Signalling of early-stage startups on crowdinvestment platforms

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

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.