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 criteria.
The research indicates that the business model concept is a suitable unit of analysis as it allows a differentiated view and enables new opportunities for the evaluation of crowdinvesting campaigns. In this context, it has been statistically proven that product-focused business models are funded more successfully than others. Generally, the results imply that startups should engage in offensive signalling to improve success of the crowdinvesting campaigns. However, there are also some indications in this context that the crowd makes decisions based on simplified evaluation processes and therefore applies binary decision heuristics.
Inhaltsverzeichnis (Table of Contents)
- Introduction
- Problem outline and relevance
- Target and approach
- Definitions and theoretical framework
- Considerations about early-stage startups
- Characteristics
- Startup funding cycle and the role of crowdinvesting
- Reasons for startup failure
- Why startup success is hard to predict
- Relevance of Business Models in the entrepreneurial context
- Definitions and origin of business modelling
- Business model ontologies
- Business model typologies
- Business models and startup innovation
- Crowdinvesting
- Concepts and classifications
- Crowdinvesting process
- Crowdinvestor profile
- Information economics in crowdinvesting
- Principal agent theory
- Signalling and decision heuristics in crowdinvesting
- Considerations about early-stage startups
- Empirical Study
- Methodology
- Definition of hypotheses
- Research model and definition of variables
- Sample and data collection
- Qualitative considerations
- Results of empirical study
- Quantitative Market Research
- Results from descriptive statistics
- Results from statistical analysis
- Discussion
- Limitations
- Quantitative Market Research
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This research study investigates the role of business models in the decision-making process of crowdinvesting for early-stage startups in Germany. The research aims to understand the relevance of signalling criteria for investors, evaluate the impact of business model presentation on project funding success, and contribute to the knowledge base for entrepreneurs, crowdinvestors, and platforms. This study focuses on the unique characteristics of early-stage startups and the specific needs and behaviours of crowdinvestors compared to traditional investors.
- The importance of business models in crowdinvesting decisions
- The impact of signalling criteria on project funding success
- The role of information economics in crowdinvesting
- The characteristics of crowdinvestors and their decision-making processes
- The evaluation of business models within crowdinvesting campaigns
Zusammenfassung der Kapitel (Chapter Summaries)
The first chapter sets the stage by outlining the problem of limited information availability in crowdinvesting and highlighting the need for understanding investor preferences and decision-making processes. It introduces the target and approach of the research, focusing on the analysis of business models and their impact on funding success.
Chapter two delves into the theoretical framework, examining the characteristics of early-stage startups, their funding cycles, and the role of crowdinvesting. It presents different frameworks for business modelling and explores the implications of information economics and behavioural science on crowdinvesting decisions.
The third chapter outlines the methodology of the empirical study, including the definition of hypotheses, research model, data collection methods, and qualitative considerations. It provides a comprehensive overview of the research design and data analysis techniques used to examine the impact of business models and signalling criteria on project funding success.
The fourth chapter presents the results of the empirical study, focusing on quantitative market research, descriptive statistics, and statistical analysis. It analyzes the data collected from the German crowdinvesting market to identify trends and patterns related to the impact of business models on project funding success.
Schlüsselwörter (Keywords)
This research study focuses on the following keywords and concepts: crowdinvesting, equity-based crowdfunding, business models, startups, signalling, information asymmetry, decision heuristics, and project funding success. These terms reflect the core themes and research areas explored in the thesis.
- Quote paper
- Pascal Mücke (Author), 2018, Signalling of early-stage startups on crowdinvestment platforms, Munich, GRIN Verlag, https://www.grin.com/document/442604
