Modeling Dynamics in Crowdfunding
Chul Kim (),
P. K. Kannan (),
Michael Trusov () and
Andrea Ordanini ()
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Chul Kim: Department of Marketing, Baruch College, City University of New York, New York, New York 10010
P. K. Kannan: Department of Marketing, Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742
Michael Trusov: Department of Marketing, Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742
Andrea Ordanini: Department of Marketing, Bocconi University, 20136 Milan, Italy
Marketing Science, 2020, vol. 39, issue 2, 339-365
We investigate various dynamics characterizing the crowdfunding process: stagnation after friend-funding, gradual increase through crowd participation, and acceleration in the last phase. We propose three mechanisms as major drivers of the crowdfunding dynamics: forward-looking delaying investment behavior, contemporaneous social interactions, and forward-looking social interactions. We apply the rational expectations equilibrium of the approximate aggregation approach to model the underlying mechanisms. Using the Bayesian IJC method, we analyze individual-level investment data from a crowdfunding platform, Sellaband. We find strong evidence for the three mechanisms and confirm that they contribute to the contrasting dynamic patterns observed in our data. We also simulate counterfactuals to derive optimal policy decisions for both fundraisers and platforms. For fundraisers, we infer the optimal goals that ensure goal completion while raising the maximum capital. For platforms, we suggest an optimal targeting strategy that identifies those crowdfunders who contribute the most to the crowding process and, ultimately, goal success. Also, we provide critical input for various resource allocation decisions by accurately predicting whether the project will succeed and when it will succeed at the time when 50% of the goal has been achieved.
Keywords: crowdfunding; forward-looking; social interactions; rational expectations equilibrium; approximate aggregation (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormksc:v:39:y:2020:i:2:p:339-365
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