Signalling of early-stage startups on crowdinvestment platforms

Table of Contents

Abstract

List of Figures

List of Tables

List of Abbreviations

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

6 List of References

Appendix

List of Figures

Figure 1 - Funding volume of crowdinvesting projects in Germany (2011- 2017)

Figure 2 - Crowdfunding Barometer 2017

Figure 3 - PwC survey results "convincing potential investors"

Figure 4 - Illustration of Research Objective

Figure 5 - Comparison ROI development based on growth rate

Figure 6 - Startup founding process

Figure 7 - Startup development phases

Figure 8 - Global and German venture financing by stage 2010-2016

Figure 9 - Utilization of funding sources of German startups in 2014-2017

Figure 10 - Investment status of German crowdinvesting startups 2016

Figure 11 - Top 20 reasons for startup failure

Figure 12 - Clustered reasons for startup failure

Figure 13 - Levels of uncertainty applied to startup development

Figure 14 - Causal Thinking vs. Effectuation

Figure 15 - Theory of Business by Peter Drucker

Figure 16 - Business model structure based on thoughts by Joan Magretta

Figure 17 - Business Model Canvas

Figure 18 - St. Galler Magic Triangle

Figure 19 - Increasing complexity of business model typologies

Figure 20 - Business Model Archetypes by Cabage & Zhang

Figure 21 - Relationship between innovation and value proposition

Figure 22 - Milestones in the digital transformation

Figure 23 - Key participation models in crowdinvesting

Figure 24 - Complexity of crowdfunding concepts

Figure 25 - Stakeholders in crowdinvesting

Figure 26 - The equity-crowdfunding process

Figure 27 - Motivation for decision-making in crowdinvesting

Figure 28 - Investor types by Ø investment and number of investments

Figure 29 - Agency conflicts in crowdinvesting

Figure 30 - Resulting problems in crowdinvesting caused by information asymmetries

Figure 31 - Signalling in crowdinvesting

Figure 32 - Structure of research study

Figure 33 - Hypotheses embedded into BMC ontology

Figure 34 - Research questions, hypothesis and determinants within the research model

Figure 35 - Market shares of platforms in Germany in 2016 by funding volume

Figure 36 - Distribution of considered projects within the collected dataset (N = 151)

Figure 37 - Early-stage startup funding campaigns from 2011 to 2017

Figure 38 - Number of individual investments by crowdinvestors

Figure 39 - Collected investment capital for the segment “startups” from 2011 to 2017

Figure 40 - Distribution of funding campaigns by business model archetypes (N = 151)

Figure 41 - Distribution of campaigns by funding level clusters

Figure 42 - Summary of regression model QTY_CROWDINV

Figure 43 - Summary of regression model F_AVG_AMOUNT

Figure 44 - ANOVA interval plot by collected funding volume (F_COLLECTED)

Figure 45 - ANOVA interval plot by collected funding volume (QTY_CROWDINV)

Figure 46 - ANOVA interval plot by collected funding volume (F_AVG_AMOUNT)

Figure 47 - ANOVA results by success variables vs. DUM_TEAM

Figure 48 - ANOVA results by success variables vs. DUM_TR_REC

Figure 49 - ANOVA results by success variables vs. DUM_BA

Figure 50 - ANOVA results by success variables vs. DUM_PATENT

Figure 51 - ANOVA results by success variables vs. DUM_REVENUE

Figure 52 - ANOVA results by success variables vs. DUM_AWARDS

List of Tables

Table 1 - Selected business model definitions

Table 2 - Business model building blocks by Osterwalder

Table 3 - Case Study Grin Verlag GmbH

Table 4 - Case Study Amazon EC2

Table 5 - Case Study Delivery Hero

Table 6 - Average characteristics of crowdinvestors on German platforms

Table 7 - Key characteristics of crowdinvestors, business angels and venture capitalists

Table 8 - Proposed assessment criteria for investors by CONDA

Table 9 - Selected research studies that deal with behaviour in crowdfunding

Table 10 - Dependent variables related to Funding Success

Table 11 - Independent variables of empirical study

Table 12 - Top German crowdinvesting platforms

Table 13 - Information building blocks on Platforms

Table 14 - Descriptive statistics for dependent variables (n=151)

Table 15 - Descriptive statistics for independent variables (n=151)

Table 16 - Mean value comparison between successful and unsuccessful campaigns

Table 17 - Interpretation of correlation coefficients by Cohen

Table 18 - Results from correlation analysis

Table 19 - Summary of expected means of 95% confidence interval from ANOVA

List of Abbreviations

ANOVA Analysis of Variance

BMC Business Model Canvas

IP Intellectual Property

KPI Key Performance Indicator

ROI Return on Investment

USP Unique Value Proposition

1 Introduction

“I think the most important impact (of my research) is the recognition that economic agents are human and economic models have to incorporate that.”^[1]

Within the past years the market environment and therefore also opportunities and challenges for innovative startups have altered fundamentally. In times of scarce venture capital the access to adequate funding capital is a prerequisite and catalyst for realizing the fast growth aspirations of startup companies. However, the range of possibilities that entrepreneurs can utilize to fund and develop their businesses has expanded due to new emerging technologies.

Crowdfunding in its various forms is a growing phenomenon around the world that can be considered as a vital part of the digital structure change and is frequently mentioned in one breath with buzzwords like Crowd Sourcing or FinTech.^[2] As an alternative to traditional funding methods crowdinvesting as a specific form of crowdfunding (equity-based crowdfunding) is on the advance. In this respect, crowdinvesting has evolved to a versatile alternative for startup entrepreneurs. On the other side it poses a new investment opportunity for a broader audience. With the advent of internet based crowdfunding platforms the equity participation in startups was no longer restricted to institutional investors like business angels or venture capitalists but became also available to private investors. In line with this process, crowdinvesting has evolved to a diversified market on which different online platforms serve as intermediaries between startup companies and private crowdinvestors. Consequently, startup entrepreneurs with promising business ideas became a valuable asset as an alternative investment opportunity for private investors in the current low-interest environment.

However, equity-based crowdfunding is a complex process especially from a communication standpoint as the complete information exchange and dealflow is online. Moreover, crowdinvestors can be considered as a completely new stakeholder class whose information requirements for decision-making are not entirely explored in line with the progressing dissemination of crowdinvesting. This development raises the question how startup founders can present and illustrate their business ideas in a way that attracts potential crowdinvestors to ultimately boost investment transactions. The present study intends to shed light on the relevance and impact of the presentation and content of business models as a decision-making factor for crowdinvestors and other related stakeholders.

1.1 Problem outline and relevance

Crowdinvesting (equity-based crowdfunding) has been measurably perceptible on the German market since 2011 as a financing alternative for young startup companies. Even today crowdinvesting can still be considered as an emerging market that has been growing constantly over the past years. Considering the current market development, the data clearly show a constant growth in terms of both funding volume and the number of successfully financed projects. This significant increase testifies growing market acceptance of this form of alternative financing. Although the real estate segment can currently be considered as the major growth driver in crowdinvesting from a holistic perspective, the segment of startup financing also gained an impelling growth push of 65% in 2017.^[3] Figure 1 illustrates the monetary development between 2011 and 2017 in Germany.

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Figure 1 - Funding volume of crowdinvesting projects in Germany (2011- 2017)^[4]

The emerging opportunities for startup entrepreneurs as well as for private investors are manifold and bear a massive economical potential as an alternative concept to classical funding types like bank loans or venture capital funds. In the meantime, Germany can be considered as the third largest market in Europe for alternative financing besides UK and France.^[5] In this respect, recent studies show that the current market penetration of crowdinvesting covers only around 4% of the addressable market. Consequently, the growth prospects of this partial segment are very positive.^[6] Numerous studies have already dealt with economics of crowdfunding.^[7] Nevertheless, many aspects in terms of function and mode of operation, opportunities, risks, but also control or regulation have not yet been sufficiently researched due to a lack of experience, statistics and history.^[8]

From financial industry’s point of view crowdfunding is not regarded as a hype, but as a trendy movement that is still in its infancy. However, the overall market has also been subject to significant changes. With the introduction of the “Kleinanlegerschutzgesetz” in 2015 a legal framework for crowdfunding was ultimately defined. Within the scope of a financing limit of up to 2.5 million Euro, issuers and platforms were exempted from expensive documentation and auditing obligations.^[9] This framework enabled a clear legal leeway for crowdinvesting in Germany but also encountered opposition as industry representatives still considered it as too restrictive.^[10] Apart from that, there has also been a continuous professionalization on the side of the provider platforms. The appearance, terms of the contract and investor models have developed continuously over the past years.

As a social phenomenon crowdinvesting is also critically followed in the media. Due to several prominent insolvencies, such as the famous startup Protonet in February 2017, the concept of crowdinvesting has already been judged to have failed fundamentally.^[11] In this context various explanatory approaches are discussed among experts. The arguments cited in this regard are the hitherto unknown risk level of this financing class, the contact design with the help of partial subordinated loans (‘Partiarische Nachrangdarlehen’) and the associated weak position of investors, as well as possible conflicts of interest in the valuation of the company.^[12]

On the other hand, the latest 2017 survey results conducted by the “Crowdfunding Barometer” show that the level of social awareness related to crowdfunding in general is increasing continuously. The results also show that two thirds of the respondents have already heard about crowdfunding and more than 35% know what crowdfunding is. Moreover, 11.7% have already participated in a crowdfunding project. Other comparable surveys come to different percentages but mainly because of different panel compositions. However, this does not change the basic positive trend. In this respect it has been shown that younger respondents are generally much more open to crowdinvesting than older respondents. The makers of the crowdfunding barometer assume that crowdfunding will naturally become established over the long term through the young and digitally socialized age groups. In this context the general awareness is stronger pronounced among high-income groups.^[13] Figure 2 illustrates the results.

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Figure 2 - Crowdfunding Barometer 2017^[14]

This development indicates that crowdinvesting as an alternative form of investment is becoming more and more present in the society. Complementary, the increased media presence and success of semi-professional TV formats such as "Die Höhle der Löwen" confirms the overarching trend that the topicality of entrepreneurship is perceived as interesting across a wide spectrum of society.^[15] The main motivations for the interest in crowdinvesting are achieving a high return on investment (ROI) and supporting convincing business ideas.^[16] In line with this trend the presented information content in crowdinvesting must be adequately prepared for a broader non- or semi-professional target group.

In this respect, the most interesting question is which selection criteria investors do apply in crowdinvesting for their investment decisions. Based on a recent survey conducted by PwC, more than 50% of startup entrepreneurs have stated to have at least difficulties to convince investors of their business idea (cf. Figure 3). The main difficulty in this respect is to create understanding for the business idea.^[17] Overall, the survey results imply the practical importance of the business model as an analytical unit when it comes to obtaining the necessary financial resources from potential investors. Consequently, there is a compelling need to examine this complex within the scope of crowdinvesting to create complementary knowledge to existing studies.

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Figure 3 - PwC survey results "convincing potential investors"^[18]

1.2 Target and approach

The main purpose of this master thesis is to analyse if the application of the business model concept as a unit of analysis is valid in the context of crowdinvesting and to determine if certain business model typologies are used more frequently and perform better in terms of funding success. In this respect, it is a second target to determine relevant signalling factors that affect the success of a crowdinvesting funding campaign. The underlying research questions are formulated as follows:

Q1 - “Considering the business model as unit of analysis, is the funding success of a crowd investment project influenced by the business model archetype and do some typologies perform better than others?”

Q2 - “Are there certain signalling criteria identifiable that result in a higher funding success?”

The criteria in crowdinvesting relevant for investment decisions have so far only been examined to a limited extent. Due to the continuous evolution of the crowdinvesting market in Germany it is reasonable to conduct a purposive analysis that reflects the current market situation. Currently, the decision-making process of crowdinvestors is largely unexplored compared to professional investor classes like business angels or venture capitalists. An analysis of possible similarities and differences as well as the consideration of possible decision heuristics should offer added value to existing knowledge in the context of crowdinvesting. Consequently, the results of this study may have high practical value because it enhances the understanding about the decision-making process in crowdinvesting.

Additionally, the results should provide a clearer picture if there are certain business model typologies preferred by the crowd. Moreover, the study intends to classify the significance of certain signalling criteria for the crowdinvesting decision-making process and to reveal potential correlations between overall funding success. Such an analysis is of special interest for early-stage startups which are characterized by short track records and associated uncertainties regarding the reliability of quantitative information.

In this respect, the approach is consistent with a major problem that was already defined by Stuart et al. „ Because the quality of young companies often cannot be observed directly, evaluators must appraise the company based on observable attributes that are thought to covary with its underlying but unknown quality. Resource holders therefore assess value by estimating the conditional probability that a firm will succeed, given a set of observable characteristics of the organization.” ^[19]

In the context of this study these observable characteristics will be related to the concept of business models as a unit of analysis. In line with the introducing quote by Thaler it must be considered that investment decisions can be considered as transactional processes that are made in a socio-economical context. This implies that the evaluation process performed by crowdinvestors may have some inherent restrictions (e.g. limited information, low cost-benefit for detailed due diligence due to small investment amounts) may force them the utilize decision heuristics based on signalling criteria to maximizing overall benefits. The identification of these factors would create added value for all stakeholders in crowdinvesting. The resultant findings should be a knowledge gain for practice and the groundwork for further scientific studies. Figure 4 illustrates the basic idea in a vivid description.

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Figure 4 - Illustration of Research Objective^[20]

As a baseline for the research it is necessary to create a common understanding for the related terminology and the underlying concepts as the entire perimeter of this research field is subject to rapid change.

Consequently, chapter two of this paper intends to provide an overview about the different business model frameworks and concepts as well as the related terminology. It constitutes the basement for all subsequent thoughts and explanations. The research in this chapter one is conducted in form of a literature review. Academic journals pose the main source of information as well as some selected secondary literature. Due to the topicality of the topic crowdinvesting and the fast development in this field the utilization of online sources is mandatory. Firstly, the characteristics of early-stage startups will be carved out which guide to an argumentation about the relevance of business modelling in the entrepreneurial context. In this light, the fundamental architecture of the busines model concept as an unit of analysis will be set out. Crucial in this respect is the identification and definition of relevant factors by dismantling existing theories related to business modelling.

In the second part of chapter two important aspects of crowdinvesting as a subcategory of crowdfunding will be presented. Finally, the role of information economics with respect to the triangular relationsship in crowdinvesting and the resultant implications on the decision-making process of investors will be emphasized. In this respect the concept of signalling as an imperative to reduce uncertainty and accompanying deficiencies in the information economics will be set out.

Chapter three deals with the definition and set up of an empirical study. In this respect, a qualitative and quantitative market research is conducted using a mixed-method approach as it includes a confirmatory as well as an exploratory component. First, it is necessary to formulate some theory-based hypotheses and a set of operationalized variables that enable empirical measurement. Moreover, the method for collecting the necessary data is explained.

Chapter four delivers a summary of the main results of the empirical study. It contains the collection, preparation, evaluation and interpretation of relevant information with regards to the underlying research questions. Finally, some implications for further research are presented.

2 Definitions and theoretical framework

2.1 Considerations about early-stage startups

In business practice, there is sometimes the common misconception that every company foundation constitutes a startup. Consequently, it is crucial to outline the specific characteristics that determine a startup business and delimits them from generic company foundations.

2.1.1 Characteristics

According to the German Startup Monitor startups are defined by the following three main characteristics:^[21]

- Startups are younger than ten years
- Startups are highly innovative with their technology and/or business model
- Startups have (or strive for) a significant employee and/or sales growth

Paul Graham, head of the famous incubator platform Y-Combinator, postulates the very straightforward definition when he says “Startup = Growth”.^[22] According to his understanding startups mainly differ from generic enterprises by their underlying endeavour to generate extraordinary growth. To enable growth, startup companies suspend the limitations related to the process of “making something lots of people want” and “reach and serve all those people”. He assumes that all other characteristics within in the DNA of startups follow from this growth orientation. He describes growth as a rationale anchor point for founders, investors and future acquirers as it leads to a high expected value even if risk is high.^[23] Moreover, growth aspirations can be considered as a threat to established companies inducing targeted acquisitions. These forces are giving startups a special position from investor perspective.

Looking at the future development of ROI based on a given growth rate it becomes obvious that the growth rate can be be defined as on of the most important key performance indicators (KPI) for startups. Figure 5 shows two graphs that compare the development of two differently growing startups after one year. It becomes obvious that a slight difference in growth rate of 2% ends up in a ROI gap of nearly 800%. Consequently, from an investor perspective growth can be considered as the most important driver for a startup investment because it has direct influence on the increasing company value which ultimately determines the ROI.

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Figure 5 - Comparison ROI development based on growth rate

Eric Ries includes another aspect when he defines a startup as a “human institution designed to deliver a new product or service und conditions of extreme uncertainty”.^[24] Ries is considered as being the founder of the Lean Startup philosophy that established a new way of thinking in terms of startup entrepreneurship. He assumes that the uncertain situation of startups regarding their future development makes them special in comparison to generic enterprises. According to this approach raising a startup is no linear process that can be planned at a high level of granularity. He asserts that a startup is built on multiple hypothesis which need to be validated sequentially through experiments – a process called validated learning.^[25]

This way of thinking implies that a static business plan is inferior compared to continuous planning – a fact can be projected on the situation of innovative early stage start ups. Based on this argumentation the elaboration of detailed number-based business plans within innovative early-stage startups is not expedient as they are misleading and encourage achieving failure as they are based on ex ante assumptions at high uncertainty leading to bad outcomes. In this respect, one has to consider that a business plan is basicly a structured summary of isolated economical aspects and projected figures that provides a delusive security. However, if the underlying value proposition is not capable to create sustainable demand, the explanatory power of all quantitative assumptions stated in the business plan is very limited. Moreover, quantitative projections often turn out to be wishful thinking. This discrepancy contains the risk of “achieving failure” with negative impact on all stakeholders.^[26] Figure 6 illustrates the differences between the linear way of thinking with detailed ex-ante planning and the iterative process which often holds true in perceived reality. This situation scrutinizes the significance of the concept of business planning in the context of early-stage startups. The necessary flexibility calls for a comprehensive approach in order to create a viable and transparent evaluation possibility for potential investors.

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Figure 6 - Startup founding process^[27]

Blank incorporates this thinking to his definition when he says “One of the critical differences is that while existing companies execute a business model, start-ups look for one. This distinction is at the heart of the lean start-up approach. It shapes the lean definition of a start-up: a temporary organization designed to search for a repeatable and scalable business model.” ^[28]

With this definition Blank pinpoints two major aspects that describe the essence of startups. The pursuit of a business model that contains the ability to be scalable which is the prerequisite for growth (in line with Paul Grahams definition) and repeatable meaning that it can generate sustainable economic success. According to this logic the formulation of suitable business model is the core driver for survival of startups and cornerstone for enabling the long-term transition into a successful enterprise. Summarizing the discussed aspects leads to the following comprehensive definition of a startup that should be referenced throughout this study:

A startup is a newly formed enterprise build on an innovative idea and striving for growth. To achieve extraordinary growth a startup must lay the foundation by the definition of a repeatable and scalable business model as a prerequisite for acquisition of appropriate funding capital. To master the transition into a mature operating business it must overcome the journey from uncertainty to proof of concept.^[29]

2.1.2 Startup funding cycle and the role of crowdinvesting

To emphasize and delimit the specific situation of early-state startups it is reasonable to regard the typical funding and development cycle of new ventures in consideration of the general conditions. The development process of startups can be divided into an ideal-typical phase model which is available in some slightly different variations in literature.^[30] Figure 7 represents a graphical illustration.

The focus of this study is on the consideration of early-stage startups which can be differentiated into seed and startup stage as highlighted in Figure 7. However, it should be noted that due to the complexity of reality the ideal-typical phases are often fuzzy and a clear demarcation between the stages is not always possible.^[31] The model also shows that along with subsequent expansion stages and the concurrent level of maturity a startup will gain a certain history of market feedback and quantitative economic data. Consequently, the reduced level of uncertainty in the later stages unlocks access to additional funding sources while at the very early stages the funding opportunities are often constrained.

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Figure 7 - Startup development phases^[32]

During seed-stage the design and the conception of the overall business idea is in the process of definition. The aim of this process is to create a sound business concept, whereby market research, development and organizational structuring are central activities. In this phase startup entrepreneurs can already make use of external support via business accelerators, incubators or business angels. Moreover, this also includes the preparation of a business plan that can be considered as the primary milestone as a prerequisite for the acquisition of venture capital. The transition into the startup phase goes along with the achievement of the so-called “proof of concept” which offers evidence that the business model is generally feasible. During this process the company is also formally founded and preparation are being made to establish its (large-scale) operational business capability.^[33]

In general, the early-stage can be considered as the most critical phase in terms of capital acquisition. The importance results from the fundamental necessity of raising external capital to drive the company's growth. The required funding mix consisting of informal and formal capital is only hard to achieve. Due to the high level of uncertainty it is often difficult to acquire suitable financing funds, especially for early-stage startups. The resulting early-stage funding gap which is often called Death Valley is a dramatic thread for many startup entrepreneurs.^[34]

The current development in global venture capital investments, shows a massive decline in closed deals since early 2015. While the global financing volume is generally increasing since 2017 the overall number of closed deal is declining. This trend indicates that venture capitalists follow the strategy to focus on making fewer but larger deals. Ultimately this development may prevent many good smaller business ideas from realization. Especially, in this respect the graphs show that seed and early-stage investments are losing ground (see Figure 12). The situation in Germany is even more dramatic, with a decline of capital invested of almost 50% between 2015 and 2016. Additionally, the overall number of closed deals is decreasing.

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Figure 8 - Global and German venture financing by stage 2010-2016^[35]

Due to this decline the startup scene in Germany is facing major challenges. Especially in the early stages entrepreneurs need comparably smaller amount as startup capital for the realisation of their plans. Future inflows of funds and thus the basis for loan repayments are difficult to estimate. Moreover, most startup entrepreneurs work full-time on their startup project so they do not have a regular income. Consequently, they are not attractive for risk-averse investors due to insufficient collateral. Due to the absence of a regular operative business the availability of reliable quantitative figures is on a very low level. This fact makes it also hard for potential investors to evaluate the future success of the respective startup as there is a lack of economic figures at this point. Even best guess projections will not solve this dilemma which can be considered as the main reason for the reluctance of banks to lend money.^[36]

Another problem is the comparably low volume of loans as startups usually require comparably low amounts of capital. Given this scale, granting loans is often not very attractive for most institutional investors because of the administrative overhead costs involved.^[37] Moreover, the related transaction costs for conducting a thorough due diligence process is way too high. Therefore, many early-stage startups are too small in terms of capital requirements to be attractive for venture capital funds.^[38]

Due to its decentralized approach, crowdinvesting bears the chance to complement the existing financing instruments during this crucial development phase as it creates a marketplace for connection of crowdinvestors and startups and therefore potentially helps the reduce the impending funding gap.^[39]

However, looking at the latest distribution by sources of financing for startups (see Figure 9) reveals that only 4.1% of the startups surveyed have received funds from crowdfunding and just 1.4% from venture debt (fixed-interest loans). This value remained very stable in the past years. From a holistic perspective it can therefore be stated that crowdinvesting still plays a subordinate role in the German startup scene so far. In general, it must be borne in mind that crowdinvesting is a comparatively new form of financing and that there are still many reservations about the concept. Additionally, the topic of crowdinvesting also continues to be critically observed in the media and the interested public.^[40] This situation indicates that crowdinvesting still bears enormous growth potential but is currently bound to the limited market awareness.

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Figure 9 - Utilization of funding sources of German startups in 2014-2017^[41]

This finding confirms to some extent the data in Figure 1 where one can see that currently majority of growth is attributable to the real estate segment while the startup segment is virtually stagnant. Lately, investors seem to be much more hesitant in the field of startup investments. One often cited reason for that reluctance could be the yet unknown risk level of this financing class. This is may be due to the current market situation and the associated uncertainty regarding investment risks and expected returns. As a rule, real estate investments have the advantage of offering an easily identifiable material counter value, which means that the investment risk is significantly lower in case of failure. As for startups, the material value of assets generally plays a minor role and the (future) enterprise value is difficult to determine especially if assessed on the base of limited information. Consequently, the capability to select promising and sort out overrated early-stage startups as early as possible can be considered as the competitive edge for outperforming investors.

The continuously updated database of the “Crowdinvesting Erfolgsmonitor” serves as a recognized data source for market data in Germany. Data is collected from the freely available primary data of the crowdfunding platforms. If information is not accessible, the platforms are asked to transmit the missing data. Generally, only investment rounds of issuers domiciled in Germany are considered.^[42] Figure 10 shows the latest success monitoring data for startup crowdinvesting in Germany.

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Figure 10 - Investment status of German crowdinvesting startups 2016^[43]

However, looking at the current figures reveals that more than 83% of the monitored crowdinvesting projects are still in progress since most of the contract have terms of 5 to 8 years. Moreover, the number of successfully completed projects is just around 5.7%. Based on that sample size no reliable statement can be made about the general success of startup crowdinvesting. This uncertainty could be one of the reasons for investors reluctance to invest.

In this context the following chapter gives an overview about the most frequent failure reasons for startups. Finally, the expected value of investments and the confidence in respective business cases can be considered as critical success factors. As soon as more projects are completed in the next few years and a larger sample size becomes available, the return and risk potential of this asset class will become clearer.

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.^[44] 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.

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Figure 11 - Top 20 reasons for startup failure^[45]

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.^[46]

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.

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Figure 12 - Clustered reasons for startup failure^[47]

The illustrated distribution indicates that the business model may represent a reasonable unit of analysis for determining the prospects of success of a startup company as more than 50% of the aggregated answers refer to a problem that relates at least to some extent to a company’s business model with its core value proposition.

2.1.4 Why startup success is hard to predict

Generally, the presented properties of early-stage startups create an unfriendly environment for investors. The high level of uncertainty leads to a complex situation with regards to potential investments as they hamper the usual decision-making process. Traditionally entrepreneurs create comprehensive business plan which describes the strategy to achieve a successful corporate future – so if this planning would really cover all important aspects leading to a significant reduction of risk it is still questionable why so many startups fail.^[48]

One major reason for this is certainly the mistaken belief that future corporate success can be reliably planned at a certain point in time based on the available underlying information. As the most common communication tool a business plan serves as a comprehensive summary of the business idea and often includes detailed plans for implementation and development. However, some authors claim the proven concept of a business plan unfolds its strenghts just primarily in the description of imitative start ups within established market environments. In the context of innovative business models with multiple unknown and unpredictable variables the classical business plan approach encounters it’s conceptual constraints.^[49]

According to (Courtney, et al., 1997) there are four levels of uncertainty leading to increasing risk potentials for entrepreneurs (see Figure 13). In a clear enough futute a certain future state can be precisely targeted by strategic planning. The second level is characterized by distinct alternative futures that form a set of different expected outcomes while the third level has a range of possible futures. Logically, the strategic efforts and entrepreneurial risks are accordingly higher. The fourth level contains true ambiguity which indicates that various unknown outcomes may result in the future and therefore all actions must be inevitably based on assumptions.^[50]

This body of thought can be perfectly adapted to describe the transition of an seed-stage startup into an operating company. As a rule, startups are based on new ideas, often with the aim of having a disruptive effect on existing markets. The initial complexity increases the uncertainty and unpredictability of future developments. In this context, managing uncertainty can be considered as fundamental.^[51]

The focus of business plans is often based on a managerial perspective and on predicting a certain future state as close as possible just because uncertainty and complexity are on a high level. Consequently, this way of thinking is counterproductive in an entrepreneurial context. According to Ries “…too many startup business plans look more like they are planning to launch a rocket ship tan drive a car. They prescribe the steps […] in such a way that even tiny errors in assumptions can lead to catastrophic outcomes.” ^[52]

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Figure 13 - Levels of uncertainty applied to startup development^[53]

In line with this argumentation Sahlman reflects on the contents and usefulness of business plans. Most business plans focus too much on anticipated figures (that are often pure best guesses) and provide too little information about the inherent logic that constitutes a sustainable the business success. Of course, numbers are still important but they should relate to the underlying business model and should reflect the venture’s key drivers for success or failure.^[54]

Consequently, the usefulness and explanatory power of business plans within the scope of innovative early stage startups is a controversial issue. This dilemma can be considered one of the main problems for misleading startup developments. Geniunely, entrepreneurs were pictured as being risk-takers opening up new paths by exploiting uncertain business opportunities.^[55] But in order to gain capital from investores the opposite should the case – successful entrepreneurs must pursue a risk reducing strategy just like potential investors do. The approach of defining a fixed business plan for the purpose of risk reduction leads this concept ad absurdum because tying a business to a fixed plan is increasing risk of sticking to outdated projections which may end up in achieving planned failure.

Another aspect is that “A small business is not a little big business” ^[56]. Derived from that finding is that approaches and methods for managing a startup cannot be compared with those of regular companies. Many reasons were discusses why strategic management and entrepreneurship have to be considered as separate disciplines.^[57] In this regard the discipline of entrepreneurship is often related to a certain kind of art while business plans are considered as being a management tool for mechanic implementation of a new formed business. Ultimately it leads to the question if entrepreneurship can be captured within management theory. This also implies that the requirements on startup entrepreneurs as well as their way of working is fundamentally different.

The theory of effectuation describes the fundamentally diametrical way of thinking between cause-related management and entrepreneurship. Sarasvathy revealed in her study that most managers follow “causality” as the highest maxim while most entrepreneurs think different. Entrepreneurs follow a mindset that she calls effectual reasoning. The difference is that causal thinkers (managers) have a pre-determined goal that they plan to achieve by finding and combining the right means. In contrast to that effectual thinkers (entrepreneurs) accept a given set of means and utilize them to create new end which do not necessarily be pre-determined. The guiding principle is this respect is to control the future and not to predict it.^[58] Figure 14 illustrates this approach.

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Figure 14 - Causal Thinking vs. Effectuation^[59]

Basically, the described aspects related to Lean Startup and Effectuation conflict with the established management approach of business planning. A way out of this dilemma is to consider business plans as a hypothesis but not as a sacred cow that is used as a rigid pattern of action. In the sense of validated learning the formulated assumptions within a business plan must be validated on a case-by-case basis by means of suitable experiments.^[60] In this respect, a business plan can be considered as an indicator but not as a clear determinant for success. What remains is the question which additional evaluation criteria are useful for investors. In this regard, the concept of business models as alternative unit of analysis will be discussed in the next chapter.

2.2 Relevance of Business Models in the entrepreneurial context

In the age of digitalisation as an overarching megatrend the complexity of doing business increased. Associated phenomena like ubiquity of information and accelerating industry clock speed make it increasingly difficult for entrepreneurs to identify suitable market gaps and to differentiate themselves from competition.^[61] Consequently, in line with the previous explanations, the assessment of the success prospects of startups is difficult for various reasons. Therefore, at least to some extent, investments can be considered as a bet against the future earning capacity of a company. To improve the odds of success the question arises based on which criteria the success of a startup can be predicted ex ante.

As an answer to the question of what determines a company's ability to succeed, the keyword business model has been used almost inflationary for some years now. Although the term is of such importance it is often used in practice very unspecific and therefore often loses its explanatory power.^[62] If one wants to understand what constitutes the long-term success of a company one should consider the reasons why successful companies once failed. Famous companies like Kodak, Nokia, Quelle or Schlecker basically missed to adapt their way of making business to the changing market requirements and finally lost their prevailing market position.^[63] On the other hand in recent years many innovative startup companies have turned entire industries upside down not by offering different or better products but by the implementation of innovative business models.^[64] There is a vast amount of practical examples available that represent the exigency of whole industries to rethink their business models.^[65]

The significance of business models as a comparative competitive advantage is already perceived reality in business practice. The business model as a unit of analysis has meanwhile gained enormous practical relevance. Compared to traditional analysis units such as organisations, branches or markets, the business model as an integrative analysis unit offers the possibility to think outside of established schemes and is now seen in many areas as a necessity for an adequate analysis and strategy finding.^[66] In many areas inimitable business models may replace products and technology as a competitive factor in the future. Consequently, the business model concept is now regarded as an independent analysis unit in the field of management research with increasing relevance for all industry sectors.^[67]

Business ventures can be considered as complex systems. As a rule, business models are itself simplified presentations of the reality so the trade-off for reduced complexity and increased visibility is a certain loss of information. There are different definitions that describe the core meaning of the business model concept. In this respect, various description systems (so-called ontologies) have been developed which define business models as a set of linked description elements. The ongoing intensive research in this field aims at answering the following main questions:

- Definition: What is a business model?
- Ontology: What does business model consist of?
- Typology: What kind of business models do exist?

However, even in 2017 there is still no generally accepted consensus on many of these questions. There are many different studies on these topics while many of them have used an explanatory approach to lay the foundation for subsequent studies. This chapter serves as an overview of the current state of science.

2.2.1 Definitions and origin of business modelling

The concept of business modelling can be considered as a comparably new field of research that gained popularity in line with the emergence of the New Economy. Along with the increased occurrence of this term since mid of the 1990s different definitions were gradually developed with each of them focusing on different aspects depending on the perspective and background of the researchers.^[68]

Peter Drucker, who is considered as being the founder of modern management, was the first researcher who set the cornerstone for what was later called business model theory but without using this specific terminology. In his article “The Theory of the Business” published in 1994 he already formulates important thoughts about the “(…) assumptions (…) about what a company gets paid for.”.^[69] He states that every profitable company must define and refine its unique theory of business constantly to maintain a competitive business. His theory is based on three parts namely the assumptions about the environment, the specific mission and about the core competencies. All aspects must fit reality to achieve a successful business.^[70] With his basic thoughts Drucker provided a rough but robust framework whose core idea is still present in all subsequent refinements. Figure 15 provides an overview about the framework.

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Figure 15 - Theory of Business by Peter Drucker^[71]

In 2002, reflecting the influence of the internet boom on entrepreneurship, Joan Magretta draws a more detailed picture of the nature of business models. She refers to Peter Drucker when she defines a good business model as being the answer to the question “What is the underlying economic logic that explains how we can deliver value to customers at an appropriate cost?”.^[72] Furthermore, she compares business models to stories that explain how enterprises work and describes them as variations on the generic value chain consisting of two major parts. On one side all activities associated with making something and on the other side activities related to selling something.^[73]

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Figure 16 - Business model structure based on thoughts by Joan Magretta^[74]

Moreover, Magretta states that beside telling a consistent story the related numbers must be coherent. In this respect, she asserts that the term business model became common along with the advent of use of spreadsheets because they enabled a quick analytic planning approach by testing connections and interdependencies along single numeric assumptions. This unlocked the possibility to literally design business models ex ante instead of estimating rough projections that in best case led to successful business models by pure chance. Basically, according to her conception a business model is something that can be constructed based on facts. According to this logic the process of business modelling can be considered as the entrepreneurial equivalent to the scientific method meaning that based on a business model hypothesis the related assumptions need to be tested and revised when necessary. That is why Magretta proposes to evaluate business models based on their coherence between “story” and “numbers” as illustrated in Figure 16.^[75]

In addition to this early definition and rather management-oriented understanding there are many other definitions in research that have proposed by various researchers. Consequently, literature research reveals that there is a vast number of facets that go along with the term business model. The understanding of what a business model describes differs significantly (see Table 1). According to modern thinking, a business model is much more than just a description of how a business idea can be monetized but goes beyond pure financial aspects. Newer definitions consider value creation as a core characteristic that must be defined, configured and balanced between companies and customers. In literature one can find a vast amount of various definitions for the term business model. To gain a broader understanding the following list gives an overview about some selected definitions.

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Table 1 - Selected business model definitions

The definition proposed by Teece merges many significant aspects that play a role in the context of a startup environment. It also highlights the importance of an appropriate business model as the main driver for sustainable success. It implies that it is not just a single element like superior assets or processes that define business success but the viable configuration of all relevant success factors. A business model should spell the inner logic of how a company intends to make profit and illustrate the necessary links and patterns between the relevant elements leading to a consistent architecture of a business.

Amit et al. assert that the subjacent scholars are dispersed and require a conceptional consolidation. Originally introduced in the technical context of the New Economy the concept of business models has still not found a determined place in academic theory. It can be considered as an interdisciplinary concept that touches various disciplines like purchasing, production, marketing and organizational theory. Nevertheless, they also identified some common topics that unify all approaches. They describe business models as a system-level unit of analysis that embraces all firm activities that are necessary to explain how value is created.^[76]

2.2.2 Business model ontologies

Various studies have dealt with the question which elements make up business models, how they interact and how these models can be structurally presented. In this respect Morris et al. have conducted a comprehensive literature review in terms of key elements that are attributed to the concept of business modelling by various researchers. The results pointed out the need for a fundamental consolidation due to the large variety of terms associated with business modelling. The result of their literature review also revealed the necessity of a reformed ontology to make the conceptual complex accessible in business practice.^[77]

In recent years this research has led to the development of various descriptive approaches, so-called ontologies. The basic idea behind these ontologies is to make business models visible and configurable in a structured manner. The term ontology originates from philosophy and was later adapted by computer science. Gruber defines an ontology as „…a set of representational primitives with which to model a domain of knowledge or discourse. The representational primitives are typically classes (or sets), attributes (or properties), and relationships (or relations among class members). The definitions of the representational primitives include information about their meaning and constraints on their logically consistent application.” ^[78]

In other words, the purpose of an ontology is the general conceptualization of a defined knowledge domain. Moreover, it describes the connection of the inherent elements as well as their interrelationships. Especially in the daily business practice where people with different professional backgrounds interact it is necessary to put complex matters (like discussions about a company’s business model) into a logical framework to enable communication based on a unified language. The practical benefit of using ontologies is the increased expressive power and the possibility to formulate abstract strategies. In addition, the underlying ontology can be understood as a blueprint that represents the logic of how companies create value for themselves and their customers. Within the economic rationale this corresponds to the purpose of a profit oriented company.^[79]

Osterwalder is one of the first movers trying to elaborate a comprehensive business model ontology as a synthesis of the existing approaches using a systematic approach. On top level, his framework addresses four major business areas. On a more detailed level these areas can be subsequently subdivided into nine building blocks which form the structure of the business model canvas (see Table 2).^[80]

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Table 2 - Business model building blocks by Osterwalder

Based on this ontology, Osterwalder et. al developed the so-called “business model canvas” (BMC) which can be considered as a versatile framework and graphical display format that serves as a common language to describe, visualize, assess and develop business models (see Figure 17). The BMC reduces the complexity of business models to a set of 9 elements representing the inner logic of a distinct business model. Since its publication in 2011 the model gained common acceptance in business practice. It’s strong focus on value proposition design takes account of the fact that a business idea must create an actual demand to survive on the market. The simplicity and visual transparency results in a common language that facilitates the communication between stakeholders.^[81]

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Figure 17 - Business Model Canvas^[82]

The arrangement of the elements follows a value-based perspective as it splits the canvas into an external customer view and an internal company view. From a structural perspective it shows similarities to Porter’s value chain but instead of illustrating the value creation activities on a process level the BMC captures the overall logic of how value is created, captured and exploited in a simplified manner. The different building blocks will be briefly described in context to the specific situation of early-stage-startups.

Customers

The module defines the customer groups to which a specific value offer is to be addressed. In this context, differentiation is generally made between different customer segments with heterogeneous requirement profiles.^[83] Segmentation is a precondition for designing an efficient sales policy.^[84] Deciding which customers to focus on is fundamental, as all other building blocks of the business model must be aligned accordingly. Especially for startups, the initial definition of the customer segments is essential, as it determines and limits the market potential on the one hand and is on the other hand necessary to bundle the limited resources in a targeted manner.

[...]

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^[1] Richard H. Thaler, professor at the University of Chicago Booth School of Business, quoted in a call broadcast at the Nobel news conference. He awarded the Nobel Prize in 2017 for his contributions to the research field of behavioural economics and their impact on economic decision-making

^[2] cf. (Dapp, 2014a, p. 1)

^[3] cf. (www.crowdinvest.de, 2017, p. 3)

^[4] Own illustration based on (www.crowdinvest.de, 2017, p. 2)

^[5] cf. (Ziegler, et al., 2018, p. 16)

^[6] cf. (Dorfleitner, et al., 2016)

^[7] Important contributions were published by (Hornuf & Schwienbacher, 2014) or (Belleflamme, et al., 2013)

^[8] cf. (Dapp, 2014b, p. 13)

^[9] cf. (Bundesministerium der Finanzen, 2015)

^[10] cf. (Dorfleitner, et al., 2016, p. 31)

^[11] cf. (Wirminghaus, 2017)

^[12] cf. (www.fuer-gruender.de, 2017)

^[13] cf. (www.crowdfunding.de, 2017a, p. 3)

^[14] Own aggregated illustration based on (www.crowdfunding.de, 2017a)

^[15] cf. (Statista, 2018c)

^[16] cf. (Marktwächter, 2018)

^[17] cf. (PricewaterhouseCoopers GmbH, 2017, pp. 10-11)

^[18] Data retrieved from (PricewaterhouseCoopers GmbH, 2017)

^[19] cf. (Stuart, et al., 1999, p. 317)

^[20] Own illustration

^[21] cf. (Deutscher Startup Monitor 2017, 2017, p. 8)

^[22] (Graham, 2012)

^[23] (Graham, 2012)

^[24] (Ries, 2011, p. 27)

^[25] cf. (Ries, 2011, p. 37 et seqq.)

^[26] cf. (Ries, 2011, pp. 21-22)

^[27] Own illustration based on (Kraft, 2016)

^[28] (Blank, 2013)

^[29] Own consolidated definition

^[30] cf. (Pott & Pott, 2015, p. 237 et seqq.)

^[31] cf. (Sixt, 2014, pp. 47-48)

^[32] Own illustration based on (Beck, 2017, p. 56)

^[33] cf. (Kulicke, 2012, p. 20 et seqq.)

^[34] cf. (Hemer, et al., 2011, p. 28 et seqq.)

^[35] Illustrations published by (KPMG Enterprise, 2017, pp. 11, 87)

^[36] cf. (Schramm & Carstens, 2014, p. 45)

^[37] cf. (Dapp, 2014b, p. 9 et seqq.)

^[38] cf. (Kortleben & Vollmar, 2012, p. 7)

^[39] cf. (Hemer, et al., 2011, p. 30)

^[40] E.g. (deutsche-startups.de, 2017), (sueddeutsche.de, 2017), (Handelsblatt GmbH, 2017)

^[41] Illustration based on (Deutscher Startup Monitor 2017, 2017, p. 52)

^[42] cf. (www.crowdinvest.de, 2017, p. 12)

^[43] Illustration based on (www.crowdfunding.de, 2016, p. 7)

^[44] cf. (Blank, 2013, p. 1)

^[45] Illustration based on (CB Insights, 2017)

^[46] cf. (CB Insights, 2017)

^[47] Aggregated visualization, for details see Appendix 1

^[48] cf. (Morris & Schindehutte, 2005)

^[49] cf. (Kunze & Offermann, 2016, pp. 6-7)

^[50] cf. (Courtney, et al., 1997)

^[51] cf. (Osterwalder, 2004, p. 13)

^[52] (Ries, 2011, p. 21)

^[53] cf. Own adapted illustration based on (Courtney, et al., 1997)

^[54] cf. (Sahlman & A., 1997, p. 98 et seqq.)

^[55] cf. (Carland, et al., 1995)

^[56] (Welsh & White, 1981, p. 1)

^[57] cf. (Kraus & Kauranen, 2009, pp. 1-3)

^[58] (Sarasvathy, 2005, p. 6 et seqq.)

^[59] Own adapted illustration based on (Sarasvathy, 2005)

^[60] cf. (Ries, 2011, p. 55)

^[61] cf. (Osterwalder, 2004)

^[62] cf. (Magretta, 2002)

^[63] (Gassmann, et al., 2013, p. 1)

^[64] cf. (Johnson, et al., 2008, pp. 59-60)

^[65] E.g. Retailer vs. Digital Downloads in Music (iTunes) & Gaming Industry (Steam)

^[66] cf. (Stähler, 2014, p. 110 et seqq.)

^[67] cf. (Zott, et al., 2011, p. 1038)

^[68] cf. (Ovans, 2015)

^[69] (Drucker, 1994)

^[70] cf. (Drucker, 1994)

^[71] Own illustration based on the thoughts by (Drucker, 1994)

^[72] cf. (Magretta, 2002)

^[73] cf. (Magretta, 2002)

^[74] Own illustration based on the thoughts by (Magretta, 2002)

^[75] cf. (Magretta, 2002)

^[76] cf. (Zott, et al., 2011, p. 1038)

^[77] cf. (Morris & Schindehutte, 2005)

^[78] (Gruber, 2009)

^[79] cf. (Stähler, 2014, p. 113)

^[80] cf. (Osterwalder, 2004)

^[81] cf. (Osterwalder & Pigneur, 2011, p. 16 et seqq.)

^[82] Own illustration adapted from (Strategyzer AG, 2018)

^[83] cf. (Gabler Wirtschaftslexikon, 2018c)

^[84] cf. (Wöhe & Ulrich, 2008, pp. 409-412)