Predicting outcomes in crowdfunding campaigns with textual, visual, and linguistic signals

Jermain C. Kaminski () and Christian Hopp ()
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Jermain C. Kaminski: Maastricht University
Christian Hopp: RWTH Aachen University

Small Business Economics, 2020, vol. 55, issue 3, No 6, 627-649

Abstract: Abstract This paper introduces a neural network and natural language processing approach to predict the outcome of crowdfunding startup pitches using text, speech, and video metadata in 20,188 crowdfunding campaigns. Our study emphasizes the need to understand crowdfunding from an investor’s perspective. Linguistic styles in crowdfunding campaigns that aim to trigger excitement or are aimed at inclusiveness are better predictors of campaign success than firm-level determinants. At the contrary, higher uncertainty perceptions about the state of product development may substantially reduce evaluations of new products and reduce purchasing intentions among potential funders. Our findings emphasize that positive psychological language is salient in environments where objective information is scarce and where investment preferences are taste based. Employing enthusiastic language or showing the product in action may capture an individual’s attention. Using all technology and design-related crowdfunding campaigns launched on Kickstarter, our study underscores the need to align potential consumers’ expectations with the visualization and presentation of the crowdfunding campaign.

Keywords: Startups; Crowdfunding; Pitch; Machine learning; Neural network; Natural language processing (search for similar items in EconPapers)
JEL-codes: C45 C55 G2 L26 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (19)

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DOI: 10.1007/s11187-019-00218-w

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