panel data on successfully and unsuccessfully funded projects listed on Kickstarter is used to empirically study the role of social information in the dynamic behavior of project backers. Building off the well-established social psychology theory around diffusion of responsibility effects, we show that additional backer support is negatively related to its past backer support. Many potential backers do not contribute to a project that has already received a lot of support because they assume that others will provide the necessary funding. Consistent with the deadline effect widely observed in bargaining and online auctions, we also show that the diffusion of responsibility effects diminish as the project funding cycle approaches its closing date. Moreover, as the project deadline draws near we find that project updates tend to increase as the project creators make a final plea for help to reach their funding goal. Reduced diffusion of responsibility effects, together with the positive influence of project updates, lead to generally increasing project support in the final stages of funding. This is particularly the case for projects that successfully achieve their goals as they are more likely to have an update in the last weeks of funding and generate more excitement from recent backers than projects that fall short.

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Journal ArticleDOI

Segmenting “digital investors”: evidence from the Italian equity crowdfunding market

TL;DR: In this article, a cluster analysis of the Italian equity crowdfunding investors' market was performed by means of a cluster-based analysis and the differences between segments in terms of socio-demographic and behavioral variables were explored.
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Investor Motivations and Decision Criteria in Equity Crowdfunding

TL;DR: The authors analyzed the motivations and decision criteria of equity crowdfunding investors using responses to a large-scale survey and found that investors form three distinct motivation-based clusters: donation-oriented supporters, return-oriented investors, and pure investors.
Journal ArticleDOI

Crowdfunding Acts as a Funding Substitute and a Legitimating Signal for Nonprofit Performing Arts Organizations

TL;DR: The authors examined the relationship between crowdfunding campaigns by nonprofit performing arts organizations and their overall fundraising portfolio using a dataset compiled from the CrowdBerger dataset, and found that crowdfunding campaigns were correlated with overall fundraising portfolios.
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How to perfectly discriminate in a crowd? A theoretical model of crowdfunding

TL;DR: A theoretical framework is proposed to capture the underlying mechanisms of the innovative online crowdfunding, by describing the capabilities unique to such model and processes stemming from them, and the producer’s chance for perfect discrimination is discussed.
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What Contributes to a Crowdfunding Campaign's Success? Evidence and Analyses from GoFundMe Data

TL;DR: This work focuses on the performance of the crowdfunding campaigns on GoFundMe over a wide variety of funding categories and develops a fusion classifier based on random forest that significantly improves the prediction result, thus suggesting effective ways to make a campaign successful.
References
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Book

Econometric Analysis of Cross Section and Panel Data

TL;DR: This is the essential companion to Jeffrey Wooldridge's widely-used graduate text Econometric Analysis of Cross Section and Panel Data (MIT Press, 2001).
MonographDOI

Microeconometrics: Methods and Applications

TL;DR: This chapter discusses models for making pseudo-random draw, which combines asymptotic theory, Bayesian methods, and ML and NLS estimation with real-time data structures.
Journal ArticleDOI

Some practical guidance for the implementation of propensity score matching

TL;DR: Propensity score matching (PSM) has become a popular approach to estimate causal treatment effects as discussed by the authors, but empirical examples can be found in very diverse fields of study, and each implementation step involves a lot of decisions and different approaches can be thought of.
Journal ArticleDOI

Interaction terms in logit and probit models

TL;DR: In this article, the authors present the correct way to estimate the magnitude and standard errors of the interaction effect in nonlinear models, which is the same way as in this paper.
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A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades

TL;DR: It is argued that localized conformity of behavior and the fragility of mass behaviors can be explained by informational cascades.
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