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.
Table of Contents
1 Introduction
1.1 Problem outline and relevance
1.2 Target and approach
2 Definitions and theoretical framework
2.1 Considerations about early-stage startups
2.1.1 Characteristics
2.1.2 Startup funding cycle and the role of crowdinvesting
2.1.3 Reasons for startup failure
2.1.4 Why startup success is hard to predict
2.2 Relevance of Business Models in the entrepreneurial context
2.2.1 Definitions and origin of business modelling
2.2.2 Business model ontologies
2.2.3 Business model typologies
2.2.4 Business models and startup innovation
2.3 Crowdinvesting
2.3.1 Concepts and classifications
2.3.2 Crowdinvesting process
2.3.3 Crowdinvestor profile
2.4 Information economics in crowdinvesting
2.4.1 Principal agent theory
2.4.2 Signalling and decision heuristics in crowdinvesting
3 Empirical Study
3.1 Methodology
3.2 Definition of hypotheses
3.3 Research model and definition of variables
3.4 Sample and data collection
3.5 Qualitative considerations
4 Results of empirical study
4.1 Quantitative Market Research
4.1.1 Results from descriptive statistics
4.1.2 Results from statistical analysis
4.2 Discussion
4.3 Limitations
5 Conclusion and Outlook
5.1 Conclusion
5.2 Implications for further research
Objectives and Research Themes
The main objective of this study is to evaluate the validity of using the business model concept as a unit of analysis in crowdinvesting. Furthermore, it investigates whether specific business model typologies perform better in terms of funding success and identifies signalling criteria that significantly impact campaign outcomes.
- Analysis of business models as a unit for crowdinvesting evaluation.
- Identification of successful business model archetypes.
- Investigation of signalling criteria affecting project funding success.
- Exploration of decision heuristics applied by crowdinvestors.
Excerpt from the Book
2.1.3 Reasons for startup failure
According to a recent Harvard Business School study 75 percent of venture-backed startups fail in returning cash to investors. In this respect, CBInsights conducted a survey by consulting 101 stranded startups and asked for the main reasons for their failure. Figure 11 displays the main results in descending order while considering that multiple answers were allowed.
It becomes evident that the dominant driver is no market need which means that the developed product was simply not demanded by customers. This is the worst scenario as a profitable business must address customer demands at a certain degree otherwise it will lose its reason for existence. Secondly, lack of cash is mentioned which can be considered as the fuel for running a startup and therefore lack of money can develop into a fatal spiral of failure. An adequate supply of capital requires that there are sufficient investors available that believe in the business case. Generally, investors will only invest if the product the product is able to achieve a profitable market response at least in the long-term.
To include all remaining aspects of the survey and to reveal the comprehensive picture it is expedient to aggregate all aspects by performing a reasonable clustering. Figure 12 summarizes the result of the clustering process and giving an indication of the relevant areas of influence in which critical success and failure factors are concealed.
Chapter Summaries
1 Introduction: Provides an overview of the changing market environment for startups and introduces crowdinvesting as a relevant and growing phenomenon in the digital era.
2 Definitions and theoretical framework: Discusses the characteristics of early-stage startups, the theory of business models, and the economics of crowdinvesting, focusing on information asymmetry and signalling.
3 Empirical Study: Outlines the research design, including the mixed-method approach, the development of hypotheses, and the operationalization of variables for the statistical analysis.
4 Results of empirical study: Presents the quantitative findings from descriptive statistics and multivariate analysis, evaluating the impact of business model archetypes and signalling factors on funding success.
5 Conclusion and Outlook: Synthesizes the core findings, discusses the implications for practitioners and platforms, and provides recommendations for future research in the field of crowdinvesting.
Keywords
Crowdinvesting, equity-based crowdfunding, business model, startups, signalling, information economics, principal-agent theory, venture capital, entrepreneurship, decision heuristics, funding success, business model canvas.
Frequently Asked Questions
What is the core focus of this research?
The research examines whether the business model can serve as a suitable unit of analysis to understand and predict the funding success of early-stage startups on German crowdinvesting platforms.
Which theoretical concepts are essential to this work?
Key concepts include information economics (specifically the principal-agent theory and the lemon market problem), signalling theory, and the application of business model frameworks like the Business Model Canvas.
What is the primary research goal?
The primary goal is to determine if specific business model typologies result in higher funding success and to identify measurable signalling criteria that investors use when making decisions under uncertainty.
Which research methodology is applied?
The study employs a mixed-method approach, utilizing descriptive and multivariate statistical analysis on a sample of 151 finished crowdinvesting campaigns.
What does the main body of the work cover?
It covers the definition and characteristics of early-stage startups, an in-depth review of business model ontologies, the mechanics of crowdinvesting, and a comprehensive statistical evaluation of campaign data.
How are the key terms for this research defined?
The work characterizes startups by their innovation, growth orientation, and high uncertainty, and differentiates crowdinvestors as a distinct, semi-professional investor class compared to venture capitalists.
How do business angels influence funding success according to the study?
The study finds that the co-investment of business angels acts as a strong signalling factor, significantly increasing the total collected funding and attracting a higher number of crowdinvestors.
What are the findings regarding the "Product" business model archetype?
Product-oriented business models (including "Ecosystems") are found to be the most successful in terms of funding volume and investor attraction, as they are easily understandable and offer tangible value.
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- Pascal Mücke (Autor:in), 2018, Signalling of early-stage startups on crowdinvestment platforms, München, GRIN Verlag, https://www.grin.com/document/442604
