Signalling of early-stage startups on crowdinvestment platforms

Master's Thesis from the year 2018 in the subject Engineering - Industrial Engineering and Management grade: 10 Kiel University of Applied Sciences language: English abstract: Crowdinvesting as a relatively new funding form for early-stage startups in Germany bears fundamental opportunities for all participants. In this respect startup entrepreneurs are facing the challenge to convince potential investors of their business idea to finally induce investments. Since crowdinvesting communication is limited to digital channels the knowledge about what information is relevant for making investment decisions is of crucial significance. Moreover the crowd can be considered as a completely new investor class with specific characteristics and information requirements compared to traditional capital providers.The purpose of this research study is to evaluate the importance of business models as a potential unit of analysis in the crowdinvesting decision-making process. In this respect the significance of selected signalling criteria that may serve as quality indicators for investors will be investigated. In order to gain a broader understanding about the preferences in crowdinvesting a comprehensive empirical study of the German crowdinvesting market is conducted. Entrepreneurs crowdinvestors as well as respective platforms and thus the entire concept of crowdinvesting will benefit from a knowledge gain.The theoretical framework describes the characteristics of early-stage startups as well as the relevance of business models in the entrepreneurial context. In this light different frameworks for the concept of business modelling will be presented and dismantled into relevant building blocks. Additionally the concept of crowdinvesting is discussed in consideration of some special implications that can be explained via information economics and behavioural sciences such as information asymmetries and the occurrence of decision heuristics based on signalling crit