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

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.

The art of crafting a systematic literature review in entrepreneurship research

“…Rauch (2019) showed some examples where software was used to analyze qualitative data (e.g. Kaminski and Hopp 2019;Short et al 2010;von Bloh et al 2019). Automated data analysis would lead to several advantages for literature reviews.…”

Crowdfunding Dynamics Tracking: A Reinforcement Learning Approach

“…With the growing popularity of crowdfunding, scholars have done much research and analysis from different perspectives (Zhao et al 2019;Liu et al 2017;Zhao et al 2017a;Zhang et al 2019b). Most of the previous works could be grouped into three categories: analyzing the influential factors (Burtch, Ghose, and Wattal 2013;Kuppuswamy and Bayus 2017;Mollick 2014;Hoegen, Steininger, and Veit 2018), predicting the funding results (i.e., success of failure) (Li, Rakesh, and Reddy 2016;Lee, Lee, and Kim 2018;Yu et al 2018;Zhang et al 2019a;Kaminski and Hopp 2019) and tracking the funding dynamics (Zhao et al 2017b;Ren et al 2018), etc. Among qualitative factors, what should be mentioned is that some scholars are committed to exploring the social effects in crowdfunding, especially the "Herd Effect" and the "Goal Gradient Effect" (Shen, Krumme, and Lippman 2010;Herzenstein, Dholakia, and Andrews 2011;Kuppuswamy and Bayus 2017), which uncovers a typical and significant pattern in funding series, i.e., U-shaped pattern.…”

Opportunities and Threats in Reviewing Entrepreneurship Theory and Practice

“…Since such analytical tools can test a large amount of data, they provide new opportunities to review text sources that could not have been analyzed with more traditional text analytical tools. For example, in entrepreneurship, researchers have used computerized text analysis to analyze shareholder letters of firms (Short, Broberg, Cogliser, & Brigham, 2010), press releases (von Bloh, Broekel, Özgun, & Sternberg, 2019), and crowdfunding campaigns (Kaminski & Hopp, 2019). Computerized tools offer several advantages compared to more traditional forms of review.…”

“…First, they help to identify relevant data, which is important given the large and growing number of scientific publications, and there are several automated processes available to locate data (O'Mara-Eves, Thomas, McNaught, Miwa, & Ananiadou, 2015). Second, computerized analysis helps analyze new data sources that were difficult to access because of the sheer amount of data involved, such as the data information repository made available by the internet or analysis that relies on multiple types of data, such as texts, videos, or verbal information (Kaminski & Hopp, 2019). Finally, there are different sets of rules—algorithms—available to analyze the data, ranging from basic methods that look, for example, at frequencies to more complex models such as natural language processing, which analyzes multiple word phrases and enables researchers to capture the syntactic relations that bind words to produce meaning (Pandey & Pandey, 2019), or sentiment analysis that helps understand an opinion about a given subject from written or spoken language.…”

“…In the domain of entrepreneurship, this type of research is just evolving and has potential to contribute to knowledge accumulation in the field as it can process huge amounts of data including data sources that might have been untapped, and it can assign meanings to the data that can only be extracted with computerized text analysis. An example of applying computerized analysis is Kaminski and Hopp (2019). Using computerized text analysis, the authors analyzed 20,188 crowdfunding campaigns.…”