Essays on crowdfunding and innovation
Aachen (2019) [Dissertation / PhD Thesis]
Page(s): 1 Online-Ressource (xv, 172 Seiten) : Illustrationen
This dissertation presents four distinct research essays with theories and computational methods that illustrate how crowdfunding data can be used to empirically study innovation and entrepreneurship. The analyses cover different levels of scale and scope, ranging from predicting aggregate venture capital investments to measuring lead user attributes and outcomes, and predictions on campaign success by analyzing text, speech and video information. The methods combine insights from data mining, time series analysis, cross-sectional analysis, causal inference, natural language processing, and neural networks, helping to improve our understanding of crowdfunding, innovation and entrepreneurship.
Kaminski, Jermain Christopher
Piller, Frank Thomas