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This book examines the adverse effects of complexity, information asymmetries, transaction costs, and uncertainty on investors' decision making. It suggests mitigating those effects using appropriate and matching signals, and analyzes a sample of 903 German startups to quantitatively highlight the distinct financing patterns and characteristics of high-tech startups. It then investigates the reasons for these patterns on the basis of a qualitative study that includes 34 interviews with investors and entrepreneurs in the US and Germany and an international expert panel. Lastly, it presents a framework that matches complexity factors with appropriate productive signals.
Inhaltsverzeichnis
Introduction: High-Tech Startup Financing.- Theory: The Liability of Complexity.- Methodology: Mixed Methods Approach.- Study I: Survey of German Startups.- Study II: Interviews with Entrepreneurs and Investors.- Framework: Matching Signals with Complexities of High-Tech Strartups.- Discussion: Why Signals Can Help to Overcome the Liability of Complexity.- Conclusion: Taming Complexity.- Appendix.
Über den Autor / die Autorin
Robin P. G. Tech is a researcher at the Humboldt Institute for Internet and Society in Berlin. He is the principal investigator of a research group that focuses on the entrepreneurial exploitation of the 'internet of things'. Prior to this, Robin studied Economics and Corporate Management at Zeppelin University (BA),Technology, Sociology, & Management (MSc) in Stockholm, and completed his MBA in Hong Kong. In his non-academic life, Robin is the co-founder of AtomLeap, a high-tech focused startup. He also worked in venture capital (2007), held a consulting position at the curating office Buro17 in Moscow (2009), worked for AUDI on inductive charging systems (2010-2011) and at the European Commission's EIT Labs (2012-2013) in the field of business modeling for renewable energies.
Zusammenfassung
This book examines the adverse effects of complexity, information asymmetries, transaction costs, and uncertainty on investors’ decision making. It suggests mitigating those effects using appropriate and matching signals, and analyzes a sample of 903 German startups to quantitatively highlight the distinct financing patterns and characteristics of high-tech startups. It then investigates the reasons for these patterns on the basis of a qualitative study that includes 34 interviews with investors and entrepreneurs in the US and Germany and an international expert panel. Lastly, it presents a framework that matches complexity factors with appropriate productive signals.
