Indicating Value in Early-Stage Technology Venture Valuation: A Design Science Approach (Schriften zum europäischen Management) 3658349433, 9783658349431

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Christoph Philipp Wessendorf

Indicating Value in Early-Stage Technology Venture Valuation A Design Science Approach

Schriften zum europ¨aischen Management Reihe herausgegeben von Roland Berger, München, Deutschland

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Christoph Philipp Wessendorf

Indicating Value in Early-Stage Technology Venture Valuation A Design Science Approach

Christoph Philipp Wessendorf Karlsruhe, Germany Dissertation genehmigt von der KIT-Fakultät für Wirtschaftswissenschaften des Karlsruher Instituts für Technologie (KIT), Karlsruhe, 2020. Tag der mündlichen Prüfung: 03. Dezember 2020 Titel: Indicating Value in Early-Stage Technology Venture Valuation: A Design Science Approach Referent: Prof. Dr. Orestis Terzidis Korreferent: Prof. Dr. Martin Ruckes Prüferin: Prof. Dr. Carolin Bock Vorsitzender: Prof. Dr. Christof Weinhardt

Schriften zum europäischen Management ISBN 978-3-658-34943-1 ISBN 978-3-658-34944-8 (eBook) https://doi.org/10.1007/978-3-658-34944-8 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Responsible Editor: Marija Kojic This Springer Gabler imprint is published by the registered company Springer Fachmedien Wiesbaden GmbH part of Springer Nature. The registered company address is: Abraham-Lincoln-Str. 46, 65189 Wiesbaden, Germany

Preface

The present dissertation was written between November 2016 and May 2020 and has been accepted by the Department of Economics and Management of the Karlsruhe Institute of Technology (KIT). Developments in venture capital investment practice were taken into account until January 2020. Potential changes in investment behavior induced by the world-wide Sars-CoV-2 outbreak are not accounted for. Today, the current positive economic environment and the resulting investment pressure felt by many investors due to the low interest rate phase are increasingly driving up investment volumes and prices for venture capital investments. With venture capital funds and respective investment volume constantly increasing in recent years and the related valuations augmenting, the need for an objectifiable valuation reinforces its high relevance. The overall volume of funds as well as the valuation of individual companies drives the interest of venture capital funds’ limited partners to optimally manage funds provided and to make investment and valuation decisions in a transparent and objective manner. This is the objective of the research project described in this dissertation. Throughout these past years that I have researched venture capital investment practice with regard to early-stage technology venture valuation, and gained relevant experience in the field myself, many people supported, motivated and inspired me, all helping to shape my way positively. I am very thankful to all of them. First, I want to thank Prof. Dr. Orestis Terzidis, Chair of Entrepreneurship and Technology Management at the Institute for Entrepreneurship, TechnologyManagement and Innovation (EnTechnon) at the Karlsruhe Institute of Technology (KIT). His vast experience in entrepreneurship research as well as strong ties to entrepreneurship and venture capital practice were of high value and helped guiding the way in this research project. Throughout the time I was working

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on this dissertation, he provided me with valuable comments and suggestions, thereby boosting my scientific curiosity. Thank you for your support and insights. I further want to thank Prof. Dr. Martin Ruckes, Prof. Dr. Carolin Bock (TU Darmstadt) and Prof. Dr. Christof Weinhardt who provided new perspectives on my work, thereby adding to its relevance. A particular role is held by my parents, Hannelore and Helmut Wessendorf, who provided me with an eager and tolerant mind, as well as providing strong family support. Thank you for laying out a solid foundation I can build on. This holds true for my wife, Ineke Schydlo who was always supportive, provided structured and well thought-through input and was tireless in reading through all my work. Thank you so much! Further, I want to thank all business angels and venture capital professionals that took part in the different studies leading to this dissertation. Your expertise, feedback and opinion provided a solid foundation and enables the present work. I am also thankful to Oliver Kuppler, Philipp Engelkamp, Benedikt Stolz, Dr. Frederik Riar, Ralph Henn, Jared Schneider, Martin Gresch, Jens Kegelmann, David Wilking, Christian Hammes, Kai Shen, Devki Rajguru, Jan-Henning Saitz and Dr. Hinnerk Oßmer, who accompanied me through this exciting project. Thank you for all the lively discussions, shared opinions and constructive input. Thank you for all your support and motivation. Thank you. Karlsruhe December 6th , 2020

Christoph P. Wessendorf

Kurzfassung

Die Mittelbeschaffung für Risikokapitalinvestitionen hat in den letzten Jahren weiter zugenommen und erreichte 2018 ein Rekordhoch von USD 13 Mrd., welche von europäischen Risikokapitalgebern eingeworben wurden (Atomico, 2019). Die eingeworbenen Mittel werden jedoch aktiv investiert, so dass auch die Investitionstätigkeit auf einem hohen Niveau bleibt. Eine kürzlich veröffentlichte Studie von Atomico (2019) gibt USD 11,6 Mrd. an, die im 2. Quartal 2019 in europäische Technologie investiert wurden. Dies spiegelt die weit verbreitete Realität wider, dass ein großer Teil der jungen Unternehmen externe finanzielle Ressourcen akquirieren muss, um Wachstum zu realisieren. Risikokapital ist in dieser Hinsicht eine wichtige Finanzierungsquelle. Ein entscheidender Schritt im Investitionsprozess eines Risikokapitalinvestors ist die Bewertung des Zielunternehmens. Investoren stehen heute wie in der Vergangenheit vor der großen Herausforderung, ein junges Unternehmen ohne Unternehmens- oder Finanzhistorie, ohne feste Kundenbeziehung oder gar ohne ein kurzfristig realisierbares Geschäftsmodell zu bewerten und gleichzeitig das potentiell enorme Wachstumspotential zu berücksichtigen. Während viele verschiedene Techniken zur Bewertung von Unternehmen im Allgemeinen entwickelt wurden, sind die meisten von ihnen für die Bewertung junger Unternehmen nicht anwendbar, insbesondere nicht in der Frühphase der Unternehmensentwicklung. Diese Diskrepanz zwischen herkömmlichen Bewertungsmethoden und charakteristischen Merkmalen, die für Unternehmen in der Frühphase spezifisch sind, entsteht z. B. durch unvorhersehbare finanzielle Prognosen oder nicht vorhandene Umsätze und Cashflows und führt zu unzuverlässigen Bewertungsergebnissen. Insbesondere die Bewertung von Technologieunternehmen, die sich in einer frühen Phase ihres Lebenszyklus befinden und daher keine oder nur geringe Cashflows generieren, ist „ein schwieriger und oft subjektiver Prozess“. (A.-K. Achleitner, 2001). In diesem

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Zusammenhang überrascht es nicht, dass Wessendorf & Hammes (2018) feststellen, dass 48% der professionellen Investoren herkömmliche Bewertungsmethoden nicht für gut geeignet halten, um das starke Wachstumspotenzial eines jungen Unternehmens zu bewerten. Um dieses Problem zu überwinden, wurde zum Beispiel die „Venture-CapitalMethode“ entwickelt, welche zwar ebenfalls einen prognostizierten Firmenwert erfordert, aber mehr Flexibilität bei den Annahmen zulässt und damit ein höheres Maß an Unsicherheit verzeiht (Scherlis & Sahlman, 1989). Dennoch stellen Wessendorf & Hammes (2018) fest, dass Risikokapitalinvestoren weiterhin skeptisch gegenüber diesen Bewertungsmetoden sind. Ein Großteil (40%) folgt keinem strukturierten Ansatz, sondern trifft situationsabhängige Entscheidungen oder Entscheidungen auf Grundlage eigener Erfahrung. Die restlichen 42% verfolgen zwar verschiedene Aspekte der Unternehmensbewertung innerhalb eines strukturierten Rahmens, bewerten diese aber in der Regel auch nach „Bauchgefühl“. Infolgedessen fehlt für die Bewertung von Venture-Capital-Investitionen in der Frühphase, insbesondere der Bewertung von Technologieunternehmen, unterstützt durch die vorhandene Literatur sowie die beobachtbare Bewertungspraxis (Wessendorf & Hammes, 2018; Wessendorf, Kegelmann, & Terzidis, 2019), ein objektiver und aussagekräftiger Bewertungsansatz, der auch dem gegenwärtigen Investitionsdruck und den steigenden Investitionsvolumina Rechnung trägt, indem er einfach strukturiert, und somit leicht und effizient operationalisierbar ist. Das jüngste enorme Wachstum der Risikokapitalfinanzierung sowie die stark ansteigende Bewertung auf Transaktionsebene bedingen eine Änderung der Bewertungspraxis für Risikokapital in der Frühphase. Die hohen Beträge, die auf dem Spiel stehen, und der zunehmende Investitionsdruck erfordern einen im Vergleich zur heutigen Praxis verständlicheren, transparenteren und fundierteren Bewertungsansatz, wobei den operativen Herausforderungen durch einen einfachen und praxisnahen Ansatz Rechnung getragen werden muss. Ein solcher strukturierter Ansatz zur Bewertung von Technologie-Risikokapital-Investitionen in der Frühphase, der die subjektive Bewertung eines Unternehmens in diesem Entwicklungsstadium berücksichtigt, wäre sowohl für die weitere Gewinnung akademischer Erkenntnis als auch für eine expandierende Risikokapitalbranche von großer Bedeutung. Ausgehend von diesen Überlegungen wird ein Design Science Research (DSR)-Projekt durchgeführt, das darauf abzielt, ein Artefakt zu entwickeln, welches die Indikation des Wertes bei der Bewertung von Technologieunternehmen im Frühstadium (später auch als „InVESt-NTBF“, i.e. Indication of Value in Early-Stage New Technology-Based Firms, bezeichnet) verbessert und gleichzeitig eine operationalisierbare und faire Bewertung ermöglicht. Dies wird den bewussten Abbau von Informationsasymmetrien zwischen Unternehmern

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und Investoren weiter unterstützen. Diese Methode soll eine aussagekräftigere Bewertung und eine bessere Anwendbarkeit für Technologieunternehmen in der Frühphase im Vergleich zu traditionellen Methoden gewährleisten. Dabei sollen insbesondere die firmenspezifischen Besonderheiten und die Anwendbarkeit in der Praxis berücksichtigt sowie ein hoher Grad an Operationalisierbarkeit auf der Grundlage leicht zugänglicher und verständlicher Unternehmensdaten erreicht werden. Um die Bewertung entsprechend zu verfeinern, ist jedoch eine granulare und aussagekräftige Informationsbasis (firmenspezifische Besonderheiten) für die Ableitung des Diskontierungsfaktors erforderlich. Diese Informationsbasis sollte einerseits die relevanten Merkmale des zu bewertenden Technologieunternehmens berücksichtigen (A.-K. Achleitner, 2001), die aufgrund des frühen Stadiums der Unternehmensentwicklung des Unternehmens überwiegend nicht-finanzieller Natur sind, und andererseits eine klare Struktur und leichte Anwendbarkeit aufweisen, um ein hohes Maß an Praktikabilität und Operationalisierbarkeit zu gewährleisten. Aus diesem Grund wurde existierende Forschung zu den Determinanten der Bewertung von Technologieunternehmen in der Frühphase durch eine systematische Literaturanalyse (Systematic Literature Review, SLR) strukturiert analysiert. Während die allgemeinen Bewertungsdeterminanten stark auf die Persönlichkeit und Erfahrung der Gründer sowie auf das Marktpotenzial ausgerichtet sind, scheint die Bewertung von Technologieunternehmen stark von der Existenz und Qualität von Allianzen mit Unternehmen und Investoren sowie von Patenten beeinflusst zu sein. Diese Determinanten werden bei der Bewertung von Technologieunternehmen als entscheidend angesehen, um den Marktzugang zu gewährleisten, einen starken Glauben von Experten an die Technologie zu signalisieren und einen hohen Innovationsgrad nachzuweisen, wodurch die technologiespezifische Komplexität und Unsicherheit spürbar verringert wird. Die daraus resultierenden Ergebnisse führten zu einer langen Liste relevanter nichtfinanzieller Determinanten für die Bewertung von Unternehmen in der Frühphase, die durch frühere Forschungsarbeiten unterstützt wurden. In einem Versuch, die Bewertung von Unternehmen in der Frühphase zu operationalisieren und zu objektivieren und damit die Unsicherheit zu verringern, wurden anschließend die nicht-finanziellen Determinanten der Bewertung von Technologieunternehmen in der Frühphase mit Hilfe eines analytischen Hierarchieprozesses (AHP) und einer Auswahl-basierten Conjoint-Analyse (CBC) analysiert. Um aussagekräftige Ergebnisse in einem anspruchsvollen Forschungsumfeld zu erzielen, wurden Triangulationsmethoden angewandt. Die durch Befragung von 75 professionellen Risikokapitalinvestoren erhaltenen Daten ermöglichten die Quantifizierung der

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relativen Bedeutung der relevanten Determinanten und die Ableitung ihrer jeweiligen Auswirkungen auf die Bewertung. Die Analyse zeigt, dass der Unternehmergeist als die wichtigste Determinante für Risikokapitalinvestoren und als ein Schlüsselfaktor für die Bewertung von Technologieunternehmen in der Frühphase angesehen wird. Als nächstes beeinflusst ein starkes Marktwachstum den Wert signifikant, gefolgt von USP, Patenten und Patentanmeldungen, Gründererfahrung sowie Allianzen. Außerdem zeigen die Ergebnisse, dass die Bewertungsfaktoren nicht wesentlich von der Art des Investors, d. h. des Business Angels oder des Venture Capital Fonds, abhängen. Die sich daraus ergebenden relevanten nichtfinanziellen Bewertungsdeterminanten und ihr jeweiliger quantifizierter Einfluss auf die Bewertung von Unternehmen in der Frühphase bilden die Grundlage des angestrebten Bewertungsansatzes. In einem letzten Schritt wird ein Ansatz zur Ableitung des Diskontierungssatzes bei Barwertbewertungsmethoden entwickelt, welche von Praktikern umfassend genutzt werden. Dieser Ansatz transformiert zunächst die identifizierten Einflüsse nicht-finanzieller Bewertungsfaktoren in eine Bewertungskennzahl und setzt sie in eine geeignete Diskontsatzstruktur um. Eine erste Validierung im Rahmen der Bewertung von drei Technologieunternehmen im Frühstadium lieferte vielversprechende Ergebnisse, wobei die modellierten Diskontierungssätze eine Abweichung von weniger als 2 %-Punkten von der angewandten realen Zielrendite zeigten. Die sich daraus ergebenden Diskontierungssätze können dann innerhalb von Barwertbewertungsmethoden verwendet werden. Um den entwickelten Ansatz und damit auch die Gesamtarbeit dieses Forschungsprojektes zu validieren, wurden fünf Experteninterviews mit professionellen Investoren und erfahrenen Jung-Unternehmern durchgeführt. Neben Anmerkungen zur Darstellung des Ansatzes sowie Empfehlungen zur weiteren Erhöhung der Aussagekraft bestätigten alle Befragten, dass der Ansatz einen Vorteil gegenüber dem Status quo in der Bewertung von Technologieunternehmen in der Frühphase darstellt und die wesentlichen Anforderungen in diesem Zusammenhang berücksichtigt. Die Ergebnisse des Interviews sind gründlich dokumentiert und werden eine solide Grundlage für eine weitere Iteration des Ansatzes bilden sowie Anregungen für künftige Forschungsarbeiten geben.

Abstract

Fundraising for venture capital investments have continued to increase in recent years to a record high of USD 13bn raised by European VCs in 2018 (Atomico, 2019). As the funds raised are actively invested the investment activity remains on a high level. A recently published study by Atomico (2019) states USD 11.6bn invested in European technology ventures in Q2/2019. This reflects the widespread reality that a large share of young ventures needs to acquire financial resources externally in order to realize growth. Venture capital is a prime source of funds in that regard. One crucial step in the investment process of a venture capital investor is the valuation of the target company. Investors today, as in the past, are faced with the great challenge of valuing a young company without a corporate or financial history, a firm customer relationship or even without a business model that can be realized in the short term, while still taking into account the potentially tremendous growth potential. While many different techniques have been developed to value companies in general, most of them are not applicable for young venture’s valuation, especially not in the early-stage of the corporate life cycle. This mismatch between conventional valuation methods and characteristic features specific to early-stage ventures, e.g. unpredictable financial projections or even non-existent sales and cash flows, leads to unreliable valuation results. Especially the valuation of technology companies, which are in an early phase of their life cycle and therefore generate no or low cash flows, is “a difficult and often subjective process” (A.-K. Achleitner, 2001). In this context, it is not surprising that Wessendorf & Hammes (2018) find that 48% of investment professionals do not consider conventional valuation methods to be well suited for evaluating the strong growth potential of a young firm. Yet, attempts have been made to overcome this problem. For instance, the development of the “Venture Capital Method”, which also requires projected firm

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value, but allows more flexibility in the assumptions made and consequently forgives a higher degree of uncertainty (Scherlis & Sahlman, 1989). Still, Wessendorf & Hammes (2018) further observe that venture capital investment professionals remain skeptical. A major part (40%) do not perform valuation according to a structured approach but make decisions based on the situation or their own experience. Although the remaining 42% pursue various aspects of company valuation within a structured framework, they generally also evaluate these aspects according to “gut feeling”. As a consequence, supported by existing literature as well as observable valuation practice (Wessendorf & Hammes, 2018; Wessendorf, Kegelmann, et al., 2019), the valuation of early-stage venture capital investments, in particular valuation of technology ventures, lacks an objective and meaningful approach to valuation that also accounts for the present investment pressure and surging investment volumes by being simple in its structure as well as easy and efficient to operationalize. The recent tremendous growth in venture capital funding as well as the rising valuation on a deal level drives a change of early-stage venture capital valuation practice. The high amounts at stake and the increasing investment pressure require a more comprehensible, transparent and founded approach to valuation compared to today’s practice while accounting for operational challenges by a simple and practical approach. Such a structured approach to early-stage technology venture valuation that accounts for the subjective assessment of a venture at this stage of development would be highly relevant to academics and an advancing venture capital industry. Motivated by these considerations, a design science research (DSR) project is carried out, which aims to develop an artifact that improves the indication of value in early-stage technology venture valuation (later also referred to as “InVESt-NTBF”) while enabling operationalizable and fair valuation. This will further support the deliberate reduction of information asymmetries between the entrepreneurs and investors. This approach ensures a more meaningful valuation and better applicability to early-stage technology ventures compared to traditional methods. The firm-specific characteristics and the applicability in practice shall be taken into account. This procedure should further have a high degree of operationalizability on the basis of easily accessible and comprehensible company data. In order to refine the valuation accordingly, however, a granular and meaningful information basis (firm-specific characteristics) is necessary for the derivation of the discount rate. This information basis should, on the one hand, take into account the relevant characteristics of the technology venture to be valued (A.-K. Achleitner, 2001), which are mainly non-financial in nature due to the early-stage of corporate development of the venture, and, on the other hand, have a clear

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structure and easy applicability in order to ensure a high degree of practicability and operationalizability. Therefore, existing research on determinants of early-stage technology venture valuation was approached in a structured way through means of a systematic literature review (SLR). Whereas general valuation determinants strongly focus on personality and experience of the founders as well as market potential, technology venture valuation appears strongly influenced by the existence and quality of alliances with corporations and investors, as well as patents and patent applications. These determinants are considered as crucial in technology venture valuation to ensure market access, to signal a strong belief in the technology and to provide proof of a high degree of innovation, thereby noticeably reducing technologyspecific complexity and uncertainty. The resulting findings led to a long list of relevant non-financial determinants for early-stage venture valuation supported by previous research. Next, in an attempt to operationalize and objectivize valuation of early-stage ventures, non-financial determinants of early-stage technology venture valuation were analyzed by means of an Analytical Hierarchy Process (AHP) and a Choice-based Conjoint Analysis (CBC). To achieve meaningful results in a challenging research setting, triangulation methods were applied. The data obtained from 75 venture capital investment professionals enabled the quantification of relative importance of relevant determinants and derive their respective impact on valuation. The analysis shows that Entrepreneurial Spirit is considered the most important determinant for venture capital investors and a key driver of earlystage technology venture valuation. Next, a strong Market Growth impacts value significantly, followed by Unique Selling Proposition, Patents and Applications, Founder Experience as well as Alliances. Further, the results demonstrate that valuation determinants do not vary significantly by investor type, i.e. business angel or venture capitalist. The resulting “high impact” non-financial valuation determinants and their respective quantified impact on early-stage venture valuation form the basis of the sought after valuation approach. In a last step, an approach to derive the discount rate in present value valuation methods, broadly used by practitioners, is developed. This approach first transforms the identified impact of non-financial valuation determinants into a valuation score and matches it to a suitable discount rate structure. An initial validation within the valuation of three early-stage technology ventures provided promising results with modeled discount rates showing a deviation of under 2% points from the real target return applied. The resulting discount rates can then be used within valuation methods based on the time value of money. In order to validate the developed approach and thereby also the overall work of this research project, five expert interviews with investment professionals and

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experienced entrepreneurs were carried out. Besides remarks with regard to the approach’s presentation as well as recommendations to further increase meaningfulness, all interviewees confirmed that it represents an advantage over the status quo in early-stage technology venture valuation and that it accounts for the main requirements in this context. The interviews’ results are thoroughly documented and will provide a solid foundation for another iteration of the approach as well as inspire avenues for future research.

Parts of this dissertation are based upon or extracts of the following contributions

Wessendorf, C. P. and Hammes, C. (2018) Methods and Criteria affecting EarlyStage Venture Valuation. doi: 10.5445/IR/1000079690. Wessendorf, C. P., Kegelmann, J. and Terzidis, O. (2019) Determinants of Early-Stage Technology Venture Valuation by Business Angels and Venture Capitalists, International Journal of Entrepreneurial Venturing, 11(5), pp. 489–520. doi: 10.1504/IJEV.2019.102259. Note: Inderscience Enterprises Ltd, trading as Inderscience Publishers retains the copyright of this publication. Wessendorf, C. P., Schneider, J., Gresch, M. A. and Terzidis, O. (2020) What matters most in Technology Venture Valuation? Importance and Impact of NonFinancial Determinants for Early-Stage Venture Valuation, International Journal of Entrepreneurial Venturing, 12(5), pp. 490–521. doi: 10.1504/IJEV.2020.111536 Note: Inderscience Enterprises Ltd, trading as Inderscience Publishers retains the copyright of this publication. Wessendorf, C. P., Schneider, J., Shen, K. and Terzidis, O. (2019) Valuation of Early-Stage Technology Ventures—A Model to Determine the Discount Rate in Present Value Valuation Methods, EntFin 2019, 4th EntFin (Entrepreneurial Finance) Conference, Trier. Wessendorf, C. P., Wilking, D. and Terzidis, O. (2019) Significance of Criteria for Early-Stage Venture Assessment. doi: 10.5445/IR/1000099413. Wessendorf, C. P., Schneider, J., Shen, K. and Terzidis, O. (2021) Valuation of Early-Stage Technology Ventures—An Approach to Derive the Discount Rate, The Journal of Alternative Investments, Winter 2021, 23(3), pp. 32–44. doi: 10.3905/jai.2020.1.114 Note: Pageant Media Ltd./ “Portfolio Management Research” (PMR) retains the copyright of this publication.

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Parts of this dissertation are based upon or extracts of the following …

Wessendorf, C. P. and Schneider, J. (work in progress): Technology Venture Financing, in: Technology Entrepreneurship, Karlsruhe.

Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Research Gap and Relevance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Research Approach and Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Theoretical Contribution and Practical Implications . . . . . . . . . . . . 1.5 Structure of this Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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2 Theoretical Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Foundations of Company Valuation . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 Value Theories in Company Valuation . . . . . . . . . . . . . . . . . 2.1.2 Value Concepts in Company Valuation . . . . . . . . . . . . . . . . 2.1.3 Purposes of a Company Valuation . . . . . . . . . . . . . . . . . . . . 2.1.4 Principles of Proper Company Valuation . . . . . . . . . . . . . . . 2.2 Company Valuation in Venture Capital Financing . . . . . . . . . . . . . 2.2.1 General Considerations of Venture Valuation . . . . . . . . . . . 2.2.2 Specific Aspects of Early-Stage New Technology-Based Firms (NTBF) Valuation . . . . . . . . . . . . 2.3 Investor Types and Specific Requirements of Venture Capital Financing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Business Angels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.2 Venture Capital Funds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Subjectivity as a Factor Determining the Value of Venture Capital Financing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.1 Systematization of Methods for the Valuation of Early-Stage NTBFs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.2 Fundamental Analysis Methods . . . . . . . . . . . . . . . . . . . . . .

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2.4.3 Market-Oriented Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.4 Total Valuation Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.5 Subjectivity in the Valuation of Early-Stage NTBFs using DCF and VCM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.6 Generalization of the Subjectivity Component . . . . . . . . . . 2.5 Determination of the Discount Rate . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.1 Choice of the Discount Rate . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.2 Discount Rate Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.3 Derivation of the Discount Rate . . . . . . . . . . . . . . . . . . . . . . 2.5.4 Components of Target Return as a Discount Rate . . . . . . .

51 53 56 60 61 61 62 64 67

3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Reflection on Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Design Science Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Fundamentals of Design Science Research . . . . . . . . . . . . . 3.2.2 Selected Research Process . . . . . . . . . . . . . . . . . . . . . . . . . . .

71 71 73 73 82

4 Application and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Problem Identification and Validation of Relevance . . . . . . . . . . . . 4.1.1 Challenges in Early-Stage Technology Venture Valuation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Definition of Solution Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Requirements and Objectives . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2 Outline of the Artifact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Design and Development of the Artifact . . . . . . . . . . . . . . . . . . . . . 4.3.1 Identification of Valuation Determinants . . . . . . . . . . . . . . . 4.3.2 Evaluation of Valuation Determinants’ Importance . . . . . . 4.3.3 Evaluation of Valuation Determinants’ Impact . . . . . . . . . . 4.3.4 Applicable Discount Rates for Early-Stage NTBF Valuation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Demonstration of the Artifact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.1 Subject of Investigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.2 Development of the Artifact . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.3 Validation of valuation approach in practical valuation setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Evaluation of the Artifact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.1 Research Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.2 Selection of Experts for Interviews . . . . . . . . . . . . . . . . . . . 4.5.3 Data Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.4 Data Collection and Analysis Method . . . . . . . . . . . . . . . . .

87 87 88 93 93 97 98 98 122 153 171 188 188 190 198 202 202 203 204 204

Contents

xix

4.5.5 Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 Summary of DSR Project Elements . . . . . . . . . . . . . . . . . . . . . . . . . 4.7 Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

211 237 237

5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Major Research Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Theoretical Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Practical Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

241 241 244 246 247 250

6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

251

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

255

Abbreviations

AHP a. k. a. AuM BA CAPM CBC cf. CVC DCF DSR e.g. FFF HB i.e. LL M&A MCMC MNL NTBF P&L RO ROA SLR VC VCM vs WACC

Analytical Hierarchy Process also known as Assets under Management Business Angel Capital Asset Pricing Model Choice-based Conjoint Analysis Confer/ see/ see also under Corporate Venture Capitalist Discounted Cash Flow Design Science Research exempli gratia/ for example Founders, Family and Friends Hierarchical Bayes Method id est/ this is to say Log-likelihood function Mergers and Acquisitions Markov-Chain-Monte-Carlo Multinomial Logit Model New technology-based firm Profit and loss statement Real Option Real Option Approach Systematic literature review Venture Capital/ Venture Capitalist Venture Capital Method versus/ compared to Weighted Average Cost of Capital xxi

List of Figures

Figure 1.1 Figure 2.1 Figure 2.2 Figure 2.3 Figure 2.4 Figure 2.5 Figure 3.1 Figure 3.2 Figure 3.3 Figure 3.4 Figure 3.5

Figure 4.1 Figure 4.2 Figure 4.3

Graphical illustration of the structure of this dissertation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conceptual framework defining the valuation outcome . . . . Network of quotations with clusters and focal points . . . . . Characterization of the entrepreneurial challenges (“Critical Tasks”) along the life cycle stages . . . . . . . . . . . . Systematization of various procedures for company valuation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Development of the discount rate as the venture matures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Design Science Research framework . . . . . . . . . . . . . . . . . . . Design science research contribution framework . . . . . . . . . Inclusive framework for entrepreneurship research as a design science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Design science research methodology process model . . . . . Design Science Research project framework „Indication of Value in Early-Stage NTBF (InVESt-NTBF)” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Question overview and structure of the questionnaire . . . . . Search string structure for Systematic Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodological framework supporting triangulation as well as specifying characteristics and strengths of AHP and CBC analysis . . . . . . . . . . . . . . . . . . . . . . . . . . .

11 14 29 32 43 68 76 78 80 81

83 89 103

127

xxiii

xxiv

Figure 4.4

Figure 4.5 Figure 4.6 Figure 4.7 Figure 4.8

Figure 4.9 Figure 4.10 Figure 4.11

Figure 4.12

Figure 4.13

Figure 4.14

Figure 4.15

Figure 4.16

Figure 4.17

List of Figures

Presentation format of an exemplary choice set with three stimuli, i.e. combinations of relevant determinants for early-stage NTBF valuation . . . . . . . . . . . . Search string structure for Systematic Literature Review—German Keywords displayed only . . . . . . . . . . . . . Selection process within Systematic Literature Review . . . . Discount rates applicable to analyzed case studies following a linear, convex and concave structure . . . . . . . . . Structural elements of the artifact “Indication of Value in Early-Stage NTBF” (InVESt-NTBF) including an overview of most relevant input and output components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Structural elements of the artifact “Indication of Value in Early-Stage NTBF” (InVESt-NTBF) . . . . . . . . . . . . . . . . Process Canvas “Indication of Value in Early-Stage NTBF” (InVESt-NTBF) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mind map created with Zenkit to deductively derive first- and second-order themes as well as aggregated dimensions from the attained statements . . . . . . . . . . . . . . . . Data structure of first-order statements originating from relevant expert interviews with regard to first iteration—Part I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data structure of first-order statements originating from relevant expert interviews with regard to first iteration—Part II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data structure of first-order statements originating from relevant expert interviews with regard to first iteration—Part III . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data structure of first-order statements originating from relevant expert interviews with regard to first iteration—Part IV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data structure of first-order statements originating from relevant expert interviews with regard to first iteration—Part V . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Improved Process Canvas “Indication of Value in Early-Stage NTBF” (InVESt-NTBF)—Version 1.5 . . . . .

162 174 176 187

192 200 206

225

226

227

228

229

230 236

List of Tables

Table 2.1 Table 2.2 Table 2.3 Table 2.4 Table 3.1 Table 3.2 Table 4.1 Table 4.2 Table 4.3 Table 4.4

Table Table Table Table Table Table Table

4.5 4.6 4.7 4.8 4.9 4.10 4.11

Overview of situation specific motivation for company valuation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overview of valuation principles . . . . . . . . . . . . . . . . . . . . . . . Definition of NTBF in later research . . . . . . . . . . . . . . . . . . . . Description of main requirements for methods underlying early-stage NTBF valuation . . . . . . . . . . . . . . . . . Archetypes of artifact functions . . . . . . . . . . . . . . . . . . . . . . . . Design Science Research guidelines and their implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Functional requirements of the artifact . . . . . . . . . . . . . . . . . . Structural, Environmental and Effect requirements of the artifact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overview of relevant studies identified during SLR Conducting Phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overview of relevant valuation determinants (cf. section 4.3.1.2) and respective selection for AHP analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overview of expert interviews conducted for pre-test . . . . . . Comparison matrix Z . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evaluation matrix Z E . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Normalized Evaluation Matrix—Iteration 1 . . . . . . . . . . . . . . Normalized Evaluation Matrix—Iteration 2 . . . . . . . . . . . . . . Normalized Evaluation Matrix—Iteration 5 . . . . . . . . . . . . . . Consistency Index R with a given number of determinants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

19 21 26 36 79 85 95 96 105

131 133 136 137 139 140 141 143

xxv

xxvi

List of Tables

Table 4.12 Table 4.13 Table 4.14 Table 4.15

Table 4.16

Table 4.17 Table 4.18

Table 4.19

Table 4.20

Table 4.21 Table 4.22

Table 4.23 Table Table Table Table Table

4.24 4.25 4.26 4.27 4.28

Table 4.29

Relative importance of relevant determinants in early-stage NTBF valuation following AHP analysis . . . . Overview of expert interviews conducted . . . . . . . . . . . . . . . . Relative importance of relevant determinants in early-stage NTBF valuation following CBC analysis . . . . Results for relative importance of determinants of early-stage NTBF valuation following CBC analysis—split by investor type . . . . . . . . . . . . . . . . . . . . . . . . Overview of relevant valuation determinants (cf. section 4.3.1.2) and their respective selection for the CBC analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Level values of determinants of early-stage NTBF valuation following CBC analysis (n = 40) . . . . . . . . . . . . . . Selection of relevant studies resulting from a systematic literature review on discount rates applicable for early-stage venture valuation . . . . . . . . . . . . . . . . . . . . . . . Selection of an exemplary NTBF valuation profile (selection made in grey) out of valuation determinants’ level values following CBC analysis . . . . . . . . . . . . . . . . . . . . Calculation of a normalized valuation score of an exemplary NTBF valuation profile (selection made in grey) out of valuation determinants’ level values following CBC analysis . . . . . . . . . . . . . . . . . . . . . . . . Description of analyzed early-stage NTBF in the context of discount rate tool validation . . . . . . . . . . . . Description of analyzed early-stage NTBF in the context of discount rate approach validation, with x = mean and x˜ = median . . . . . . . . . . . . . . . . . . . . . . . Background of interviewed experts within artifact evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overview of expert interviews conducted . . . . . . . . . . . . . . . . Deductive categories for artifact evaluation . . . . . . . . . . . . . . Interview guideline for expert interviews . . . . . . . . . . . . . . . . Supporting data for each first-order category . . . . . . . . . . . . . Descriptive statistics of attained first-order statements and deductively derived categories . . . . . . . . . . . . . . . . . . . . . Summary of DSR Project Elements . . . . . . . . . . . . . . . . . . . .

145 146 148

152

158 170

177

195

196 198

201 203 204 205 208 211 232 238

1

Introduction

1.1

Motivation

Today, the success stories of young companies that shape our day-to-day life, such as AirBnB, Facebook or Google, are publicly present. Not long ago, these companies were Start-Up companies aiming to exploit technology in their respective markets. This development demonstrates that young companies play a key role in the structural change of economies. They create new industry sectors and force established companies to adapt to these changed conditions by flexibly reacting to market conditions or environmental changes (J. Egeln, 2000). In order to grow, ventures in an early stage of development need substantial resources to drive their development. As debt capital (e.g. in the form of loans), in particular with a long-term term to maturity, is generally only provided to young and innovative ventures that can provide real collaterals or sufficient equity, access to traditional financing instruments such as loans or borrowings remains insufficient for the wide majority of Start-Ups (Kulicke, 1997; Nathusius & Szyperski, 1999). Further, the traditional credit approval process is based on an ex-post analysis of financial information that is generally not suited to provide a perspective on the future development of the venture (Kußmaul & Richter, 2000; Pleschak, 1999). In consequence, several players such as governments, hoping to increase the overall growth of their economies, or established companies, recognizing the innovative potential of young growth companies, as well as financial investors, aiming for a highly lucrative investment, perform equity investments in order to gain access to new products, technologies or market potential. As a consequence, the problem of scarce financial resources for young ventures has diminished, especially with the establishment of venture capital as a funding instrument. These funds are

© The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2021 C. P. Wessendorf, Indicating Value in Early-Stage Technology Venture Valuation, Schriften zum europäischen Management, https://doi.org/10.1007/978-3-658-34944-8_1

1

2

1

Introduction

provided by highly specialized venture capital companies. Today, fundraising for venture investments is continuously increasing (HighTech Startbahn, 2017). This is still a widespread reality today, as more and more young companies need to acquire resources for their strong and rapid growth. A recently published study by KPMG Enterprise (2017) shows that venture capitalists are providing higher sums each year. In 2010, USD 45 billion (8,459 deals) were invested worldwide in venture capital, whereas six years later, in 2016, the figure had already risen to USD 127 billion (13,665 deals). However, the peak of investment activity can be observed in 2015. With USD 141 billion (17,992 deals), more than three times the amount invested in 2010, a clear peak was set. The current positive economic environment and the resulting investment pressure felt by many investors due to the low interest rate phase are increasingly driving up the prices for venture capital investments. In a recent study by Wessendorf & Hammes (2018) focusing on early-stage venture capital investors1 in Germany (n = 19), 74% of participants confirm this phenomenon. Furthermore 79% are of the opinion that the valuation proposed by young ventures is “strongly increased” to “clearly too high”. The company value derived by the ventures therefore appears in most cases to be exaggerated or the procedure used in the valuation is not comprehensible. One crucial step in the investment process of a venture capital investor or business angel, is the valuation of the target company. Even though conventional valuation methods are known among venture capital investors (Wessendorf & Hammes, 2018), the ventures’ characteristics in an early stage of development will mostly prevent their application in a valuation setting, as crucial aspects of the valuation cannot be sufficiently reflected. In contrast to established companies, the lack of a company and financial history, recognized products and experienced management teams as well as missing stable turnovers, profits and cash flows increases risk and uncertainty (Kaserer, Achleitner, von Einem, & Schiereck, 2007). Venture capital investors today, as in the past, are faced with the great challenge of valuing a young venture based on limited data available, while still taking into account the potentially enormous growth potential. While many different techniques have been developed to value companies in general, most of them are not applicable for early-stage venture valuation. Missing financial time series or even non-existent sales and cash flows lead to unreliable valuation results. Especially the valuation of technology companies, which are in an early phase of their life cycle and therefore generate no or low cash flows, is “a difficult and 1 To concretize the research question and to derive relevant fields of action, a survey of n = 19 venture capitalists was conducted in the period April–May 2017. The participants are private venture capital funds (42%), business angels (21%), public venture capital funds (21%), corporate venture capital funds (16%) and others (11%) from Germany.

1.1 Motivation

3

often subjective process” (A.-K. Achleitner, 2001). Yet, attempts have been made to overcome this problem. For instance, the development of the “Venture Capital Method”, which is based on more easily projected or modelled future financial data (Scherlis & Sahlman, 1989). Nevertheless, these (conventional) valuation methods are usually either inappropriate, as they do not take sufficient account of the characteristics of such technology companies (e.g. discounted cash flow method), or impractical, as the valuation effort is very high and the result is still questionable (e.g. real option approach). This problem was largely addressed by deriving a value on the basis of estimates based on experience (e.g. discount rate for the discounted cash flow method) or comparable transactions (e.g. multiples). However, this valuation is not always comprehensible to all parties involved in a venture capital investment process. In consequence, value is mostly determined by negotiating skills rather than the true value of the venture. This is supported by 48% of those surveyed by Wessendorf & Hammes (2018) that do not consider conventional valuation methods2 to be well suited for evaluating the strong growth potential of a young company, which is particularly noticeable in technology companies. This suggests that the conventional valuation methods will be adjusted freely and, if necessary, depending on the situation, in order to reveal potential. The skepticism towards these valuation methods is clear. A major part (40%) therefore do not invest according to a structured approach but make decisions based on the situation or their own experience. Although the remaining 42% pursue various aspects of company valuation within a structured framework, they generally also evaluate these aspects according to “gut feeling” (Wessendorf & Hammes, 2018). This phenomenon is particularly pronounced for the valuation of early-stage technology ventures [i.e., new technology-based firms; NTBF (Almus & Nerlinger, 1999; Storey & Tether, 1996), which require not only the valuation of the ventures’ future market potential, but also their technological feasibility as well as the technology’s suitability for commercialization. Therefore, this research project will put particular emphasis on the field of early-stage technology venture valuation. The challenge of finding an objective valuation result, comprehensible to all parties involved and deliberately reducing information asymmetries, becomes apparent. This observation underpins the necessity for a comprehensible and operationalizable valuation approach that is suitable to early-stage technology ventures.

2

e.g. Multiples, Venture Capital Method, Discounted Cash Flow Method.

4

1.2

1

Introduction

Research Gap and Relevance

When turning to existing literature two related fields emerge. First, there is entrepreneurship research, which is, in the context of venture capital, mostly focusing on crucial criteria a venture has to fulfill in order to successfully pass the venture capital investment process. Within this field, a smaller subsection can be identified that actually discusses determinants of value in a venture capital context (Köhn, 2017; Wessendorf, Kegelmann, et al., 2019). Yet, these publications refrain from quantifying the impact of these determinants on valuation. So far, only one publication approached this topic. Festel, Wuermseher, & Cattaneo (2013) developed a tool whereby a venture was assessed along various defined criteria in order to derive an adjusted beta factor. This adjusted beta factor represents the link to conventional valuation methods by directly influencing the discount rate in present value valuation method. While this approach was certainly a big step towards a more transparent, objective and also practical and operationalizable valuation, questions with regard to the determinants’ selection, relevance and impact on valuation remain. This will be addressed in sections 4.3.2 and 4.3.3. Second, there is finance research, which is however, primarily focusing on valuation of established private or listed corporations. When turning to previous literature, one will recognize that in this field, the contribution to the present discussion is of minor scale, with the exception of using a real options approach to value venture capital investments (A.-K. Achleitner & Nathusius, 2003; Dittmann, Maug, & Kemper, 2004). It appears that entrepreneurial finance will remain in the entrepreneurship field as it is today mostly qualitative and less data-driven as general finance research. Existing literature as well as observable valuation practice (Wessendorf & Hammes, 2018; Wessendorf, Kegelmann, et al., 2019) show that valuation of early-stage venture capital investments, in particular valuation of technology ventures, lacks an objective approach to valuation that also accounts for the present investment pressure and surging investment volumes by being simple in its structure, yet easy and efficient to operationalize. This gap provides the basis for meaningful functional requirements (cf. section 4.2.1) that will define the creation of a suitable tool as a result of this present work and thereby improve the status quo. Such a tool, providing a structured approach to early-stage-technology venture valuation while accounting for the subjective assessment of a venture, and thus improving the status quo of early-stage venture valuation practice, would be highly relevant to academics and a surging venture capital industry. Next to

1.3 Research Approach and Objective

5

the tremendous growth of global venture capital in recent years (cf . section 1.1.) Wessendorf, Kegelmann, et al. (2019) outline that “In Europe alone, venture capital fundraising has peaked at e 6.4bn in 2016, a 16% increase compared to e 5.5bn in 2015 (HighTech Startbahn, 2017). Further growth is expected in 2017 with venture capital fundraising reaching e 7.0bn (HighTech Startbahn, 2017). At the same time, venture capital investments in Europe totaled at e 5.0bn in Q2 2017, thereby representing a strong increase from its Q2 2016 level of e 3.4bn (Dealroom, 2017). […] However, the steady European venture capital fundraising and investment cannot solely be attributed to a boosted investment activity but does also reflect an increasing price level.”

With venture capital funds (i.e. USD 13bn raised by European VCs in 2018 (Atomico, 2019)) and respective investment volume constantly increasing in recent years (i.e. USD 11.6bn in Q2/2019, which was the largest ever quarter for capital invested in European technology ventures (Atomico, 2019)) and the related valuations augmenting, the need for an objectifiable valuation reinforces its high relevance. The overall volume of funds as well as the valuation of individual companies drives the interest of venture capital funds’ limited partners to optimally manage funds provided and to make investment and valuation decisions in a transparent and objective manner.

1.3

Research Approach and Objective

Reviewing previous sections 1.1 and 1.2 one will recognize that the recent tremendous growth in venture capital funding, as well as the surging valuation on a deal level, demands for a change of early-stage venture capital valuation practice. The high amounts at stake and the increasing investment pressure require a more comprehensible, transparent and founded approach to valuation compared to today’s practice while accounting for operational challenges. As early-stage ventures do mostly not yet dispose of a corporate history, a financial track record, recognized products as well as an established customer basis (Damodaran, 2009; Kaserer et al., 2007), conventional valuation methods in general are deemed unsuitable. A particular challenge is represented by the valuation of early-stage technology ventures, that require not only the valuation of the venture itself but also the underlying technology (e.g. feasibility, ability to be commercialized). Further, due to necessary additional effort on the underlying technology, which is a prerequisite to developing a commercially viable offer, technology ventures in general require

6

1

Introduction

more resources until a successful product-market fit and a subsequent market entrance can be realized. Motivated by these considerations, a design science research (DSR) project is carried out, which aims to develop an artifact that improves the indication of value in early-stage technology venture valuation (later also referred to as “InVEStNTBF”) while enabling operationalizable and fair valuation. This will further support the deliberate reduction of information asymmetries between entrepreneurs and investors. DSR is “a research paradigm in which a designer answers questions relevant to human problems viathe creation of innovative artifacts, thereby contributing new knowledge to the body of scientific evidence” (A. Hevner & Chatterjee, 2010, p. 5). Whereas empirical research desires to “describe, explain, and predict”, design science pursues “to change the world, […] improve it, and […] create new worlds […] by developing artifacts that can help people fulfil their needs, overcome their problems, and grasp new opportunities” (Johannesson & Perjons, 2014, p. 1). An artifact can be defined as “an object made by humans with the intention to be used for addressing a practical problem” (Johannesson & Perjons, 2014, p. 7). It is intriguing, that even though design activities are central to most applied disciplines and have a long history in research fields such as building, engineering, material science, and in particular information technology, (A. Hevner & Chatterjee, 2010), DSR is a young but emerging field in management and entrepreneurship literature (Dimov, 2016; Romme, 2016; Romme & Reymen, 2018). The surging interest in DSR within management and entrepreneurship literature can be attributed to DSR pursuing to understand the “how” rather than the “why” and “what” of entrepreneurship (Stevenson & Jarillo, 1990, p. 21). A resulting alternative approach in entrepreneurship research, besides the positivist and narrative research methods, intends to bridge the relevance gap between management and entrepreneurship research and practice (Van Aken, 2005; Van Aken & Romme, 2009; van Burg & Romme, 2014). Hence, this dissertation is conceptualized as a design science research project in the field of entrepreneurial finance. It aims to develop an approach for indicating value that ensures a better applicability for early-stage technology ventures compared to traditional methods. Thus, firm-specific characteristics in the earlystage of corporate development and the applicability in practice shall be taken into account. This procedure should also have a high degree of operationalizability on the basis of easily accessible and comprehensible company data, in order to further facilitate continuous value monitoring. The venture capital method, which is simple, fast and widely accepted in the venture capital industry (objective of operationalizability and practicability), is a

1.4 Theoretical Contribution and Practical Implications

7

special approach to venture valuation. However, it can be described as partially inadequate, at least conceptually, since it only provides an estimated value based on limited data available (A.-K. Achleitner & Nathusius, 2003). Yet, in order to refine its valuation accordingly, a granular and meaningful information basis (firm-specific characteristics) is necessary. This information base should, on the one hand, take into account the relevant characteristics of the technology venture to be (A.-K. Achleitner, 2001), which are mainly non-financial in nature due to the early-stage of corporate development of the venture, and, on the other hand, have a clear structure and easy applicability in order to ensure a high degree of practicability and operationalizability. The discount rate used for valuation by e.g. the venture capital method or discounted cash flow method is used as the starting point. Besides the overarching goal of creating and evaluating an artifact that improves the indication of value in early-stage technology venture valuation while enabling operationalizable and fair valuation, this dissertation aims to outline meaningful avenues for future research.

1.4

Theoretical Contribution and Practical Implications

The clear trend of a strongly increasing level of funds provided to venture capital investors as well as the subsequent high volume of investments and high valuations per deal, establish the importance of a thorough valuation (cf . sections 1.1 and 1.2). With venture capital investments being, as many other investments and asset classes, driven by a general investment pressure due to the prevailing low interest environment, competition among investors gets fierce, potentially resulting in higher valuations and quicker investment decisions. It is therefore in the best interest of venture capital investors as well as limited partners in a fund, to follow a structured valuation approach that does account for the specifics of early-stage (technology) ventures, is easy to operationalize (i.e. time-efficient) and provides a comprehensive valuation. Turning to academia, these results contribute to existing literature on earlystage technology venture valuation within the entrepreneurial finance literature. Analyzing previous works in that field, and also entrepreneurship research in general, three main conclusions can be drawn. First, a significant part of relevant existing research investigated the influence of different determinants (both financial and non-financial in nature) in a venture capital investment context. Yet, the majority of these studies did not specifically focus on valuation of early-stage ventures but venture capital decision-making and underlying factors as well as

8

1

Introduction

ventures’ success factors. Still, a subsegment of relevant literature focusing on determinants in a venture valuation context can be identified. Interestingly, these publications focus on ventures in general and only a limited few have a focus on technology ventures, which become more and more present nowadays (Wessendorf, Kegelmann, et al., 2019). Second, analyzing these publications, a great number of valuation determinants and their underlying rationale for driving value becomes apparent. However, the present studies mostly refrain from specifying a clear ranking of importance among these determinants as well as quantifying their impact. Therefore, the findings are hard to use in valuation practice and thus allow for further research within the field of entrepreneurial finance. Lastly, when analyzing valuation approaches for early-stage venture capital that strongly account for non-financial valuation determinants, previous research is scarce. Only one relevant publication can be identified, which suggests a refined valuation approach accounting for different static levels of relevant valuation determinants, thereby expanding on the existing research in this field (Festel et al., 2013). Yet, the fundamentals are questionable with regard to using a CAPM-like approach to derive a discount rate, because the use of CAPM in a venture capital context conceptually is not fully accepted by researchers. Further, the suggestion that all valuation determinants included in the approach are of equal importance and have theoretically the same impact on value is questionable. In this context, the present work will contribute to research in entrepreneurship and entrepreneurial finance by adding relevant insights in the field of early-stage venture valuation. The major contributions are threefold. First, with early-stage ventures being increasingly driven by new technology, this study provides a differentiated view on technology venture investments and their valuation. Second, by focusing on non-financial determinants only in order to account for the specific characteristics of early-stage ventures, this work contributes by investigating the determinants’ relative importance and provides a quantifiable impact on value. Thereby, this work allows for the quantitative measurement of valuation determinants that are mainly driven by subjective impressions. Lastly, the attained findings are implemented into a comprehensive approach, that allows for an objectivized assessment leading to a discount rate, which can be used for valuation in the context of accepted valuation methods, such as the Venture Capital Method (c.f. section 2.4.4.1). Currently, investors cannot rely on conventional valuation methods in practice, as these are mostly not suitable for early-stage ventures. These methods are in general not sufficiently able to account for the large growth potential of a young venture and are mostly driven by financial data and company history – which cannot yet be provided by an early-stage venture. Other methods, like e.g. the

1.4 Theoretical Contribution and Practical Implications

9

Venture Capital Method, provide an alternative to tackle these mentioned flaws but are often not able to provide a precise assessment due to a lack of structure and proven approach to derive the many assumptions needed. Therefore, investors often follow their own “experience” or “gut feeling” or some other form of subjective impression, in a mostly unstructured way (Wessendorf & Hammes, 2018). These subjective impressions are not negative per se and can be a good approximation for a venture’s value. Yet, they are not comprehensible, not transparent and therefore hard to trust in or rely on for entrepreneurs sitting at the other side of the table or third parties involved in the transaction (e.g. limited partners). In this context, the present research contributes to early-stage technology venture valuation practice in several regards. First, the developed approach provides a clear structure to collect necessary information for the valuation. The valuation follows a clear process, starting by assessing a defined set of non-financial valuation determinants that prove to be the most relevant value drivers in early-stage venture capital. This does not only enable an easy to operationalize approach to data collection but also allows the investor to provide subjective input data (i.e. by deciding on the observed intensity of the individual determinants in a venture) which will be objectivized in a subsequent step. Second, the subjectively assessed intensity of most relevant non-financial valuation determinants will be objectivized following a transparent and easy to understand process. At this step, the scientific background underlying the process’s rationale originates strongly from the present research and is consequently well documented. Thus, clear statements can be made about how different subjective assessments of different valuation determinants impact the value of a venture. The objectivized valuation assessment will then be transformed to a discount rate, following an interest rate structure that can be adjusted to the specific industries the investor is active in. Thereby, the investor is in a position to not only reflect venture specific-information but also industry-specific information. Finally, the investor can use this discount rate in his own investment calculations, compare it against his target return or decide on using it within a conventional and accepted valuation method, such as the Venture Capital Method. To the best of our knowledge, and proven by the present work, no such valuation approach is broadly used in early-stage venture capital and hence contributes venture capital practice strongly by providing a new way on how to indicate value in an early-stage venture. Therefore, these findings are relevant to practitioners of the buy- and sell-side of an early-stage technology venture investment as they provide a comprehensible, transparent, structured and proven approach to uncover value, which can subsequently be used in tried and tested valuation methods.

10

1.5

1

Introduction

Structure of this Dissertation

This dissertation is divided into six main parts: introduction, theoretical background, methodology, application and results, discussion and conclusion. The first chapter describes the motivation of this dissertation, as well as the research gap and relevance. It also introduces the chosen research approach and defined research objective along with the dissertations’ theoretical contribution and practical implications. The second chapter provides the theoretical background on which the present work is based. It provides a deep dive into company valuation as well as the specific challenges of valuation within venture capital investments in early-stage technology ventures. This is followed by an introduction to subjectivity as a factor determining the value of venture capital financing. Chapter three details the methodological reflections and subsequently describes the chosen DSR approach. Next, it details the research design with specific reference to DSR principles. Further, the main methodological terms and concepts used throughout this study are defined. In chapter four, the artifact will be developed according to DSR principles. Thus, the data, its analyses and respective scientific research methodology as well as subsequent findings following a DSR approach are presented. Further, the resulting artifact, i.e.an operationalizable and fair approach to indicate value in early-stage technology ventures will be validated with real valuation cases and discussed with investment professionals and experienced entrepreneurs. To conclude, chapter five discusses the dissertation’s major research results as well as its practical implications, theoretical contribution, limitations and avenues for future research identified. After a brief conclusion (chapter 6), relevant material is presented in the appendix and references are listed (cf . figure 1.1).

•

3 METHODOLOGY • Reflecon on Methodology

• • Praccal Implicaons Limitaons

Design and Development of the Artefact with: _ Systemac Literature Review (SLR) _ Analycal Hierarchy Process (AHP) _ Choice-based Conjoint Analysis (CBC)

Design Science Research

Investor Types and VC-specific Requirements Subjecvity as a Factor for Value Determinaon

Research Approach and Objecve Theorecal Contribuon & Praccal Implicaons

Figure 1.1 Graphical illustration of the structure of this dissertation

APPENDIX AND REFERENCES

6 CONCLUSION

5 DISCUSSION • Major Research Results • Theorecal Contribuon

•

• •

2 THEORETICAL BACKGROUND • Principles of Company Valuaon • Company Valuaon in VC Financing

4 APPLICATION AND RESULTS • Problem Idenficaon and Relevance • Definion of Soluon Space

• •

1 INTRODUCTION • Movaon • Research Gap and Relevance

•

• • •

•

•

Future Research

Demonstraon of the Arfact Evaluaon and Communicaon Summary of the DSR Project Elements

Determinaon of the Discount Rate

Structure of this Dissertaon

1.5 Structure of this Dissertation 11

2

Theoretical Background

The present dissertation builds on principles and concepts that are widely accepted within academia (in particular finance research) as well as investment practice. In order to define a clear frame of reference for the present work and provide an understanding of relevant knowledge, this chapter will detail the required theoretical background.

2.1

Foundations of Company Valuation

The outcome of a company valuation as performed in today’s investment practice is shaped by the underlying value theories and concepts as well as the intended purpose of the valuation. Yet, these will affect the valuation outcome on a theoretical level and only partially impact the application of a valuation process. For the latter, principles of proper company valuation need to be respected and suitable valuation methods need to be applied. This ensures that the valuation outcome is, on one hand, theoretically sound, and, on the other hand, a result of a correct and appropriate valuation process (cf . figure 2.1). The following sections will detail the different aspects influencing the valuation outcome, with a particular emphasis on early-stage venture valuation.

© The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2021 C. P. Wessendorf, Indicating Value in Early-Stage Technology Venture Valuation, Schriften zum europäischen Management, https://doi.org/10.1007/978-3-658-34944-8_2

13

Figure 2.1 Conceptual framework defining the valuation outcome

VALUE CONCEPTS cf. secon 2.1.2

Principles of proper company valuaon respected cf. secon 2.1.4 cf. secon 2.4

Suitable valuaon methods applied

VALUATION OUTCOME

VALUE THEORIES cf. secon 2.1.1

VALUATION PURPOSE cf. secon 2.1.3

14 2 Theoretical Background

2.1 Foundations of Company Valuation

2.1.1

15

Value Theories in Company Valuation

Essentially, two different value theories are distinguished: objective and subjective value theory. Objective value theory was the predominant value theory in Germany until the 1960s (A.-K. Achleitner & Nathusius, 2004, p. 12; Drukarczyk & Schüler, 2007, p. 101). According to objective value theory, the value of an asset is determined by its objective characteristics. In the context of venture valuation, the venture’s value is determined by the value of the venture’s assets, resulting in only one correct value and as such being independent of the valuation’s purpose and the valuing party’s interests. Thus, the predominant valuation method following an objective value theory is the net asset value method. It belongs to the category of individual valuation methods, since its application determines the values of the individual components of the enterprise and adds them to the total value of the enterprise. The values of the individual components are valued either at their liquidation values or at their reconstruction values. Starting in the 1960s, objective value theory was strongly criticized, in particular because important but intangible assets, such as a customer base, an established brand or a leading market position, are ignored (Rudolf & Witt, 2002, p. 56). Further, as can be particularly observed with the net asset value method, which follows objective value theory, future development of the enterprise is not accounted for. As argued in section 2.4.1, the net asset value method will not be discussed in detail within this work due to its conceptual flaws with regard to valuing early-stage ventures, whose existing assets are generally insignificant compared with their future earnings prospects (Rudolf & Witt, 2002, p. 56). This conceptual flaw with regard to early-stage growth companies holds true for the remaining situation-unspecific fundamental-analytical individual valuation methods (cf. figure 2.4; section 2.4.2). Hence, the development of subjective value theory and the associated valuation methods must be seen as a consequence of the criticism of objective value theory, as it specifically addresses the previously outlined general flaws of objective value theory. It prevailed in Germany during the 1970s and is considered the predominant view today (Drukarczyk & Schüler, 2007, p. 101). According to subjective value theory, the value of an asset cannot be determined by an isolated view on its properties alone, but needs to also account for the assets’ utility to its owner (A.-K. Achleitner & Nathusius, 2004, p. 11). Thus, with regard to venture valuation, the value depends on the utility that current or future shareholders receive from owning the venture (or parts of it). This will include future growth, value of a brand or customer base. The agreement between the buyer and the seller, or investor and entrepreneur, will then set the market price.

16

2 Theoretical Background

The valuation methods detailed in section 2.4 follow subjective value theory and represent valuation methods accepted in today’s company valuation practice. Yet, only a subset of these valuation methods is considered suitable to value earlystage ventures and growth companies.

2.1.2

Value Concepts in Company Valuation

In addition to the different perceptions of value, which are reflected in objective and subjective value theory, there are also different approaches to how such a value is to be conceptually understood. Kuhner and Maltry distinguish six different types of value concepts (Kuhner & Maltry, 2017, p. 38): balance sheet value, market capitalization, liquidation value, reproduction or reconstruction value, net asset value in terms of expenditure savings, present value of expected surpluses or capitalized earnings value. In addition to these value concepts, there are also heuristic approaches and mixed approaches of value perception. However, these do not represent independent value concepts and are therefore excluded from the more detailed examination in the subsequent sections (Kuhner & Maltry, 2017, p. 50–55).

2.1.2.1 Balance Sheet Value The balance sheet value in the context of company valuation describes the book value of equity as a balance sheet item as a point of orientation for the company’s value. Yet, the book value of equity is generally not regarded as a good approximation of the company’s value due to differing accounting purposes as well as accounting specific rules such as the reference to the past and the principle of prudence (Kuhner & Maltry, 2017, p. 39).

2.1.2.2 Market Capitalization Market capitalization is generally regarded as a good approach to determine a company’s value as it reflects in essence the aggregated “expectations of investors as to what volume of financial returns can be expected from a security” (Kuhner & Maltry, 2017, p. 39). A company’s value is determined by multiplying the current stock market price by the number of fully diluted shares issued. Yet, several effects are known to impact the determination of a company’s value by means of market capitalization. These are primarily stock market-specific, such as the stock markets’ exposure to exogenous events that cannot be rationally explained as well as to random price fluctuations or systematic price distortions. However, only a very small number of all companies are listed on the stock exchange and can consequently be valued based on market capitalization.

2.1 Foundations of Company Valuation

17

2.1.2.3 Liquidation Value The liquidation value of a company is based on the assumption that the company’s operations are discontinued and is consequently determined by estimating the proceeds from the company’s liquidation. Thus, it is in strong contrast to the prevailing perception of value following subjective value theory, which is based on “financial utility that the company can realize in the future” (Kuhner & Maltry, 2017, p. 49). It is incompatible with the objective of determining the “value for the person who is actually running its operations” (Kuhner & Maltry, 2017, p. 47). Yet, certain strategic circumstances of the seller or market developments leading to a company’s distress might justify this value concept (Kuhner & Maltry, 2017, p. 47 ff).

2.1.2.4 Reproduction Value or Reconstruction Value The reproduction value or reconstruction value of a company is based on the assets’ replacement value rather than future earnings surplus. Thus, similar to the liquidation value, it is in contrast to the prevailing perception of value following subjective value theory, which is based on “financial utility that the company can realize in the future” (Kuhner & Maltry, 2017, p. 49). However, certain strategic circumstances of the buyer might justify this value concept (Kuhner & Maltry, 2017, p. 47 ff).

2.1.2.5 Net Asset Value in Terms of Expenditure Savings The net asset value in terms of expenditure savings is a very particular value concept. Conceptionally, it is similar to the capitalized earnings value (cf. section 2.1.2.6), which reflects an orientation towards the future, a holistic determination of value and subjectivity. In the event that an investor has already decided to invest in a particular company, the net asset value in terms of expenditure savings determines whether she should buy the company or build an identical new one. If the expenditures from both options are to be regarded as equivalent, “the value of the company up for sale is determined by the extent to which the existing substance of the property being valued saves, reduces or at least postpones the future expenses (or rather payments) necessary for an alternative new construction” (Kuhner & Maltry, 2017, p. 51). This concept is typically used in specific circumstances, e.g. if it is difficult to determine a company’s future revenues.

2.1.2.6 Present Value of Expected Surpluses or Capitalized Earnings Value The capitalized earnings value is mostly in line with prevailing value theory. It reflects the future financial surplus of the company and thus its financial utility. This financial surplus is discounted to the time of valuation. According to Kuhner

18

2 Theoretical Background

and Maltry all other value concepts “only have a purpose if they are to serve as an estimate of the capitalized earnings value” (Kuhner & Maltry, 2017, p. 52). Even though the capitalized earnings value appears to be the best value concept from that perspective, it is not entirely accepted in academia and practice. First, the future financial surplus needs to be forecasted, thus being subject to uncertainty and subjectivity as well as the future owner’s strategic decisions. In summary, the determination of capitalized earnings values must be classified as a complex and subjective undertaking, even though it represents the most appropriate concept for most valuation purposes (Kuhner & Maltry, 2017, p. 53 f).

2.1.3

Purposes of a Company Valuation

Besides value theories and value concepts, it is the valuation purpose also impacts the valuation outcome. The valuation purpose depends on both the motivation for the valuation and the function it is intended to fulfil. Yet, different functions of company valuation can be applied to different motivations for a valuation (A.-K. Achleitner & Nathusius, 2004, p. 18). Firstly, with regard to the motivation to perform a valuation, Künnemann (1985, p. 58 ff) distinguishes those where shareholder structure changes (i.e. decision-dependent occasions) and those where this is not the case (i.e. decisionindependent occasions). Further, decision-dependent occasions for a company valuation can also present themselves as dominated or non-dominated situations, whereby a dominant situation reflects that one of the parties involved can force a change in shareholder structure against the will of the other parties (A.-K. Achleitner & Nathusius, 2004, p. 15 f). Table 2.1 provides an overview of the differing motivations to perform a company valuation. An alternative classification of valuation motivations is suggested by the German Institut der Wirtschaftsprüfer (IdW) (Standard S1 for company valuation), which distinguishes entrepreneurial initiative, where the valuation is part of the decision-making (e.g. buy-out or IPO), external accounting (e.g. investment or mergers and acquisitions) or matters of legal or contractual nature (e.g. squeeze-out procedure) (Tinz, 2010, p. 14 ff). Second, Hayn (2000, p. 34, 38) suggests that subjective value theory evolved to functional company valuation. A.-K. Achleitner & Nathusius (2004, p. 16) propose to follow objective or subjective value theory dependent on the function a company valuation has to fulfil. Thus, the appropriate company value represents the one that is most appropriate for the intended function (Drukarczyk & Schüler, 2007, p. 100). Two different theories with regard to a company valuation’s function exist today and are widely accepted: the Cologne Function Theory (i.e.

2.1 Foundations of Company Valuation

19

Kölner Funktionslehre) and the function theory developed by the German Institut der Wirtschaftsprüfer, which is sometimes described as phase-oriented function theory (A.-K. Achleitner & Nathusius, 2004, p. 16).

Table 2.1 Overview of situation specific motivation for company valuation (own presentation based on A.-K. Achleitner & Nathusius (2004); Künnemann (1985)) Decision-dependent occasions Decision-independent occasions Dominated situation

• Resignation and exit of a shareholder • Determination of compensation payments within the context of a control/profit transfer agreement • Expropriations

• N/A

Non-dominated situation

• Purchase/sale of a company • Voluntary restructuring of companies • Voluntary mergers • Entry of a new shareholder

• Credit ratings • Taxation of company assets • Determination of leverage

The Cologne Function Theory distinguishes main functions and side functions of company valuations. Main functions are considered to have an impact on the decision-making within the context of a change in shareholder structure. Thus, Cologne Function Theory identifies three main functions for valuation. First, the advisory function that provides a subjective decision value to a party involved in a transaction (e.g. the maximum price a buyer – thus subjective value – is willing to pay and vice versa) (A.-K. Achleitner & Nathusius, 2004, p. 17; Hayn, 2000, p. 43). Second, the mediation function that describes the range of price negotiation between the parties, which is of subjective nature (A.-K. Achleitner & Nathusius, 2004, p. 17; Hayn, 2000, p. 44). Lastly, the argumentation function, which allows the counterparty to identify a company value that serves as an argument to achieve a specific negotiation goal. It is made known to the other party and is used to negotiate a value below (buyer’s view) or above (seller’s view) the decision value (A.-K. Achleitner & Nathusius, 2004, p. 17; Hayn, 2000, p. 45). In contrast to the described main functions, side functions of company valuation aim for a representation of value for reporting, legal and taxation purposes. Therefore, first, the accounting function (also described as information and communication function) identifies the company value on the basis of commercial

20

2 Theoretical Background

regulations by means of their annual financial statements (A.-K. Achleitner & Nathusius, 2004, p. 17; Hayn, 2000, p. 40). Second, the fiscal assessment function identifies the company value to assess the basis of corporate taxation (A.-K. Achleitner & Nathusius, 2004, p. 17; Hayn, 2000, p. 40). Lastly, the contract drafting function allows for the agreement of contractual terms, which determine the approach to value a company when certain circumstances arise (A.-K. Achleitner & Nathusius, 2004, p. 17; Hayn, 2000, p. 40). Further side functions can be found in relevant literature. However, common to all, the approach to company valuation is determined by external requirements (Hayn, 2000, p. 40). Therefore, the detailed descriptions of further side functions do not add value to the intended discussion of valuation in a venture capital financing context. The function theory developed by the German Institut der Wirtschaftsprüfer describes the functions of company valuation as a function of an auditor’s tasks in the course of his engagement with a company. First, this leads to a valuation as a neutral evaluator or expert, who identifies a value determined under impartial and factual objectivity, which, according to the subjective value theory, corresponds to the seller’s lower price limit (Hayn, 2000, p. 48 f). Second, she will assume the function of a consultant, whereby she bases her valuation on the previously defined objectivized value and determines the (subjective) decision value of the party she is advising (cf. advisory function of the Cologne Function Theory) (Hayn, 2000, p. 49 f). Lastly, the auditor will assume the function of an arbitrator, whereby she leads the subjective value conceptions and the resulting decision values of the parties involved to a negotiation result (Hayn, 2000, p. 50).

2.1.4

Principles of Proper Company Valuation

In order to ensure a proper valuation outcome, essential principles for conducting a such company valuation need to be respected. The principles widely accepted today (cf. Standard S1 for company valuation by the German Institut der Wirtschaftsprüfer (IdW)) originate from Moxter (1983) and were regularly adapted to modern valuation practice, especially as some principles experienced justified criticism (Kuhner & Maltry, 2017, p. 71). Standard S1 for company valuation by the German Institut der Wirtschaftsprüfer (IdW) essentially suggests six principles to be respected in company valuation (cf . table 2.2). It needs to be pointed out, that Standard S1 for company valuation by the German Institut der Wirtschaftsprüfer (IdW) does not explicitly consider the valuation of young growth companies (A.-K. Achleitner & Nathusius, 2004, p. 22). As of

2.2 Company Valuation in Venture Capital Financing

21

Table 2.2 Overview of valuation principles (own presentation) Principle

Description

Scope of the valuation purpose

The valuation’s motivation and function determine the value concept and valuation method (Tinz, 2010, p. 19)

Valuation of the economic entity

The enterprise value is not determined by the sum of its individual assets, but rather its “by the interplay of all values” (Tinz, 2010, p. 19)

Valuation as of the reference date

The enterprise value depends on the point in time at or in relation to which it is to be determined (Tinz, 2010, p. 20)

Comprehensibility of the valuation approach

The valuation is based on transparent and comprehensible assumptions (Tinz, 2010, p. 20)

Valuation of operating assets (future financial surpluses)

The valuation is based on the net cash flow that actually accrues to the investor from operations and is at his free disposal (Tinz, 2010, p. 20)

Separate valuation of non-operating assets

The valuation of non-operating assets requires the comparison of the assets’ retention with their liquidation value (Tinz, 2010, p. 20)

Disregard of the principle of accounting prudence

If the valuing party acts in the capacity of a neutral advisor/ expert, she is obliged to be impartial, thereby contradicting the principle of accounting prudence (Tinz, 2010, p. 20)

today, no official guidelines specifically developed for the valuation of young growth companies exist. Thus, a potential mismatch remains possible between the specific valuation requirements for early-stage growth companies and general principles for company valuation. Nonetheless, as these general principles are considered to reflect good valuation practice, they should also be seen as an important guideline for venture capital financing.

2.2

Company Valuation in Venture Capital Financing

2.2.1

General Considerations of Venture Valuation

Performing a venture valuation assumes an important role in the process of venture capital financing. It generally follows subjective value theory and, dependent on the specific case, the value concept of Market Capitalization (cf.

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2 Theoretical Background

section 2.1.2.2) or Present Value of Expected Surplus (cf. section 2.1.2.6). The reason for the venture valuation at the time of investment by a venture capital investor is defined as a non-dominated, decision-dependent motive, which changes the ownership structure. In most cases, new shares in the company are created in this process. This changes the share ownership in percentage terms. In the end, the valuation represents the price agreed between the parties involved and, together with the investment sum to be provided, determines the extent of the new shares that the venture capitalist receives. After first rough value approximations by the venture capital investor in the context of the screening process for potential venture investments, a valuation will be carried out during the due diligence phase. This is a phase of detailed analysis of the early-stage growth business seeking venture capital financing. Existing research shows that approximately 80% of ventures fail before reaching this stage in the investment process (Franke, Gruber, Henkel, & Hoisl, 2004). After the investor has familiarized herself with the specific background and the current situation of the company during the due diligence process, she now determines a valuation. The valuation of the venture reflects the venture capital investor’s assessment of the investment risks. The common methods of valuing young growth companies comprise means of mapping the identified risks. These risk considerations lead to corresponding premiums and discounts on the valuation. The resulting venture valuation is to be understood as a decision value that takes into account the subjective circumstances of the investor. This value and further details will be negotiated in a subsequent step. The firm value or price, which the parties ultimately agree on, is subsequently also dependent on the negotiating position and skills of both parties (A.-K. Achleitner & Nathusius, 2004, p. 19). Here, potential differences between the founders and the venture capital investor have to be overcome, which relate to the value as well as the approach of the valuation. In general, the goal of the venture’s entrepreneurs is to transfer as few shares as possible against the background of the venture’s capital requirements and thus to achieve the highest possible firm valuation. In contrast, the venture capitalist aims to obtain as many shares as possible, which in turn requires the lowest possible firm value. Furthermore, there is often a significant difference in that the entrepreneurs tend to think retrospectively, and the investors tend to think prospectively. Whilst it is important for existing shareholders to appreciate the commitment, personal risk and financial resources they have invested in the venture, the venture capital investor focuses on the future profitability of the company in accordance with recognized valuation concepts. As long as the entrepreneurs are unable to show to what extent their past commitment will have a profit-increasing effect in the future, there will not be an increasing effect on the investor’s company valuation (A.-K. Achleitner & Nathusius, 2004, p. 13). Other subjective influencing

2.2 Company Valuation in Venture Capital Financing

23

factors may also complicate an agreement. Thus, the investor might value his contributions necessary for a positive development of the venture as a discount on value perceptions by the entrepreneurs (e.g. value added due to management support and access to the investor’s networks) (A.-K. Achleitner & Nathusius, 2004, p. 13), or strive for risk-reducing contractual clauses (e.g. liquidation preferences and anti-dilution clauses). Overall, there are numerous factors that influence the final price of a venture capital investment upwards or downwards. In addition to the current market situation, i.e. the scope of supply and demand for capital on the venture capital market, as well as the strategic considerations that entrepreneurs and investors make in order to arrive at their decision values. These are subject to the influence of certain determinants that the investor uses to assess the investment and their characteristics for the specific venture. Many of these important valuation determinants must be subjectively assessed by the investor. Against this background, the question of how and by means of which methodology early-stage ventures are to be evaluated focuses on how the decision values are to be determined by a venture capital investor (Hayn, 2000, p. 73 f) and should accordingly be the emphasis of this research project.

2.2.2

Specific Aspects of Early-Stage New Technology-Based Firms (NTBF) Valuation

In principle, a number of essential distinctions have to be made with regard to founding a company, which ultimately lead to different classes of business creation (A.-K. Achleitner & Nathusius, 2004). First of all, the legal and economic independence of the newly founded company must be assessed. In contrast to dependent company foundations (e.g. the foundation of a subsidiary by a commissioned employee of the parent company), independent foundations are characterized by the fact that the founders are “not bound to any superordinate organization” (A.-K. Achleitner & Nathusius, 2004, p. 1). Second, a distinction is made in terms of the venture’s structure. Original foundations are characterized by a completely new foundation of a company, whereas derivative foundations refer to companies that build on an existing company substance. Third, company foundations can be classified according to their growth potential (A.-K. Achleitner & Nathusius, 2004, p. 2 f). Finally, there is a difference between imitative and innovative company foundations. An innovative company foundation is defined as one in which the business model or the products or services offered are based on innovation, whereas an imitative company foundation copies existing business models and products and services as far as possible.

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2 Theoretical Background

However, despite the different classes of young ventures, that allow for a distinction among each other, the specific properties of young ventures in general and new technology-based firms (NTBF) in particular need to be detailed. It is these properties which will hence reveal the specific valuation challenges and represent the emphasis of this research project.

2.2.2.1 Classification of Start-Up Companies The term “Start-Up”, even if widely used, is not clearly defined. As a result, typical features, which can clearly define the subject of this study, often describe different phenomena. Since a detailed description of the different attempts to define the term “Start-Up” does not correspond to the aim of this work, the focus will be on typical characteristics that such companies can exhibit, thereby indicating which character traits play a role in the relevant definitions. The annual German Start-Up Monitor publishes a classification for Start-Ups that is applied to its surveys and research (Kollmann, Hensellek, Jung, & Kleine-Stegemann, 2019). The first of the following three characteristics must necessarily be given, whereas only one of the two remaining characteristics need to be fulfilled (Kollmann, Stöckmann, Hensellek, & Kensbock, 2016, p. 16): 1. Start-Ups are younger than ten years 2. Start-Ups are (highly) innovative in terms of their technology and/or business model 3. Start-Ups have (or are aiming for) significant growth in employees and/or turnover Following these characteristics, a Start-Up company can be classified as a young growth company or growth-oriented company foundation (A.-K. Achleitner & Nathusius, 2004, p. 1). Thus, it describes an independent, original and innovative company foundation with high growth potential.

2.2.2.2 Characterization of Young Growth Companies Following the classification of young growth companies as independent, original and innovative company foundations with high growth potential, other characteristics are typically observed. First, in line with Kollmann, Stöckmann, Hensellek, & Kensbock (2016, p. 16)’s mandatory characteristic (cf. section 2.2.2.1), the duration of company existence is decisive. In some cases, this can be defined according to the company’s age in terms of years. In some particular cases, especially in fast-moving markets or, in contrast, in markets with slow and long-term dynamics, the duration of company existence

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might be more meaningful when described in terms of its degree of establishment (cf. section 2.2.2.4). In this context, the focus lies on the company’s economic existence, since a merely new legal existence is a purely formal step which does not entail the kind of new challenges associated with managing young enterprises. This requirement has strong ties to the company’s independence, as companies with a purely new legal existence are often dependent companies (e.g. subsidiaries or corporate spin-offs) (Hayn, 2000, p. 15). In the context of this work, a young growth company is characterized by a young age and a low degree of establishment, thus a phase of product development, first-time acquisition of customers, the establishment of supplier relationships and internal processes as well as the development of organizational structures to master initial growth. Second, a young growth company is characterized by a high degree of dynamism. As a result, it is subject to almost constant adaption (Hayn, 2000, p. 17). On the one hand, the need to adapt results from changes in the environment of the company, especially in new markets. On the other hand, dynamism is also a characteristic that is induced by young growth companies themselves. In this way, they themselves create an environment of strong momentum by means of their innovations. Third, growth represents an important characteristic to define young growth companies. A.-K. Achleitner & Nathusius (2004) suggest that young growth companies demonstrate a high growth potential. Thus, past or present growth is secondary in comparison to future anticipated growth. Thereby, companies that have not yet demonstrated growth can also classify as a young growth company. Yet, the relevant indicator for growth as well as the necessary level of growth are not defined and might therefore vary in consequence. With regard to company growth indicators, qualitative (e.g. negotiating power) and quantitative (e.g. sales, earnings, cash flow, number of employees) indicators can be used as benchmarks (Tinz, 2010, p. 8). With regard to the level of growth, there is no unified or clear definition of high, above average or disproportionate growth. Since not only the company itself, but also its environment can show growth rates, the above-average nature of a company’s growth can therefore only be meaningfully determined by comparison with market growth rates. On the other hand, the specific valuation problems of growth companies can also occur when they show high growth in absolute terms (Rudolf & Witt, 2002, p. 21). In addition, a distinction must be made between organic and inorganic growth, since only the former comes from the company itself, is based on its own resources and is achieved through innovation (Tinz, 2010, p. 9).

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2.2.2.3 Definition of New Technology-Based Firms The characteristics described in section 2.2.2.2 apply to each type of young growth company. However, this research project has a particular focus on a special group of young growth companies to be detailed: new technology-based firms (NTBF), also referred to as technology ventures. Storey & Tether (1996) define NTBF by the following characteristics: 1. 2. 3. 4.

NTBF are younger than 25 years NTBF are based on the exploitation of an invention or technological innovation NTBFs are exposed to substantial technological risks NTBF are not subsidiaries of an established company, but founded by entrepreneurs

This definition reflects the characteristics of young growth companies described in section 2.2.2.2., i.e. age, independence and innovativeness. In addition, NTBF are subject to the specific technology-related criteria (cf. characteristics 2 and 3). Yet, innovation is not synonymous with technology, so an innovative product does not necessarily have to be a technological product and vice versa. Thus, NTBF are young, independent companies that market technological innovations that entail technology-specific risks. Following the initial definition of Storey & Tether (1996), later research adapted this definition to reflect current observations and relevant dynamics (cf . table 2.3):

Table 2.3 Definition of NTBF in later research (own presentation) Author

Definition

Luggen & NTBFs are companies operating in high technology sectors, are less than Tschirky 10 years old and are managed by the original founding team. (2003) Luggen & NTBFs are entrepreneurial organizations in the survival or growth phase with Tschirky a focus on the creation, development and exploitation of technological (2003) innovations through a strong research and development orientation in high technology sectors. Mäki & Hytti (2008)

NTBFs are independent companies, not older than 10 years, whose operations are based on exploiting the company’s technological resources, which means that the company actively develops, produces and commercializes technology.

Runge (2014, p. 16)

NTBF are entrepreneurial organizations with the goal of actively creating, developing and/or commercializing offerings based on technology or research, especially innovative products, processes, applications and services, that are not older than 12 years and are usually managed by the original founding team or at least one of its founders.

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Based on this definition of NTBF, Runge (2014, p. 702) further distinguishes academic and non-academic (or other) NTBF. Academic NTBF are further differentiated in Research-Based Start-Ups (RBSU), which build on the results of research groups at universities or other public research institutions, and other academic NTBF (Runge, 2014, p. 702). He defines RBSU as “commercial entities deriving a substantial part of their commercial activities from the application or exploitation of a technology or know-how resulting from a research program carried out by a university or other non-profit and usually public research organization” (Runge, 2014, p. 17). Other academic NTBF are defined as being founded by entrepreneurs that have been studying in a higher academic institution, whereas other NTBF match the definition of NTBF but do not have an academic background (Egeln, Gottschalk, Rammer, & Spielkamp, 2002, p. 9). The different types of NTBF can be further distinguished according to their offerings (i.e. product or service) and R&D intensities (defined as the quotient of R&D expenditures and earnings or sales; in percent) (Runge, 2014, p. 4). Based on this Metzger, Niefert, & Licht (2008, p. 3) distinguish between high technology with an R&D intensity of at least 8 percent and advanced technology with an R&D intensity of between 3.5 and 8 percent. This research project will follow the above-mentioned definition of NTBF according to Runge (2014, p. 16) and the high technology classification according to Metzger, Niefert, & Licht (2008, p. 3).

2.2.2.4 Characterization of the Early-Stage Phase It seems indisputable today that companies go through various stages of growth in the course of their development. In this context, metaphors with a reference to biology or organisms are regularly used to illustrate the development (Lippitt & Schmidt, 1967), which is supported by the term “life cycle”. Over the last few years, these life cycle models have been continuously sharpened and improved, so that a deep understanding of the company’s development has been created. This understanding is particularly important for young companies, as each phase of development, each stage of the life cycle, presents concrete challenges to the company and thus defines important tasks that are relevant in the respective phase. This is a good indicator of whether a comspany has identified the relevant challenges and addressed them correctly in order to further drive its development. It is precisely this identification and addressing of relevant challenges that has a special significance in the valuation of companies. Especially in the early phases of a company’s development, management and investors need to be sure that the company is on the right track. For this reason, this section will take a closer

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look at the life cycle stages of companies, especially technology companies, and discuss the critical tasks in the context of corporate development.

2.2.2.4.1 Literature Analysis The theoretical foundations of life cycle stages in corporate development were laid in the 1960s and 1970s. Lippitt & Schmidt (1967) state that organizations are subject to a life cycle (“Like people and plants, organizations have lifecycles”). The definition of life cycle stages at this time is strongly oriented towards this analogy of biology, which is why a distinction is made between three phases: Birth, Youth, Maturity. Subsequently, further theoretical models have been developed, which have been strongly influenced by later research and literature in this field. Greiner (1972, 1998) has identified 5 different phases in company development (Creativity, Direction, Delegation, Co-ordination, Collaboration) by analyzing existing studies, the attributes of which can be found in numerous later models. These phases are further elaborated by Adizes (1979).1 In the following, a variety of models have been developed, which clearly differ in their industry/company focus and in their perspective on organizational challenges. In the context of this research project, technology companies have a special interest, which is why the theoretical approach of Galbraith (1982) is particularly worth mentioning. Just like other work in the field of life cycle stages, Galbraight (1982) dealt with theoretical work and did not conduct any empirical analysis. However, he has focused on technology companies and has laid a significant foundation in this area. His life cycle stages (proof of principle, model shop, start-up/volume production, natural growth, strategic maneuvering) can also be found in the basic features in later empirical models. With the promotion of empirical analyses, the literature on life cycle stages of companies has undergone an enormous development. The focus of analysis has become much more acute in many areas and has led to interesting, wellfounded results. An analysis of the existing and relevant literature shows a close interlinking of the various studies, but also illustrates the existence of four major clusters (cf . figure 2.2). In addition to the general theoretical foundations (Cluster D: Theoretical; Adizes, 1979; Greiner, 1972; Lavoie & Culbert, 1978), which have already been described in more detail above, a distinction can be made between further publications, mostly of an empirical nature, which deal specifically with technology companies (Cluster A: new technology-based firms; Galbraith, 1982; Hanks & 1

Adizes ( 1979) distinguishes ten phases of company development: Courtship, Infancy, Go-go, Adolescence, Prime, Stable, Aristocracy, Early bureaucracy, Bureaucracy, Death.

SMALL AND MEDIUM-SIZED ENTERPRISES

C

D

B

THEORETICAL

OTHER SECTORS

Figure 2.2 Network of quotations with clusters and focal points (arrow direction corresponds to the direction of referencing; red border indicates focus publications; own presentation)

NEW TECHNOLOGY-BASED FIRMS

A

2.2 Company Valuation in Venture Capital Financing 29

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Chandler, 1994; Hanks, Watson, Jansen, & Chandler, 1994; Kazanjian, 1988; Kazanjian & Drazin, 1990; Koberg, Uhlenbruck, & Sarason, 1996; McCann, 1991; K. G. Smith, Mitchell, & Summer, 1985), small and medium-sized enterprises (Cluster C: Small and Medium-Sized Enterprises; Bhave, 1994; Dodge, Fullerton, & Robbins, 1994; Dodge & Robbins, 1992; Eggers, Leahy, & Churchill, 1994; Lewis & Churchill, 1983; Rutherford, Buller, & Mcmullen, 2003; Scott & Bruce, 1987; Steinmetz, 1969; Terpstra & Olson, 1993) or other industries and classes of companies (Cluster B: Other Sectors; Beverland & Lockshin, 2001; Z. Block & Macmillan, 1985; Gupta & Chin, 1993; Kimberly, 1979; Miller & Friesen, 1984; Quinn & Cameron, 1983; Tushman, Newman, & Romanelli, 1986). The relationships between the individual publications and their respective intensity (i.e. number of citations expressed by arrows and their indicated direction) suggest that there are focus publications that have shaped the work in their respective cluster as well as influenced other publications (marked with a red border). In order to achieve the highest possible relevance of the examined literature in the area of life cycle stages within the scope of this research project, Cluster A: High-Tech will be further detailed in the following. This ensures that the company development, the challenges to be addressed and the necessary tasks can be clearly carried out and provide a solid basis for the results of this dissertation.

2.2.2.4.2 Relevant Models An analysis of Cluster A: High-Tech shows a clear focus on two publications (Kazanjian, 1988; Kazanjian & Drazin, 1990), which is made clear by the numerous references from other publications (cf . arrow direction to named publications in figure 2.2). The results of the above-mentioned studies are based on the empirical analysis of dominant problems in 105 technology-driven firms. In the following, Kazanjian (1988) distinguishes four life cycle stages in technology companies: Conception & Development, Commercialization, Growth, Stability. These are further developed by Kazanjian & Drazin (1990). In the first phase “Conception & Development” the business/product idea is developed and implemented in a first prototype. This phase is strongly limited to conceptual activities and a first market testing in the form of fund raising as well as having first pilot partners to further develop the idea. In the second phase “Commercialization” the focus is on the production of first marketable products and organizational development. The definition of processes for production and corporate management as well as market response are of particular importance. In the third phase, “Growth”, the company experiences strong market-driven growth. Sales and market share

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increase significantly. After a phase of strong growth, the company reaches “Stability” in the final, fourth phase. The focus here is on optimizing the status quo by increasing profitability and laying the foundations for future growth. Other publications and research results of Cluster A: High-Tech often develop multi-level models or vary in the choice of focus in organizational terms. What they all have in common, however, is that they overlap greatly with the characteristics of life cycle stages and the fundamental challenges of the models of Kazanjian (1988) and Kazanjian & Drazin (1990). These challenges and the tasks necessarily arising from them will be further detailed in the following section.

2.2.2.4.3 Challenges and Tasks (“Critical Tasks”) Section 2.2.2.4.2 “Relevant Models” shows that the understanding of life cycle stages in technology companies is significantly influenced by the work of Kazanjian (1988) and Kazanjian & Drazin (1990). Within the framework of this research, four life cycle stages are defined, which highlight the challenges for technology companies and clarify the necessary tasks required: Conception & Development, Commercialization, Growth, Stability. Since the present study deals with the indication of value of young technology companies, it will not discuss with the fourth phase “Stability” in detail. Thus, in the context of this work, the early-stage will refer to “Conception and Development”, “Commercialization” as well as the early “Growth” phase. In these phases, however, the entrepreneurial challenges and tasks (“Critical Tasks”) are very clear and differentiated. Figure 2.3 shows that a young technology company within the first stage of the life cycle, “Conception & Development”, primarily focuses on the development of the business idea and the development of the technological basis (i.e. prototype) and makes a great effort to acquire resources for the further development of the company. Provided that these challenges have been sufficiently met, the company will deal with product manufacturing and organizational development in phase 2, “Commercialization”. On the one hand, this implies a further technological development towards a producible demonstrator and, on the other hand, increased marketing activities to achieve first sales. In the subsequent “Growth” phase, market entry has already taken place and the basic features of production have been established. The company must now achieve production in larger quantities in order to grow sales and market shares. This first stage of growth, where scalable processes and infrastructure have to be developed is considered part of the early-stage, as this represents the tipping point of a company turning from low turnover to a high turnover and professionalized organization. Sufficiently competent personnel and process efficiency are becoming increasingly important to reflect the strong growth in the company.

Business idea development

Prototype product construcon

Selling the idea to financial backers

•

•

Technology development (construcon of a prototype product)

•

•

Resource acquision (selling the idea to financial backers)

•

Beginning manufacturing Gearing up first markeng Solving inial engineering difficules Developing nucleus of administrave system

• • •

Organizaonal task system definion

•

•

Producon related startup (make product work well and produce it beyond the model shop prototype approach)

•

COMMERCIALIZATION

•

•

•

Managing personnel problems associated with high growth

Establishing market share

Manufacturing efficiently and with high quality

Manufacturing in volume

Organizaonal issues (efficiency, effecveness, personnel)

•

•

Sales/ market share growth (produce, sell and distribute product in volume)

•

GROWTH

•

Launching a second generaon product while simultaneously managing the efficiency of the exisng product line

Future growth base (2nd generaon product)

Internal controls (formalizaon, standards, maintain growth and market posion)

•

•

Profitability

•

STABILITY

Figure 2.3 Characterization of the entrepreneurial challenges (“Critical Tasks”) along the life cycle stages. (own presentation based on Kazanjian, 1988; Kazanjian & Drazin, 1990)

Kazanjian, Drazin (1990)

Kazanjian (1988)

CONCEPTION AND DEVELOPMENT

32 2 Theoretical Background

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33

2.2.2.5 Specific Challenges in Early-Stage NTBF Valuation and Methodological Requirements NTBF in the early stage display certain characteristics (cf. sections 2.2.2.3 and 2.2.2.4), in particular their focus on commercially exploiting a new technology as well as the limited corporate history, which result in specific challenges in the context of valuation. Yet, these challenges drive the particular requirements for the methodologies used in early-stage NTBF valuation. Both, the specific challenges and the resulting requirements shall be discussed in greater detail.

2.2.2.5.1 Representativeness and Availability of Relevant Data In general, the valuation of a NTBF requires relevant company and market data as input variables. Depending on which valuation concept is underlying the valuation, for example, book values of assets, income indicators, capital ratios or capital market interest rates must be collected, or qualitative characteristics must be estimated and quantified. For a NTBF, this necessary database has two problematic aspects. First, it is very small, as company-specific data can only be collected for a very short period of time due to the short business history. Second, due to the dynamics and the expected growth of the NTBF, these few historic data points can be classified as not representative of the NTBF’s future characteristics and growth, especially because the company’s development is extremely uncertain at this stage (Kaserer et al., 2007). This uncertainty can be described as a broad spectrum of possible future paths of development. In addition to the realization of attractive growth and extensive profitability, the failure of the company as the opposite extreme needs to be considered. As such, both a positive and a negative variation in value (i.e. risk) need to be properly reflected. The associated high uncertainty, which is a characteristic relevant to valuation and must be taken into account in the methods underlying the valuation, may also be partially driven by innovation. Furthermore, the implementation of new technologies, products and services is more difficult to predict than that of already known and established offerings. This challenge is not reduced by the assessment of future development based on planning calculations, because the preparation of such planning is subject to exactly the same logic of deriving future values from those of the past or present. Besides, a sound procedure for planning is regularly not yet available in a young NTBF, so that the insufficient data basis is also due to the lack of an insufficient assessment of future planning values (Hayn, 2000, p. 26). Moreover, overly rigid and detailed planning is at odds with the flexibility that the management of a NTBF must retain due to the dynamic environment (Hayn, 2000, p. 25 f).

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With company dynamics and uncertainty being difficult to forecast, also methodological challenges need to be taken into account. Growth, in particular, represents a fundamental challenge for most conventional valuation methods. These either do not consider the growth of the company at all or assume that it is in the low single-digit percentage range of maturity (Tinz, 2010, p. 76). Moreover, besides of being (mathematically) challenged by higher growth rates,2 these methods do not provide for a comprehensible system to derive the growth rates, in particular in terms of including company-specific features in the growth assessment. They rather require the valuing party to make a general and therefore not situation-specific quantitative determination of growth. As a consequence, relatively fixed growth ranges have been established, which contradicts the fact that growth is company-specific and, in the case of early-stage NTBFs also potentially above-average (Tinz 2010, p. 77).

2.2.2.5.2 Suitability of Company-Specific Data for Valuation Furthermore, although company-specific data may be available, it may not be suitable for use in its substance or form. This is often driven by the observable dynamics, the innovation underlying the NTBF’s business and the resulting growth. For instance, the high degree of innovation underlying a NTBF often requires long and intensive research and development. This results in an aboveaverage time-to-market and long conception and development phases that need to be financed, and usually exceed the internal financing capacity (A.-K. Achleitner & Nathusius, 2004, p. 5). This in turn leads to high capital and resource requirements (in particular for technology research, product development, marketing and sales) and, thus, in an increased investment risk for the respective investors (Hayn, 2000, p. 33 f). In such context, negative income values or cash flow, especially in the early stages of the NTBF’s development are regularly observed in practice. These often occur when lengthy research and development work is required to successfully complete a new product or technology. A negative cash flow in the first few years of the NTBF’s development is not only unrepresentative of how a NTBF may develop in the future, but also leads to a higher rather than a lower current value, for example, when discounted using financial mathematics. In such a valuation context, initial losses in early years that may only represent expected or typical start-up costs and are therefore not representative of future success and value, significantly reduce the current value. Mathematically, there is an overemphasis 2

Growth rates higher than discount rates will e.g. distort the results of discounted cash flow valuation.

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on circumstances with little relevance to the future, which distorts the investment’s attractiveness for an investor. It might be argued that the value is only created in a later year and the initial losses thus reduce the value. However, this view disregards the fact that the losses in the first few years are a necessary component of developing the business and thus only form the basis for later value creation (A.-K. Achleitner & Nathusius, 2004, p. 45). Due to their inevitability, they are therefore to be seen as value-adding rather than value-decreasing. However, this circumstance cannot be reflected in most calculation methods, such as the discounted cash flow methods.

2.2.2.5.3 Importance of Intangible Assets Along with a lack of (financial) resources goes the great importance of intangible assets, including in particular the know-how of the NTBF’s founders and early employees as well as its protection in the form of patents. The resulting high knowledge intensity is a typical feature of innovative, growth-oriented ventures and is a major determinant of its success (A.-K. Achleitner & Nathusius, 2004, p. 5). Furthermore, the nature of NTBF is characterized by their dependency on individuals, which is often very pronounced. The founders frequently combine motivation and know-how in a highly concentrated manner. The qualities that are required of the NTBF in order to be successful must therefore be represented to a high degree by the people leading it. In the absence of established structures and extensive resources, their abilities and character traits determine the initial development and future success in an unusually strong way, both positive and negative. 2.2.2.5.4 Specific Requirements for Valuing Early-Stage NTBF The specific challenges in the valuation of early-stage NTBF, which result from their specific characteristics, result in concrete requirements for methods to be used for their valuation. A.-K. Achleitner & Nathusius (2004, p. 6 ff) suggest the main requirements for early-stage NTBF valuation as outlined in table 2.4 below. According to A.-K. Achleitner & Nathusius (2004, p. 8), the complete fulfilment of all four criteria is ideal. However, it is rarely possible to achieve all four, for instance future orientation and adequacy of representation increase complexity and thus impair practicability, which in turn makes acceptance more difficult. The goal should therefore realistically be to achieve the best possible fulfilment of the criteria.

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Table 2.4 Description of main requirements for methods underlying early-stage NTBF valuation (own presentation, based on A.-K. Achleitner & Nathusius (2004, p. 6 ff)) Requirement

Description

Orientation towards the future Since the decisive part of the development of NTBFs and thus their potential for success lies in the future, valuation methods based on past experience are not suitable. The short and non-representative data history requires the comprehensive prognosis of future variables. Adequacy of representation

The special features of young NTBFs in general and the characteristics of the NTBF to be valued in pasrticular must be reflected in the valuation methods under consideration. These include negative cash flows, growth, uncertainty, risks and opportunities, intangible assets, management capability, flexibility and other subjective determinants.

Practicability

The valuation methods must be as simple as possible to apply in view of the complexity resulting from uncertainty and future forecasts. This includes the availability of reliable and valid data at reasonable (time) expense. Specialist knowledge in statistics, mathematics or other scientific disciplines should not or hardly be required.

Acceptance

Moreover, the fulfilment of the theoretical quality criteria must result in the valuation method finding industry-wide approval. Otherwise, its use would permanently encounter resistance based on established valuation practices.

2.3

Investor Types and Specific Requirements of Venture Capital Financing

Venture capital describes the financing of young, innovative and growth-oriented ventures with equity. This equity capital is provided by venture capital investors who act either independently (known as “business angels”) or as part of a company or fund. In contrast to debt capital, which requires interest-bearing repayment, the venture capitalist receives shares in the venture, which she aims to increase in value and subsequently sell after a period of approximately 3–6 years (A.-K. Achleitner & Nathusius, 2004). She further contributes to the increase in value through her commitment to the venture in the form of management support and the provision of expertise and networks (Stressing et al., 2018, p. 11 ff). Due to the considerable risk associated with its business model, venture capital is also characterized by significantly higher expected returns on the investments

2.3 Investor Types and Specific Requirements …

37

(A.-K. Achleitner & Nathusius, 2004). For entrepreneurs, this form of financing represents an opportunity to obtain additional capital even before an adequate creditworthiness of the company is reached, because the venture capitalist does not require collateral in the form of guarantees or real assets, but manages the investment risk by actively influencing the business development and requiring high returns. Furthermore, the venture capital investor is specialized in better handling the high risk of a such financing compared to banks, state institutions or private individuals (apart from business angels or private venture capital investors). Nevertheless, the various types of venture capital investors differ in terms of their investment approach, the extent to which they support a venture they invest in as well as their objectives and expected returns. In the following, the main groups of venture capital investors will be described in detail.

2.3.1

Business Angels

After being financed by FFF (i.e. founders, family and friends) a venture will usually turn to external equity investors to fund its development. At this early stage of development, with almost no access to debt capital, business angels are usually representing the most attractive source of funding. In general, they are either wealthy individuals with a diverse set of motivations and a variety of backgrounds that prefer to invest alone or as a group/ syndicate. The latter allows business angels to significantly increase the investment amount and share operational tasks attached to the investment among each other. Both will allow the individual business angel to better diversify her investments (Wessendorf & Schneider, work in progress). Early-stage ventures often turn to a very diverse set of investors that have different motivations and preferences with regard to their investment. Thus, a general description of business angels is a difficult task (M. Van Osnabrugge & Robinson, 2000). Some business angels are interested in the specific topic addressed by the venture (e.g. because they spent some time working in the specific field and have relevant knowledge), some are motivated by a potential financial return, and others pursue a (partially) strategic role within a venture they invest in. Still, they represent a very advantageous type of investor for an early-stage venture, due to a suitable set of investment preferences: • Funding high-risk entrepreneurial firms in the first stages of company development (Freear, Sohl, & Wetzel, 1991);

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• Providing smaller amounts that are often needed to realize a first proof of principle or to launch the early organization (M. Van Osnabrugge & Robinson, 2000); • Seeking higher risk investments and having lower return expectations compared to other investors (M. Van Osnabrugge & Robinson, 2000); • Favoring an efficient and quick investment process, thus they usually put less emphasis on a due diligence. However, they have become increasingly professionalized in recent years (Wessendorf, Kegelmann, et al., 2019); • No strict investment focus, thus open to invest in different kinds of industries or technologies and generally more geographically dispersed (M. Van Osnabrugge & Robinson, 2000). In essence, business angels invest in those early-stage ventures where institutional venture capital investors are often reluctant to invest, e.g. because of risk or difficulty of a proper due diligence (M. Van Osnabrugge & Robinson, 2000). Nonetheless, there are negative aspects to be considered by entrepreneurs pursuing funds from business angels (M. Van Osnabrugge & Robinson, 2000): • Hard to find: Business angels tend to not publicly advertise their interest to invest. Thus, entrepreneurs will usually get in touch with business angels through their own professional or private network or by introduction at specific investment events and pitches. • No or limited follow-on money: Business angels generally do not participate in following funding rounds due to a lack of available funds (i.e. in the context of a multi-million Euro Series A). • Active decision-making: Business angels motivated by having a strategic role might want to have a final say in decisions. This can potentially slow down decision-making processes. With regard to their investment strategy, business angels generally follow a valueadd strategy within their investments, also referred to as a buy-and-hold strategy. In order to implement a such strategy, they will often have a straight equity investment in the venture (i.e. own a direct share in the venture) or hold a convertible loan that will be converted to shares at a future event. Thus, business angels will hold onto the shares of an investment for a longer period of time in order to sell at a future higher price (M. Van Osnabrugge & Robinson, 2000).

2.3 Investor Types and Specific Requirements …

2.3.2

39

Venture Capital Funds

As with business angels, it is very difficult to provide a general description of venture capital funds. This is mainly driven by very heterogenous motivations to invest as well as strategies, especially when it comes to asset size, industry focus, preferences for the investment stage or reputation. Wessendorf & Schneider (work in progress) distinguish two main groups. The first group, the classical form of a venture capital fund, presents itself as “an independent venture capital fund that will invest funds from its investors (the so-called limited partners, that are wealthy individuals, corporations [, funds of funds] or even public authorities) in a defined perimeter, mostly investment stage and industry.” The second group are corporate venture capital investors that do not necessarily have a separate fund to invest from but make investments from the corporation’s resources. Yet, these will also invest in high-risk ventures but do not have the same level of independence, as the interest of the corporation they make investments for is generally more clearly defined and narrow. Thus, “these venture capitalists will generally follow a more strategic investment approach looking for ventures that potentially represent a good strategic fit to the corporate partner, e.g. with regards to technology or customers” (Wessendorf & Schneider, work in progress). Additionally, hybrid investors mixing characteristics of these two groups in one or another way also exist. Within both groups, however, an entrepreneur looking for funding will find investors that mainly provide expansion capital for a later stage of company development. However, there is also a small number of funds that focus on early-stage investments. With regard to investment strategy, Wessendorf & Schneider (work in progress) suggest different approaches, depending on the type of venture capitalist: “The traditional venture capital company is a financial investor that will look for a return based on the sale of its shares at a future date, thereby realizing a value add. These investors are therefore more exit driven and focus on realizing a profit within the time-frame of their fund (in general 7-10 years for a technology oriented VC). In contrast, a corporate venture capitalist is not as exit driven or does not necessarily have to respect a fixed time frame.” “Yet, one factor unifies the different types of venture capitalists. They all will require a product or service that has high up-side potential (ideally scalability, i.e. low input but large output) but is in essence already proven and has a clear unique selling proposition (USP). Further, these investors will particularly focus on a skilled management team that is knowledgeable in the relevant market. This market will ideally be a high growth market enabling a strong company growth and solid revenue streams (Wessendorf, Schneider, Gresch, & Terzidis, 2020).”

40

2 Theoretical Background

As with business angels, there are some positive implications from an investment by a venture capital investor that need to be considered by an entrepreneur (M. Van Osnabrugge & Robinson, 2000): • Provision of larger amounts of funding, which will allow the venture to strongly invest into its development (i.e. organizational built-up, production, market entry) and thus drive its growth; • Providing access to a strong network relevant to the investor’s particular investment focus, which is actively used for the venture’s further development, i.e. by introduction to potential partners and clients; • Providing strong expertise in relevant markets but also with regard to building a firm and scale its business. As a result, an entrepreneur with a venture capital fund among its shareholders will have a strong partner to shape the phase of growth as well as to enable organizational readiness when it comes to a potential exit event. Yet, there are negative aspects to be considered by entrepreneurs pursuing funds from venture capital funds (M. Van Osnabrugge & Robinson, 2000): • Minimal funding ticket size: Venture capital funds will require a minimum amount to invest in a venture in order to justify the required efforts of carrying out a comprehensive due diligence; • Investment in growth stage: Venture capital funds usually focus on later stages in company development as larger funding needs will increase the efficiency of their investments, thereby making it difficult for early-stage entrepreneurs to access this source of funding; • Defined investment process: The venture capital fund will follow a defined investment process in order to ensure that only such investments are made, which are in line with internal policies and the limited partners’ investment intentions (i.e. investment focus); • Complex and time-intensive investment process: The defined investment process of a venture capital fund will potentially slow down the fundraising process and require the entrepreneur to prepare various types of supporting documentation. Further, a lot of time and financial resources are spent on negotiations, preparation of the due diligence, legal advice etc.; • High return expectations: In order to deliver on the return promises made to the venture capital fund’s limited partners, the venture capital fund is likely to focus on high growth cases only.

2.4 Subjectivity as a Factor Determining the Value …

41

With regard to their investment strategy, similar to business angels, venture capital funds generally follow a value-add strategy, but only for a limited amount of time. They will thus acquire a direct share of the venture in the form of a straight equity investment or hold a convertible loan that will be converted into shares of the venture at a future date or event. The latter is usually observable among venture capital funds investing in early-stage ventures. In both cases, the venture capital fund will hold the shares until an opportunity to sell the shares for a profit within the fund’s defined maturity can be realized. Corporate venture capitalists, however, represent a potential exception from this investment strategy as they have mostly strategic motives for an investment. They will generally hold their shares until this strategic objective is met or until it becomes clear that it will not be met (Wessendorf & Schneider, work in progress).

2.4

Subjectivity as a Factor Determining the Value of Venture Capital Financing

2.4.1

Systematization of Methods for the Valuation of Early-Stage NTBFs

The valuation of a company is usually carried out on special occasions, such as the purchase or sale of a company or a company merger (cf. section 2.1.3). However, a valuation can also be carried out on numerous other occasions, such as the equity investment in a company. In any case, a clear distinction must be made between the value of a company, which may be different for each party involved, and the price of a company or share of a company, which is determined by mutual agreement between the parties involved. For this reason, company valuations tend to be subjective and not always directly comprehensible to all parties involved. In the early days of company valuation, the aim was to identify the sole and true value of a company. This value should be objective and comprehensible. To achieve this goal, the net asset value method was generally used (B. H. Meyer, 2006). However, since the motivations of a corporate transaction vary widely, this striving was broken with and the valuation of the company became increasingly subjective, thus shaped by the motivations of the parties involved (cf . section 2.1.1) This has led to the development of a large number of valuation methods, which are now firmly established in practice and have proven themselves in academia. Engel (2003) structures this abundance of valuation methods into two main groups, whereby the first group comprises classical valuation methods which

42

2 Theoretical Background

can be applied independently of the objectives of the valuation, i.e. situationunspecific. The second group comprises situation-specific valuation methods, which take into account the specific challenges of an investment in young companies. These two groups are further subdivided by Achleitner & Nathusius (2003) in order to achieve an in-depth systematization of the evaluation procedures (cf. figure 2.4). In the first subcategory of the first group, fundamental analytical methods, there are two subgroups. The individual valuation methods aim to express the value of the company’s total assets as the sum of the individual asset components (Festel et al., 2013). The focus of the valuation is therefore on the assets and liabilities that can be directly valued. In contrast, the total valuation methods not only focus on the valuation of the company’s assets, but also include subjective aspects, explicit or implicit, in the valuation. For this purpose, the company to be valued is understood as a single unit. In addition to the widely used discounted cash flow method (DCF), the total valuation methods also include valuation by the real options approach and the capitalized earnings value method. The second subcategory of the first group reflects market-oriented methods, often referred to as multiples in practice. Here, the basis of the company valuation is provided by the determined value of a comparable company (so-called peers), e.g. in the context of a recent transaction. Various indicators or ratios can serve as a reference for adjusting this value to the company to be valued. The first subcategory of the situation-unspecific methods (i.e. the second group), the total valuation methods, includes the venture capital method, which is widely used in practice, and its meaningful extension, the First Chicago Method (A.-K. Achleitner, 2001; A.-K. Achleitner & Nathusius, 2003). The second subcategory of the second group, the rules of thumb, includes various procedures which, however, only provide a rough estimate of the enterprise value (A.-K. Achleitner & Nathusius, 2003). On the basis of this systematization, the most common and relevant procedures for company valuation in venture capital (Wessendorf & Hammes, 2018) are explained below and the underlying methodology or theoretical approaches are detailed. In view of their relevance in venture capital valuation practice, the remaining valuation methods are not detailed in the context of the present work (A.-K. Achleitner & Nathusius, 2003; Engel, 2003).

Rules of Thumb

Figure 2.4 Systematization of various procedures for company valuation. (own presentation based on Engel, 2003 and Achleitner & Nathusius, 2003)

Recent Acquisions Method

Inial Public Offering Method

Similar Public Company Method

Market-oriented Methods

Total Valuaon Methods • Capitalized Earnings Value Method • Discounted Cashflow Methods • Real Opons Approach

First Chicago Method

Venture Capital Method

Total Valuaon Methods

Fundamental Analysis Methods

Individual Valuaon Methods • Liquidaon Value Method • Net Asset Value Method

Situaon-specific Methods

Situaon-unspecific Methods

METHODS OF COMPANY VALUATION

2.4 Subjectivity as a Factor Determining the Value … 43

44

2.4.2

2 Theoretical Background

Fundamental Analysis Methods

The description of valuation methods within this group will refrain from detailing the Liquidation value method as well as the Net asset value method as these are not considered to account for potentially strong growth in a young venture but focus on assets currently on the balance sheet (objective value theory; cf. section 2.1.1).

2.4.2.1 Capitalized Earnings Value Method (German Income Approach) The capitalized earnings value method is one of the first valuation methods to include an increasingly pronounced subjective component. Valuation using the capitalized earnings value method is based on the assumption that the subjective benefit for the buyer or seller determines the value of a company. Besides a financial component, this value is also expressed by non-financial aspects driving earnings and cash flow(e.g. strategy, market position). However, these aspects are not directly taken into account as they cannot be quantified in monetary terms (Schacht & Fackler, 2009, p. 18). The company’s value is thus determined as the market value of the equity capital. This is determined by the present value of the net financial surpluses that will be allocated to the shareholders in the event of a going concern and the sale of non-operating assets from the company. This value, also known as the future profit value, represents the central figure in the valuation. If the present value of the net financial proceeds from the sale of the assets less debts and liquidation costs on winding up the company exceeds the calculated future profit value, the liquidation value is used as the market value (Schacht & Fackler, 2009, p. 171).

2.4.2.2 Discounted Cash Flow Methods Discounted cash flow methods are some of the most widely used valuation methods and are regularly applied both in practice and in academia. Over the years, a large number of approaches to valuation following the underlying logic of discounted cash flows have been developed, which differ mainly in the definition of the payment surpluses that form the basis of the valuation and the discount rate. What they all have in common, however, is that the future payment surpluses of a company are discounted to the current observation date in order to obtain a current value (cf. equation 2.1). However, discounted cash flow methods are not only used for company valuations, but also for other valuations, provided that future cash flows can be derived (Meyer, 2006, p. 33 f).

2.4 Subjectivity as a Factor Determining the Value …

DPV =

T  t=1

E(FC Ft ) (1+r )t

W ith: D P V E(FC F) r t g

= = = = =

+

45

E(FC FT +1 ) (r −g)(1+r )T

Discounted Pr esent V alue in period 0 E x pected value o f f r ee cash f lows in period t Cost o f capital rate Period index Gr owth rate

(2.1)

In principle, discounted cash flow methods can distinguish between net and gross approaches. With the net or equity approach (cf. section 2.4.2.2.1), the value of the equity is determined. For this purpose, the cash flows, which without exception accrue to the equity providers, are discounted to their present value using the cost of equity. The relevant cash flows are the excess cash and cash equivalents after payment of taxes, interest on borrowings and other payments to be brought forward. In contrast, the gross or entity approach (cf. section 2.4.2.2.2) measures the cash flows to which all investors, equity and debt capital providers, are entitled. These are discounted at the total cost of capital to obtain the current enterprise value. The equity value can also be derived from the gross approach, in which the market value of the debt capital is deducted from the total value of the company (Meyer, 2006, p. 33 f). Some of the most common variations of discounted cash flow methods are discussed below. Provided that the same assumptions are made with regard to the future financing behavior of the company being valued, however, the valuation results are the same for all variations mentioned, even though they might differ in the respective calculation (e.g. equity value vs enterprise value).

2.4.2.2.1 Net Application of the Discounted Cash Flow Method The net approach (i.e. equity approach) of the discounted cash flow method aims to directly determine the market value of a company’s equity, taking into account payment surpluses to which only equity providers are entitled (in particular dividends and withdrawals). The net cash flows are usually discounted at the corresponding cost of equity (cf. equation 2.1), which is generally determined by the Capital Asset Pricing Model (CAPM) (Sharpe, 1964) (cf . equation 2.2). Thus, an expected return for equity providers is derived from the risk-free interest rate Rf and the beta factor βi weighted market risk premium E(Rm ) − R f . The risk-free interest rate Rf is usually determined by a risk-free investment on the capital market, e.g. based on the yield of a long-term government bond (remaining term ≥ 10 years) of the

46

2 Theoretical Background

highest credit rating (e.g. AAA). The beta factor is a measure of the risk of the company under consideration. However, it only represents the systematic risk (i.e. market risk), as the company-specific risk can be completely eliminated through portfolio diversification and therefore does not require any additional remuneration of the investors (Seppelfricke, 2007, p. 212 f). The market risk premium is the difference between the market yield E(Rm) and the risk-free interest rate Rf . In practice, the market yield is usually the yield of a broadly diversified stock index, such as the MSCI World Index, which is comparable to the company to be valued in terms of its structure and focus (Schacht & Fackler, 2009, p. 211 f).   r E K = E(Ri ) = R f + βi E(Rm ) − R f W ith: r E K E(Ri) Rf βi E(Rm)

= = = = =

Cost o f equit y E x pected r etur n Asset i Risk − f r ee inter est rate Mar ket risk E x pected mar ket r etur n

(2.2)

A change in the amount of borrowed capital also changes the overall capital structure. In theory, the discount rate would subsequently have to be adjusted to a corresponding mixture of cost of equity and cost of debt, but this is rarely done in practice to reduce complexity (Schacht & Fackler, 2009, p. 225 f). However, as mentioned above, the cost of equity is typically used for discounting, even though the Operating Cash Inflow Surplus is not equal to the Free Cash Flow accruing to equity holders. This is mostly done in order to reduce complexity as it is sufficiently close to a pure private perspective.

2.4.2.2.2 Gross Approach of the Discounted Cash Flow Method The gross approach of the discounted cash flow method basically involves numerous variations of this approach. In contrast to the net approach and the equity approach, the gross approach takes into account all cash flows in order to obtain the enterprise value of the valuation object. The most common variations of the gross approach are discussed in more detail below. 2.4.2.2.2.1 Weighted Average Cost of Capital Approach The weighted average cost of capital (WACC) approach is a widely used valuation approach in practice. To measure total capital (i.e. enterprise approach), the period-specific future gross cash flows are calculated and discounted using the WACC.

2.4 Subjectivity as a Factor Determining the Value …

47

Gross cash flows are discounted using the corresponding total cost of capital r tc , which is generally determined using the WACC approach. This approach is used to determine the weighted average cost of capital based on the cost of equity and debt and the fair values of equity and debt3 (cf. equation 2.3) (Achleitner & Nathusius, 2003; Seppelfricke, 2007, p. 21). rtc =

E F ∗ rE + ∗ r D ∗ (1 − tax) (E + F) (E + F)

W ith: E D rE rD tax

= = = = =

Equit y (mar ket value) Debt capital (mar ket value) Cost o f equit y Cost o f debt capital T ax rate

(2.3)

The Capital Asset Pricing Model (CAPM) is used to determine the cost of equity (cf. equation 2.3). The cost of debt capital is either derived from actual cost for debt capital of the firm or comparable market cost. After determining the relevant gross cash flows and the capital costs, the enterprise value can be calculated according to equation 2.1. 2.4.2.2.2.2 Total Cash Flow Approach The total cash flow approach is closely related to the WACC approach and thus shows strong similarities. The main difference between the two approaches lies in the consideration of corporate tax savings due to pro rata debt financing (i.e. tax shield). Under the total cash flow approach, the deductibility of debt capital costs from the income tax base is already taken into account when calculating the cash flow. When choosing the total cash flow approach, the discount rate is determined according to the WACC approach, but the borrowing costs before tax are used to avoid double consideration of the tax shield. According to Schacht & Fackler (2009, p. 209), the total cash flow approach is highly relevant, especially in Germany. By taking the tax shield into account when determining the cash flow, it allows the special features of the German tax system to be taken into account without much effort.

3

Due to variations in the capital structure of a company, the target capital structure is usually assumed here.

48

2 Theoretical Background

2.4.2.2.2.3 Adjusted Present Value Approach In practice, the adjusted present value approach is less widespread than the discounted cash flow approaches already presented. The special feature of this valuation approach is that value-influencing characteristics of a company are differentiated and thus a distinction can be made between value drivers in the context of operating activities and the capital structure. It is therefore a multi-stage procedure that determines the total market value of a company by adding individual value components. In a first step, the value of future gross cash flows is calculated, not taking into account the tax deductibility of interest on borrowed capital. This methodology therefore assumes to be purely equity financing, which is why the future gross cash flows are discounted using a risk-adjusted cost of equity. The result of the first valuation step is thus an enterprise value that is not based on the capital structure and its specific risk, but is solely attributable to the operating activities of the company being valued. The impact of the capital structure on the enterprise value, through the taxreducing effect of the borrowing costs, is derived in a second step. This is done in an isolated calculation of the present value of the tax benefits (tax shield) and interest subsidies. The two identified value components are finally added together (cf. equation 2.4), since the total market value of the indebted company must result from the market value of the company without debt and the value contribution of the debt financing according to the adjusted-present-value approach (Schacht & Fackler, 2009, p. 223 f). A P V = −I0 +

W ith: A P V I0 E(C F) rE rD t T axt Subt

T t=1

= = = = = = = =

T T E(C Ft ) T axt Subt + t + t t=1 (1 + r D ) t=1 (1 + r D )t (1 + r E ) (2.4)

Ad justed Pr esent V alue in period 0 I nvestment in period t E x pected value o f gr oss cash f lows in period t Cost o f equit y (total equit y f inancing assumed) Cost o f debt capital (be f or e tax) Period index T ax savings in year t I nter est subsidies (be f or e tax) in year t

2.4 Subjectivity as a Factor Determining the Value …

49

2.4.2.3 Real Option Approach Financial options are widely used in practice and discussed in finance research. Yet, real options transfer the concept of financial options to the real world. The concept of real options (RO) is based the work of Myers (1977): “The value of the firm as a going concern depends on its future investment strategy. Thus it is useful for expositional purposes to think of the firm as composed of two distinct asset types: (1) real assets, which have market values independent of the firm’s investment strategy, and (2) real options, which are opportunities to purchase real assets on possibly favorable terms.” (Myers, 1977, p. 163)

According to Myers (1977), a real option provides the right to buy a real asset at favorable terms, thus being strongly comparable to a financial call option. In contrast to the financial option, however, the real option refers to a real asset. More recent research defines real options more broadly, such as a right, but not an obligation, to perform an action in real life (Kodukula & Papudescu, 2006). Copeland und Antikarov (2001) second a strong analogy to financial options and define a real option as follows: “A real option is the right, but not the obligation, to take an action (e.g., deferring, expanding, contracting, or abandoning) at a predetermined cost called the exercise price, for a predetermined period of time – the life of the option” (Copeland & Antikarov, 2001, p. 5).

According to Brach (2003, p. 44) this analogy extents to characteristics of financial options such as flexibility, irreversibility and uncertainty. Nevertheless, some aspects of a real option differ greatly from financial options. In contrast to a financial option, the investor or holder of a real option can intervene in a real asset (e.g. a venture) and thus influence the value of the underlying asset (T. E. Copeland, Koller, & Murrin, 1994, p. 403). Further, exclusivity, tradability, the compound character and the variability of its parameters distinguish real options from purely financial options. As such, financial options provide their holder with the exclusivity to exercise the option, whereas real options are generally not considered exclusive. An exception are real options that are protected by e.g. intellectual property rights. Further, in contrast to organized markets for financial options, no efficient markets exist to trade real options. Existing literature describes real options as a method (i.e. real option approach; ROA) to value risky ventures with high growth potential, that is able to identify room for maneuver. It often considers the real option approach as an extension

50

2 Theoretical Background

to conventional valuation methods (Hilpisch, 2006, p. 38), thus as a mean to calculate an extended enterprise value (cf. equation 2.5). E Vex = N P V DC F + N P V R O W ith: E Vex = E xtended Enter prise V alue N P V DC F = N et Pr esent V alue deter mined by DC F N P V R O = N et Pr esent V alue deter mined by R O

(2.5)

As a result, the (extended) enterprise value comprises a rigid value component from the DCF method, that takes a predetermined path of investment projects as a basis, and the value contribution of the real option, which measures flexibility to react to changing environmental conditions over time that can be exploited by the venture’s management. However, the resulting enterprise value does not represent an absolute total value, but rather a value interval whose lower limit is defined by the NPV DCF and whose upper limit is reached by adding the NPV RO . The actual enterprise value lies between these two limits and depends on the actual value of the real option (Dietmar, Schneider, & Thielen, 2006, p. 311 f), i.e. Enter prise V alue  (N P V DC F ; N P V DC F + N P V R O ). According to A.-K. Achleitner & Nathusius (2003) real options are valued using classic option pricing models that were developed for determining the value of financial options. This is justified by strongly comparable constitutive characteristics of both financial options and real options. Yet, as described above, clear differences such as influence on the real asset, exclusivity, and tradability are observed. This results in the obligation to adapt the option pricing models intended for valuation of financial options and to take real option-specific effects into account. As a result, these real option pricing models show a very high degree of complexity (Pritsch, 2000, p. 174 ff), thereby tremendously reducing practicability and acceptance for the application of the ROA in valuation practice. Further, the short representative history of growth-oriented ventures complicates a reliable forecast of relevant financial data. A.-K. Achleitner & Nathusius (2003) conclude that “the added value of the real options approach therefore lies not in the determination of a quantitative result, but in qualitative results, i.e. in the identified real options.”

2.4 Subjectivity as a Factor Determining the Value …

2.4.3

51

Market-Oriented Methods

Market-oriented valuation methods are simple and easy to understand and are therefore often used, typically in combination with other valuation methods. For this reason, however, they are increasingly not presented in the literature as fullyfledged valuation methods, but rather as simplified procedures for initial price determination. In market-oriented valuation methods, prices observable in the market, e.g. share prices of companies traded on a stock exchange or in transactions of comparable companies, serve as the basis for valuation. This implies that the price observed in the market reflects the value of the company, or that the average price of companies that can be compared to the market is correct. However, this also results in a certain degree of imprecision due to speculation, strategic purchases or macroeconomic influences as well as uncertainty with regard to aspects underlying the benchmark data. Nevertheless, current price points can thus be defined and support price negotiations accordingly (Achleitner & Nathusius, 2003; Meyer, 2006, p. 64 f). In order to make meaningful use of market-oriented valuation methods, four basic steps are basically followed.4 In a first step, benchmark companies (i.e. peers), which can be compared with the company to be evaluated on the basis of various criteria, e.g. industry, market coverage, product range and company size, have to be identified. In a second step, it must be determined whether a current, ideally comprehensible valuation result is available for these companies. Thirdly, it must be ensured that uniformly defined indicators or key figures (e.g. sales, profits or book values) are available for both the benchmark company or companies and the company to be valued. In the fourth step, these indicators or key figures are analyzed within the group of comparable companies in order to identify and avoid the influence of possible extreme values (Meyer, 2006, p. 64 f; Nowak, 2000). Afterwards, a valuation can be carried out by placing the observed company valuations in a corresponding relationship to the valuing company on the basis of the indicators or ratios (cf. equation 2.6). B E VV O =

4

E VB b=1 X B

b

∗ XV O

However, the order may occasionally vary in practice as well as in literature.

52

2 Theoretical Background

W ith: E VV O E VB XV O XB b

= = = = =

Enter prise value o f the valuation object Enter prise value o f benchmar k company B V aluation object ratio Benchmar k company ratio Benchmar k company index

(2.6)

However, it is apparent that the individual characteristics of the companies to be valued are not or only barely considered in the valuation, which is why marketoriented procedures should be used in addition to other valuation procedures (A.-K. Achleitner & Nathusius, 2003). Furthermore, valuations in the market may be erroneous due to its imperfections (Meyer, 2006, p. 71). Depending on company maturity or the market in which the prices for the benchmark companies were formed, three different approaches can be distinguished: the Similar Public Company Method, the Initial Public Offering Method and the Recent Acquisitions Method.

2.4.3.1 Similar Public Company Method In the context of the similar public company method, the current market value of relevant comparable companies is used. The real-time reporting of valuations can clearly be described as a strength of this approach. However, due to the structure of the selected market, the international stock exchanges, publicly listed companies are largely in an advanced phase of corporate development (late growth or stability, cf. section 2.2.2.4), which is why their valuations should generally only be used to a limit when valuing young companies (Nowak, 2000).

2.4.3.2 Initial Public Offering Method The initial public offering method is based on the prices of initial public offerings on the international stock exchanges (Nowak, 2000). This partially addresses the problem that the comparable companies on the stock exchange are companies in an advanced stage of development, as initial listings also regularly include companies that are in an intermediate stage of development (late growth, cf. section 2.2.2.4).

2.4.3.3 Recent Acquisitions Method The recent acquisitions method uses recent corporate transactions observed in the market, often also in non-public markets, as a basis for valuation (Nowak, 2000). The price of these transactions (or the ultimately negotiated transaction value) from comparable companies is applied to the company to be valued, although

2.4 Subjectivity as a Factor Determining the Value …

53

there is usually a pronounced lack of knowledge about the underlying rationale for pricing or comparable metrics (e.g. comparably structured EBITDA), as these transactions are often not made available to the general public. This approach is interesting for the valuation of young as well as small and medium-sized companies, as their transactions and valuations are usually carried out in non-public markets.

2.4.4

Total Valuation Methods

2.4.4.1 Venture Capital Method The venture capital method (VCM) is one of the situation-specific methods that are only applicable in the case of venture capital financing. It was specifically designed to address the challenges of young growth companies, i.e. no earnings, high cash burn followed by later cash generation and profits in the form of a hockey stick (M. Van Osnabrugge & Robinson, 2000). At its core, the venture capital method is comparable to market-oriented valuation method as it makes use of relevant multiples to identify a future enterprise value. Yet, the valuation is made from the perspective of the venture capitalist, who seeks a capital gain on the future sale of her shares. Thus, her required rate of return will be included in the valuation to derive a proper value at the valuation date. This is important as the investor’s return depends on the value development up to an exit event (A.-K. Achleitner & Nathusius, 2003). According to A.-K. Achleitner & Nathusius (2003) the venture capital method requires two steps in order to derive the value of a venture. First, the future value of the company is forecasted with the help of multiples. The time chosen for this forecast corresponds to the end of the investment horizon, i.e. the exit time (cf. equation 2.7). VT = M ∗ X W ith: VT M X T

= = = =

Enter prise value at time T I ndustr y average multi plier , e.g. pr o f it multi plier Company ratio, e.g. pr o f it End o f investment hori zon

(2.7)

A.-K. Achleitner & Nathusius (2003) suggest that the capital gain generated by selling the venture’s shares at a future point in time is the only financial return to

54

2 Theoretical Background

the venture capital investor. Therefore, in addition to the venture’s future enterprise value, today’s enterprise value needs to be determined. This is achieved by applying a discount rate that equals the investor’s target return. The resulting enterprise value includes the capital sum provided by the venture capitalist, i.e. the venture’s post-money valuation. V0 =

VT

(1 + r )T W ith: V0 = Enter prise value at time 0 (day o f valuation) r = T arget r etur n o f ventur e capital investor

(2.8)

The target returns required in valuation practice are often very high, especially in the financing of ventures that are in a very early phase of their corporate life cycle (i.e. early-stage). These high discount rates are partly due to the high risk of investing in such companies (cf. section 2.5.1). A.-K. Achleitner & Nathusius (2003) observe that the venture capital method is a preferred valuation method of venture capital investors to value seed and startup ventures (i.e. in the early-stage, cf. section 2.2.2.4) as a solid forecast of the venture’s development is often not yet possible and the method thus only requires rough estimations. Nevertheless, for the purpose of forecasting the venture’s development, the forecasted figures set out in the business plan should not be used as presented, but adjusted in line with market conditions and the investor’s specific expertise in the relevant field (A.-K. Achleitner & Nathusius, 2003; Damodaran, 2009). Yet, the venture capital method is not considered an appropriate tool to reflect a precise future of the venture and thus to provide an accurate valuation. It does not account for venture-specific information, which is considered to influence the venture’s value to an investor (e.g. dilution effects due to capital increases by e.g. additional investors joining during the investment period). However, it represents a good approach to approximate the venture’s value that is widely used and accepted in valuation practice due to its simple and efficient application (A.-K. Achleitner & Nathusius, 2003).

2.4.4.2 First Chicago Method The First Chicago method is a multiple scenario approach to value a venture with an inherent high level of risk (Scherlis & Sahlman, 1989). As a consequence,

2.4 Subjectivity as a Factor Determining the Value …

55

previous research sometimes refers to the First Chicago method as being a compromise between the venture capital method (due to the individual valuations performed) and a real option approach (due to scenario-based weighting of valuations) (Desaché, 2014). Yet, the different scenarios considered within the method are seen as a useful extension of the venture capital method (A.-K. Achleitner & Nathusius, 2003). In a first step, the First Chicago method requires the definition of different scenarios, varying in numbers depending on the potential scenarios applicable. In this regard, the investor disposes of a high degree of flexibility within the valuation. A probability of occurrence is assigned to each scenario which will later serve as a weighting. In a second step, the enterprise value is determined for each scenario and discounted by a realistic cost of capital, e.g. by applying the venture capital method or even performing a discounted cash flow valuation. The resulting values will then be weighted with the respective probability of occurrence, and summed up to an expected enterprise value (cf. equation 2.9). E VE =

I i=1

W ith: E VE V0i pi i

= = = =

pi V0i E x pected enter prise value Enter prise value in period 0 f or scenario i Pr obabilit y o f scenario i Scenario index

(2.9)

As a consequence, the First Chicago method is considered to have certain advantages over other methods, such as the venture capital method. First, it requires the valuing investor to reflect on possible outcomes of the business under valuation. Therefore, the growth potential but also increased level of risk inherent in an early-stage venture is taken into account in a more refined manner. Further, it also covers a part of the value imbedded in real options by means of various scenarios. This provides the investor with a higher degree of flexibility in accounting for the venture’s reality. Nevertheless, the First Chicago method remains very judgmental and subjective (Schumann, 2006). This is reflected in every step of the calculation, starting with the scenario definition and assignment of probabilities as well as the determination of value in each scenario based on future enterprise values. Consequently, a discrepancy between the entrepreneur’s valuation and the venture capital investor’s valuation is considered to be likely.

56

2.4.5

2 Theoretical Background

Subjectivity in the Valuation of Early-Stage NTBFs using DCF and VCM

2.4.5.1 Suitability of Valuation Methods for Early-Stage NTBF Valuation In the sections 2.4.2 to 2.4.4 several valuation methods used in a venture capital context were presented. Yet, their suitability to early-stage venture capital investments and subsequent acceptance in venture capital valuation practice differs. Thus, the presented methods will be discussed with regard to comprehensibility of the valuation result, flexibility in accounting for venture-specific information within the valuation as well as practicability. This discussion aims to identify a subset of relevant valuation methods to be further detailed.

2.4.5.1.1 Comprehensibility of the Valuation Result Comprehensibility of the valuation result is achieved by providing a reasoning as well as the underlying rationale for the attained value. Therefore, the party responsible for valuation but also other parties involved will have transparency on assumptions made and the respective implication on the calculated valuation. With regard to the described valuation methods, such a transparent approach on valuation is achieved by fundamental analysis methods as well as, even though to a reduced extent, by total valuation methods. Yet, the market-oriented valuation methods do not fulfill this important aspect as the parties involved in valuation will not know about the underlying rationale for the attained valuations of peer companies. Thus, in market-oriented valuation, an opaque valuation result is taken and subsequently interpolated or extrapolated. The valuation itself is not comprehensible. 2.4.5.1.2 Flexibility in Accounting for Venture-Specific Information Flexibility in accounting for venture-specific information is achieved by being able to reflect knowledge of future effects (e.g. changes in cash flow or capital structure) or alternative strategies to choose within the valuation. In order to do so, a valuation method needs to offer the optionality to adjust individual variables, ideally at different points in time. Again, this aspect is fulfilled by fundamental analysis methods (in particular the real option approach and discounted cash flow method) as well as total valuation methods. Market-oriented valuation methods, however, do not offer this flexibility as they are strongly dependent on the valuations of observable peer companies.

2.4 Subjectivity as a Factor Determining the Value …

57

2.4.5.1.3 Practicability of the Valuation Method Practicability of valuation methods is achieved by applying a methodology that is easy to use and hence is not characterized by a high degree of complexity. In this regard, market-oriented valuation methods as well as total valuation methods appear particularly strongly positioned, as both can be applied with limited resources. Fundamental analysis methods are more complex in their structure (in particular the real option approach) and therefore not always well suited to valuation reality. 2.4.5.1.4 Intermediate Conclusion Having analyzed the described valuation methods with regard to comprehensibility of the valuation result, flexibility in accounting for venture-specific information within the valuation as well as practicability, a subset can be selected to be further detailed. Market-oriented valuation methods are not considered to fulfill the requirements of comprehensibility and flexibility. The real option approach does not appear to meet the requirements for practicability due to its high level of complexity. This leaves the capitalized earnings value method, the discounted cash flow method, the venture capital method and the First Chicago method. However, as the capitalized earnings value method appears to be more of local relevance (a. k. a. German Income Approach) and was not particularly commonly mentioned as a valuation method used in practice in the context of the performed empirical analysis of the present work (cf. section 4.1), it will be omitted and not discussed in greater detail. Moreover, as the First Chicago method is considered a useful extension of the venture capital method (A.-K. Achleitner & Nathusius, 2003) and can also be applied to extend the discounted cash flow method, it classifies more as a method to enhance the scope of valuation rather than a standalone method. Thus, it will also be omitted in order to focus on the more relevant standalone valuation methods. Hence, subjectivity inherent in the discounted cash flow method and the venture capital method will be discussed in the following sections.

2.4.5.2 Discounted Cash Flow Methods Subjective elements can be found at various stages of the discounted cash flow valuation. First, the forecast of future cash flows is driven by subjective expectations and estimates. The inherent uncertainty of future cash flows increases with its distance from the valuation date. Yet, uncertainty of the forecasted cash flows is also dependent on the venture itself. NTBF for instance are considered to evolve with even greater uncertainty and dynamism and very rarely allow for a derivation from sound historical data due to their strong reliance on time- and

58

2 Theoretical Background

resource intensive-technology development and subsequent commercialization (cf. section 2.2.2.3). Since the cash flow forecast represents the main value driver for the DCF methods, their characteristics have a great influence on the resulting enterprise value. Second, in line with the uncertainty about future cash flows, the growth rate for the rough-planning phase, which is a simplification of forecasting single cash flows in the distant future, is determined subjectively. It is generally based on an estimate and therefore not subject to any well-founded venture-specific system accounting for various types and sources of growth. This potentially has major implications that need to be taken into account: Firstly, the rough planning phase generally has the largest contribution to the value of the venture within a DCF analysis. Hence, the growth rate chosen has a great impact on the value of the venture. Secondly, the value contribution by the rough-planning phase is based on the calculation of an infinite series of cash flows, which cannot be applied if the growth rate is greater than or equal to the discount rate. A difference between the two rates of zero would require a mathematically impossible division by zero, whereas a negative difference would result in a reduction in enterprise value. Both scenarios are incompatible with the logic of discounted cash flow valuation. Nevertheless, since the growth rates of early-stage NTBFs can be above average, the potential implications on valuation mentioned above are not only relevant in theory but also in practice. In the scenario of very high assumed growth rates and the simultaneous willingness to base the valuation on the discounted cash flow method, the valuation requires to either subjectively reduce the growth rate or to subjectively increase the discount rate. Both, however, would be unfounded and thus subject to complete subjectivity or even pure arbitrariness. Third, the determination of the cost of capital, which is used as a discount rate, is often also highly subjective. Even if the selection of debt capital interest rates to calculate the WACC, subsequently used as a discount rate, is ignored in the context of this discussion, which can be argued as valid as most early-stage ventures are almost entirely financed by equity (Festel et al., 2013), a high level of subjectivity remains. This subjectivity is inherent to the models used to calculated the cost of equity, e.g. the CAPM. Within the CAPM, the risk-free interest rate and an average stock market index matching the dynamics of the venture to be valued must be chosen. This is generally be done in line with accepted recommendations by investment professionals. Yet, the subsequent choice of the beta factor is a central subjective factor in the valuation of young growth companies (Kruschwitz, 2007, p. 402 f). Due to the present lack of fundamental data (Damodaran, 2009; Kaserer et al., 2007) and other historical correlations of returns, beta values have to be derived from peer groups. Both the choice of a peer group (in particular

2.4 Subjectivity as a Factor Determining the Value …

59

privately held, non-listed young companies) and the selection of a comparable sector might be objectively difficult, dependent on the venture’s business and, even more crucially, its technology to be commercially exploited. The difficulty of determining appropriate beta values and subsequently calculate an appropriate cost of capital leads to avoidance of the respective financial models in practice. Discount rates as well as beta values are consequently determined by means of subjective risk additions and deductions, explicitly taking into account factors other than the systematic risk of the investment (A.-K. Achleitner & Nathusius, 2004, p. 52; Festel et al., 2013, p. 222; Wessendorf & Hammes, 2018, p. 3). While this approach is considered intentional in the context of other valuation methods, e.g. the venture capital method, DCF methods applied to such a valuation are subjectively modified. To conclude, the valuation of early-stage ventures and NTBF by means of DCF methods may be regarded as conceptually correct. Yet, their suitability and resulting valuation in practice demonstrates several aspects worth questioning due to the methods’ strong adaption to early-stage ventures’ specifics as well as the influence of subjectivity.

2.4.5.3 Venture Capital Method Subjectivity is inherent in the venture capital method because of the consciously adopted investor perspective and subsequent inclusion of non-measurable investor experience, e.g. to value characteristics that are difficult to describe quantitatively (A.-K. Achleitner & Nathusius, 2004, p. 181 f). The determination of value is subjective mainly because of the specific investment conditions for venture capitalists. On one hand, this applies to the optimal exit point and hence the duration of the investment period. These are driven by the intended period in which a certain return is promised to the venture capitalist’s limited partners as well as by risk and diversification considerations influencing the duration of an investment. As a consequence, the optimal exit point and duration are thus triggered by internal considerations of the venture capital investor or agreed targets and do not necessarily reflect the optimal point in the venture’s development. This will potentially lead to a subjective value determination in order to meet agreed targets as well as possible. On the other hand, in addition to the optimal exit time, the future value is subject to the same subjective influences regarding the expected cash flows, costs of capital and growth rate that are also present in the DCF methods. Discounting to the valuation date also takes place in the venture capital method. The discount rate

60

2 Theoretical Background

thus has the same effect on the enterprise value as in the DCF methods. As mentioned above, its determination is subject to a high degree of subjectivity. The level of such discount rates falls empirically with the progressive development of the venture in question (cf. section 2.5.4). Until today, neither the premiums nor the discounts on systematic risk, as widely used to determine target return in the venture capital method, can be systematically quantified and are consequently subject to subjective determination by the investor. Yet, these premiums and discounts represent a major contribution to the attributed enterprise value. However, it is crucial to consider that other valuation methods are not necessarily less susceptible to subjectivity (A.-K. Achleitner & Nathusius, 2004, p. 149).

2.4.6

Generalization of the Subjectivity Component

The fact that the usual valuation methods leave scope for subjective assessments or even require these as a necessary valuation input is not limited to discounted cash flow methods and the venture capital method. Both conventional (e.g. market-oriented methods a. k. a. multiples) and less conventional methods (e.g. real option approach) are characterized by subjectivity. According to A.-K. Achleitner (2002, p. 4), the valuation in the context of venture capital financing therefore resembles more a pricing process than a real valuation process. Consequently, it is potentially irrelevant which methods the venture capitalist uses as basis of her valuation; she will, in the end, use her individual experiences and impressions to determine individual parameters. With regard to early-stage ventures in general and early-stage NTBFs in particular, there are important determinants whose characteristics cannot be measured concretely but are assessed intuitively. Taken together, the assessment of these determinants results in a profile-like overall assessment of the venture, which is interpreted individually, and most likely differently, by each venture capital investor. Consequently, this forms the basis for subjective considerations within the valuation by the investor. In terms of a comprehensible valuation, which takes into account the specifics of the early stage, there is a need for a valuation method that allows an intuitive and subjective valuation. However, this valuation method should also be transparent, i.e. it should provide an objective framework for the subjectivity of the valuation. This enables the intuitive valuation logic to be translated into a measurable valuation approach.

2.5 Determination of the Discount Rate

2.5

61

Determination of the Discount Rate

Valuation practice of early-stage ventures in general and early-stage NTBFs in particular relies on different kinds of discount rates to be applied to different valuation methods. The target return is considered to be among the most prominent concepts of discount rates. Yet, the target returns used within an early-stage valuation context fluctuate significantly. This is mainly due to different points of reference, when determining an “appropriate” target return, as it often remains unclear whether the returns apply per investment, per investor or per company, whether they are equal, weighted by initial investment amount or weighted by investment duration (McDonald & DeGennaro, 2016, p. 719 ff). In addition, return levels depend on market conditions and thus on the period to which they relate. Also, it is essential to consider the development phase of the venture in which the investment is about to be made, the returns that apply and the venture’s type (e.g. NTBF or not). Further, the form of the exit will have an additional impact on the level of realized returns (McDonald & DeGennaro, 2016, p. 725). The same phenomenon can be observed for returns calculated using the CAPM (Sharpe, 1964). Even though the model provides a clear approach on how to calculate the “appropriate” discount rate for a valuation, the underlying assumptions are not fully applicable to the valuation of early-stage ventures (i.e. as the CAPM was originally designed to value firms listed on a stock market) and estimating the necessary variables is usually very challenging for private, non-listed firms. Nevertheless, previous literature provides several perspectives on the determination of the discount rate within an early-stage venture or NTBF valuation setting.

2.5.1

Choice of the Discount Rate

The criteria underlying the choice of an appropriate discount rate are very heterogeneous in nature. In essence, the choice of discount rate will follow the investor’s decision if a hypothesized “real” discount rate, comparable to the ones “observable” in relevant markets but approached via a theoretical model, or a targeted rate of return would best reflect her understanding of proper valuation. It becomes clear, that the fundamental question underlying this decision is whether value is reflected by what the market might potentially be willing to pay for a particularly intended investment or what level of value an individual investor might (want to) realize in the same context. This decision is individually made and thus not further explored in this research project.

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2 Theoretical Background

Second, the choice of discount rate will most likely follow the choice of the valuation model to be applied. This is especially true for the venture capital method that builds on the investor’s required return (A.-K. Achleitner & Nathusius, 2004) and therefore needs to be based on some level of target return. Other valuation methods, such as the DCF, can be performed following different discount rate concepts (e.g. CAPM). Lastly, when choosing an appropriate discount rate, an investor is dependent on the available information. In order to properly derive a discount rate according to the CAPM, the investor needs to specify several variables. If these variables cannot be properly specified due to a lack of information, it will most likely to change the perspective on valuation from an external market-view to an internal return-view. In consequence, the target return might become the discount rate concept of choice.

2.5.2

Discount Rate Concepts

In practice, the discount rate is regularly based on the investor’s target return or derived from models, such as the CAPM. Yet, the choice of the correct discount rate appears to be divided into two different categories: probability-based model adjustments on the one hand and risk-based model adjustments on the other. In probability-based models, the discount rate is mainly influenced by the default risk of the venture. In risk-based approaches, on the other hand, the discount rate depends mainly on the changing risk in defined risk categories of the venture. However, the methodological approaches described must be viewed critically and evaluated in the light of their applicability for the valuation of early-stage ventures.

2.5.2.1 Capital Asset Pricing Model (CAPM) The CAPM is a model originating from capital market theory and is often used to determine a company’s cost of equity (A.-K. Achleitner & Nathusius, 2004; Brealey, Myers, & Allen, 2017). The CAPM is established as the most common method for determining the discount rate, and investors are therefore highly familiar with this model. It is much more objective than target return approaches and thus provides a good and commonly understood valuation basis. According to Smith and Smith (2000), the CAPM predicts the average expected returns of venture capital investors very well, as the theoretical statement that diversifiable risk does not influence investors’ returns has been mathematically proven and empirically confirmed. Therefore, if the cash flows within a DCF model correspond to

2.5 Determination of the Discount Rate

63

the expected cash flows as intended and are not modeled more optimistically, the CAPM is a very good model for determining the discount rate. The early CAPM of Sharpe (1964) and Lintner (1965) results in very different valuation results for small companies (Banz, 1981). Fama and French (1992) have addressed this issue in an extended version of the CAPM, which addresses in particular the risks associated with size and value. This means that a size premium for companies with a low market capitalization on the one hand and a value premium in relation to increased book-to-market valuations on the other hand are taken into account (Fama & French, 2012). Festel et al. (2013) make use of this approach and extend the original CAPM, in which the specific risks of evaluating early-stage ventures are reflected in the derivation of the beta factor. This approach enables a differentiated derivation of the beta factor, a selective calculation of the discount rate and thus a comprehensible valuation. However, the challenges posed by the CAPM for venture investments have many roots. First, venture capital investors, in particular when they are specialized in certain industries or technologies are not fully diversified, so that the consideration of systematic risk only is insufficient (Damodaran, 2009). Second, the CAPM assumes perfect capital markets, which do not exist in reality (A.-K. Achleitner & Nathusius, 2004). Finally, the difficulty in applying the CAPM to early-stage ventures lies within the determination of a suitable beta factor. Normally, fluctuations of historical stock returns are used for this purpose, which is not easily applicable for private companies (A.-K. Achleitner & Nathusius, 2004). There are different approaches for determining the beta factor, such as the identification of a peer group of comparable companies providing an average group beta factor (A.-K. Achleitner & Nathusius, 2004; Lerner & Willinge, 1996; Livingston, 2014; Metrick & Yasuda, 2007; J. K. Smith & Smith, 2000). This peer group can be identified by selecting comparable companies based on industry, investment opportunities, margins and long-term productivity (Metrick & Yasuda, 2007) or other aspects such as the comparability of business models (Livingston, 2014) or industry-specific key performance indicators (Lerner & Willinge, 1996). A second approach consists of using the beta factor of listed venture capital firms (Smith and Smith, 2000). The portfolio of these companies usually consists only of start-ups, which is why the beta factor of venture capital firms is a good approximation for a venture’s beta factor. As a third approach, it is also common to use the average industry beta factor (A.-K. Achleitner & Nathusius, 2004).

2.5.2.2 Target Return In contrast, the investor’s target return is a subjective return expectation of a venture capital investor and is applied as a discount rate primarily within the venture

64

2 Theoretical Background

capital method (A.-K. Achleitner & Nathusius, 2004; Lerner & Willinge, 1996). However, there are several reasons why a majority of investors still use the target return as the basis for the discount rate (A. K. Achleitner, Zelger, Beyer, & Müller, 2004), such as its simple structure, practical usage and fast result delivery (A.-K. Achleitner, 2001; A.-K. Achleitner & Nathusius, 2004; Scherlis & Sahlman, 1989; J. K. Smith & Smith, 2000). Besides, surveys have shown that investors tend to discount with empirical values, which is why the target return represents a sound basis for the discount rate (A. K. Achleitner et al., 2004). The influencing factors within the target return can be roughly divided into systematic and unsystematic risk of investing in a NTBF (A.-K. Achleitner & Nathusius, 2004; Scherlis & Sahlman, 1989). In addition to these forms of risk accounted for within the target return, the investor will apply various risk premiums that are specific to venture capital investment (cf. section 2.5.4).

2.5.3

Derivation of the Discount Rate

2.5.3.1 Derivation of the Discount Rate for Discounted Cash Flow Methods While describing valuation methods widely accepted in valuation practice, in particular discounted cash flow methods (cf. section 2.4.2.2), it became apparent that discount rates are a key component within several valuation methods. In particular the mentioned discounted cash flow methods naturally rely on a discount rate, which is intended to reflect the financing structure of the firm to be valued. The WACC (cf. equation 2.3) approach is one of the most common methods, since both equity and debt components make up the financing or capital structure. When financing early-stage ventures, however, it must be taken into account that these companies are typically fully equity-financed. This applies in particular to young technology ventures (Festel et al., 2013). The calculation of the discount rate is therefore primarily based on the approach for calculating the equity value and thus follows the CAPM,5 as defined in equation 2.2. This means that the chosen discount rate reflects the cost of equity of the young venture on the one hand and the return expected by the investor on the other. The CAPM considers the individual systematic risk of the investment by choosing the beta factor, a relative measure of risk compared to a relevant peer group

5

Other approaches for calculating the cost of equity capital are also pursued in theory and practice, e.g. the arbitrage price theory (Kruschwitz, 2007, p. 403).

2.5 Determination of the Discount Rate

65

of companies or investment assets (Berk & DeMarzo, 2007, p. 350). The calculation of the CAPM shows that the beta factor has a major influence on the nature of the cost of equity and hence the discount rate in the valuation of a young venture (Kruschwitz, 2007, p. 402 f). In general, there are two ways in which the beta factor can be determined. However, both have in common that the change in value of an investment is analyzed in relation to changes in a market, a portfolio or another group of cash flows (Berk & DeMarzo, 2007, p. 350). This is, on the one hand, the derivation of the beta factor from the accounting history of a company and, on the other hand, the determination of the beta factor by a sensitivity analysis based on the forecasted cash flows. Different types of beta factors are known, depending on the application area (e.g. leveraged beta or asset beta) (Berk & DeMarzo, 2007). In the context of early-stage venture valuation, however, the challenge arises that these ventures usually neither have an accounting history nor can they reliably estimate the variability of their future cash flows. The calculation of a discount rate based on equity financing alone, as done with the CAPM, thus becomes imprecise. This might serve as confirmation as to why the existence of a cash flow adjustment component in the discount rate (cf. section 2.5.4) is not disputed in theory or practice (A.-K. Achleitner, 2002).

2.5.3.2 Derivation of the Discount Rate in the Venture Capital Method The first factor relevant for the target return is the systematic risk of investing in an early-stage NTBF. The systematic risk indicates the extent to which the return on investment is exposed to the overall economic environment. In the case of early-stage NTBF, it can be classified as particularly high. Further risk premiums are charged for the unsystematic risk borne by the venture capital investor, which is based on limited diversification possibilities. On the one hand, this is due to the fact that a venture capital investor has a limited ability to diversify risk because she has to put a lot of effort and attention into her portfolio companies’ development and can therefore not easily increase the number of investments. Furthermore, the possibility of risk diversification is limited by focusing investments on individual markets or sectors. At this point, a clear break with the CAPM becomes apparent, which assumes perfect diversification and therefore only considers systematic risks as relevant for returns. In addition to the systematic risk, an additionally premium for the higher illiquidity of shareholdings in NTBF, the so-called illiquidity or general fungibility premium, is applied (cf. section 2.5.4). This premium results from the fact that

66

2 Theoretical Background

such investments are much more difficult to liquidate at market value or to be reallocated to other investments. This is partly true since capital gains in such cases only occur after several years and an earlier sale is rarely considered (Damodaran, 2005; Scherlis & Sahlman, 1989; J. K. Smith & Smith, 2000). In addition, the business plan of entrepreneurs is often very optimistic, meaning that cash flow models usually only represent the best case and their realization remains highly uncertain. In order to compensate for any negative deviations, a cash flow adjustment surcharge is often applied to the discount rate, even if an appropriate correction of the forecasted figures themselves would be preferable, but often proves to be too unsubstantiated (A.-K. Achleitner & Nathusius, 2004; Scherlis & Sahlman, 1989). Finally, venture capital investors participate very actively in their portfolio companies also and therefore weigh up how much non-financial support the venture will require from them. This support, which includes contacts, mentoring, strategic advice and management support, may require a higher return for the value-add provided (Scherlis & Sahlman, 1989; M. Van Osnabrugge & Robinson, 2000). Since many of the services a venture capital investor has to offer are difficult to obtain in any other way, a simple premium on the target return is a way out that bypasses determining market-driven service prices. Please refer to section 2.5.4 for an overview of target return’s single components. Further, a possible discount to account for the fact that venture capital investments are mostly minority investments is often considered. In such cases, it is common practice to apply a minority discount to reflect the weak(er) or lacking influence of a minority investor. This discount is usually applied to the defined value of the equity capital, instead of a premium on the target return (Cheridito und Schneller 2008, p. 418). Nevertheless, it represents a subjectively determined correction. On the other hand, there is a control premium or package premium on the value of equity capital, if the value of the growth company is determined by comparison with share values that are lower in percentage terms than typical venture capital investments (Achleitner und Nathusius 2004, p. 126 f). Smith and Smith (2000) believe that the entrepreneurs’ optimism about cash flows is more likely to be countered with a higher discount rate rather than slowing optimism by renegotiating optimistic cash flow modeling. Achleitner and Nathusius (2004) are of the opposite opinion, arguing that cash flows should be adjusted. This view is also supported by Damodaran (2009), who explains that the target return is in danger of becoming a vehicle in which all factors for uncertainty are modeled. Also, the value-added factor should not be reflected in the target return, but modeled in the cash flow using comparable market prices (Lerner & Willinge, 1996). Damodaran (2009) further criticizes the included default risk, as this suggests that the risk does not change over the business cycle.

2.5 Determination of the Discount Rate

67

According to Achleitner (2002) the target returns applied show a range of 50 to 100%, but generally around 60% in the seed and start-up phase (corresponding to the “Conception & Development” and “Commercialization” phases; cf. Kazanjian (1988) und Kazanjian & Drazin (1990) as well as section 2.2.2.4.2). In the subsequent phase, the so-called first-stage phase (corresponding to the “Growth” phase; cf. Kazanjian (1988) und Kazanjian & Drazin (1990) as well as section 2.2.2.4.2), target returns of 40 to 60%, followed by 20 to 35% in the transition to an established company (corresponding to the “Stability” phase; cf. Kazanjian (1988) und Kazanjian & Drazin (1990) as well as section 2.2.2.4.2) are observed.

2.5.4

Components of Target Return as a Discount Rate

According to Achleitner (2002), the values for the discount rate to be applied can be determined by three additional components in addition to the classic factors, such as the risk-free interest rate and systematic risk: the liquidity premium, compensation for the added value through operational use of the venture capital investor and cash flow adjustment. The importance of these components is very high at the beginning of the venture’s life cycle and continuously diminishes as the company matures (cf. figure 2.5). The liquidity premium aims at compensating for the increased illiquidity of an investment in an early-stage venture compared to an investment in a listed company. Due to the lack of a market for shares in early-stage ventures, the sale of such shares is very difficult or even impossible. The compensation for the value added through operational commitment compensates the venture capital investor for providing operational support to the venture. This operational support may relate to advisory activities, access to networks or activities directly within the venture. Furthermore, this factor aims at compensating the reputational contribution by the entry of an established and renowned venture capitalist or business angel. The cash flow adjustment is of particular importance as this factor compensates the venture capitalist for the fact that she miscalculates or makes mistakes in the assessment of the venture’s development and thus also in its valuation. It is therefore a factor that is intended to price in the uncertainty in the venture’s development in the context of valuation. Achleitner (2002) notes that this factor can be reduced if the data on which the assessment is based is examined more closely and adjusted. However, it remains decisive, since it compensates for “the risk of default […] up to bankruptcy, which can be reduced to a certain level, but which is always present with such investments”. This risk that the company or

Jusfiable Discount Rate

Growth

Development Stage of the Firm

Commercialisaon

Stability

Figure 2.5 Development of the discount rate as the venture matures. (own presentation based on Achleitner (2002) and Scherlis & Sahlman (1989))

Concepon & Development

Base rate (risk-free interest rate)

Corresponds to Total Discount Rate

68 2 Theoretical Background

2.5 Determination of the Discount Rate

69

investment in question does not develop as expected must be taken into account by investors in order to achieve the target return in the portfolio (Scherlis & Sahlman, 1987). The existence of these components is not disputed in theory (A.-K. Achleitner, 2002) and generally not in practice. However, with regard to the last component of the discount rate, the cash flow adjustment, it becomes clear that valuation methods which aim at discounting the cash flows of young ventures (e.g. discounted cash flow method and venture capital method) have a certain imprecision inherent based on the chosen discount rate. This is sometimes due to the fact that the available data does not necessarily have the required accuracy for a valuation. This imprecision could presumably be significantly reduced if the discount rate, in particular the cash flow adjustment component, could be derived from a solid and comprehensible corporate data basis.

3

Methodology

3.1

Reflection on Methodology

The present research project aspires to bundle relevant determinants for the valuation of early-stage technology ventures in an operationalizable and scientifically substantiated approach. A noteworthy feature of this research is the close interaction between valuation practice and valuation theory. For this reason, in addition to existing research, contributions from practice will be iteratively and scientifically processed. High interdependencies between specific conditions (influenced by, among other things, corporate strategy, team, organization) and the valuation logic (based on the theory of traditional valuation methods and adjustments resulting from practice) are to be assumed. In the field of information technology, the method of Design Science Research (DSR) (A. R. Hevner, March, Park, & Ram, 2004) has established itself as an accepted research methodology. It is important to understand that design in this context can be expressed as a process (i.e. set of activities) on the one hand and as a product (i.e. artifact) on the other. The product is evaluated, generates feedback and a better understanding of the problem in order to improve its quality and the design process. This build-and-evaluate-loop is usually iterated several times before the final design of the product is determined (Markus, Majchrzak, & Gasser, 2002). It becomes apparent that DSR has an inherent need to improve both process (i.e. design and evaluation) and product (i.e. models, methods, etc.). A. R. Hevner, March, Park, & Ram (2004) state that the aim of DSR is to create practical utility, which extends and develops existing theory. Thereby, A. R. Hevner, March, Park, & Ram (2004) clearly distinguish DSR from routine design, which applies existing knowledge (e.g. best practices) to organizational problems. DSR,

© The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2021 C. P. Wessendorf, Indicating Value in Early-Stage Technology Venture Valuation, Schriften zum europäischen Management, https://doi.org/10.1007/978-3-658-34944-8_3

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Methodology

in turn, addresses important, yet unsolved problems in an innovative, more effective and efficient way. Existing knowledge may be included where necessary, even if it is usually not available (Markus et al., 2002). Thus, creativity and a trial-anderror approach are typical characteristics of the DSR approach. DSR primarily addresses “wicked problems” (Rittel & Webber, 1984), such as those arising from unstable requirements and constraints due to poorly defined environmental conditions and complex interactions between subcomponents of the problem and the corresponding solutions. Sein, Henfridsson, Purao, Rossi, & Lindgren (2011) take the approach of DSR even further, arguing that a research method is needed which recognizes an [IT] artifact (i.e. product) as “shaped by the interests, values, and assumptions of a wide variety of communities of developers, investors, users” (Orlikowski & Iacono, 2001) without losing the essence of design science research (i.e. innovation and the treatment of a problem class). Sein, Henfridsson, Purao, Rossi, & Lindgren (2011) thereby express that the organizational context in the development of a design and a product is not yet sufficiently reflected in the DSR approach presented by A. R. Hevner, March, Park, & Ram (2004). As a consequence, Sein, Henfridsson, Purao, Rossi, & Lindgren (2011) address this problem by combining DSR with action research. This combined approach, also known as Action Design Research (ADR), now enables an in-depth evaluation and learning process by the actual user of a product (Action Research component), which is integrated into the research project. Iivari (2007) advocates this and suggests a two-step process: first, the design of a product by DSR, followed by second, the evaluation and implementation in an organization by Action Research. Similarly, Cole, Purao, Rossi, & Sein (2005) argue that adding a “reflection phase” to DSR for improved “learning outcomes” and adding a “development phase” to Action Research for “more concrete learning”, results in an integrated research approach from the combination. The approach of Action Design Research also seems to be appropriate in the context of the present research work, as in this concrete context, a complex problem (similar to the described wicked problems)—the valuation of early-stage technology ventures on the basis of limited data—is approached in an innovative and creative way by developing a valuation logic (similar to an algorithm in information technology). This valuation logic is intended to be evaluated iteratively, in different frameworks (e.g. case study), further developed on the basis of the feedback received and checked again for its practical suitability and validity. In the context of this research project, the central building blocks and principles of DSR will be taken into account in order to attain results that can be formalized and generalized. However, the evaluation of the developed concept in its ultimately

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intended organizational context, as envisaged by ADR, must be viewed critically in terms of implementation. The in-depth evaluation and learning process by the actual user of an artifact in this context appears to be difficult to realize due to the high level of discretion (sometimes even required by law) demonstrated by investment professionals and institutional investors. Thus, the present work will refrain from the described Action Research components and fully focus on following a DSR approach in order to still address this “wicked problem” in an innovative and creative way.

3.2

Design Science Research

Management and entrepreneurship research, as part of social science research, traditionally aims for understanding, describing, explaining, and possibly predicting the natural or social subject matter of its research (Van Aken, 2005). In this context, research can be described as a “systematic, intensive study directed toward fuller scientific knowledge of the subject studied” (Blake, 1978, p. 3) thereby emphasizing on the “scientific knowledge”. Yet, with regard to the identified research gap and the resulting research objective, this dissertation does not aim at understanding and explaining the challenges inherent in early-stage technology venture valuation practice but is seeking to develop an artifact addressing the pressing need in that matter. This is envisaged to be realized through a dedicated approach to operationalizable and fair valuation of early-stage technology ventures, thereby deliberately reducing information asymmetry between investors and entrepreneurs. Hence, a design science approach, reducing the gap between theory and practice, is followed. However, this does not come at the cost of sacrificing rigor for the benefit of professional relevance. In contrast, it is the right balance of rigor and professional relevance towards complementary, symbiotic activities that is creating the real benefit of this research methodology (Dimov, 2016), which addresses “the relevance problem of academic management research” ( van Aken, 2004, p. 241) and thus facilitates research that matters, or at least has the potential to matter.

3.2.1

Fundamentals of Design Science Research

Design science as a research approach is central to most applied sciences, with a particularly pronounced history in building, engineering and material science (A. Hevner & Chatterjee, 2010, p. 9). Yet, it is a relatively new research approach as

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the discussion on the relationship between design and science reaches back to the 1920s (Nigel Cross, 2001). Originating in the sciences of the artificial (Simon, 1996), design science “refers to an explicitly organized, rational and wholly systematic approach to design” (Cross, 2001, p. 51). Due to DSR’s high relevance to the field of computing and information technology (Hevner & Chatterjee, 2010), it is seen as an “equal companion” (Hevner, 2007, p. 87) to other methodologies in natural sciences. Nevertheless, in the fields of management, finance and entrepreneurship research DSR remains an uncommon and rather unexplored research method (Dimov, 2016; Romme, 2016; Romme & Reymen, 2018). Yet, in order to bridge the relevance gap of management and entrepreneurship research (Van Burg & Romme, 2014), scholars call for focusing research in these fields on the “how” rather than the “why” and “what” (Stevenson & Jarillo, 1990, p. 21). In supplement to empirical research, DSR does not limit itself to describe, explain, and predict. “It also wants to change the world, to improve it, and to create new worlds. Design research does this by developing artifacts that can help people fulfil their needs, overcome their problems, and grasp new opportunities” (Johannesson & Perjons, 2014, p. 1). In this context, a design science approach connects the retrospective and prospective perspective of research (Romme & Reymen, 2018). The definition of and the approach to solve the particular problem originating from venture capital investment practice is thus turned into an object of research (Dimov, 2016). This research approach aims at investigating the conditions for understanding and the emergence of knowledge, thereby classifying as an epistemological belief in research. Epistemology differentiates three main path of scientific reasoning: induction, deduction and abduction (Döring & Bortz, 2016, p. 35). Whereas induction describes the conclusion from the special to the general, i.e. by observing empiricism and consequently developing a resulting theory, which is mainly established in the qualitative paradigm of empirical social science research, deduction describes the conclusion form the general to the specific, i.e. the formulation of a theory and consequently developing empirically verifiable hypotheses. Thereby, on the basis of analyzed data, the formulated hypotheses and the overarching theory will either be refuted or provisionally confirmed. Thus, deduction is mainly established in the quantitative paradigm of social science research. In abduction, similar to induction, the scientific reasoning begins with the data. Yet, unlike induction, recognizable patterns in data are not systematically analyzed step by step. Incomprehensible combinations of characteristics within the data are considered in order to form new explanatory hypotheses. This generation of hypotheses is done in a creative, mental process, whereby the intellectual attitude of the researcher plays a decisive role (Reichertz, 2003). With this, DSR follows abductive

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reasoning based on epistemological beliefs in research (Dorst, 2011). Nevertheless, the DSR process might include qualitative and quantitative elements and aspects.

3.2.1.1 Design Science Research Framework The very core of the DSR framework is represented by the design product, e.g. an artifact, as well as the design process. This design product and the design process are not created independent of natural laws or behavioral theories, but strongly rely on kernel theories (Walls, Widmeyer, & El Sawy, 1992). Thereby, the design product’s creation relies on existing kernel theories “that are applied, tested, modified, and extended through the experience, creativity, intuition, and problem solving capabilities of the researcher” (Hevner, March, Park, & Ram, 2004, p. 76). Hevner, March, Park, & Ram (2004) propose a research framework that integrates the behavioral science and design science paradigms. This provides a holistic view on the interplay of key aspects within DSR, thereby showing their complementary nature. Even though it is originally intended for the field of information systems, the proposed research framework of Hevner, March, Park, & Ram (2004) can in essence be applied to a broader spectrum of applied sciences. The framework of Hevner, March, Park, & Ram (2004) (cf . figure 3.1) inherently contains three DSR cycles: the design cycle, the relevance cycle, and the rigor cycle (A. R. Hevner, 2007; A. R. Hevner et al., 2004). The design cycle is at the heart of the DSR project. It contains the creation of the design product as well as the design process itself. Alternative designs are iteratively generated, evaluated and refined until a satisfactory design product, e.g. artifact, is obtained. The relevance cycle connects the research project with the contextual environment and ensures that the developed design product can be applied to it, or ideally can improve it. The contextual environment, consisting of people, organizations or technical systems, provides the problem space as well as the evaluation context. By addressing business needs defined by goals, tasks, problems, and opportunities perceived by people within the organizational context and positioned relative to the existing technology, relevance of the research is ensured. The rigor cycle ensures that the design product is designed upon the state-of-the-art knowledge within a specific research field and based on the use of scientific theories and methods. In consequence, the developed design product further provides additions to the existing knowledge base.

Applicaon

Communicaons Architecture

Development Capabilies

•

•

•

Applicaon in the appropriate environment

•

Infrastructure

• Simulaon

Refine

Infrastructure

Measures Validaon Criteria

•

Techniques

Data Analysis •

•

•

Methodologies

•

Methods

Models

•

Constructs

•

Addion to the knowledge base

Applicable Knowledge

Instruments

•

Frameworks

•

3

Figure 3.1 Design Science Research framework (Hevner, March, Park, & Ram, 2004, p. 80)

•

Experimental Field Study

Case Study

Analycal

• •

Processes

•

Jusfy/ Evaluate

Assess

•

Structure & Culture

•

Business Needs

Technology

Strategies

•

Organizaon

Characteriscs

•

Theories

•

Assets

•

Capabilies

•

•

Foundaons

Theories

•

KNOWLEDGE BASE

Develop/Build

RIGOR

Risks

IS RESEARCH

•

RELEVANCE

People

ENVIRONMENT

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3.2.1.2 Design Science Research Contributions The design cycle, representing the core of the DSR framework, is enclosed by the relevance cycle, assuring the connection with the research project’s environment, and the rigor cycle, assuring the research project’s interlocking with state-of-theart knowledge. Thereby, the product of the design cycle will necessarily have implications on its environment as well as contribute to its knowledge base. Both will come in the form of knowledge contributed. Yet, a DSR project may have different types of knowledge contribution depending on the nature of the designed product, e.g. an artifact (Gregor & Hevner, 2013). Gregor & Hevner (2013, p. 345) propose a framework that positions the knowledge contribution of a DSR project along two dimensions: solution maturity and application domain maturity. This results in four distinguishable forms of knowledge contribution i.e. improvement, invention, exaptation and routine design. Apart from the routine design, which applies known solutions to known problems, all other forms of knowledge contribution provide a research opportunity (cf. figure 3.2). The research output of a DSR project is a design product, i.e. a theory, an abstract artifact (with relevance to the contextual environment) or a concrete artifact. Johannesson & Perjons (2014, p. 7) define artifacts as “an object made by humans with the intention to be used for addressing a practical problem”. These artifacts can be classified as (Johannesson & Perjons, 2014, p. 29; March & Smith, 1995): • Construct: terms, notations, definitions, and concepts needed for formulating problems and their possible solutions. • Model: a set of propositions or statements demonstrating relationships among constructs that represent possible solutions to practical problems. • Method: formal or informal set of steps describing the performance of a task, i.e., guidelines, process definitions or judgment skills. • Instantiation: working system used in practice to operationalize or precede constructs, models, and methods. In contrast to the above classification, Iivari (2007) proposes a function-oriented classification of artifacts. The seven archetypes identified in his work differentiate primarily in accordance with the artifacts’ function to or role for their respective users (cf. table 3.1). In accordance with the initially presented research framework (Hevner, March, Park, & Ram, 2004), the product of the design cycle can be either a concrete artifact or a theory. Thus, in contrast to the different classes of research output

Low

Soluon maturity

High

No major knowledge contribuon

Low

Research opportunity and knowledge contribuon

Extend known soluons to new problems (e.g. adapt soluons from other fields)

Applicaon domain maturity

Apply known soluons to known problems

EXAPTATION

Research opportunity and knowledge contribuon

Research opportunity and knowledge contribuon

ROUTINE DESIGN

Invent new soluons for new problems

Develop new soluons for known problems

INVENTION

3

Figure 3.2 Design science research contribution framework. (own presentation based on Gregor & Hevner (2013))

High

IMPROVEMENT

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Table 3.1 Archetypes of artifact functions (own presentation based on Iivari (2007); Johannesson & Perjons (2014)) Role/ function

Archetype

Examples

To automate

Processor

Embedded and transaction processing systems

To augment

Tool

Personal productivity systems, computer aided design, word processors, spreadsheets

To mediate

Medium

E-mail, instant messaging, chat rooms, blogs, electronic storage systems, social software

To inform

Information source

Information systems

To entertain

Game

Computer games, edutainment

To artisticize

Piece of Art

Compute art

To accompany

Pet

Digital (virtual and robotic) pets

stated, several researchers propose to add design theory itself as a research output (Gregor, 2006; Gregor & Hevner, 2013; Gregor & Jones, 2007). The classification of the knowledge contribution of this dissertation will be discussed and specified in chapter 5.

3.2.1.3 Design Science Research Process Even though the majority of DSR projects root in the same or highly comparable research frameworks (i.e. the fundamental reasoning of this research approach), the structure of a DSR project and the resulting process of performing DSR is shaped by the existence of different research methodologies (Johannesson & Perjons, 2014; Peffers, Tuunanen, Rothenberger, & Chatterjee, 2007), including a specific design science approach based on action research, i.e. Action Design Research (Sein et al., 2011). With regard to entrepreneurship research, Romme & Reymen (2018) propose a research process that distinguishes design activities (i.e. creating and evaluating) and validation activities (i.e. justifying and theorizing) (cf. figure 3.3). These interdependent activities serve to address both rigor and relevance inherent to DSR. Yet, even though this framework is conclusive and compelling due to its simplicity, it lacks the necessary level of detail to reliably guide a researcher performing a DSR project. This fact becomes particularly apparent when turning to respective literature in the field of information systems (Peffers et al., 2007; Sein et al., 2011). In order to provide a better guidance, the commonly accepted and well elaborated process framework proposed by Peffers et al. (2007) inspires the present work

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3

Pracces, values, constructs, models, and/ or principles

CREATING

Pracces, values, constructs, models, and/ or principles

JUSTIFYING

Methodology

EVALUATING Pracces, values, constructs, models, and/ or principles

THEORIZING

Pracces, values, constructs, models, and/ or principles

Figure 3.3 Inclusive framework for entrepreneurship research as a design science. (own presentation based on Romme & Reymen (2018, p. 6))

(cf. figure 3.4). This process framework clearly distinguishes six main activities and their interdependencies: problem identification and motivation, definition of the objectives for a solution, design and development, demonstration, evaluation, and communication. First, the problem is identified, specified, as well as its relevance established. Second, the objectives of a solution to the specified problem are defined, which provide the characteristics of the artifact to be developed. Thereby, a theory on how to design a matching artifact is developed. Next, third, this theory is implemented by designing and developing the artifact. The result of this step provides an approach to the specified problem, which needs to be tested (i.e. “how-to” knowledge). Fourth, the artifact is demonstrated within a suitable context in order to solve the previously specified problem. Fifth, by observing the artifact’s effectiveness and efficiency as well as fulfillment of other requirements its suitability to address the problem is evaluated. Lastly, sixth, the results will be communicated among relevant experts in order to gain relevant feedback and deeper insights

ProblemCentered Iniaon

• Show importance

Design- & DevelopmentCentered Iniaon

• Develop an arfact

• Design an arfact

Possible Research Entry

ObjecveCentered Iniaon

• Define characteriscs of a beer arfact

Design & Development

Client-/ ContextCentered Iniaon

• Use arfact to solve problem

• Find suitable context

Demonstraon

• Iterate back to design

• Observe how effecve, efficient

Evaluaon Disciplinary Knowledge

Metrics, Analysis, Knowledge

Communicaon

• Professional publicaons

• Scholarly publicaons

Figure 3.4 Design science research methodology process model. (own presentation based on Peffers et al. (2007))

Nominal Process Sequence

• Define problem

Interface

Define Objecves of a Soluon Theory

Idenfy Problem & Movate How to Knowledge

Process Iteraon

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in the artifacts ideal design. The last two steps “evaluation” and “communication” might trigger new DSR process iterations by redefining objectives as well as redesigning and developing the artifact until and ideal result is achieved. Additionally, the DSR process as well as its various iterations can be initiated at different stages of the process. The process does not need to be a full cycle process as it is the case for a problem-centered initiation. It might also focus on the objective (i.e. objective-centered), thereby starting at stage two, design and development-centered, thereby focusing on stage three or client/ context centered, thereby having its focus on step four.

3.2.2

Selected Research Process

Since the proposed model by Peffers et al. (2007, p. 73) can be referred to as a “general methodological guideline for effective DS research” it needs to reflect the specific aspects of the present DSR project. Thus, this work will be based on the proposed process framework by Peffers et al. (2007) (cf. figure 3.4). The resulting DSR process covers six activities. First, the problem is identified, specified, and a knowledge base created. Second, the requirements are determined, in order to, third, create the artifact accordingly. This includes an iterative refinement of selected aspects of the overall artifact. Fourth, the artifact is demonstrated and, fifth, evaluated in a single iteration. The fact of a single iteration of the overall artifact is due to the high amount of resources needed to construct the artifact in its total complexity. Thus, the present work refrains from additional iterations to develop, demonstrate and evaluate. Still, new insights from evaluation as well as the created and enhanced knowledge base were obtained and documented. Lastly, sixth, communication accompanies every step of the selected DSR process and serves as an activity supporting the evaluation and validation of individual steps. Since the individual steps and the respective DSR activities have specific goals and characteristics, different research methods appear valuable and are hence not excluded beforehand (Johannesson & Perjons, 2014, p. 77). Next to a clearly defined process, guidelines assuring a high quality of DSR results need to be taken into account. In this context, Hevner, March, Park, & Ram (2004) propose seven guidelines to understand, execute and evaluate good DSR. These guidelines will be respected and thus integrated into this DSR project as follows: • Guideline 1 – Design research must produce an artifact: The tool elaborated hereafter for indicating value in early-stage NTBF represents such an artifact

(cf. secon 4.2)

Derivaon of requirements and objecves Relevant Valuaon Determinants (cf. secon 4.3.1) Determinants’ Importance (cf. secon 4.3.2) and Impact (cf. secon 4.3.3) Applicable Discount Rates (cf. secon 4.3.4)

•

•

•

Design and development of the arfact

Design & Development

(cf. secon 4.4.3)

Demonstraon of the arfact in one iteraon cycle by means of three case studies

Demonstraon Demonstrated Arfact

(cf. secon 4.5)

Evaluaon of arfact by means of expert interviews

Evaluaon

Figure 3.5 Design Science Research project framework „Indication of Value in Early-Stage NTBF (InVESt-NTBF)”

Communicaon Academic conferences and publicaons (cf. secon 4.7)

(cf. secon 4.1.1)

Problem-centered iniaon of the project, specificaon of the problem and creaon of a relevant knowledge base by means of an empirical study

Explicated Problem and Knowledge Base

Definion of Soluon Space Arfact

Problem Specificaon & Knowledge Base Requirements, Objecves and Input

One arfact iteraon cycles

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Evaluated Arfact

84

• •

•

•

•

•

3

Methodology

envisaged as an instantiation, i.e. working system that can be used in practice (March & Smith, 1995). Guideline 2 – The addressed problem needs to be relevant and important: In the present work, the problem’s relevance and importance is shown by systematic literature review as well as empirical studies in the form of surveys. Guideline 3 – The evaluation of the artifact respects scientifically recognized methods and standards: The applied methods and research standards of the present work are well suited to demonstrate the utility, quality and efficacy of the artifact (cf. section 4.5.5). Guideline 4 – Clear and verifiable contributions in the area of the design artifact must be demonstrated: Clear research contributions to academia but also practical implications are highlighted and discussed in detail in sections 5.2 and 5.3. Guideline 5 – Application of rigorous methods in both construction and application of the artifact: Scientific methods to reach an understanding of the problem, build the knowledge base as well as conduct and evaluate the artifact are thoroughly selected and applied. Guideline 6 – The DSR process reflects a search for an effective artifact utilizing available means to reach desired ends: Each step of the valuation tool’s elaboration presented hereafter is followed by a reflection on the results and the necessary steps forward to reach the desired outcome. Yet, available resources of this DSR project allow for one iteration only. Restrictions of the environment, specifically the particularities of venture capital investors such as time limitations, discretion and special requirements for comprehensible valuation, are considered. Guideline 7 – The research must be presented to technology-oriented as well as management oriented audiences: The interim results but also the final results of this work were regularly presented (i.e. at conferences or scientific papers) and discussed with relevant stakeholders from academia, investment practice and funding-experienced entrepreneurs.

Table 3.2 summarizes the proposed guidelines (Hevner, March, Park, & Ram, 2004) and their implementation in this DSR project: The further structure of the present work will follow the specifically adapted DSR framework (cf . figure 3.5) based on the six DSR project activities defined in the DSR process model by Peffers et al. (2007) and are detailed in the following chapters.

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Table 3.2 Design Science Research guidelines and their implementation (own illustration in reference to Hevner, March, Park, & Ram (2004)) Guideline

Description

Implementation

Guideline 1: Design as an Artifact

Production of a viable artifact in Development of a method in the the form of a construct, a model, form of a framework that has the a method, or an instantiation. function of a tool (cf. section 4.2.2)

Guideline 2: Problem Relevance

Development of technology-based solutions to important and relevant business problems.

Outline the relevance and importance of indicating value in early-stage NTBF to investment practice (cf. section 1.1 and 4.2.1).

Guideline 3: Design Evaluation

Rigorous demonstration of the design artifact’s utility, quality, and efficacy via well-executed evaluation methods.

Analysis of qualitative assessments from relevant experts stating the artifact’s utility, quality, and efficacy (cf. section 4.5.5) along with the derived requirements.

Guideline 4: Research Contributions

Provision of clear and verifiable contributions in the areas of the design artifact, design foundations, and/or design methodologies.

Outline the theoretical and practical contributions (cf. section 5.2 and 5.3.)

Guideline 5: Research Rigor

Application of rigorous methods Rigorous selection and application in both the construction and of methods used to understand the evaluation of the design artifact. problem, developing and evaluating the artifact while respecting scientific principles.

Guideline 6: Design as a Search Process

Utilization of available means to reach desired objectives while satisfying laws in the problem environment

Guideline 7: Communication of Research

Effective presentation to both Presentation of the research results, technology-oriented and as well as interim results, at management-oriented audiences. academic conferences and scientific journals as well as discussion with relevant stakeholders, i.e., investment professionals and funding-experienced entrepreneurs.

Consideration of the current state of knowledge, taking into account the specific characteristics and requirements of early-stage venture valuation by professional investors. The DSR process ends after the first iteration by reaching the desired objective and a lack of resources for further iterations.

4

Application and Results

4.1

Problem Identification and Validation of Relevance

In a first step of this research project, the direction of research, e.g. solution to a problem or improvement, needs to be defined. Therefore, Peffers et al. (2007) propose four different research entre points in DSR: a problem-centered initiation, an objective-centered initiation, a design- and development-centered initiation, and a client-/ context-centered initiation (cf. figure 3.4). As the challenges within earlystage venture valuation laid out in the introduction suggest, this DSR project will follow a problem-centered initiation (cf. chapter 1) in order to develop an appropriate approach to indicate value in early-stage NTBF. Chapter 2 provided the necessary theoretical background to frame this DSR project and define the relevant terminologies and concepts. The present research will be positioned within the current scientific literature in the following sections of this chapter. Therefore, several systematic literature reviews of the respective major components of an approach to indicate value in early-stage NTBF have been conducted. Hevner, March, Park, & Ram (2004) state, that a DSR project needs to build upon existing knowledge in a rigorous way. The already existing knowledge, outlined by the performed systematic literature reviews, therefore provides a basis for the construction and evaluation of an artifact (cf. section 3.2.1) and will further extend the knowledge base by generating new knowledge (Baskerville, Baiyere, Gregor, Hevner, & Rossi, 2018; Gregor & Hevner, 2013). The process of building new knowledge upon an existing knowledge base is considered to ensure the balance of rigor in the research conducted (Dimov, 2016; A. R. Hevner et al., 2004). The objectives of the solution should be derived “from the problem definition and knowledge of what is possible and feasible” (Peffers et al., 2007, p. 55). Required

© The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2021 C. P. Wessendorf, Indicating Value in Early-Stage Technology Venture Valuation, Schriften zum europäischen Management, https://doi.org/10.1007/978-3-658-34944-8_4

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resources for the definition of objectives for a solution are “knowledge of the state of problems and current solutions, if any, and their efficacy” (Peffers et al., 2007, p. 55). Hence, as a starting point for the present research, an empirical survey among relevant investment professionals focusing on early-stage technology venture investments is conducted in order to understand the current process and challenges in the respective valuation practice. This allows for the identification of persisting problems as well as the indication of relevance of an envisaged solution.

4.1.1

Challenges in Early-Stage Technology Venture Valuation

This section is based on: Wessendorf, C. P. and Hammes, C. (2018) Methods and Criteria affecting Early-Stage Venture Valuation. https://doi.org/10.5445/IR/1000079690.

4.1.1.1 Research Purpose and Design of the Empirical Survey Wessendorf & Hammes (2018) describe the research purpose of the survey conducted as: “[…] to get a deeper understanding of the valuation practice of German-speaking Venture Capitalists and Business Angels. […] first, the knowledge and usage of valuation methods for Start-Up valuation needed to be explored. Second, the criteria and according performance indicators considered to drive Start-Up value and thus serving as the necessary basis for valuation methods needed to be analyzed in greater detail. Lastly, the current situation in venture […] to meaningfully discuss the responses given.”

In order to fulfill the objectives, set out by the defined purpose, a quantitative research approach has been chosen. Thus, an online survey on the platform www. typeform.com was developed, that could be accessed by invited participants from April 22nd , 2017 to July 22nd , 2017. “Each participant was asked a minimum of 9 questions up to a maximum of 45 questions”, span over three main topics: investor characteristics, valuation methods, current situation in venture capital investment practice (Wessendorf & Hammes; 2018). The structure of the questionnaire and the questions to relevant participants are displayed in figure 4.1. With regards to participation, a high number of relevant participants has been aimed for. Thus, three different channels were used to reach potential candidates from the relevant investment community: desk research on relevant venture capital firms and business angels as well as the subsequent contact of these firms

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Figure 4.1 Question overview and structure of the questionnaire. (Wessendorf & Hammes, 2018)

and persons via telephone to initiate participation, “desk research on relevant venture professionals and business angels via professional associations (i.e. Bundesverband Deutscher Kapitalbeteiligungsgesellschaften) and professional social networks (i.e. LinkedIn and Xing)” and subsequent contact through these platforms

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or available e-mail addresses, distribution of the online survey’s link “via relevant mailing lists including venture capital professionals and Business Angels only [and] administered by the “CyberForum e.V.” (Karlsruhe) and the “Vereinigung Baden-Württembergische Wertpapierbörse e.V.” (Stuttgart)”.

4.1.1.2 Results and Discussion With regard to investor characteristics, the participants (n = 54) of Wessendorf & Hammes’ (2018) survey demonstrate the following: “47% of respondents described themselves as Private Venture Capital Funds, 24% as Business Angels, 11% as Public Venture Capital Funds, 9% as Corporate Venture Capital Funds and the remaining 9% as other venture investor types. Confronted with the question in which life cycle/ investment phase the investor is investing (multiple answers possible), 78% of respondents reported to be active within the “Conception & Development” stage, 76% in the “Commercialization” stage, 33% in the “Growth” stage and only 2% are active in later stages [cf. section 2.2.2.4 for a description of stages]. Thus, on a combined basis, 96% of respondents are active during the “Seed” and “Start-Up” stages (i.e. “Conception & Development” until early “Growth”) of a venture. Following the question on whether an investment focus is pursued, 20% of the participants declined to have an investment focus. The remaining 41% of respondents stated to follow a cross-sector investment strategy including both technology ventures and business model driven ventures, 35% mentioned a focus on technology ventures only (i.e. New Technology-Based Firms; NTBF[…]) and 4% mentioned a focus on business model driven ventures. Thus, on a combined basis, 76% of respondents are either generally or specifically focusing their investments on early-stage technology ventures and are thus expected to be knowledgeable on how to value technology.”

With regard to valuation methods, the participants (n = 54) of Wessendorf & Hammes’ (2018) survey were initially asked which valuation methods they are knowledgeable about and which valuation methods are actually used in venture capital valuation practice. The participants’ answers present themselves as follows: “With 91% of respondents (total n=54) knowing about valuation by multiples, this method represents the most well-known valuation method. This is followed by the Discounted Cash Flow Method (85%), the Venture Capital Method (69%), Scenario Analysis (54%) and Real Option Approach (30%). The remaining 2% of respondents stated to apply personal experience and other, less conventional methods in the valuation context. Interestingly, the usage of these methods for Start-Up valuation differs greatly. Even if the valuation by multiples is not only the most-well known method but also the most applied method in a valuation context of early-stage ventures (74% of

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respondents agreed to apply multiples for Start-Up valuation purposes), the Venture Capital Method becomes second (43%) passing the Discounted Cash Flow Method (37%). This is followed by Scenario Analysis (26%), other, less conventional valuation methods (15%) and finally, the Real Option Approach (4%). It is striking that the other, less conventional methods stated by relevant respondents are in general based on own experience with Start-Up valuation (9%), no methods at all (4%), as these are expected to not reflect the real value of the Start-Up, or own negotiation skills to define a value (2%).”

In order to gain an even more concrete understanding of valuation practice, participants in the study of Wessendorf & Hammes (2018) were asked which valuation method they primarily use for valuation. “Relevant respondents (n=46) mentioned Multiples (52%), Discounted Cash Flow Method (22%), Scenario Analysis (4%), Venture Capital Method (2%) and Real Option Approach (2%). Intriguingly, 28% of respondents mentioned personal experience and gut feeling as the primary approach to derive a valuation. It needs to be pointed out that multiple answers were needed to be given due to the fact that the valuation methods chosen differ in function of the life cycle stage of the Start-Up to be valued.”

This further revealed new aspects compared to the prior question, where valuation methods generally used in venture capital valuation were explored. First, some valuation methods known to venture capital investment professionals are not used in practice. Thus, differences between knowledge and usage of these valuation methods are often clearly observable. For the sake of example, the valuation by discounted cash flow methods (cf. section 2.4.2.2) is known to 85% but only used by 22% of participants (Wessendorf & Hammes; 2018). The real options approach (cf. section 2.4.2.3) is known to 30% but only applied by 2% of participants (Wessendorf & Hammes; 2018). This can either be due to the methods being fundamentally inappropriate for early-stage NTBF valuation (e.g. because of underlying mathematics in DCF valuation, cf. section 2.2.2.5.2) or too complex to apply (e.g. real options approach (A.-K. Achleitner & Nathusius, 2004)) and thus not operationalizable. Second, a large number of investment professionals state personal experience and gut feeling as an approach to valuation. Hence, the underlying reason for this needed to be further explored by Wessendorf & Hammes (2018). “Even though the wide majority of respondents actively use the above described valuation methods in practice, out of n=54 respondents, only 52% consider these valuation methods as appropriate to account for the potentially strong growth of Start-Ups. The remaining 48% do not consider these valuation methods as appropriate. It further remains a possibility, that the real number of investment professionals considering conventional valuation methods as inappropriate is much higher. This hypothesis is based on a potential bias in the way the present questions were answered. We believe,

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that some investors would in general agree that certain valuation methods are not suitable but would not admit to be using valuation methods that do not serve the pursued purpose. The fact that the questionnaire could have been answered anonymously is mitigating the risk of a strong bias in this case. Nevertheless, certain explanations for this present result become apparent in the answers given and are further reflected in relevant literature. First, the necessary data for applying specific valuation methods cannot yet be provided by the Start-Ups analyzed. This is mainly due to the fact that these Start-Ups are in a very early stage of the life cycle and thus do not yet dispose of a track record or company history (Kaserer et al., 2007). Consequently, there is no basis in order to correctly implement and adjust these valuation methods, thereby increasing the subjectivity of the valuation process (A.-K. Achleitner, 2001). Second, the conventional valuation methods are not suitable to value high-growth companies as they cannot account for growth that is not yet reflected in the present data. As the value of Start-Up companies is mainly defined by its future (A.-K. Achleitner, 2002), growth becomes a crucial aspect in Start-Up valuation. Therefore, the discussed valuation methods are not in any case suitable in valuation practice – at least in the early stages of Start-Up life- cycles.”

Even though some participants state that early-stage ventures do not yet dispose of a solid history to correctly implement some of the listed valuation methods, these were still described as being used in valuation practice. Thus, 68% of the respondents (n = 34) asked how they derive the discount rate for a discounted cash flow, answered that this is done based on experience. The remaining answers state the discount rate is derived by the target return of investors, the Weighted Average Cost of Capital (WACC) and the Capital Asset Pricing Model (CAPM). In this context it has to be noted that the regularly observed negative cash flow of earlystage technology ventures mathematically challenges some of these specifically mentioned valuation methods (cf. section 2.2.2.5.2). In a next step, the valuation models used for venture valuation were investigated in greater detail. Here, the authors tried to understand drivers for value. It has been hypothesized that these drivers could be clearly described and measured in order to allow for a targeted selection of high-value ventures. The survey’s results of Wessendorf & Hammes (2018) depict a divided situation with regard to objectivity and subjectivity in valuation, further suggesting a diverse and heterogeneous, yet mostly subjective set of determinants. “[…] the respondents (n=33) were asked to define performance indicators, preferably measurable, in order to enable a meaningful assessment. However, out of n=243 mentions of indicators within the investment decision process, only n=29 mentions are of quantitative nature. Within the remaining n=214 indicators, “personal impression” (n=29), “experience” (n=23) and “gut feeling” (n=20) score among the Top 3 indicators.”

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With regard to the current situation in venture capital investment practice, the respondents to this set of questions (n = 53) of Wessendorf & Hammes’ (2018) survey clearly mentioned that the proposed valuation by early-stage ventures themselves were generally too high (i.e. 8.02/10.00). This suggests also a lack of valuation transparency and possibility for verification by other parties involved in the process, therefore leading to exaggerated valuations. Further, the current interest rate environment and the resulting investment pressure were considered an important driver for early-stage venture valuation by 74% of respondents. To conclude, the performed survey among relevant investment professionals (Wessendorf & Hammes, 2018) indicates a regularly unstructured approach to early-stage technology venture valuation that is strongly shaped by “gut feeling” and “experience”. This phenomenon is potentially explained by structured approaches being often considered as inappropriate, e.g. due to a lack of data. Yet, the observed approaches are not only mostly unstructured but also highly subjective, thereby leading to strongly differing expected valuations by investors and entrepreneurs. With venture capital investment activity constantly on the rise and valuations increasing, an efficient and objective approach to indicate value in early-stage NTBF is relevant to investors, their limited partners and entrepreneurs. As the discussed empiricism suggests, such an approach currently does not existing and thus is not followed by investment professionals. Therefore, a solution to these challenges, observable in early-stage technology venture valuation, is highly relevant and urgently needed.

4.2

Definition of Solution Space

Having analyzed the inherent challenges in early-stage technology venture valuation in an empirical survey questioning venture capital investment professionals (cf . section 4.1) and thus establishing their relevance, a respective solution space is presented. According to Johannesson & Perjons (2014, p. 103 ff) and Peffers et al. (2007), a solution space specifies requirements and objectives of a possible solution, as well as identifies and outlines the artifact.

4.2.1

Requirements and Objectives

Johannesson & Perjons (2014, p. 103) define a requirement as a “property of an artifact that is deemed as desirable by stakeholders in a practice and that is to be used for guiding the design and development of the artifact. A requirement can

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concern the functions, structure, or environment of an artifact as well as the effects of using the artifact.” Functional requirements refer to the functions of the artifact and are specific to the DSR project’s context. The identified problems and stakeholders’ wants and needs provide the necessary basis. In contrast, structural requirements are more generic and refer to the artifact’s structure, such as coherence, consistence, or conciseness. Environmental requirements, also classifying as generic requirements, refer to the artifact’s relationship with the environment and disintegrate into three main categories: usage qualities, management qualities and generic environmental qualities. Usage qualities refer e.g. to an artifact’s usability, comprehensibility, customizability, suitability, and accessibility. Management qualities consider e.g. maintainability, flexibility, and accountability of an artifact. Generic environmental qualities relate e.g. to the expressiveness, correctness, generality, interoperability, autonomy, proximity, completeness, effectiveness, efficiency, robustness, and resilience of an artifact. In addition, requirements that refer to the effects of using the artifact can also be formulated (Johannesson & Perjons, 2014, p. 103 ff). Yet, the requirements need to be carefully developed based on the stakeholders’ problems, needs and the desired outcome. Overall, the results of the performed empirical survey suggest a high level of subjectivity within early-stage venture valuation. This confirms the motivation outlined in section 1.1 as well as findings of existing research in this field (A.-K. Achleitner, 2001; Damodaran, 2009; Kaserer et al., 2007). Wessendorf & Hammes (2018) find that: “[…] the valuation methods applied in an early stage of the Start-Up life cycle have a clear tendency towards subjectivity (i.e. multiples) and are largely shaped by personal experience of the investor. More sophisticated valuation methods, such as the Discounted Cash Flow Method, are […] based on data and criteria that are influenced by assumptions, personal experience and gut feeling. This is confirmed by the majority of respondents (68%) stating to derive the discount factor based on experience. Intriguingly, the results of this questionnaire further prove that the majority of respondents is active in very early-stages of Start-Up life cycle and is thus relying on multiples and personal experience to derive a valuation. Even if a diverse set of valuation methods is used and also applied, the method of choice for more complex valuation remains the Discounted Cash Flow Method. […] analyzing the criteria taken into account within the valuation process in greater detail, the above stated tendency towards subjectivity, in particular in an early stage, becomes more pronounced. A structured investment approach is followed by only 61% of respondents. Thus 39% of respondents stated to not follow a structured investment approach and therefore pursue a potentially opportunistic and subjective investment approach not taking into account a defined set of criteria. Out of the

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remaining investment professionals (61%), who describe themselves as following a structured investment approach, a wide majority (88%) does not have measurable performance indicators for these criteria and thus mentions subjective performance indicators such as “personal impression”, “gut feeling” and “experience” as the basis of their decisions.”

Further, as outlined in section 1.2, with venture capital funds and respective investment volume constantly increasing in recent years and the related valuations augmenting, the need for an objectifiable valuation becomes highly relevant. The overall volume of funds as well as the valuation of individual companies drive the interest of venture capital funds’ limited partners to optimally manage funds provided and to make investment and valuation decisions in a transparent manner. These findings reveal a set of industry customs in valuation practice as well as challenges that are at the core of the problem to be addressed in this research project. The following construction of an artifact, highly relevant to valuation practice facing rising deal volumes, increasing investment activity and valuations, will thus need to reflect (cf . table 4.1): Table 4.1 Functional requirements of the artifact Challenges

Functional Requirements

(1) Methods for firm valuation are generally based on input variables derived from the past, which is not representative of early-stage NTBF mostly realizing potential in the future.

(1) The artifact must allow for an orientation towards the future development of a NTBF representing the core of value creation.

(2) Early-stage NTBF are characterized by negative cash flow and high capital needs that mathematically challenge conventional valuation methods.

(2) The artifact must reflect special features of early-stage NTBF development in the valuation method’s fundamental approach.

(3) Early-stage venture valuation is characterized by subjectivity as objective and quantifiable data are rarely available.

(3) The artifact must allow for subjective assessment and valuation based on non-financial valuation determinants.

(4) Early-stage venture valuation (4) The artifact must provide a clear ranking determinants are very diverse and of relevance for valuation determinants heterogeneous, thereby complicating an to allow for efficient valuation. efficient assessment (continued)

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Table 4.1 (continued) Challenges

Functional Requirements

(5) Early-stage venture valuation is unstructured, thus not transparent and verifiable, in particular to other parties involved in the process.

(5) The artifact must allow for the subjective assessment of value drivers to be formalized and structured for easy understanding.

(6) Early-stage venture valuation is not easily compatible with conventional valuation methods, widely accepted in valuation practice.

(6) The artifact must allow for the formalized and structured value drivers to be implemented into an accepted valuation method.

(7) Early-stage venture valuation is complex and thus does not allow for a simple and operationalizable valuation approach.

(7) The artifact must allow the reduction of complexity in order to provide a practical and operationalizable approach to valuation practice.

In addition to these functional requirements, which can be interpreted as the artifact’s “usefulness”, structural, environmental as well as effect requirements are elaborated (cf . table 4.2):

Table 4.2 Structural, Environmental and Effect requirements of the artifact Requirement Category

Requirements

Structural requirements

The artifact must be • Coherent • Concise

Environmental requirements

The artifact must be • Easy to use • Comprehensible • Complete • Adequately complex • Efficient • Effective • Accountable

Effect requirements

The artifact must provide some advantage to status quo

With regard to the purpose of this research project of improving the indication of value in early-stage technology venture valuation, these requirements represent the criteria, that constitute the artifact’s viability. Thus, these criteria must be

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fulfilled by the artifact to enable the users (venture capital investors and entrepreneurs seeking venture capital funding) to conduct a viable, transparent and concise value indication process accounting for the specifics of early-stage NTBF.

4.2.2

Outline of the Artifact

Following discussions with fellow researchers and venture capital investors, the author decided upon the fundamental characteristics of the artifact. A possible solution was expected by providing a defined and concise set of non-financial valuation determinants, that allow for subjective assessment of the venture while reflecting operational requirements of valuation practice, thereby further accounting for a lack of meaningful financial data. The relation among these determinants as well as their impact on valuation should be transparent, leading to an interpretable and meaningful valuation score. This score should provide the basis to transform the valuation, driven by subjectivity into a discount rate to be applied in conventional valuation methods, such as the Venture Capital Method. Thus, the artifact classifies as a design instrumentality (Vincenti, 1993, p. 219 f.), method (March & Smith, 1995) or a tool (Iivari, 2007) that transforms a subjective valuation into a variable to be used in conventional valuation methods. Rigby (2001, p. 139) defines management tools as involving “a set of concepts, processes, exercises, and analytic frameworks”. Doskoˇcil (2016) ascertain that a tool may serve to optimize workflows and support decision-making. Tools further provide the necessary elements for performing specific tasks, while allowing the user to concentrate on the essentials. Yet, a tool is no guarantee that investors will perform a thorough and transparent valuation. Hence, the artifact to be developed will augment (cf. table 3.1) early-stage investors to perform their technology-venture valuation in a meaningful and efficient manner. The proposed tool is intended to be implementable in conventional table calculation programs, such as Microsoft Excel, commonly used among investment professionals, or other mathematical software. Yet, in a first development iteration of this DSR project, the artifact will be implemented in the form of a canvas to allow for easy discussion.

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Application and Results

Design and Development of the Artifact

The determination of meaningful, relevant value drivers is crucial for company valuation. However, valuation determinants vary in the course of the company’s development, depending on the phase in the corporate life cycle, and are taken into account differently in the investment process (C. Mason & Rogers, 1996; Riding et al., 1993). Wessendorf & Hammes (2018) showed that, particularly in the case of early-stage ventures, a wide range of aspects must be attentively assessed in order to consider both future sustainability and strong growth potential. Due to an observable lack of company history among early-stage ventures, the range of fluctuation of risk is usually particularly pronounced. The aspects to be taken into account rather include qualitative than quantitative factors, such as the competence of the management team, which clearly distinguishes the valuation of early-stage ventures from the valuation of established and large companies. In the case of the latter, the company is usually already consolidated by an organizational structure and has found a clear place in the market. In contrast to early-stage ventures, the dependence on e.g. individuals is often secondary. As a consequence to the findings in section 4.1.1.2, the described situation calls for an in-depth investigation of the relevance and impact of company characteristics on value, aiming to provide an important basis for a meaningful and comprehensible valuation. Hence, in order to provide the necessary fundamentals to construct an artifact capable of addressing the previously laid out requirements, over the course of the following sections, relevant determinants for early-stage technology venture valuation will be identified (cf. section 4.3.1) and their respective relevance for (cf. section 4.3.2) as well as impact on (cf . section 4.3.3) value quantified. Next, the resulting information will be processed to derive a suitable and meaningful discount rate (cf. section 4.3.4) to be applied in present value valuation methods (cf. section 4.4).

4.3.1

Identification of Valuation Determinants

This section is based on: Wessendorf, C. P., Kegelmann, J. and Terzidis, O. (2019) Determinants of Early-Stage Technology Venture Valuation by Business Angels and Venture Capitalists, International Journal of Entrepreneurial Venturing, 11(5), pp. 489–520. https://doi.org/10.1504/ IJEV.2019.102259.

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As a first step, the necessity for a systematic literature review needs to be verified. Thus, previous research addressing the same set of questions in the context of a literature review needs to be identified in a structured approach. The hence applied search string to search various relevant databases (i.e. EBSCO Business Source Premier, Web of Science, Springer and Nexis) has been constructed of four elements containing relevant keywords and Boolean operators “AND” and “OR”: 1. 2. 3. 4.

Research objective, i.e. “determinants" OR its synonyms; AND Valuation objective, i.e. “NTBF” OR its synonyms; AND Valuating entity, i.e. “venture capitalist” OR its synonyms; AND Type of research to be addressed, i.e. “SLR” OR its synonyms; AND

After screening title and abstract the resulting publications, and if deemed relevant, by subsequently reading through the whole publication in order to verify its relevance, only one relevant publication was identified. Köhn (2017) studied determinants of Start-Up Valuation and thereby performed a SLR, analyzing 58 scientific publications. Following his analysis, Köhn (2017) elaborates a holistic model on Start-Up valuation comprised of cultural, environmental, market specific, venture capital specific and Start-Up specific factors. The resulting determinants of Start-Up valuation are then categorized within this model and thoroughly described. Yet, he refrains from rating the determinants’ importance or influence on valuation. In order to differentiate from previous research and provide a sound knowledge base to this DSR project, the study of Wessendorf, Kegelmann, et al. (2019) focusses on “valuation factors directly related to the start-up and in the direct sphere of influence of the parties involved in the evaluation.” Hence, first, the particular attention on early-stage venture valuation that is mainly driven by qualitative assessment requires a narrowed and thereby differentiating focus on non-financial valuation determinants. This does not necessarily imply to compromise the results, as Sievers et al. (2013) show that the variation in value explainable by solely nonfinancial information is strongly comparable to the variation in value explainable by solely financial information. The differentiation of valuation determinants from investment criteria needs to be stressed at this point, as the latter is more widely discussed in scientific literature as criteria applied to the decision if an investment is made or not. Secondly, the attention on early-stage venture valuation further requires to limit the scope of the analysis to publications addressing the early stage of the organizational life cycle, i.e. “Conception and Development”, “Commercialization”, and first phases of “Growth” (Kazanjian, 1988; Kazanjian and Drazin, 1990). Lastly, in order to further increase meaningfulness of the analysis,

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the SLR concentrates on NTBFs, also often referred to as “technology ventures” within this work. By this, a specific and more homogenous set of characteristics (i.e. high R&D expenses, long R&D duration, high market-related uncertainty, uncertain time-to-market, difficult R&D customer integration, extensive need of change in customer behavior) is expected as NTBF all share an increased technological uncertainty compared to e.g. conventional web applications (Gerpott, 2005), not accounting for (potentially smaller) differences subject to different high technology sectors, thereby potentially leading to a more homogenous set of valuation determinants.

4.3.1.1 Systematic Literature Review (SLR) on Valuation Determinants Following major studies outlining the methodological approach of an SLR (Tranfield et al., 2003; Kitchenham, 2004; Budgen and Brereton, 2006; Kitchenham et al., 2009; Crossan and Apaydin, 2010; Tahir et al., 2016), this SLR will disintegrate into three main steps: Planning, Conducting, and Reporting. The planning phase needs to establish the necessity for an SLR, leading to the formulation of research questions and the definition of a review protocol. The conducting phase describes the review itself, further detailing the identification and selection of primary studies as well as the analysis and discussion of relevant content. Finally, the reporting phase addresses the result presentation and thereby concludes the review.

4.3.1.1.1 Planning Phase The planning phase of an SLR is comprised of three steps that will be elaborated in the following: necessity of the SLR, research questions, and review protocol: 1. Necessity of the SLR: Wessendorf et al. (2019) state that “early-stage venture valuation remains a challenge for academics and practitioners alike (Achleitner, 2001; Shane and Cable, 2002; Kaserer et al., 2007; Maxwell et al., 2011; Valliere, 2012; Festel et al., 2013; Wessendorf and Hammes, 2018) thereby creating the necessity to systematically examine existing research in order to reduce its present level of complexity and draw practicable conclusions.” As no such work is known to the author in the defined area of research, even after systematically reviewing existing research (cf . section 4.3.1), the need for the present study can be established. 2. Research questions: Two research questions will shape this SLR. First, it is intended to identify relevant early-stage valuation determinants for technology venture valuation leading to

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RQ1: Which early-stage venture valuation determinants are defined within the relevant academic literature?

Next, the relevant valuation determinants specific to early-stage technology ventures needed to be identified, leading to RQ 2: Which early-stage valuation determinants have a particular influence on technology ventures?

With research questions and thereby scope of the analysis defined, the process of analysis has to be specified within the review protocol. 3. Review protocol: Wessendorf et al. (2019) define the review protocol along six sections, that all have a clear link to the review process and the defined scope. a. Search process: According to García et al. (2006), finding relevant publications might be threatened by a lack of consistency (i.e. with regard to terminology) within the search process. Therefore, appropriate concepts and terminology in the field of study as well as relevant keywords related to the defined research questions were identified in the first step. Next, relevant business dictionaries and thesauri (i.e. Wirtschaftslexikon Gabler and www.thesaurus.com) were consulted to gather appropriate synonyms for these keywords. “Finally, wildcard characters (“*”) and Boolean operators (“AND” and “OR”) were used to formulate the search string. The applied search string consisted of four elements all connected by the Boolean operator “AND”: (1) the valuation object, e.g. “NTBF”, (2) the research object, e.g. “determinants”, (3) the relevant activity to be addressed, e.g. “Investment”, and (4) the valuating entity, e.g. “venture capitalist”. Within these elements, Boolean operator “OR” in combination with wildcard operator “*” were used to consider relevant synonyms, both in English and German” (cf . figure 4.2) (Wessendorf, Kegelmann, et al., 2019). The search and selection of relevant publications followed a three-step approach. First, the defined search string was used to search relevant databases (i.e. EBSCO Business Source Premier, Web of Science, Springer and Nexis). Based on the title’s fit to the search string, a long list of relevant publications was created. Next, the resulting publications’ abstract and conclusion sections were analyzed in order to further establish relevance of the publications to the intended search. Next, the reference management software Mendeley is used to identify additional publications, proposed by the Mendeley Suggest algorithm that bases its recommendation on literature

102

b.

c.

d.

e.

f.

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stored in Mendeley users’ libraries relying on the citation graph of Scopus and a proprietary predictive model based on users actions (Hoey, 2015). The publications proposed by Mendeley Suggest, if deemed relevant, were as well analyzed by reading the respective abstracts and conclusion sections to further establish relevance. Lastly, relevant references within the selected publications were screened (i.e. “snowball tracking”) in order to reduce the risk of omitting relevant primary studies and to further increase the sample size (Horsley et al., 2009). Study inclusion criteria: A study’s fit to the defined research question can be seen as the fundamental selection criterion. Thus, the publications selected all needed to discuss venture valuation based on non-financial valuation determinants in an early stage of the venture’s life cycle. Further, a clear link to technology ventures had to be visible in order to provide answers to RQ2. Next, with the aim of increasing practical relevance, publications that derived valuation determinants by analyzing a specified set of own primary data (i.e. empirical evaluation) were of particular interest. In consequence, publications deriving their results from other publications (i.e. secondary data) were omitted. Study exclusion criteria: The quality of the selected publications is deemed critical to the SLR’s validity. Therefore, selected primary studies needed to be published in scientific journals and available in full as well as written in English or German. In consequence, secondary studies or commercial studies as well as studies requiring paid access were excluded. Quality assessment criteria: In order to ensure high quality as a basis for reliable results and conclusions, Wessendorf et al. (2019) deem “good SLR planning, a thorough choice of keywords as well as well-defined inclusion and exclusion criteria as crucial. The studies’ context and assumptions, its theoretical basis as well as the identified findings were therefore checked for validity within the objective of this SLR. The reference management software Mendeley was used to manage the [identified relevant] primary studies.” Data extraction: A data extraction form was designed using Microsoft Excel in order to extract data in a structured, consistent, and uniform manner. The relevant data from relevant publications was recorded and analyzed in a subsequent step. Information on empirical study: The data to be gathered needs to be defined beforehand in order to allow for a concise database. Thus, data captured from the selected publications were assigned to two sections: general information (i.e. title, author, year of publication, investor type covered, sample

I

OR

OR

OR

OR

Deeptech*

Ntbf*

Cleartech*

“Venture Funded”

Startup*

II

III

Kriteri*

IV

Investor*

OR

OR Finanz*

Business Angel

OR

BA

OR

Venture Capitalist

OR

VC

VALUATING ENTITY

Invest*

OR

OR

OR

Back*

OR

Fund *

ADRESSED ACTIVITY

Financ*

AND

Bewertungskriteri*

OR

Determinant*

OR

Criteria

RESEARCH OBJECT

AND

AND

Figure 4.2 Search string structure for Systematic Literature Review. (own illustration in reference to Wessendorf, Kegelmann, et al. (2019))

Nanotech*

High*tech*

Biotech*

“Venture Backed”

Start-Up*

VALUATION OBJECT

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size, data collection method and technology focus) as well as specific information on the analyzed determinants within the selected publications (i.e. determinants, comments on determinants and categorization).

4.3.1.1.2 Conduction Phase The conduction phase, as the name implies, presents the major aspects of how the SLR was conducted, following the previously defined SLR plan. 1. Search and selection of primary studies: The defined systematic search for relevant publications discussing early-stage (technology) venture valuation was carried out in the three months period spanning from September to November 2017. The defined search string lead to 120 potentially relevant scientific publications that were screened according to the search process outlined in 4.3.1.1.1 (3) a. Thereby, 32 relevant publications were extracted. Following snowball tracking and the subsequent application of secondary search as defined in the search process as well as inclusion and exclusion criteria, the total number of relevant publications resulted in 45. The relatively high number of identified studies that were excluded from further analysis is driven by the applied inclusion and exclusion criteria, thereby leading to a research scope focusing on rigor and relevance. Yet, the share of relevant studies to studies found in primary research is in line with other comparable studies in this field (Köhn, 2017). The quality of the identified studies was continuously examined and ensured. 2. Data extraction and analysis: Data has been extracted according to the previously prepared data extraction forms (cf . section 4.3.1.1.1 (3) e) and quantitatively as well as qualitatively analyzed. Data of 45 publications dealing with determinants of early-stage venture valuation in general as well as a subset of 15 publications dealing with early-stage technology venture valuation in particular were gathered (cf. table 4.3). It was a prerequisite that the described determinants in these publications were demonstrated as being relevant to valuation by the primary studies’ results. Wessendorf et al. (2019) found that the number of relevant publications over time is increasing.

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Table 4.3 Overview of relevant studies identified during SLR Conducting Phase (own illustration in reference to Wessendorf et al. (2019)) Author (s)

Year Sample Data Size

Cumming, Douglas; Henriques, Irene; Sadorsky, Perry

2016 31

Investor Technology Type Focus

Determinants covered

VentureOne VC

Cleantech

Market; Market Growth

Munari, Federico; 2015 332 Toschi, Laura

VentureOne VC

Nanotech

Patents and Applications

Carpentier, Cécile; Suret, Jean Marc

Prop. Database

BA

Diverse

Market Growth; Management Experience; Industry Experience

Criaco, Giuseppe; 2014 262 Minola, Tommaso; Migliorini, Pablo; Serarols-Tarrés, Christian

Prop. Database

VC; BA

Diverse

Start-Up Experience; Alliances

Hsu, Dan K.; Haynie, J. Michael; Simmons, Sharon A.; McKelvie, Alexander

2014 85

Survey

VC; BA

Diverse

Market Growth; Management Experience; Personality

Block, Joern H.; De Vries, Geertjan; Schumann, Jan H.; Sandner, Philipp

2014 2,671

VentureOne VC

Diverse

Patents and Applications

Prop. Database

Biotech

Patents and Applications

2015 85

Hoenen, 2014 580 Sebastian; Kolympiris, Christos; Schoenmakers, Wilfred; Kalaitzandonakes, Nicholas

VC

(continued)

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Table 4.3 (continued) Author (s)

Year Sample Data Size

Investor Technology Type Focus

Determinants covered

Sievers, Soenke; 2013 127 Mokwa, Christopher F.; Keienburg, Georg

Thomson One

VC

Diverse

Market; Market Growth; Start-Up Experience; Management Experience; Investor Reputation

Streletzki, Jan Georg; Schulte, Reinhard

2013 64

Survey

VC

Diverse

Team Completeness; Industry Experience; Education; Management Experience

Conti, Annamaria; Thursby, Marie; Rothaermel, Frank T.

2013 226

Survey

VC; BA

High Tech

Personality; Patents and Applications

Miloud, Tarek; Aspelund, Arild; Cabrol, Mathieu

2012 102

Survey

VC

Diverse

Market; Industry Experience; Management Experience; Industry Experience; Team Completeness; Team vs Solo; Alliances

Yoo, Changsok; Yang, Dongwoo; Kim, Huykang; Heo, Eunnyeong

2012 56

Survey

VC; BA

Game Tech

Product Status; Market; Personality

Wang, Jue; Shapira, Philip

2012 244

Prop. Database

VC; BA

Nanotech

Alliances (continued)

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Table 4.3 (continued) Author (s)

Year Sample Data Size

Investor Technology Type Focus

Determinants covered

Maxwell, Andrew 2011 150 L.; Jeffrey, Scott A.; Lévesque, Moren

Survey

BA

Diverse

USPs; Market Growth; Product Status; Management Experience; Patents and Applications

Stankeviˇcien˙e, Jelena; Žinyt˙e, Santaut˙e

2011 6

Survey

VC

Diverse

Market; Market Growth; Management Experience; Start-Up Experience; Industry Experience; Alliances

Zhang, Junfu

2011 5,972

VentureOne VC

Diverse

Start-Up Experience

Pina-Stranger, Alvaro; Lazega, Emmanuel

2011 88

Survey

VC

Biotech

Team vs Solo

Ivanov, Vladimir I.; Xie, Fei

2010 1,510

Thomson One

VC

Diverse

Investor Reputation

Knockaert, 2010 68 Mirjam; Clarysse, Bart; Wright, Mike

Survey

VC

Diverse

Industry Experience; Management Experience; Start-Up Experience; Personality; Team Completeness; Presentation; Market Growth; Market; USPs

Zheng, Yanfeng; 2010 170 Liu, Jing; George, Gerard

Prop. Database

VC

Biotech

Age; Alliances (continued)

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Table 4.3 (continued) Author (s)

Year Sample Data Size

Investor Technology Type Focus

Determinants covered Education; Management Experience

Franke, Nikolaus; 2008 51 Gruber, Marc; Harhoff, Dietmar; Henkel, Joachim

Survey

VC

Diverse

Hsu, David H; Ziedonis, Rosemarie H

2008 370

Thomson One

VC

Semiconductor Patents and Tech Applications

Hsu, David H

2007 149

Survey

VC

Diverse

Education; Start-Up Experience

Paul, Stuart; Whittam, Geoff; Wyper, Janette

2007 30

Survey

BA

Diverse

Personality; Team vs Solo

Sørensen, Morten

2007 22,747

VentureOne VC

Diverse

Investor Reputation

Sudek, Richard

2006 173

Survey

BA

Diverse

Personality; Team Completeness

Ge, Dingkun; Mahoney, James M; Mahoney, Joseph T

2005 210

Thomson One

VC

Diverse

USPs; Market Growth; Management Experience; Start-Up Experience; Team vs Solo; Alliances

Nicholson, S; Danzon, P M; McCullough, J

2005 539

Prop. Database

VC

Biotech

Alliances

Baum, Joel A.C.; Silverman, Brian S

2004 204

Prop. Database

VC

Biotech

Team Completeness; Alliances; Patents and Applications

Mason, Colin; Stark, Matthew

2004 10

Survey

VC; BA

Diverse

Market Growth; Industry Experience; Education; Management Experience (continued)

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Table 4.3 (continued) Author (s)

Year Sample Data Size

Investor Technology Type Focus

Determinants covered

Dittmann, Ingolf; 2004 53 Maug, Ernst; Kemper, Johannes

Survey

VC

Diverse

USPs; Market Growth; Management Experience

Silva, Jorge

2004 16

Survey

VC

Diverse

USPs; Market Growth; Structure; Personality; Management Experience

Hsu, David H.

2004 149

Survey

VC

Diverse

Investor Reputation

Janney, Jay J.; Folta, Timothy B.

2003 328

Prop. Database

VC; BA

Biotech

Alliances

Feeney, Lisa; Haines, George H.; Riding, Allan L.

1999 303

Survey

BA

Diverse

Market Growth; Expertise; Management Experience; Personality

Stuart, Toby E.; Hoang, Ha; Hybels, Ralph C.

1999 301

Survey

VC

Biotech

Alliances; Investor Reputation

Zacharakis, 1998 53 Andrew L; Meyer, G.Dale

Survey

VC

Diverse

Market Growth

Mason, Colin; 1996 36 Harrison, Richard

Survey

BA

Diverse

Team vs Solo; Team Completeness

Lerner, Joshua

Survey

VC

Biotech

Patents and Applications

Hall, John; Hofer, 1993 16 Charles W.

Survey

VC

Diverse

Presentation; Personality

Macmillan, Ian C; 1987 150 Zemann, Lauriann

Survey

VC

Diverse

USPs; Market; Management Experience

Macmillan, Ian C; 1985 100 Siegel, Robin; Narasimha, P. N Subba

Survey

VC

Diverse

Management Experience; Expertise

1994 173

(continued)

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Table 4.3 (continued) Author (s)

Year Sample Data Size

Investor Technology Type Focus

Determinants covered

Tyebjee, Tyzoon T.; Bruno, Albert V.

1984 121

Survey

VC

Diverse

USPs; Management Experience

Tyebjee, Tyzoon T.; Bruno, Albert V.

1984 41

Survey

VC

Diverse

Market; Market Growth; Structure; Management Experience

Wetzel, William E.

1983 133

Survey

BA

Diverse

Market; Personality

“Dividing the considered time line into four phases, i.e. 1980–1989 (phase 1), the time before the dot-com bubble 1990–1999 (phase 2), the time after the burst of the dot-com bubble 2000–2008 (phase 3) and the era starting with [the] financial crisis 2009 until today (phase 4), we observe a different level of activity and focus. In the first phase, with only 5 relevant publications, the foundation of entrepreneurship and venture valuation research was set. The second phase, represented by 6 relevant publications and driven by the dot-com euphoria, dealt with valuation determinants in a more practical way. The third phase, strongly influenced by the burst of the dot-com bubble, saw a tremendous increase in relevant literature (14 publications) mostly focusing on underlying reasons for an investment as well as value determinants. Finally, the fourth phase covering the era of the financial crisis followed the reasoning of the previous phase and demonstrated an increasingly constant level of publications (20 publications in total).”

Intriguingly, valuation determinants also became increasingly diverse over time. Whereas a limited selection of seven valuation determinants become apparent in early research of phase 1 (i.e. Market Acceptance, Market Growth, Structure, USPs, Expertise, Management Experience, Personality), phase 2 already scientifically analyzed 13 different determinants deemed relevant for early-stage venture valuation (i.e. with Presentation, Team vs Solo, Team Completeness, Alliances, Investor Reputation and Patens and Applications being added to the previous group of determinants). Phase 3 brought new determinants to be researched (i.e. Education, Industry Experience and Start-Up Experience, thereby leading to a total of 16 determinants). Firm Age and Product Status are the last determinants to be added to the group of relevant

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determinants for early-stage venture valuation, becoming apparent in the last and current phase 4. Several observations need to be detailed following this analysis. First, “IP and Alliances” does not play a role in the defined phase 1. Yet, the interest in that category rose significantly, from 3 publications in phase 2 to 12 publications in phase 4, thereby potentially implying a higher degree of relevance of the subject matter. Second, determinants being part of the category “Entrepreneur and Team” marked another trend in research. Starting off equally with “Start-Up Characteristics”, this category became the clear focus of research interest, with almost half of the relevant publications discussing determinants of this category. Also, this observation is potentially implying a higher degree of relevance to the subject matter. Third, on a single determinant level, it is remarkable that Management Experience, Market Acceptance and USP emerged relatively early in relevant research. In contrast, other determinants such as Market Growth or Start-Up Experience of the founders appear with a pronounced time lag. Wessendorf et al. (2019) hypothesize that “early research […] can be considered to focus primarily on classic business concepts such as Market Acceptance, USPs and Management Experience. The research focus was subsequently widened […]. It was team dynamics and founder characteristics driving the research of that era leading to the dot-com bubble. This might have been motivated by increasing firm valuations seen in practice and thus raising interest among academics. Historical events such as the dot-com bubble might also provide the rationale for a sudden increase of research focusing on market characteristics as well as formalized determinants such as Patents and Applications as well as Alliances during the subsequent phases. Even if these results show an expanding research scope over time, it remains unclear if this development is based on a more thorough valuation process or the improved accessibility of data for a more differentiated research. Based on the observations made, it is hypothesized that an initial improvement in accessible data and researchers’ preferences preceding the dot-com bubble (i.e. more deals, more data, more practical relevance) was followed by the objective to attain a more refined view on early-stage venture valuation post-dot-com bubble (i.e. search for a refined approach for risk mitigation).” With regard to the specific field of early-stage technology venture valuation, the identified 15 relevant publications covered biotechnology (8 publications), nanotechnology (2 publications), cleantech (1 publication) as well as diverse fields of high-tech such as software, semiconductors and technology-driven university spin-offs. It was only after the burst of the dot-com bubble that research interest really surged, with 13/15 publications appearing in phase 4.

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3. Threats to validity: The limited sample size of 45 relevant publications is considered as a main threat to validity by Wessendorf et al. (2019). The even smaller sample size of publications in the specific field of technology venture valuation (n = 15) adds to this threat to validity and needs to be taken into account when drawing respective conclusions in the following reporting phase.

4.3.1.1.3 Reporting Phase The reporting phase will detail the identified valuation determinants and their underlying rationale. Following the holistic model of Köhn (2017), the identified valuation determinants will be subdivided into three main parts: Start-Up Characteristics, Entrepreneur and Team, IP and Alliances. 1. Start-Up Characteristics: Following Köhn (2017) this category describes characteristics of a venture that cannot be directly influenced by the entrepreneur or management team and that relate to the venture itself as well as its market and product characteristics. a. Age: With the organizational development of a venture proceeding over time (i.e. firm age), the venture will generate more information on its markets, products and organization as well as a financial history, thereby facilitating the valuation by venture capital investors. This especially benefits valuation methods based on the time value of money, such as the Venture Capital Method. A.-K. Achleitner (2002) demonstrates that a component of the discount rate to be used in these valuation methods serves as a mean to adjust planned cash flows for uncertainty. Following A.-K. Achleitner (2002) findings, this uncertainty is reduced over the course of the organizational life cycle, i.e. with increasing firm age, thereby leading to more relevant information to base a valuation on as well as a lower variance in discount rates. With firm age increasing, Zheng et al. (2010) observe that technology ventures in particular will face a higher probability of having created a network that benefits the innovation capability of the venture. Yet, these originate from observations made among biotechnology firms, where a certain track record already implies success due to the stretched-out organizational life cycle. In this context it has to be mentioned, that increasing firm age does not necessarily equal a development across organizational development stages. However, for investors, it is a good approximation of such a development. Remarkably, other studies find

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that firm age is insignificant in explaining a venture’s valuation. The number and quality of financing rounds appear to be more informative in this context (Sievers et al., 2013). b. Market (Acceptance and Competition): The venture’s positioning in its key market is of importance in a venture valuation context with regard to two dimensions. First, the acceptance as well as the need for a venture’s offer by market participants provides important information in order to assess the venture’s market access (Tyebjee and Bruno, 1984a). Market acceptance is assessed by analyzing data from first products being sold in the target market and how these products are accepted by customers. This is particularly important in existing and stable markets as a new offer needs to replace an existing one (Macmillan et al., 1987; Stankeviˇcien˙e and Žinyt˙e, 2011). Second, the structure of a market needs to be understood in order to assess market attractiveness (Knockaert, Clarysse, & Wright, 2010). This includes in particular the evaluation of the competitive landscape as well as the maturity and growth rates of the relevant market (Cumming et al., 2016). Other factors influencing market dynamics, e.g. regulation, are of additional interest. Wetzel (1983) suggests that an investor’s own relevant market expertise is crucial to meaningfully assess market attractiveness. c. Market Growth: With the exception of very specialized investors, venture investors in general prefer investments in ventures that are active in markets showing strong potential for growth (Carpentier and Suret, 2015; Dittmann et al., 2004; Feeney et al., 1999; Ge et al., 2005; Hsu et al., 2014; Mason and Stark, 2004; Maxwell et al., 2011; Miloud et al., 2012; Sievers et al., 2013; Silva, 2004; Stankeviˇcien˙e and Žinyt˙e, 2011; Tyebjee and Bruno, 1984a; Zacharakis and Meyer, 1998). Already observable growth rates will benefit the assessment of positive growth potential and thereby driving firm value. Miloud et al. (2012) analyzed 184 early-stage investments and were able to confirm that industry growth is positively and significantly related to pre-money valuations. This provides further support for the hypothesis that industry structure partially determines valuation. Strong market growth might even compensate for new competitors entering the market. In addition, high market demand forces ventures to a higher degree of organizational efficiency, thereby minimizing cost and benefitting an investor’s valuation. Further, Stankeviˇcien˙e and Žinyt˙e (2011) propose that a higher product differentiation will have a positive effect on market growth as it enables higher potential growth rates for individual companies.

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d. Presentation: An initial valuation of a venture is already performed based on the venture’s presentation and supporting documents. As venture capital investors only have limited time resources to assess their interest in a venture, the presentation and supporting documents have to demonstrate high quality, thorough quantitative information as well as professionalism and transparency by addressing all information needs of the investor. Thus, a professional and pleasant presentation of the entrepreneurs and their venture, which is not limited to the personal impression given by the entrepreneurs but also expands to a complete and thorough presentation of the business plan and related documents, has a positive effect on its valuation by investors (Hall and Hofer, 1993). e. Product Status: A functioning prototype or market-ready product is considered to reduce uncertainty with regard to a venture’s market offer. With the reduction of uncertainty being valued by investors, a resulting effect on a venture’s valuation is considered to be positive (Yoo et al., 2012; Maxwell et al., 2011). Analyzing new media companies in Korea, Yoo et al. (2012) demonstrate this effect to extend also on the reduction of uncertainty in technological feasibility and functionality of the product. f. Structure: With structural robustness of a venture (e.g. robustness of its organization and process) increasing, a venture is considered to become more resilient towards unexpected risks. A high level of robustness will support the reduction of uncertainty and thereby positively affect valuation (Silva, 2004; Tyebjee and Bruno, 1984a). g. USPs: A strong Unique Selling Proposition (USP) is considered to increase the appeal of a venture for investors (Dittmann et al., 2004; Ge et al., 2005; Macmillan et al., 1987; Knockaert et al., 2010; Maxwell et al., 2011; Silva, 2004; Tyebjee and Bruno, 1984b). Unique differentiation factors towards an existing offer imply a strong potential profitability of a venture (Tyebjee and Bruno, 1984b), thereby positively affecting its valuation. Additionally, USPs are considered to represent added value for customers and thus allow for strengthening the positioning towards competition (Silva, 2004). 2. Entrepreneur and Team: Köhn (2017) describes the founders to represent the core of a venture. Thereby, their individual attributes are considered to have an influence on venture valuation. a. Education: The education of an entrepreneurial team is perceived to provide hints on how problems will be handled as well as to increase credibility of the entrepreneurs’ expertise. Franke et al. (2008) find that a heterogeneous education within a team is considered to drive the success of a venture. With regard to technology ventures, Hsu (2007) proposes that founders

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with a Ph.D. are a strong signal for scientific expertise and credibility for technological development. b. Expertise: With regard to technology ventures, a team disposing of market expertise in general and technological know-how in particular is considered to be strongly beneficial for the venture’s success. Yoo et al. (2012) even find that specialist knowledge of the entrepreneurial team is even more important than the entrepreneurs’ personality and investor fit. Feeney et al. (1999) justifies this by a strong level of expertise enabling the entrepreneurial team to perform more realistic assessments of the venture’s risks and opportunities. This is considered to reduce uncertainty and thus positively drive a venture’s value. Macmillan et al. (1985) detail that expertise is not only limited to technological expertise but also extents to market risks. This allows for the right product positioning accounting for relevant market dynamics as well as for agile and successful reactions to impending risks. c. Industry Experience: Experience in an industry relevant to the venture by at least one member of the entrepreneurial team, preferably gained in previous work for a large corporation, is perceived as a positive signal in the investment and valuation process (Carpentier and Suret, 2015; Franke et al., 2008; Mason and Stark, 2004; Knockaert et al., 2010; Maxwell et al., 2011; Miloud et al., 2012; Stankeviˇcien˙e and Žinyt˙e, 2011; Streletzki and Schulte, 2013). Relevant industry experience is considered to allow for a clear analysis of the market and potential market risks as well as to draw meaningful conclusions for a successful market strategy (Carpentier and Suret, 2015). Fiet (1995) argues that the absence of relevant industry experience will increase the probability of failure tremendously. d. Management Experience: Experience gained in a previous (higher) management position supports meaningful leadership in a venture. This is considered to positively affect the probability for funding as well as a venture’s valuation (Tyebjee and Bruno 1984a; Macmillan et al., 1985; Macmillan et al., 1987; Feeney et al., 1999; Silva, 2004; Ge et al., 2005; Franke et al., 2008; Stankeviˇcien˙e and Žinyt˙e, 2011; Miloud et al., 2012; Streletzki and Schulte, 2013; Sievers et al., 2013; Hsu et al., 2014; Carpentier and Suret, 2015). Wessendorf et al. (2019) summarize the underlying rationale as follows: “A founder with management experience knows the necessary strategies as well as organizational structures and market dynamics that drive growth of a young company (Feeney et al., 1999; Miloud et al., 2012; Carpentier and Suret, 2015). He knows how an idea can turn into a viable business plan and has experience in

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dealing with employees and colleagues. Management experience further helps to assess risks correctly and to react to them. This corresponds to statements made in comparable studies, that investors are afraid to work with inexperienced founders, which might cause the company to fail (Macmillan et al., 1985). Management Experience is considered proof of the entrepreneur being able to motivate people and to be in charge as well as showing a higher level of perseverance, which is considered important (Knockaert et al., 2010). Moreover, founders with extensive management experience are more often willing to invest a sufficiently large amount of time in the start-up (Macmillan et al., 1987).”

e. Personality: The entrepreneur’s personality in general but also the personal fit between the entrepreneur(s) and the investor are considered to be an important determinant within the investment and valuation process (Knockaert et al., 2010). Business angels in particular place emphasis on the entrepreneurs’ personality matching their own, thereby gaining motivation to personally engage with the venture and support its development (Hall and Hofer, 1993). Silva (2004) suggests the founders’ passion and their willingness to sacrifice to be a strong signal positively influencing the investment and valuation process. f. Start-Up Experience: Previous experience as a founder of a venture or employment in a Start-Up is perceived to have a positive effect on valuation (Hsu, 2007; Zhang, 2011; Miloud et al., 2012; Sievers et al., 2013; Criaco et al., 2014) as its suggests that founders dispose of a strong network within relevant communities as well as know-how to cope with Start-Upspecific challenges (Miloud et al., 2012; Zhang, 2011; Hsu, 2007). Start-Up Experience further implies knowledge on handling investors and their most important points of interest in a venture. Hsu (2007) suggests that Start-Up Experience is particularly beneficial if the experience gained relates to a venture undergoing several successful financing rounds. g. Team Completeness: A complete, interdisciplinary founding team is perceived to improve a venture’s resilience towards a wide variety of threats (Baum and Silverman, 2004; Mason and Harrison, 1996; Miloud et al., 2012; Streletzki and Schulte, 2013; Sudek, 2006). Knockaert et al. (2010) find that complementarity of competencies of both technical and commercial nature are of particular interest to investors. Further, a complete team potentially saves an investor’s resources by not having to employ an additional manager, engage with an external service provider to compensate the missing capabilities or sacrifice the investor’s management capabilities (Miloud et al., 2012), thus dealing with day to day business more efficiently (Mason and Stark, 2004). Miloud et al. (2012) analyzed 184 rounds

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of early-stage investment and were able to establish a significantly higher valuation for ventures disposing of a complete management team. h. Team vs Solo: In line with the benefits of a complete, interdisciplinary team described above, investors prefer founding teams to individual founders (Mason and Harrison, 1996; Ge et al., 2005; Pina-Stranger and Lazega 2011; Miloud et al., 2012). This is justified by an increasing level of complexity related to a venture’s operation and long-term strategy requiring a diverse set of capabilities easier to be found in a team (Miloud et al., 2012) as well as the reduction of ill-considered, potentially emotional decisions made by one person alone. 3. IP and Alliances: Köhn (2017) attributes a crucial role to IP and Alliances in a venture valuation context, due to the importance of a venture’s position within its relevant market, as well as the connections of the organization and its ideas to other corporations, partners (e.g. clients and suppliers). a. Alliances: As alliances and partnerships foster the impression that other corporations, disposing of relevant market and specialist knowledge, believe in the venture’s offering, a high number of such alliances and partnerships is perceived to have a positive impact on valuation (Criaco et al., 2014; Ge et al., 2005; Janney and Folta, 2003; Miloud et al., 2012; Nicholson et al., 2005; Stankeviˇcien˙e and Žinyt˙e, 2011; Wang and Shapira, 2012). This suggests a reduction in information asymmetry present in an earlystage investment context. Horizontal alliances are seen positively as these partners are closer to potential customers and thus have a more profound understanding of customer needs (Baum and Silverman, 2004). Nicholson et al. (2005) find that entering partnerships in the biotechnology industry results in a value increase between 46.2 and 48.0 percent. A positive influence on valuation holds for alliances to banks and investors (Stuart et al., 1999), partners for cost reduction in development and production (Zahra and George, 2002) as well as partners improving the venture’s learning curve (Zahra et al., 2000). Intriguingly, Zheng et al. (2010) find that the impact of alliances on valuation decreases with increasing maturity of the venture. b. Investor Reputation: Analogue to the impact of Alliances outlined above, Hsu (2004) proposes that a known and respected investor funding a venture is a strong signal of the quality of a venture’s business concept. Thus, other venture capital investors, in particular smaller investors, are more likely to (co-)invest in such a venture, thereby positively influencing a venture’s valuation. Additionally, Ivanov and Xie (2010) find that larger investors often provide valuable support and services to the ventures they are invested

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in, thereby enabling a higher level of growth and increasing the chance for success. c. Patents and Applications: Previous research proposes a strong correlation between the number of patents as well as patent applications and a venture’s valuation as well as funding success (Baum and Silverman, 2004; Block et al., 2014; Conti et al., 2013; Hoenen et al., 2014; Hsu and Ziedonis, 2008; Lerner, 1994; Knockaert et al., 2010; Maxwell et al., 2011; Munari and Toschi, 2015). Hsu and Ziedonis (2004) establish a significant valuation increase by 24 percent triggered by a doubling in patent application stock of analyzed ventures. Three important aspects can be distinguished when evaluating the importance of patents to valuation. First, patents enable a venture to protect themselves from competitors’ market access (Macmillan et al., 1985; Hoenen et al., 2014). Second, patents suggest innovative capabilities implying a strong competitive advantage as well as a mean to reduce information asymmetry. Third, patents as well as patent applications suggest an advanced level of technological maturity and product development (Baum and Silverman, 2004). Intriguingly, Lerner (1994) finds that a venture’s valuation and the patent scope appear to be negatively related. Thus, patents with a very specific and narrow scope have a higher relevance in a valuation context.

4.3.1.2 Findings Following the thorough planning and conduction of the SLR, the identified results provide a solid basis to discuss and answer the set out research questions. 1. RQ1: Which early-stage venture valuation determinants are defined within the relevant academic literature?

The analysis of relevant publications on general determinants of early-stage venture valuation leads to 18 determinants identified and detailed. Wessendorf, Kegelmann, et al. (2019) rank them according to the number of mentions with the analyzed relevant literature. “Management Experience with 18 mentions can be considered to have a consistently positively related influence on valuation. […] The second most mentioned determinant with 14 mentions is Market Growth, expected to be of significant importance to investors […]. Third, with 10 mentions each, are Alliances and Personality. Alliances have a positively related and quantifiable influence on valuation as these are considered […][to] reduce uncertainty and drive growth of an early-stage venture. Personality is considered to enable a trustful and passionate cooperation [between the investor and]

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the founding team, which is valued by many investors. Following are determinants that are considered to demonstrate a positive influence on venture valuation as they either ensure a competitive edge, i.e. Patents and Applications (8 mentions), USPs (7), or provide a mean to reduce uncertainty driven by information asymmetries and inappropriate resource allocation, i.e. Market (9 mentions), Start-Up Experience (7), Industry Experience (6), Team Completeness (6), Investor Reputation (5), Education (4), Team vs solo Entrepreneurs (4), Product Status (2), Expertise (2), Structure (2), Presentation (2) and Age (1).”

Analyzing these results, two major conclusions can be drawn. First, the presented results indeed suggest a strong importance of non-financial determinants for the valuation of early-stage ventures. This is in line with previous literature, that finds that non-financial information’s explainable impact on the variation in pre-money valuation is highly comparable to the respective financial information’s impact (Sievers et al., 2013). Second, previous research studies are generally limited to detailing the characteristics of the identified determinants as well as the reasoning for their influence on valuation. Thus, the general influence of the mentioned determinants is empirically proven. Yet, it becomes apparent, that only a part of the analyzed publications focusses on the determinants’ impact on venture valuation, thereby only allowing for a partial assessment of relative importance. If relative importance of determinants is discussed, however, only a rough order is provided. Therefore, a clear ranking of importance among these determinants as well as a measurable impact cannot be established. Wessendorf, Kegelmann, et al. (2019) hypothesize that the relative frequency of these determinants being mentioned in scientific literature represents a first hint not only on consistency of relevant research but also on the determinants’ relevance to early-stage venture valuation.

2. RQ2: Which early-stage valuation determinants have a particular influence on technology ventures?

Turning to determinants of early-stage technology venture valuation, the analyzed relevant literature leads to 14 determinants identified. (Wessendorf, Kegelmann, et al., 2019) rank them according to the number of mentions in relevant literature. “The determinants Alliances as well as Patents and Applications, with 6 mentions each, combine the large majority of mentions […]. Both determinants are considered to have a positive and empirically quantifiable relation to early-stage technology venture valuation as they distinctively reduce uncertainty for the investor. This is realized by Alliances providing proof [by] […] relevant market participants sharing the positive

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belief in the new venture as well as Patents and Applications demonstrating technological feasibility and competitive edge. [Further][…], Personality and Market are considered to have a positive effect on valuation and thus follow with 3 each mentions in relevant publications. Especially in technology venture valuation, the investor needs to establish a relation of trust with the founders as well as foster his own belief in the technology, the founders’ capabilities and ambitions as well as a proven market acceptance. Finally, Team Completeness (2 mentions) as well as Age, Industry Experience, Investor Reputation, Management Experience, Market Growth, Presentation, Product Status, Start-Up Experience and USPs with 1 mention each in relevant publications have a positive effect on technology venture valuation. These determinants suggest a strong reduction of uncertainty driven by information asymmetries and inappropriate resource allocation, thereby positively supporting the venture’s valuation.”

Analogue to the general determinants of early-stage venture valuation presented above, previous research on determinants for early-stage technology venture valuation limits itself to describing the determinants’ characteristics and their underlying rationale for driving value. A clear ranking of relative importance as well as a measurable impact on valuation can generally not be established. To conclude, the analysis of 45 relevant publications investigating early-stage venture valuation (including a subset of 15 publications focusing on technology venture valuation) lead to 18 general determinants and 14 technology-specific determinants of early-stage venture valuation. All identified determinants have an empirically proven influence on valuation. Yet, a clear ranking of importance as well as a measurable impact on value cannot be established. Slight differences between general venture valuation and technology-venture valuation are observable, such as a tendency towards hard facts and proof of innovation for the latter. Wessendorf, Kegelmann, et al. (2019) “hypothesize that especially in a technologydriven venture, the particular risks inherent to technology as well as its rationale and measurable characteristics put the emphasis in valuation on understanding and controlling the technological foundation first, and, to a certain extent, independently of market-specific or internal characteristics.” Further, no differences with regard to investment and valuation determinants among venture capital investors and business angels could be established, which is congruent with previous research results focusing on the investment behavior of business angels and venture capitalists (Van Osnabrugge, 2000; Mason and Stark, 2004; Sudek, 2006; Wiltbank et al., 2009; Maxwell et al., 2011). Limitations of the performed analysis can mainly be attributed to a limited sample size as well as to the research process’s methodology. With regard to the first, it remains unclear if the identified determinants for technology-venture valuation are merely a selected subset of the general valuation determinants, thereby implying that the whole range of determinants is of relevance, but the

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identified subset is of particular interest, or if indeed they represent an exhaustive group of valuation determinants. Concerning the methodological limitations, the specific steps chosen to conduct this SLR are on the one side strengthening consistency and rigor, on the other side imposing additional limitations. Thus, the specific focus of the SLR, the defined review protocol and the selection of primary studies are potential sources of bias, e.g. with regard to overrepresented geographies. Further, the general focus on technology ventures might create a bias by generalization, i.e. not differentiating by the specific industry or technology addressed in the individual studies. Yet, the present analysis needs to be understood as a first step to understand value-driving determinants, thereby potentially requiring a broader approach towards the subject matter. With regard to constructing an artifact improving the indication of value in early-stage technology venture valuation (InVESt-NTBF), while addressing the set out requirements (cf . section 4.2) the following has been achieved: 1. Non-financial determinants relevant to early-stage (technology) venture valuation were identified based on previous scientific literature. This allows for the construction of an artifact accounting for the specific challenges in valuation of early-stage ventures, i.e. a lack of financial history as well as measurable track record. 2. The influence of non-financial determinants for (technology) venture valuation are empirically proven by existing scientific literature. Yet, certain aspects leading to the construction of a meaningful artifact remain unclear: 1. No clear ranking of importance among the identified non-financial determinants for (technology) venture valuation could be established. This prevents the construction of the intended artifact as the requirement of comprehensibility of venture valuation, especially the practical and operationalizable use of the artifact is complicated. 2. No measurable impact of the identified non-financial determinants for (technology) venture valuation could be established. Thereby, an indication of value is impossible at the current stage. These two aspects, currently unresolved, will shape the next steps in designing and developing a meaningful artifact.

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Evaluation of Valuation Determinants’ Importance

This section is based on: Wessendorf, C. P., Schneider, J., Gresch, M. A. and Terzidis, O. (2020) What matters most in Technology Venture Valuation? Importance and Impact of NonFinancial Determinants for Early-Stage Venture Valuation, International Journal of Entrepreneurial Venturing, 12(5), pp. 490–521. https://doi.org/10.1504/IJEV.2020. 111536

The relative importance of non-financial determinants of early-stage NTBF valuation needs to be investigated in order to create an artifact improving the indication of value in early-stage NTBF (InVESt-NTBF), while addressing the set out requirements (cf . section 4.2.1), in particular comprehensibility and efficiency of venture valuation. This is reflected in its practical and operationalizable use.

4.3.2.1 Literature Review Previous literature shows that relative importance of non-financial determinants is either specified for determinants affecting the investment decision process in general (Elango et al., 1985; Khan, 1987; Hisrich and Jankowicz, 1990; Knight, 1994; Muzyka et al., 1996; Bachher and Guild, 1997; Bachher et al., 1999; Zutschi et al., 1999; Van Osnabrugge and Robinson, 2000; Eisele et al., 2002; Shepherd et al., 2003; Sudek, 2006; Zinecker and Bolf, 2014), for determinants referring to a venture’s success or failure, thereby influencing the investment decision (MacMillan et al., 1985; Keeley and Roure, 1989; Meyer et al., 1993) or specifically for determinants of venture valuation. Yet, only a rough order of importance among determinant categories is available for the latter. Thus, the relative importance of individual non-financial determinants of early-stage technology valuation remains largely unclear. Still, a review of selected existing literature providing an order of importance related to either determinants of investment decision-making, success and failure of a venture as well as related to determinant categories of venture valuation will be briefly outlined. At this stage, it is remarkable that highly comparable results are achieved within the analysis of the first two groups. Whereas at least one of the top-5 determinants within each respective study on determinants for venture capital investment decision-making falls into the category Founder Personality (rank 2.1), Founder Experience (2.3), Product Characteristics (3.6) or Market Characteristics (3.6), in particular MacMillan et al. (1985) find, with regard to determinants affecting a venture’s success or failure, that founders’ Enthusiasm, Experience

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and Market Growth are all scoring in the top-5 of their ranking (Wessendorf, Schneider, Gresch, et al., 2020). Van Osnabrugge and Robinson (2000, p. 120 ff.) examine differences in the prioritization of different investment criteria between business angels and venture capital investors. They find that there are clear similarities, such as a strong focus on the founder/team of founders and management, followed by criteria for evaluating the product and the market, and then financial factors. In this context, they also observe different tendencies (Van Osnabrugge & Robinson, 2000; Van Osnabrugge, 2000): Whereas a venture capital investor usually pays more attention to financial aspects, if only for the reason of delivering on her return promise to limited partners, a business angel may pay more attention to the possibility of taking an operational stake in the company himself. In the course of their investigation, Van Osnabrugge und Robinson (2000, p. 121 f.) identify 17 criteria that are important to all investors when assessing a company and distinguish between business angels and venture capital investors in the priority of the criteria. Due to a strong tendency of the surveyed investors to invest in young technology companies, 18 criteria were also identified which are of particular importance for investors assessing technology ventures. These 18 criteria can also be distinguished in terms of priority between business angels and venture capital investors. Of these 18 criteria, 5 are in the category “founder/management team”, 5 in the category “market/product”, 3 in the category “key financial figures” and 2 in the category “intellectual property rights and competitive hurdles”. The remaining three criteria deal with the role of the investor in the company and investment process as well as the geographical location of the company. Mason und Stark (2004) contribute by discussing and assessing the importance of different valuation determinants along all categories of a business plan, with bankers, venture capital investors (VC) and business angels (BA) It shows that financial ratios are at the top of the list in terms of importance (for 22.5% of the BA and 21.3% of the VC), followed by market characteristics (for 19.8% of the BA and 22% of the VC), the founding team (for 16.8% of the BA and 12.0% of the VC), the investor fit (for 13.5% of the BA and 1.0% of the VC). Furthermore, in order of importance, the corporate strategy, the business plan, the developed and offered product, processes and other criteria are also mentioned. Mason und Stark (2004) were thus able to show, on the one hand, that the main aspects of a business plan or a company are of particular importance to investors, and on the other hand, that the importance can vary depending on the type of investor and thus the investment strategy pursued. Yet, only determinant categories (e.g. the founding team) are ranked without specifying individual determinants (e.g. expertise or experience).

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Sudek (2006) follows a two-phase approach, first a qualitative phase to collect relevant criteria, followed by a quantitative phase for evaluation and prioritization, in order to investigate valuation criteria of a group of business angels in Southern California (n = 72). He generally confirms the findings of Van Osnabrugge und Robinson (2000, p. 121) and adds that these previously ranked criteria for investment decision-making play a special role for investors in the context of venture valuation. Beyond this, however, Sudek (2006) identifies additional criteria (i.e. the quality of the management team, entry barriers for competitors and the operational commitment of the advisory board/consultants), which are classified as important valuation criteria in his study. A major difference to the work of Van Osnabrugge und Robinson (2000), however, is that he observes a sometimes very different prioritization of the criteria. According to Sudek (2006), these differences may be due to geographical differences between the USA and the UK and to different investment behavior under different market conditions. Thus, he suggests that a deeper understanding of how investors prioritize these criteria should be sought in future research.

4.3.2.1.1 Differentiation to Previous Research The present work differs from previous research in several aspects and thereby establishes its relevance to academia and valuation practice alike. It is considered to further deepen the existing knowledge in the field of early-stage technology venture valuation. First, this work focusses on the importance of relevant valuation determinants for early-stage technology ventures. As outlined previously, existing research mainly discusses and ranks determinants affecting the investment decision-making or determinants of success and failure of a venture. The remaining publications that show a relation to early-stage venture valuation refrain from providing a quantified and detailed order of importance. Second, this work will focus on non-financial determinants only, as early-stage ventures are not considered to dispose of a meaningful corporate history and data for valuation (Kaserer et al., 2007; Damodaran, 2009). Yet, this limitation in scope is not considered to compromise the results, as Sievers et al. (2013) find that “the variation in value explainable by solely non-financial information is strongly comparable to the variation in value explainable by solely financial information”. Yet, the meaningfulness of the non-financial determinants needs to be ensured, supporting the relevance of the present work.

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Third, in contrast to previous literature, the present work undertakes a clear focus on the valuation of NTBF, which represents a particularly interesting valuation subject due to the strong impact of technology on the overall firm value. This strict focus cannot generally be established for previous literature identified. Fourth, the present work focuses on the early stage of the organizational life cycle, i.e. “Conception and Development”, “Commercialization”, and first phases of “Growth” as defined by prior literature (Kazanjian, 1988; Kazanjian and Drazin, 1990). According to Wessendorf et al. (2020) “this represents a rather interesting stage within the organizational life cycle in a valuation context due to the associated challenges faced by conventional valuation practice (Kaserer et al., 2007; Damodaran, 2009)”. Fifth, the present work will differentiate itself in terms of the geographic scope chosen. Whereas US based venture capital dominates the relevant research in the field chosen, only a few selected publications cover European influences (Muzyka et al., 1996; Knockaert et al., 2010). This attempt to include different geographic customs and practices with regard to valuation, is continued within the present work by a data set focusing on the German-speaking region in Europe, which represents 26% of AuM by European venture capital funds (Mueller et al., 2017).

4.3.2.2 Research Question Based on the previous research identified, which mainly discusses determinants’ importance in venture investment decision-making or provides a generalized ranking of determinant categories, the present work will further investigate the relative importance of individual determinants affecting early-stage NTBF valuation. Thereby, RQ3 is defined as: RQ3: What is the relative importance of determinants affecting early-stage NTBF valuation?

In order to elicit meaningful results, Wessendorf et al. (2020) suggest a twostep approach consistent with triangulation principles in research: “First, building up the empiricism through the Analytical Hierarchy Process (AHP) in order to understand determinants’ relevance for early-stage [NTBF] valuation, [followed by] validating the derived relevance […] through a Choice-based Conjoint Analysis (CBC).” This will provide a strong foundation in order to develop an artifact to improve the indication of value in early-stage technology venture valuation (InVESt-NTBF), while respecting the specific requirements outlined in section 4.2.1.

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4.3.2.3 Triangulation as a Methodological Framework Driven by risk and uncertainty inherent to early-stage NTBF investment (Storey and Tether, 1996; Achleitner, 2001; Kaserer et al., 2007), various challenges in conducting the necessary analysis to answer the defined research questions become apparent. As a result, the limited number of investors active in early-stage NTBF investments as well as the associated discretion with regard to details of the investment, further adding to uncertainty in the industry, can be considered of primary importance in terms of their direct effect on sample size available for analysis. Yet, as a high degree of objectivity—and thereby reliability and validity—is crucial for meaningful findings, this must be properly reflected within the dataset analyzed and the methodology chosen. In consequence, the methodological framework of the present research project builds on triangulation by data source and by method, to ensure a high degree of objectivity. Wessendorf et al. (2020) state that “triangulation in social science research refers to a research practice that validates a finding by revealing that independent measures of it do either agree on it or do not contradict it (Miles and Huberman, 1994).” With regard to research practice, Wessendorf et al. (2020) suggest that “it often aims to complement measures with regard to their respective strengths (Bryman, 2006) in order to describe complex phenomena (Cohen and Manion, 1994).” Five different types of triangulation can be distinguished (Miles and Huberman, 1994), out of which two types are relevant for the analysis performed within this research project (cf . figure 4.3).

4.3.2.3.1 Triangulation by Data Source As the term implies, data triangulation processes data originating from different sources to balance potential biases of different data material (Brown, 2001). The form of triangulation by data source relevant to the present research project focusses on data collection at different points in time. Following expert opinion, Wessendorf et al. (2020) suggest that around 190 institutional investors and 360 business angels and family offices in Germany focus at least a part of their investment activity on early-stage NTBF. Considering this limited population size of 550 individual investors and the generally limited availability of investors to participate in research, the present work followed an approach whereby relevant investors were contacted more than once in different research settings in order to capture as many answers as possible at different points in time. Thereby, biases driven by the investors’ respective current deal pipeline were accounted for. In consequence, “a total sample size of N = 75 relevant participants (13.6% of derived population) was reached in complementary surveys, out of which N = 35

• multi-criteria method • measures subjective preferences • allows for a relatively high number of determinants to be covered • allows for quantified comparison of different determinants

Triangulation by method

• multiattributive method • measures impact on a target parameter, thus analyses a directional context • depicts decision-making in real time • allows for experiment that is time-saving and confidential

Relevant sample of VCs, BAs; Data collected Q3 2018; N=40

Choice-based Conjoint Analysis (CBC)

Figure 4.3 Methodological framework supporting triangulation as well as specifying characteristics and strengths of AHP and CBC analysis. (own representation, based on Wessendorf et al. (2020))

(characteristics & strengths)

Relevant sample of VCs, CVCs, BAs; Data collected Q1 2018; N=35

Triangulation by data source

Analytical Hierarchy Process (AHP)

Challenges with implications on methodology: Limited number of investors active in early-stage technology venture investments Associated discretion with regard to details of their investment process

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participated in an Analytical Hierarchy Process (AHP) study and N = 40 participated in a Choice-based Conjoint Analysis (CBC) study” (Wessendorf, Schneider, Gresch, et al., 2020). With a median overall sample size of n = 51 in comparable research on early-stage venture investment and a median sample size of n = 44 in comparable work on early-stage NTBF investment, this research project attains a particularly strong sample size (MacMillan et al., 1985; Riquelme and Richards, 1992; Knight, 1994; Elango et al., 1995; Bachher and Guild, 1997; Landström, 1998; Bachher et al., 1999; Shepherd, 1999; Zutshi et al., 1999; Eisele et al., 2002; Franke et al., 2004; Sudek, 2006; Franke et al., 2008; Knockaert et al., 2010; Hsu et al., 2014; Zinecker and Bolf, 2014; Hoenig and Henkel, 2015).

4.3.2.3.2 Triangulation by Method Kopinak (1999) describes triangulation by method as the collection of “information pertaining to the same phenomenon through more than one method, primarily in order to determine if there is a convergence and hence, increased validity in research findings” (Kopinak, 1999, p. 171). Thus, the use of an increasing number of research instruments is considered to provide more detailed and multi-layered information about the subject under study (Kopinak, 1999). In consequence, the present research project follows two methodologies with complementary strengths, thereby adding to the project’s validity: The Analytical Hierarchy Process (AHP) and the Choice-based Conjoint Analysis (CBC).

4.3.2.4 Analytical Hierarchy Process (AHP) The Analytical Hierarchy Process (AHP) intends to systematically solve multicriteria decision problems. Thus, the method determines the relative priority of each determinant in relation to the achievement of a goal or state (Saaty, 1987). This enables the evaluation of selected determinants relative to each other (scoring) and thereby provides a clear order. This is of particular importance in a setting, where investigated determinants are not directly to be compared quantitatively. The AHP combines certain methodological aspects that are considered as strengths and thus beneficial in the context of triangulation by method. These aspects further result in practical implications that allow for a simple but meaningful usage in research. 1. Several determinants can be accounted for in parallel (multi-criteria method); 2. Subjective preferences can be assessed and measured; 3. A high number of determinants can be analyzed by a reduced number of pairwise-comparisons;

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4. Determinants can be compared to each other quantitatively. In consequence, the application of AHP in this research project aims for determining the relative importance of relevant determinants for early-stage NTBF valuation among each other, thereby potentially resulting in a subset of determinants that are deemed to be of particular importance and thus affecting practicability of early-stage NTBF valuation in a positive way. In principle, the AHP can be subdivided into four main parts (Mühlbacher and Kaczynski, 2013): Study Design, Data Collection, Data Analysis, and Data Interpretation.

4.3.2.4.1 Study Design A study following AHP methodology needs to build its design on defined decision-making problems (Saaty, 1990a; 2008). Thus, before designing an empirical survey intended to derive determinants affecting the overall objective of the study, the relevant influencing factors for solving the set out problem situation need to be identified. 4.3.2.4.1.1 Determinant Selection According to Helm and Steiner (2008) existing publications, comprehensive literature research (such as a systematic literature review), or other alternative techniques/ sources are potentially to be used for determinant selection. Therefore, the relevant findings of section 4.3.1.2 will serve as a sound basis for the AHP. These findings do not only reflect a similar research scope (i.e. early-stage NTBF and non-financial valuation determinants) but also provide clear definitions of each determinant to be used. In addition, due to the followed Systematic Literature Research approach, it provides a sound methodical foundation to identify a meaningful and comprehensive set of determinants. Yet, in order to decrease the complexity for participants in the survey on preferences, which provides the relevant data for AHP, the set of determinants to be analyzed following AHP methodology is intentionally limited. Besides decreasing methodological complexity for comfortable participation in the required survey, the limitation of determinants further aims to increase the determinants’ operationalizability in a valuation context (i.e. identification of fewer but more meaningful determinants for a more efficient valuation process). Thus, out of the 18 determinants identified, a subset of 11 determinants hypothesized to be most relevant in early-stage venture valuation is extracted (Wessendorf, Kegelmann, et al., 2019). This hypothesized relevance is mainly driven by the intensity and quality of the determinants’ discussion in scientific literature (i.e. the more often and the more

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refined a determinant is discussed by several researchers, the more relevant this determinant is considered to be for venture valuation). These selected determinants are partially aggregated to form a group of 9 determinants (cf . table 4.4) to be used in this AHP analysis. This is intended to further reduce complexity for participants in the required survey. Additionally, previous relevant research was taken into account to further support the importance of the 9 determinants identified. As a result, the top-7 non-financial determinants identified by Knockaert et al., (2010), whose work is considered to have a highly comparable research scope, are fully reflected within the set of determinants chosen for this analysis. 4.3.2.4.1.2 Survey Concept Development Pairwise comparisons are considered a central element of a survey following the methodical approach of AHP (Saaty, 1999). Yet, with AHP methodology employed as a first step, two major strengths become apparent in the context of a triangulation by method approach, in particular when comparing AHP methodology to Conjoint Analysis methodology. First, AHP requires a reduced number of pairwise comparisons in order to compare all nine determinants in pairs, resulting in 36 (= 9 over 2 combinatorically) pairwise comparisons. A pairwise comparison will be in a format that allows to compare a given determinant A (e.g. Patents and Applications) directly with a given determinant B (e.g. USP). In contrast to a conjoint analysis approach, the AHP appears to result in a reduced complexity and thus a simplified participation in the survey at this stage of the analysis. Second, the AHP mathematically allows for direct comparison of two determinants with each other. The attained results are thus directly comparable by means of measuring the importance of each individual determinant (scoring). In contrast, a conjoint analysis approach would qualify as more indirect as other determinants’ influence within a bundle need to be taken into account for comparison in the first place. In addition to the pairwise comparisons, the survey’s design is shaped by a multi-step scale that allows for a refined comparison. Yet, in contrast to the 9step scale used by Saaty (1990a; 1999) the participants in the following survey are asked to provide answers through a 5-stage, balanced scale in order to facilitate decision-making of respondents. Such a scale will comprise the following 5 steps: • • • • •

Determinant Determinant Determinant Determinant Determinant

A A A A A

is is is is is

strongly more important than Determinant B moderately more important than Determinant B equally important as Determinant B moderately less important than Determinant B strongly less important than Determinant B

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Table 4.4 Overview of relevant valuation determinants (cf. section 4.3.1.2) and respective selection for AHP analysis (Wessendorf, Schneider, Gresch, et al., 2020) Determinants identified in Wessendorf et al. (2019)

Definition of determinant

Determinants selected for Analytical Hierarchy Process (AHP)

Personality

Founders’ passion and their willingness to sacrifice

Entrepreneurial Spirit

USP

Strength of Unique Selling Proposition (USP)

USP

Patents and Applications

Number of patents and patent Patents and Applications applications and respective scope

Management Experience

Experience gained in a higher Management Experience management position originating from a previous employment

Industry Experience

Existing industry experience of the founders, preferably originating from previous work for a large corporation

Industry Experience

Start-Up Experience

Previous experience as a founder of a venture or employment in a Start-Up

Start-Up Experience

Market Growth

Current market growth as well Market Growth as potential for market growth

Market

Acceptance of a venture’s offer by market participants as well as competition. Both are considered to drive “Market Growth”

Alliances

Number of alliances and partnerships entered by the venture

Alliances

Education

The founder’s level of education

Education & Expertise

Expertise

Specialist knowledge of the founders (especially. For technology ventures) (continued)

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Table 4.4 (continued) Determinants identified in Wessendorf et al. (2019)

Definition of determinant

Team Completeness

Completeness of founding team, meaning a team covering all necessary functions

Investor Reputation

Presence of a known and respected investor in a venture

Team vs Solo

Founding team vs individual founders

Presentation

A professional and pleasant presentation of the founders and their venture

Product Status

Current status of product development defines certainty in the venture’s process of developing a market offer

Structure

The structural robustness of a venture (e.g. organization and processes)

Age

The age of a Start-Up (i.e. firm age)

Determinants selected for Analytical Hierarchy Process (AHP)

Nevertheless, the evaluation is fully compliant with AHP methodology at the mathematical level, with usual values for AHP secured by design—in the sense that the results of the 5-stage balanced scale will provide a comparable level of information (but less detail) as the originally used 9-step scale—and thus entirely usable within AHP analysis (Wessendorf, Schneider, Gresch, et al., 2020). 4.3.2.4.1.3 Pre-test The defined study design is based on theoretical considerations. Thus, practical suitability needs to be ensured, which was validated in a “pre-test”. This approach is deemed to ensure the informative value of the results. Thus, qualitative interviews with relevant venture capitalists, business angels and corporate venture capitalists were carried out in order to uncover potential weaknesses of the design, and to ensure practical relevance. The interviews were conducted between January 8th , 2018 and January 31st , 2018 (cf . table 4.5).

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Table 4.5 Overview of expert interviews conducted for pre-test Expert Code Name

Interview length (hh:mm:ss)

Interview type

Background

E1

00:41:05

Telephone Interview

Business Angel

E2

00:26:19

Telephone Interview

Venture Capitalist

E3

00:34:46

Telephone Interview

Venture Capitalist

E4

00:39:20

Telephone Interview

Corporate Venture Capitalist

E5

00:54:34

Telephone Interview

Corporate Venture Capitalist

E6

00:36:54

Telephone Interview

Venture Capitalist

E7

00:45:12

Telephone Interview

Business Angel

E8

00:42:46

Telephone Interview

Business Angel

Sum

05:20:56

Average

00:40:07

Resulting from these interviews, it can be hypothesized that certain determinants in early-stage NTBF valuation are “must-have determinants”. In consequence, the survey allows for a neutral answer option in case of a pairwisecomparison of two “must-have determinants”. The time frame and scope of the survey was considered appropriate by all respondents of the pre-test. The final questionnaire accounts for all necessary adjustments originating from the pre-test.

4.3.2.4.2 Data Collection After successfully validating the survey concept in a pre-test and making final adjustments to the survey, relevant investment professionals are contacted and motivated to participate in the survey. Since the AHP provides a mathematical solution to the pairwise comparisons made by relevant investment professionals, these must be mapped numerically (Saaty, 2008). As the numerical retrieval increases the complexity of a later evaluation, the data collection of this research project is conducted electronically. 4.3.2.4.2.1 Contact to Relevant Investment Professionals In order to derive meaningful results in the context of developing the envisioned artifact, only investors with experience as venture capitalists, business angels or corporate venture capitalists are considered as study participants. Next, in order

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to better reflect the defined research subject in the later results, the research focus is narrowed down. Thus, first, only investors who invest in early-stage NTBF are allowed to participate, as valuation determinants potentially differ across industries and business sectors. Second, these relevant investors are required to invest in German-speaking Europe. Thereby a total of 280 experts were contacted in the two months period spanning from February 2018 to March 2018 in order to motivate them for participation in the survey. 4.3.2.4.2.2 Response by Relevant Investment Professionals As a result of the high number of relevant investors invited to participate in the survey (n = 280), 35 (12.5%) relevant investors participated and completed the survey. This can be interpreted as the survey being comprehensible, thereby supporting the overall reliability of the attained results.

4.3.2.4.3 Data Analysis Following successful data collection, where the pairwise comparisons performed by relevant investors participating in the survey were registered numerically, the resulting data needs to be processed and analyzed. 4.3.2.4.3.1 Weight Calculation In a first step, comparison matrices Z are created, which contain n elements A1 , A2 , A3 , …, An whose vector of corresponding weights w1 , w2 , …, wn is known. A comparison matrix Z consist of n x n elements, with n describing the number of determinants. Please note that notation of matrices follows Saaty (1990a) and is intended to improve readability by including element denomination to specify columns and rows. ⎡

Z=

A1 A2 .. . An

⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣

A1 A2 w1 /w1 w1 /w2 w2 /w1 w2 /w2 .. . wn /w1 wn /w2

An w1 /wn ··· w2 /wn .. .. . . · · · wn /wn

⎤

⎡

⎥ A1 ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ = A2 ⎢ ⎥ ⎢ .. ⎢ ⎥ ⎦ . ⎣ An

A1 A2 a 11 a 12 a 21 a 22 .. . a n1 a n2

⎤ An . . . a 1n ⎥ ⎥ ⎥ a 2n ⎥ ⎥ .. ⎥ .. . . ⎦ · · · a nn (4.1)

The determined values from the pairwise comparisons allow for priority calculation and weight calculation (Manthey, 2007; Klein and Scholl, 2012). According to Saaty (1987) the balanced scale applied does not allow for the assumption

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that the values are evenly distributed within the scale. There might be a “tendency towards the middle” (Schnell et al., 1999). AHP fundamentally assumes that decision-makers behave “reciprocally” and therefore consistently. Thus, comparisons do not have to be inverted, which holds true for the balanced scale used within this work. The comparison matrix Z resulting from the data obtained in this research projects presents itself as (cf . table 4.6): Yet, as a consequence of applying a balanced scale in pairwise comparisons, the values of comparison matrix Z need to be adjusted. Following the model of Salo & Hämäläinen (1997), the adjusted values c are computed taking into account the rebalanced weights wbal as follows: c=

wbal 0.45 + 0.05 x = 1 − wbal 1 − (0.45 + 0.05 x)

(4.2)

By adjusting all values of the comparison matrix Z, the evaluation matrix Z E is established. Please note that notation of matrices follows Saaty (1990a) and is intended to improve readability by including element denomination to specify columns and rows. ⎡

ZE =

A1 A2 .. . An

⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣

An A1 A2 c 11 c 12 · · · c 1n c 21 c 22 c 2n .. .. .. . . . c n1 c n2 · · · c nn

⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦

(4.3)

The evaluation matrix Z E resulting from adjusting the comparison matrix following the model of Salo & Hämäläinen (1997) presents itself as displayed in table 4.7:

1.00 0.16

0.16 0.17 0.18

Entrepreneurial 3.69 Spirit

0.18

0.18

4.83

0.19

0.16

Start-Up Experience

Management Experience

Market Growth 0.20

3.51

Industry Experience

Education and Expertise

Alliances

USP

0.15

0.17

0.19

3.86

4.94

0.20

0.17

1.00

6.14

5.51

0.19

0.20

3.40

4.31

4.60

1.00

5.74

6.71

5.69

0.17

0.19

3.80

0.20

1.00

0.22

5.00

6.31

0.21

0.18

0.19

4.20

1.00

5.10

0.23

0.20

6.03

5.00

0.15

0.17

1.00

0.24

0.26

0.29

0.26

5.69

0.28

0.18

1.00

6.03

5.23

5.17

5.00

5.23

6.20

5.40

1.00

5.46

6.66

5.63

5.86

5.17

6.03

6.77

6.14

4

0.16

0.15

0.27

1.00

Patents and Applications

Patents and Entrepreneurial Industry Start-Up Management Market Education Alliances USP Applications Spirit Experience Experience Experience Growth and Expertise

Table 4.6 Comparison matrix Z

136 Application and Results

0.306

0.361

2.241

Start-Up Experience

Management Experience 0.361

0.268 3.753

1.670

0.389

0.321

8.526

Education and Expertise

Alliances

USP  column

0.316

0.331

Market Growth 0.429

0.273

0.321

0.378

Industry Experience

0.576 1

1

Entrepreneurial 1.736 Spirit

Patents and Applications

12.381

0.331

0.405

1.801

2.300

0.429

0.357

1

3.115

2.643

16.817

0.411

0.429

1.632

1.990

2.125

1

2.802

3.662

2.766

10.468

0.346

0.411

1.778

0.418

1

0.471

2.333

3.264

0.446

12.398

0.367

0.405

1.941

1

2.390

0.503

0.435

3.024

2.333

7.219

0.277

0.331

1

0.515

0.563

0.613

0.555

2.766

0.599

19.842

0.383

1

3.024

2.466

2.431

2.333

2.466

3.167

2.571

25.132

1

2.610

3.608

2.724

2.891

2.431

3.024

3.728

3.115

Patents and Entrepreneurial Industry Start-Up Management Market Education Alliances USP Applications Spirit Experience Experience Experience Growth and Expertise

Table 4.7 Evaluation matrix Z E

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4.3.2.4.3.2 Weighting Vector Determination The relative importance or target weights, which represent the sought after information to answer RQ3, can be determined from the evaluation matrix by an eigenvector calculation, subsequently referred to as weighting vector determina tion (Saaty, 1988; 1990a; 1990b). First, the column sum ( column) in Z E (cf. table 4.7) is calculated in order to normalize the values c of the evaluation matrix.  For example: The value c(2;1) = 1.736 is divided by column 1 c(x;1) = 8.526, which results in a normalized value for evaluation matrix cn(2;1) = 0.204. Second,  the row elements of the normalized matrix are summed up to row totals row .  This allows local determinant weights wloc to be determined by dividing row by the number of determinants n (cf. equation 4.4). r ow c(1; x) n W ith: wloc (A1 ) = Relative local weight o f V alue A1 wloc (A1 ) =

n

= N umber o f Deter minants

(4.4)

This determined value corresponds to the principal right eigenvector, which forms the relative weight (wloc ) for the corresponding element (Mühlbacher and Kaczynski, 2013). The local weight wloc must be iteratively approximated to a global result in order to transform local weights wloc into global weights wglobal for relative weighting of determinants (Wessendorf, Schneider, Gresch, et al., 2020). Therefore, the power iteration method is employed, which builds on the property of the principal right eigenvector. Following Saaty (1987; 1990b), the power iteration method consists of three steps and is repeated until the calculated relative weights differ only slightly or in the amount of the respective determined value of the previously potentiated matrix. These steps are described by (i) the potentiating of the values within the evaluation matrix c2 , (ii) the normalization of the matrix by dividing the values by column totals c2 n, and (iii) the calculation of local weights wloc . With each iteration, the deviations between the calculated weights are reduced, thereby leading to an approximation of global relative weights wglobal (Saaty, 1990a). Within this research project, a total of five iterations is required until a global value for the relative weights wglobal is determined (cf . tables 4.14 to 4.16) (Wessendorf, Schneider, Gresch, et al., 2020) (cf . tables 4.8, 4.9, 4.10).

0.071 1.000

0.046

0.038

1.000

USP  column

0.084

0.096

0.088

0.082

Alliances

0.263

Management Experience

0.073

0.086

0.050

0.042

Start-Up Experience

0.196

0.044

Industry Experience

0.266

0.154

Education and Expertise

0.204

Entrepreneurial Spirit

Market Growth

0.117

Patents and Applications

1.000

0.027

0.033

0.145

0.186

0.035

0.029

0.081

0.252

0.213

Table 4.8 Normalized Evaluation Matrix—Iteration 1

1.000

0.024

0.025

0.097

0.118

0.126

0.059

0.167

0.218

0.165

1.000

0.033

0.039

0.170

0.040

0.096

0.045

0.223

0.312

0.043

1.000

0.030

0.033

0.157

0.081

0.193

0.041

0.035

0.244

0.188

1.000

0.038

0.046

0.139

0.071

0.078

0.085

0.077

0.383

0.083

1.000

0.019

0.050

0.152

0.124

0.122

0.118

0.124

0.160

0.130

1.000

0.040

0.104

0.144

0.108

0.115

0.097

0.120

0.148

0.124

Row

9.000

0.320

0.460

1.296

0.867

1.109

0.588

0.957

2.186

1.216



100.0%

3.6%

5.1%

14.4%

9.6%

12.3%

6.5%

10.6%

24.3%

13.5%

Relative weight wloc

4.3 Design and Development of the Artifact 139

0.042 1.000

0.052

0.041

1.000

USP  column

0.056

0.139

0.091

0.125

Alliances

0.114

Management Experience

0.065

0.096

0.075

0.063

Start-Up Experience

0.145

0.119

Industry Experience

0.248

0.137

Education and Expertise

0.281

Entrepreneurial Spirit

Market Growth

0.109

Patents and Applications

1.000

0.041

0.050

0.144

0.093

0.123

0.055

0.095

0.260

0.140

1.000

0.044

0.055

0.132

0.104

0.089

0.062

0.099

0.273

0.143

1.000

0.043

0.053

0.146

0.075

0.129

0.061

0.104

0.270

0.118

1.000

0.047

0.059

0.129

0.091

0.109

0.066

0.098

0.265

0.138

1.000

0.038

0.047

0.142

0.092

0.122

0.059

0.101

0.264

0.136

1.000

0.037

0.049

0.143

0.094

0.121

0.063

0.102

0.256

0.135

Row

9.000

0.376

0.474

1.265

0.800

1.073

0.553

0.889

2.374

1.197



100.0%

4.2%

5.3%

14.1%

8.9%

11.9%

6.1%

9.9%

26.4%

13.3%

Relative weight wloc

4

1.000

0.043

0.053

0.145

0.085

0.141

0.059

0.075

0.258

0.142

Table 4.9 Normalized Evaluation Matrix—Iteration 2

140 Application and Results

0.043 1.000

0.054

0.043

1.000

USP  column

0.054

0.139

0.089

0.118

Alliances

0.119

Management Experience

0.063

0.099

0.089

0.063

Start-Up Experience

0.140

0.099

Industry Experience

0.262

0.133

Education and Expertise

0.262

Entrepreneurial Spirit

Market Growth

0.133

Patents and Applications

1.000

0.043

0.054

0.139

0.089

0.118

0.063

0.099

0.262

0.133

Table 4.10 Normalized Evaluation Matrix—Iteration 5

1.000

0.043

0.054

0.139

0.089

0.118

0.063

0.099

0.262

0.133

1.000

0.043

0.054

0.139

0.089

0.118

0.063

0.099

0.263

0.133

1.000

0.043

0.054

0.139

0.089

0.119

0.063

0.099

0.262

0.133

1.000

0.043

0.054

0.139

0.089

0.118

0.063

0.099

0.263

0.133

1.000

0.043

0.054

0.139

0.089

0.118

0.063

0.099

0.262

0.133

1.000

0.043

0.054

0.139

0.089

0.118

0.063

0.099

0.262

0.133

Row

9.000

0.383

0.487

1.255

0.799

1.066

0.563

0.890

2.362

1.196



100.0%

4.3%

5.4%

13.9%

8.9%

11.8%

6.3%

9.9%

26.2%

13.3%

Relative weight wloc

4.3 Design and Development of the Artifact 141

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4.3.2.4.3.3 Consistency Test The significance of AHP results depends largely on their consistency. This, in turn, is based on the consistency of the answers to a number of pairwise comparisons to be as high as possible. Yet, due to the need to precisely specify the evaluation judgements as well as the limited information processing capacity required by the pairwise comparisons in AHP, Haedrich et al. (1986) find that it is often difficult for respondents to make fully consistent value judgements. Therefore, the consistency and the transitivity of the data need to be evaluated. This is an important and AHP-specific step to assess the validity and reliability of the data (Helm and Steiner, 2008). A decision is considered consistent if, for example, “A is twice as important as B, B is three times as important as C and A is therefore six times as important as C” (Belton 1986). Transitivity exists as soon as the preference order is unambiguous. Thus, the evaluation matrix is consistent if “aij × ajk = aik applies to any i, j and k” (Nitzsch 1993). Equation 4.5 describes the maximum principal eigenvalue, which is subsequently used to measure the consistency of the evaluation matrix (Saaty, 1987). λmax =

n j=1

W ith: λmax ai j W

ai j

Wj Wi

= Maximum Princi ple Eigenvalue = V alue o f N or mali zed Evaluation Matri x = Relative W eight

(4.5)

Ideally, if the decision registered within an AHP analysis is completely consistent, the maximum principle eigenvalue (λmax ) corresponds to the total number of column elements n of the required eigenvector (i.e. n = 9). However, in reality λmax > n is observed (Saaty, 1987), which is reflected by the present work with λmax amounting to 9.826. Yet, the maximum principal eigenvalue is hard to be interpreted in terms of consistency. It provides a target value for complete consistency but refrains from a simple interpretation once the ideal value is not attained. Thus, in pairwise comparison, the consistency index (CI) measures the inconsistency. Inconsistencies are to be expected in practice. For this reason, a ratio between CI and an average value R (for “random”) is considered (cf. equation 4.6). The value R represents empirical values, which have been verified in test series. The resulting consistency ratio (CR) is a measure of coherence and supports consistency judgement as it can be used to determine to what extent the inconsistency is acceptable.

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CI λmax − n ;CR = n−1 R = Maximum Princi ple Eigenvalue

CI = W ith: λmax

n = N umber o f Column Elements R = Random V alue (Saat y, 1987)

(4.6)

Consistency depends on the number of determinants, thus the more determinants are retrieved, the higher the probability of inconsistent responses (cf. table 4.11).

Table 4.11 Consistency Index R with a given number of determinants (Saaty, 1987) Number of determinants

1

2

3

4

5

6

7

8

9

10

Consistency Index R

0.00

0.00

0.52

0.89

1.11

1.25

1.35

1.40

1.45

1.49

Saaty (1987) sets a very strict limit for sufficient consistency (CR < 0.1) compared to the limits (CR < 0.2) of other relevant studies (Dolan, 2008; Til et al., 2008). The data collected in this research project are on one hand strongly consistent, with CI = 0.10 and, on the other hand, strongly coherent, with CR = 0.07. Thus, the stricter limit defined by Saaty (1987) is withstood. This, in consequence, is considered as a strong proof of the data’s high validity.

4.3.2.4.4 Data Interpretation The results of Data Analysis serve as a basis for finding initial answers to the defined research questions. By interpreting this data, the last step of the AHP is reached. The result of the empirical survey, its normalization and the subsequent iterations described within the AHP thus lead to relative weights for determinants used in the valuation process of early-stage NTBF. Analyzing these results, it becomes apparent that, for example, “Entrepreneurial Spirit” represents 26.2 percent of the valuation decision-making for an early-stage NTBF and is therefore considered as most important. As the determinants are compared directly with each other in pairwise comparisons and thus put in relation to each other, this allows, for example, for the statement that “Education and Expertise” (4.3 percent) is not even half as important as “Market Growth” (8.9 percent). “USP” and “Management Experience” make up 25.7 percent of the decision, but it cannot be concluded that combined they are as important as “Entrepreneurial Spirit” (26.2 percent).

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A combination of determinants was not considered by the pairwise comparisons, therefore no conclusions can be drawn about the importance of one determinant with regard to a combination of determinants. For the sake of easy reference and clear structure, the data processed according to AHP methodology will be interpreted and discussed in greater detail within the subsequent section 4.3.2.7 “Findings”.

4.3.2.5 Qualitative Validation of AHP Results by Means of Expert Interviews Validation of identified relative determinant importance wglobal is crucial in order to obtain meaningful results. With the goal being the creation of practical utility, relevant respondents of the survey designed for AHP analysis will be confronted with the attained results based on a Delphi study-like approach. This approach follows a 4-step approach (Häder, and Häder 1994):

4.3.2.5.1 Operationalization of Main Question Aiming for Concrete Determinants for a Later Assessment The defined research question RQ3 represents the basis for result validation and further insights from relevant investment professionals. Therefore, the research question was put in context of the achieved AHP results in order to conduct a targeted discussion. This represents the basis of the subsequent standardized question program. 4.3.2.5.2 Development of Standardized Question Program for Anonymous Questioning of Experts on a Subject During the interviews, relevant investment professionals will be confronted with the results of the survey. In order to provide the interviewees with a better picture of the topic as well as to ensure that the results and open questions are clearly understood, a summary document consisting of a short introductory text as well as main results is sent to them prior to the interview. With presenting these main results (cf. table 4.12), the defined two main questions are formulated: 1. Do you agree with these results? 2. Which of these determinants presented can be fully assessed and operationalized in your opinion?

4.3 Design and Development of the Artifact Table 4.12 Relative importance of relevant determinants in early-stage NTBF valuation following AHP analysis

145

Determinant

Relative weight

Entrepreneurial Spirit

26.2%

USP

13.9%

Patents and Applications

13.3%

Management Experience

11.8%

Industry Experience

9.9%

Market Growth

8.9%

Start-Up Experience

6.3%

Alliances

5.4

Education and Expertise

4.3%

Sum

100.0%

4.3.2.5.3 Preparation of Survey Results and Anonymous Feedback to the Interviewees Involved During the interview, interviewees can, with the knowledge of the decisions of the other participants, look at the determinants one more time and reconsider their opinion on the importance ranking. Thereby, an open discussion is to be developed in which the investment professional presents his opinion on the results and puts them in a valuation context as well as answers the main questions defined. The results obtained from the interviews with relevant investment professionals are not numerically evaluable and statistically reliable, thus minutes need to be kept for documentation purposes. Nevertheless, the results obtained enable a good understanding of practical perspectives, an assessment of the validity of the empirical study as well as provide initial indications for later conclusions. 4.3.2.5.4 Evaluation of the expert interview Seven interviews conducted with relevant investment professionals of approximately 30 minutes each in April 2018 (cf. table 4.13) lead to differing results, potentially driven by different backgrounds (e.g. Venture Capital vs Corporate Venture Capital) and seniority (e.g. Investment Manager vs Analyst) of the interviewees. However, the feedback confirms that the order of importance of early-stage technology venture valuation determinants in itself appears plausible. Still, the relatively high importance of “Entrepreneurial Spirit” was surprising, as smaller distances between main determinants were suspected. Further, the ranking of the least important determinants is confirmed several times during the interviews and additionally supported by practical examples, with for example “Education” often being documented, but not included in the valuation.

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Table 4.13 Overview of expert interviews conducted Expert Code Name

Interview length (hh:mm:ss)

Interview type

Background

E1

00:37:12

Telephone Interview

Business Angel

E2

00:33:48

Telephone Interview

Venture Capitalist

E3

00:29:10

Telephone Interview

Venture Capitalist

E4

00:32:29

Telephone Interview

Corporate Venture Capitalist

E5

00:34:42

Telephone Interview

Corporate Venture Capitalist

E6

00:24:05

Telephone Interview

Corporate Venture Capitalist

E7

00:30:52

Telephone Interview

Business Angel

Sum

03:39:18

Average

00:31:20

On the other hand, there are three determinants considered to have the highest level of relative importance to early-stage NTBF valuation: “Entrepreneurial Spirit” (ES), “Unique Selling Proposition” (USP) and “Patents and Applications” (IP). Here, the expert interviews provide a link to “must-have determinants”. The majority of investment professionals confirmed the statement of one interviewee who concludes: “USP is an absolute must for every Start-Up and applies to every industry. Without a USP created by an idea, technology or business model, the Start-Up makes no sense at all. The USP is the essence of a Start-Up.” Consequently, a USP must exist for a Start-Up to be considered for an investment and its uniqueness must be understood for proper valuation. This can be realized through unique know-how, a time advantage or, in the case of technology-oriented Start-Ups, through IP. As a first conclusion, the experts identify USP and IP as mandatory determinants for consideration of technology ventures as an investment opportunity as well as their valuation. Further, ES is the determinant that has the highest importance to investors when investing and valuing a technology venture, as investors require a high level of confidence in the entrepreneurs. The determinant ES is therefore not a “musthave determinant” for the consideration of NTBF in the investment process, but a “must-have determinant” for the execution of the investment and its valuation. The great importance of ES is based on the lacking ability to operationalize the entire investment and valuation process. As valuation in an early-stage NTBF is

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147

not entirely reflected by numbers, a subjective component in the assessment is required. One investment professional described the importance of ES as follows: “I trust less in the experience of a person in certain areas or in his alliances. That’s too indirect. I trust the man himself to make the Start-Up successful.” As all other determinants have less influence on the investment activity and valuation, investors do not regard them as “must-have determinants”. Thus, three “must-have determinants” become apparent that apply at different points in time: USP and IP in the sourcing of investment opportunities and ES in the decisionmaking phase, with all having an important influence on valuation. With relevant investment professionals having a common understanding of the three “must-have determinants” as well as the three least important determinants in a valuation context, three remaining determinants need to be further explored. “Industry Experience”, “Management Experience” or “Start-Up Experience” give the assurance that the entrepreneur knows what she is doing but does not offer any guarantee to the investor with regard to valuation. This leads to the investment professionals assigning more relative weight to their own judgement with regard to performance of the entrepreneur than to the evaluation of such indicators. Finally, no expert interview revealed a contradiction to the identified ranking of relative importance. Further, the selection of determinants was confirmed and considered to be correct, with the exception of a specific personal component not included in “Entrepreneurial Spirit” being missed. The subjective “first impression” (for example sympathy) of the investor appears as an important determinant of the later investment. The fact that intuition or so-called “gut feeling” plays a role in many investment decisions, explains why no suitable formulation of this missing determinant could be attained. However, it could be agreed that this missing element is reflected in various determinants and therefore cannot be substantiated individually.

4.3.2.6 Choice-based Conjoint Analysis (CBC) Note: The Choice-based Conjoint Analysis (CBC) performed in this research project is primarily intended to provide answers on valuation determinants’ impact. Yet, some valuable information related to valuation determinants’ importance (i.e. this section 4.3.2) can also be derived. In order to ensure a clear communication structure with regard to results presented within this dissertation, results relating to valuation determinants’ importance are described within the following section, whereas the overall methodology of the CBC is being detailed at a later point in section 4.3.3.3 Following a triangulation by method approach, an additional perspective on relative importance of relevant determinants in early-stage NTBF valuation has

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to be gained. This additional perspective is intended to complement to previously attained results from AHP analysis, which were subsequently validated by means of expert interviews. Based on the specific methodological strengths (cf. section 4.3.3.3), a Choice-based Conjoint (CBC) analysis was chosen. As the CBC analysis performed followed the primary objective of retrieving relevant data to answer RQ4, thus measuring the impact on valuation by relevant determinants for early-stage NTBF valuation, the methodology applied is described in greater detail within section 4.3.3.3. In essence, the CBC analysis within this research project followed four main steps: Study Design, Data Collection, Data Analysis and Data Interpretation. Yet, a secondary outcome of the CBC analysis, even though aiming for measuring relevant determinants’ impact on early-stage NTBF valuation, is a measure on relative weight, thus relative importance, of these respective determinants (cf. equation 4.13). As these results provide valuable additional insights to find a comprehensive answer to RQ3, the resulting relative weights will be included in the step “Evaluation of Valuation Determinants’ Importance” (cf. section 4.3.2) of the “Design and Development” (cf. figure 3.5) stage of this research project’s artifact. The relative weights resulting from CBC analysis span over six main determinants that are considered to be of high relevance to early-stage NTBF valuation (cf. table 4.14).

Table 4.14 Relative importance of relevant determinants in early-stage NTBF valuation following CBC analysis

Determinant

Relative weight

Entrepreneurial Spirit

33.4%

USP

19.4%

Market Growth

18.4%

Patents and Applications

13.7%

Founder Experience

11.5%

Alliances

3.6%

Sum

100.0%

Note: In order to reduce complexity induced by CBC with regard to a high number of attribute combinations, the nine determinants previously analyzed within an AHP context, needed to be reduced to six determinants. Thus, first, the experiential determinants (i.e. Industry Experience, Management Experience and Start-Up Experience) were combined to form a single variable “Founder Experience”. Second, the variable

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149

“Education & Expertise”, which appeared to be of little significance within the AHP, was excluded (cf. table 4.16). This reduction in determinants and thereby resulting combinations aims for increasing the quality and amount of data to be retrieved from the analysis.

For a detailed description on CBC methodology in general and the specific steps applied within this research project, section 4.3.3.3 is recommended.

4.3.2.7 Findings In order to attain a meaningful answer to RQ3, the present research project builds on triangulation by data source and by method to ensure a high degree of objectivity. This was realized in a two-step approach. In a first step, an empirical survey with 35 investors in early-stage NTBF following AHP methodology was completed. Participants in this survey had to evaluate one determinant relevant to early-stage NTBF valuation over another, for nine value-driving determinants in total. “Reliability was assured by the survey’s design and participant selection, whereas validity was proven by a consistency ratio CR = 0.07” (Wessendorf, Schneider, Gresch, et al., 2020). Wessendorf et al. (2020) continue: “Due to the AHP methodology applied, a direct comparison of these determinants among each other with regard to their importance in early-stage NTBF valuation becomes possible. Analyzing the obtained results, we find a clear ranking in relative importance of valuation determinants. In descending order of importance, we find that “Entrepreneurial Spirit” is the most important value driving determinant (with an importance weight of 26.2%), followed by “USP” (13.9), “Patents and Applications” (13.3), “Management Experience” (11.8), “Industry Experience” (9.9), “Market Growth” (8.9), “Start-Up Experience” (6.3), “Alliances” (5.4) and “Education and Expertise” (4.3). The top-3 determinants are highly important to valuation, with a weighting factor of 53.4% compared to the remaining six determinants with 46.6%. A combination of determinants was not considered by the pairwise comparisons, therefore no conclusions can be drawn about the importance of one determinant with regard to a combination of determinants.”

In a second step, to further deepen the understanding gained and validate the results attained within AHP analysis, a survey with n = 40 participating investment professionals following the CBC methodology is carried out. In this context, participants assessed bundles of determinants (i.e. stimuli) over another, for six value-driving determinants in total. “Reliability was assured by the survey’s design and participant selection, whereas validity was proven by a McFadden’s pseudoR2 of 72%” (Wessendorf, Schneider, Gresch, et al., 2020). According to the

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empirical survey, by far the most influential and thus the most important determinant for the valuation of an early-stage NTBF is the “Entrepreneurial Spirit” (33.39%). “’USP’ (19.35) and ‘Market Growth’ (18.43) are the next most important determinants and are almost on a par. The determinants ‘Patents and Applications’ (13.68) and ‘Founder Experience’ (11.52) follow at a smaller distance. The determinant ‘Alliances’ (3.62) represents the lowest ranking determinant” (Wessendorf, Schneider, Gresch, et al., 2020). These results are partially supportive of the previous results attained in AHP analysis, as they confirm the priority of the determinants “Entrepreneurial Spirit”, “USP”, “Patents and Applications” and “Alliances”. Nonetheless, the experience-driven determinants (aggregated to “Founder Experience” within CBC analysis) and the determinant “Market Growth” appear to be differently valued by the participants of both surveys. In consequence, also with regard to determinants for early-stage NTBF valuation, venture capitalists appear to emphasize the “jockey” (i.e. the entrepreneur(s)) rather than the “horse” (i.e. the product of the venture), which is an analogy used by Macmillan et al. (1985) and Kaplan (2019) to describe important criteria in venture capital investment decision-making and venture selection. Linking the attained results to non-financial determinants analyzed in relevant previous research, Wessendorf et al. (2020) find a comparable but slightly different picture. “[…] Founder Personality, comparable with the “Entrepreneurial Spirit”, attains the highest score on average. This holds for the highly comparable study of Knockaert et al. (2010), who rank the entrepreneur (score 12.64) and the team (11.85) highest. Next, “Market Growth” (8.84) scores in the top third of the ranking, same as with the performed CBC analysis. Nevertheless, the results of the AHP locate this determinant at the bottom half of the ranking established. Following are “USP” (8.53) and “Protection” (7.76) scoring in the top half, again comparable to the results of our CBC analysis, whereas our AHP results for “USP” and “Patents and Applications” locate it clearly in the top third. The remaining determinants are not directly accounted for but indirectly reflected in the determinants chosen. However, the ranking of “Market Acceptance” (6.52), “Geography of the Market” (5.27), the “General-purpose of the Technology” (5.03) and “Market Size” (4.39) are clearly assigned a reduced level of importance, thus comparable to our results. Finally, the determinant “Contact” (7.81) ranks in the top half of relevant previous research but was not fully reflected in our analysis. However, it is surprising that Knockaert et al. (2010) do not directly analyze the effect of “Management Experience”, “Industry Experience” and “Start-Up Experience” on decision-making. Looking at these experience-driven determinants across all relevant studies analyzed, they rank in the top-half (2.3), which is reflected within our AHP analysis. The identified slight differences might result from a different research focus (that is venture valuation vs. venture investment decision-making). Still, this

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151

comparison supports the general structure of the importance ranking to be correct, in particular with regard to the top-5 determinants.”

In addition, several publications forming part of relevant previous literature mention differences in venture capitalist and business angel investment and valuation behavior (M. Van Osnabrugge & Robinson, 2000). These differences might originate in the respective institutional background, investment motivation and objectives. Yet, the findings of the conducted AHP and CBC analyses are strongly differing from these previous findings. Looking at venture capitalists and business angels that participated in the empirical surveys of this research project, the observed valuation focus presents itself as highly comparable with no significant differences. Wessendorf et al. (2020) observe that participants of the survey analyzed in an AHP approach were relevant venture capitalists (n = 15) as well as corporate venture capital investors and business angels (n = 20). As some of the participants assigned to this last group invest their private money while others strategically invest the money of their own company, a clear distinction cannot be made. “Nevertheless, across all answers, the consistency ratio CR = 0.07 proves that the results derived are highly comparable, independently of the investor type” (Wessendorf, Schneider, Gresch, et al., 2020). Turning to the survey’s results analyzed by CBC methodology, both investor types rank “Entrepreneurial Spirit” first among valuation determinants with an importance weight of 33.0 for venture capitalists and 34.3 for business angels. Wessendorf et al. (2020) establish concordance “with ‘Alliances’ having the least importance in an early-stage technology venture valuation context with an importance weight of 3.6 and 4.0 respectively. The remaining determinants analyzed vary slightly in their ranking among venture capitalists and business angels. However, their importance weighting is highly comparable with slight differences in the range of 0.8 to 2.9” (cf. table 4.15). These results further support Wessendorf et al. (2019) who find that business angels have a tendency towards entrepreneur and team determinants (i.e. “Entrepreneurial Spirit”) in a valuation context. In contrast, venture capitalists have a stronger focus on measurable and proven determinants (i.e. “USP” or “Patents and Applications”). Yet, considering the finding of no significant difference in investment and valuation behavior among venture capitalists and business angels, it is hypothesized that business angels nowadays, and particular in the very complex investment segment dealing with NTBF, become increasingly professionalized. This drives business angels to adopt valuation determinants and investment decision-making practice that is increasingly comparable to venture capital funds.

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Table 4.15 Results for relative importance of determinants of early-stage NTBF valuation following CBC analysis—split by investor type (Wessendorf, Schneider, Gresch, et al., 2020) Determinant Importance for Relative Determinant Importance for Relative Venture Capitalists Importance/ Business Angels and CVC Importance/ n = 27 Weighting n = 13 Weighting Entrepreneurial Spirit

33.0

Entrepreneurial Spirit

USP

20.0

USP

17.7

Patents and Applications

14.5

Patents and Applications

11.6

Founder Experience

11.3

Founder Experience

12.1

Market Growth

17.6

Market Growth

20.3

Alliances Sum

3.6 100.0

Alliances Sum

34.3

4.0 100.0

With regard to constructing an artifact improving the indication of value in early-stage technology venture valuation, while addressing the set out requirements (cf . section 4.2) the following has been achieved: 1. Recent empirical information on non-financial determinants specific to earlystage NTBF valuation was gathered. 2. A clear ranking of relative importance of non-financial determinants relevant to early-stage NTBF valuation in a venture capital valuation context was established. This allows the construction of an artifact accounting for the specific challenges in valuation of early-stage NTBF, i.e. a lack of financial history and measurable track record as well as, in particular, the set out requirements for the artifact with regard to transparency. 3. A clear focus on value-driving determinants derived from the ranking of importance of non-financial determinants for NTBF valuation is established, thereby improving the artifacts operationalizability by a reduction of complexity and number of variables.

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Evaluation of Valuation Determinants’ Impact

This section is based on: Wessendorf, C. P., Schneider, J., Gresch, M. A. and Terzidis, O. (2020) What matters most in Technology Venture Valuation? Importance and Impact of NonFinancial Determinants for Early-Stage Venture Valuation, International Journal of Entrepreneurial Venturing, 12(5), pp. 490–521. https://doi.org/10.1504/IJEV.2020. 111536

The impact of non-financial determinants on early-stage NTBF valuation needs to be investigated in order to create an artifact improving the indication of value in early-stage NTBF valuation (InVESt-NTBF), while addressing the set out requirements (cf . section 4.2.1). A clear understanding on the determinant’s effect on value will reflect the necessary transparency and provide a further aspect to formalize its structure.

4.3.3.1 Literature Review Similar to the attained findings related to investment determinants’ relative importance, also investment determinants’ impact can be separated in differing groups. Two groups emerge that need to be discussed in greater detail. The first group focusses on determinants’ impact to the decision-making in the context of a venture capital investment process (Franke et al., 2008, 2004; Hoenig & Henkel, 2015; D. K. Hsu et al., 2014; Knockaert et al., 2010; Landström, 1998; Colin Mason & Stark, 2004; Riquelme & Rickards, 1992; D. A. Shepherd, 1999). The majority of these studies (8 out of 9) employ a conjoint analysis methodology as the basis of their respective surveys, which are mostly following the full-profile approach (6 out of 8). In addition, this group shows a particularly unusual research scope, compared to the wider group of publications dealing with determinants in venture capital investments, as a clear focus on technology ventures as well as a geographic scope including Europe, is established by a relatively large fraction of the analyzed publications (Hoenig & Henkel, 2015; Knockaert et al., 2010; Landström, 1998). Turning to the attained quantification of identified determinants, Wessendorf et al. (2020) find: “The highest impact on the venture investment decision-making among non-financial determinants is observed with Founders’ Experience, e.g. Industry Experience (33%). This is followed by Founders’ Personality, e.g. Passion (18%), Product Characteristics, e.g. USP (10%), Market Characteristics, e.g. Market Growth (10%), Intellectual Property, e.g. Applications and Patents (9%), Alliances, e.g. to Strategic Partners (5%), Existing Investors (4%), Operations (4%) and other determinants (3%).”

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One study is of particular importance to the present work, as it investigates the impact of determinants on investment decisions in European early-stage technology venture investments (Knockaert et al., 2010). Knockaert et al. (2010) follow an inductive research design and a conjoint analysis to derive a clear ranking of determinant’s impact. This is based on a dataset originating from 68 European early-stage high-technology venture capitalists. “Varying in relation of the investor’s strategic focus, Return on Investment scores the highest in importance (13.55), followed by the entrepreneur (12.64), the team (11.85), market growth (8.84), USP (8.53), time to break even (7.82), contact (7.81), protection (7.76), market acceptance (6.52), geography of the market (5.27), the general-purpose of the technology (5.03) and market size (4.39).” (Wessendorf, Schneider, Gresch, et al., 2020)

For the second group, a clear focus on determinants for early-stage venture valuation can be established. Yet, this group consists of only one study, that addresses the quantification of valuation determinants’ impact within a venture valuation context (Festel et al., 2013). Similar to the already existing literature, relevant criteria for the valuation of early-stage ventures are identified. Here Festel et al. (2013) are guided on the one hand by existing literature, but on the other hand by the concrete valuation process of 16 early-stage high-technology start-ups (i.e. biotechnology, nanotechnology, clean-tech and medical technology). In addition to the 20 identified valuation determinants, however, an attempt is made to quantify the influence of the characteristics of these criteria. Festel et al. (2013) thus describe an approach that has a direct influence on the valuation result by deriving an adjusted beta factor to be applied within the CAPM from an assessment of the venture along 20 valuation determinants. The procedure is as follows: (1) classification of the early-stage technology venture to be valued according to the defined 20 valuation determinants along a 5-step scale, (2) determination of the adjustment of the basic beta factor in steps of 0.5 depending on the determinant presentation, (3) adjustment of the beta factor, (4) calculation of the equity capital costs with CAPM on the basis of the adjusted beta factor and finally (5) valuation of the technology venture with e.g. the discounted cash flow method or venture capital method and adjusted discount rate. With regard to the work of Festel et al. (2013), it is important to question the extent to which the approach presented corresponds to reality, as the framework follows an equal scaling and weighting of impact. The present work thus aims to empirically investigate if such an equally weighted approach can be justified.

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4.3.3.1.1 Differentiation to Previous Research The present work differs from previous research in several aspects and thereby establishes its relevance to academia and valuation practice alike. It is further considered to deepen the existing knowledge in the field of early-stage technology venture valuation. First, this work focusses on the impact of defined determinants on valuation of early-stage technology ventures. As outlined previously, existing research mainly discusses determinants’ impact on the investment decision-making. Only one publication relevant to the early-stage NTBF valuation can be identified. Yet, the approach demonstrated in previous research triggers information needs that will be investigated by the present research project. Second, in line with section 4.3.2.1, this work will focus on non-financial determinants only, as early-stage ventures are not considered to dispose of a meaningful corporate history and data for valuation (Kaserer et al., 2007; Damodaran, 2009). Yet, this limitation in scope is not considered to compromise the results, as Sievers et al. (2013) find that “the variation in value explainable by solely nonfinancial information is strongly comparable to the variation in value explainable by solely financial information”. Nonetheless, the meaningfulness of the non-financial determinants needs to be ensured, supporting the relevance of the present work. Third, in line with section 4.3.2.1, the present work assumes a strict focus on the early stage of the organizational life cycle, i.e. “Conception and Development”, “Commercialization”, and first phases of “Growth” as defined by prior literature (Kazanjian, 1988; Kazanjian and Drazin, 1990). According to Wessendorf et al. (2020) “this represents a rather interesting stage within the organizational life cycle in a valuation context due to the associated challenges faced by conventional valuation practice (Kaserer et al., 2007; Damodaran, 2009)”.

4.3.3.2 Research Question After having analyzed the determinants’ importance compared to each other, these determinants need to be further characterized in order to derive meaningful insights on their impact on early-stage NTBF valuation. Thus, RQ4 is formulated as: RQ4: What is the impact of determinants identified on early-stage NTBF valuation?

This will provide strong foundation in order to develop an artifact to improve the indication of value in early-stage NTBF (InVESt-NTBF) valuation, while respecting the specific requirements outlined in section 4.2.1.

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4.3.3.3 Choice-based Conjoint Analysis (CBC) After evaluating the results attained by AHP methodology, a need to better account for the specifics of early-stage NTBF valuation within determinant characterization was identified. Therefore, a methodology that allows for experimentbased analysis of a directional relationship among several subjectively assessed determinants with different manifestations each was intended. This resulted in a methodological shift towards a Choice-based Conjoint Analysis (CBC), which is considered to be the conjoint analysis variant that best reflects realistic decisionmaking situations (Herrmann et al., 2008, p. 690) and is, thus, predominantly used in scientific analysis (Herrmann et al., 2008, p. 689). Besides some crucial challenges faced by this analysis methodology (cf. section 4.3.2.4), the measurement of subjective assessments became more central. The methodological strengths of CBC can be considered to complement AHP and are essentially the following: 1. Preference between independent and metric dependent variables that have a directional relationship can be measured (i.e. multi-attributive method) (Herrmann et al., 2008, p. 164, 654). Thus, it allows for the analysis of bundles of the subject matter, and for its respective manifestations; 2. The directional context is analyzed by enabling the measurement of impact on a target parameter; 3. The decision-making situation and individual preferences are depicted in real time, thereby avoiding distortions by intentional or unintentional biases (Shepherd and Zacharakis, 1999) caused by self-assessments or retrospective justification for a subjective decision; 4. Implementability within the framework of an experiment that is resourceand time-saving as well as confidential for the test persons is given, thereby potentially allowing for a high number of quality responses given. As several variants of conjoint analyses are used in research settings, their respective advantages and disadvantages need to be evaluated against the intended analysis objective. CBC is considered the predominant variant in scientific contexts (Herrmann et al., 2008, p. 689), having clear advantages over traditional variants of conjoint analyses. Thus, CBC allows for a more realistic simulation of the decision situation, thereby facilitating the specification of preferences by test persons (Herrmann et al., 2008, p. 690). A CBC analysis “requires test persons to repeatedly decide on selecting an attribute among several options offered at the same time” (Wessendorf, Schneider, Gresch, et al., 2020). The CBC’s respective utility function contains a deterministic and a stochastic component, as the underlying “model assumes that the selection decisions of the respondents cannot be fully

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explained by the specified model consisting of attributes, their characteristics and the utility function. The stochastic parameter therefore represents utility influences caused by unrecorded attributes, unobservable heterogeneity, measurement errors, or an inaccurately suitable utility function” (Wessendorf, Schneider, Gresch, et al., 2020). Predictions on the preference of test persons are probability weighted, due to the stochastic component inherent to CBC (Herrmann et al., 2008, p. 692 ff.). The CBC approach will be discussed in four main steps: Study Design, Data Collection, Data Analysis and Data Interpretation.

4.3.3.3.1 Study Design According to Backhaus et al. (2006, p. 522), the basic procedure to design a study following conjoint analysis methodology consists of five steps: Determination of characteristics and variants, Determination of the Survey Design, Evaluation of Stimuli, Estimation of Utility Values, and Aggregation of Utility Values. 4.3.3.3.1.1 Determination of Characteristics and Variants The characteristics, or attributes, to be specified in this analysis are non-financial determinants of early-stage NTBF valuation. In line with the previous AHP analysis, the specific determinants analyzed by CBC are identified and characterized in section 4.3.1 (Wessendorf, Kegelmann, et al., 2019). Yet, for the sake of reducing complexity induced by CBC with regard to a high number of attribute combinations, the nine determinants previously analyzed within an AHP context, needed to be reduced to six determinants. Thus, first, the experiential determinants (i.e. Industry Experience, Management Experience and Start-Up Experience) were combined to form a single variable “Founder Experience”. Second, the variable “Education & Expertise”, which appeared to be of little significance within the AHP, was excluded (cf . table 4.16). This reduction in determinants and thereby resulting combinations aims for increasing the quality and amount of data to be retrieved from the analysis. A total of three variants (hereafter also referred to as “manifestation”) for each characteristic (hereafter also referred to as “determinant”) will be presented within the CBC analysis. The determinants’ variants can be in the form of a “low”, a “medium” or a “strong” manifestation, thereby expressing the determinant’s under proportional, comparable or over proportional manifestation in a venture compared to the benchmark of the respective participant. 4.3.3.3.1.2 Determination of the Survey Design All previously defined determinants will be reflected in the CBC with varying manifestations. Each combination of determinants’ manifestation, i.e. stimuli, will be presented to the test person. Thus, this research project applies the full profile

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Table 4.16 Overview of relevant valuation determinants (cf. section 4.3.1.2) and their respective selection for the CBC analysis (Wessendorf, Schneider, Gresch, et al., 2020) Determinants identified in (Wessendorf, Kegelmann, et al., 2019)

Definition of determinant

Determinants selected for Analytical Hierarchy Process (AHP)

Determinants selected for Choice-based Conjoint Analysis (CBC)

Personality

Founders’ passion Entrepreneurial and their willingness Spirit to sacrifice

Entrepreneurial Spirit

USP

Strength of Unique Selling Proposition (USP)

USP

USP

Patents and Applications

Number of patents and patent applications and respective scope

Patents and Applications

Patents and Applications

Management Experience

Experience gained in Management a higher management Experience position originating from a previous employment

Industry Experience

Existing industry experience of the founders, preferably originating from previous work for a large corporation

Industry Experience

Start-Up Experience

Previous experience as a founder of a venture or employment in a Start-Up

Start-Up Experience

Market Growth

Current market growth as well as potential for market growth

Market Growth

Founder Experience

Market Growth

(continued)

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Table 4.16 (continued) Determinants identified in (Wessendorf, Kegelmann, et al., 2019)

Definition of determinant

Determinants selected for Analytical Hierarchy Process (AHP)

Determinants selected for Choice-based Conjoint Analysis (CBC)

Market

Acceptance of a venture’s offer by market participants as well as competition. Both are considered to drive “Market Growth”

Alliances

Number of alliances and partnerships entered by the venture

Alliances

Alliances

Education

The founder’s level of education

Education & Expertise

Expertise

Specialist knowledge of the founders (especially. For technology ventures)

Team Completeness

Completeness of founding team, meaning a team covering all necessary functions

Investor Reputation

Presence of a known and respected investor in a venture

Team vs Solo

Founding team vs individual founders

Presentation

A professional and pleasant presentation of the founders and their venture (continued)

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Table 4.16 (continued) Determinants identified in (Wessendorf, Kegelmann, et al., 2019)

Definition of determinant

Product Status

Current status of product development defines certainty in the venture’s process of developing a market offer

Structure

The structural robustness of a venture (e.g. organization and processes)

Age

The age of a Start-Up (i.e. firm age)

Determinants selected for Analytical Hierarchy Process (AHP)

Determinants selected for Choice-based Conjoint Analysis (CBC)

method, resulting in the simultaneous evaluation of all determinant manifestations, instead of comparing only two determinant manifestations against each other. This approach is considered to reflect a greater realism (Backhaus et al., 2006, p. 525), even though it places increasing cognitive demands on the test persons. Yet, these were regarded as potentially unproblematic, as the test persons participating in this research project are considered experts in their field. Still, the cognitive demands and resulting difficulties placed upon participants need to be reduced as much as possible in order to allow for a high quality of answers retrieved. The potential information gain per selection decision needs in consequence to be weighed against the increasing difficulty of providing meaningful comparisons. Thus, in a first step, since a symmetrical study design with six determinants, each with three manifestations leads to a very high number of 36 = 729 stimuli following the full profile method, the systematic reduction of the design by fractionation became indispensable. In consequence, a fractional-factorial design was applied to the study, which considers interaction effects between the determinants (Herrmann et al., 2008, p. 672) and optimizes important efficiency criteria, aiming for the determination of stable results even in cases with few respondents (Herrmann et al., 2008, p. 697).

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Next, in a second step, a number of three stimuli was chosen for this research project. This is in line with the recommendation of relevant literature to include three to five stimuli per choice set in order not to complicate the assessment of stimuli by participants (Herrmann et al., 2008, p. 696; Backhaus et al., 2015, p. 185). Only complete stimuli were shown, which were randomly assigned to the choice sets and followed a random sequence within a choice set. The information gained from a CBC analysis increases with the number of choice sets submitted by relevant participants. Yet, if a participant’s choices are consistent, the additional information benefit decreases with an increasing number of choice sets. This is mostly due to redundancy of information or fatigue of the participants. In consequence, the number of choice sets is recommended to be limited to a minimum of six choice sets (Herrmann et al., 2008, p. 698) and a maximum of 15 choice sets (Backhaus et al., 2015, p. 185). For the present research project, a total of 16 choice set blocks were created, within each of which a different series of 12 choice sets to be shown to the participant was generated. Thus, a test person would be randomly assigned to one of the 16 choice set blocks and presented with the corresponding, also randomized series of 12 choice sets. By this, potential distortions and bias from the choice set’s presentation or sequence can be strongly reduced. In addition, each choice set contained a simultaneously offered None-option, conducive to the realism of the experiment (Herrmann et al., 2008, p. 698). Since the stimuli were to represent early-stage NTBF, the ventures’ appropriate presentation based on their determinant manifestations had to be elaborated. Since a verbal description was considered too extensive and error-prone, a mostly visual, diagrammatic representation of the venture’s was chosen. Thus, the choice sets and respective stimuli were presented in visual form, e.g. on a computer or mobile screen (cf . figure 4.4). Additionally, each participant was requested to read the initial instructions, which precisely defined what type of venture (i.e. early-stage NTBF) needed to be valued, thereby supporting reliability of the survey. “These instructions also included explanations of the purpose and the objectives of the survey, the definitions of the six determinants and their characteristics, as well as a description of the assumed framework conditions, which applied to the ventures as well as to the given valuation context” (Wessendorf, Schneider, Gresch, et al., 2020).

0

0

0

0

0

0

1

1

1

1

SELECT

intermediate

weak

1

intermediate

weak

1

intermediate

2

2

2

2

2

2

strong

3

3

3

3

3

3

0

0

0

0

0

0

1

1

1

1

1

SELECT

intermediate

1

intermediate

weak

weak

weak

2

2

2

2

2

2

VENTURE B

strong 3

3

3

3

3

3

1

1

SELECT

intermediate

weak

1

1

1

1

2

2

2

2

2

2

strong

strong

strong

strong

3

3

3

3

3

3

I WOULD NOT INVEST IN ANY OF THESE VENTURES.

0

0

0

0

0

0

VENTURE C

4

Figure 4.4 Presentation format of an exemplary choice set with three stimuli, i.e. combinations of relevant determinants for early-stage NTBF valuation

USP

Market Growth

Patents and Applicaons

Founder Experience

Entrepreneurial Spirit

Alliances

VENTURE A

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4.3.3.3.1.3 Evaluation of Stimuli Test persons assess stimuli by repeatedly comparing stimuli and selecting a stimulus from a choice set. Thus, the selections made by the participating test persons are initially only available as nominally scaled data (Backhaus et al., 2015, p. 196). 4.3.3.3.1.4 Estimation of Utility Values Based on the evaluations of stimuli observed in the decision-making experiment designed (cf. section 4.3.3.3.1.2), partial utility values (i.e. the utility contributed by each determinant to the total utility of any stimulus) were estimated. A linearadditive compensatory utility model is followed, whereby utility contributions consist of a deterministic and a stochastic component (Herrmann et al., 2008, p. 693). Ui,k =



Vi,k, j,n + εi,k

j∈J n∈N j

W ith: Ui,k = T otal utilit y value o f stimulus k f or r espondenti J = Amount o f deter minant s N j = Amount o f values o f deter minant j ∈ J Vi,k, j,n = U tilit y contribution f or then − th value o f deter minant j f or stimulus k and r espondent i εi,k = Stochastic utilit y component o f stimulus k f or r espondent

(4.7)

Herrmann et al. (2008, p. 693 f.) suggest that the stochastic utility components are usually distributed independently of each other, following a Gumbel distribution. Further, CBC builds on the assumption that the stimulus, which is assigned the highest utility, is selected in decision-making. Thus, when two stimuli are compared, a respondent i will select a stimulus k if the difference between the deterministic utility components of k and an alternative stimulus is greater than the difference between the stochastic utility components of the two stimuli (cf. equation 4.8). Thereby, the respondent assigns a higher utility to k.

j∈J

n∈N j



 Vi,k, j,n − Vi,k  , j,n > εi,k  −

εi,k ∀ i ∈ I , k, k  ∈ K and k   = k

(4.8)

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However, a clear difference between the stochastic components cannot be observed. Thus, the selection of a stimulus can only be predicted with a certain probability. The partial utility value model is assumed as an evaluation function for the determinant manifestations. This leads to a separate partial utility being “estimated for each discrete value of a determinant instead of assuming a given functional relationship for the partial utility values. [Thus, in line with Herrmann et al. (2008, p. 660),] the partial utility value of a determinant results from the estimated partial utility values of its characteristics] […]” (cf . equation 4.9) (Wessendorf, Schneider, Gresch, et al., 2020). Uk, j,n =

N

β j,n ∗ xk, j,n

n=1

W ith: Uk, j,n = Par tial utilit y value o f deter minant j f or stimulus k with valuen N = Amount o f possible values f or deter minant j ß j,n = Estimated par tial utilit y value f or value n o f deter minant j xk, j,n = Binar y dummy − variable with value 1, i f deter minant j f or stimulus k has the value n, other wise value 0

(4.9)

Even though the sample of participating venture capital investors might be considered as homogenous in its structure, heterogeneity must be assumed in their behavior with regard to the assessment of determinants and their respective manifestations and characteristics. Hence, partial utility values of the determinant manifestations were estimated by applying the appropriate Hierarchical Bayes (HB) method. Thereby, the determination of individual partial utility parameters within the aggregated sample data on choices is enabled, while accounting for the mentioned heterogeneity of preference. Next, aiming for comparable partial utility parameters, a standardization of the values becomes necessary. In this context, the HB method assumes that the investigated sample follows a continuous distribution of preferences, whereby each respondent may have individual preference structures that differ from those of the sample. In consequence, two model levels are combined: first, a superordinate distribution of the individual utility functions and, second, a model of the observed selection behavior of the respondents. As a result, Wessendorf et al. (2020) suggest that “from the observed selection decisions of the respondents, estimation parameters for the individual utility functions are

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determined and from these, in turn, their distribution on an aggregated level. The aggregated values are then used to improve the individual values.” These outlined steps are iteratively repeated until no significant improvement in the estimated distribution function can be established. The behavior of selecting a defined choice set is assumed to follow a multinomial logit model (MNL). Thus, the probability that a respondent i selects a stimulus k from a choice set a is expressed by equation 4.10. This equation thereby reflects the choice model and the estimation procedure used for the partial utility values of the determinant manifestations. e

Wi,k =



 j∈J n∈N j βi, j,n ∗xk, j,n

k  ∈Ca e



 j∈J n∈N j βi, j,n ∗x

∀ i ∈ I , k ∈ Ca and Ca ⊆ K

k  , j,n

W ith:Ca = I ndex quantit y o f stimuli in choice seta K = Amount o f all stimuli

(4.10)

By transforming the probability W i,k into the logarithm of its associated chance (i.e. logarithmic odds), the selection probability that a respondent i selects a stimulus k from a choice set a is alternatively expressed as:      = ln logit pr ob k| k

pr ob(k| k  ) 1 − pr ob(k| k  )

 (4.11)

Once the experiment is finished, the choice decisions of the respondents are only available as nominally scaled data. In order to attain maximum plausibility (likelihood), the maximum likelihood method is used for estimation (Backhaus et al., 2015, p. 196). Hence, the modelled probability for a stimulus k which was selected from a given choice set by a respondent i in the experiment must be as high as possible, resulting in, for ease of calculation, a logarithmic log-likelihood function (LL) as expressed in equation 4.12. LL =

K R

ln[ pr obr (k)] ∗ xk,r

r =1 k=1

W ith: R = Amount o f choice sets K = Amount o f possible stimuli pr obr (k) = pr obabilit y f or the selection o f stimuluskin choice setr

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xk,r = Dummy − variable with value 1, i f stimulus k has been chosen in choice setr , other wise value 0

(4.12)

In a final step, the partial utility values of the determinant manifestations are estimated in order to maximize LL. Using the conjoint analysis software conjoint.ly, this is realized by operating the Markov-Chain-Monte-Carlo (MCMC) method, which uses a hybrid Gibbs sampler with a random walk metropolis step. 4.3.3.3.1.5 Aggregation of Utility Values Since the loss of information as a result of nominal selection data prevents an estimate at the individual level, the utility estimate is made at an aggregated level. Yet, using the HB approach described in the previous section, the aggregated utility estimate is already carried out.

4.3.3.3.2 Data Collection In the first place, the study design was discussed in a phone-based pre-test with six relevant venture capital investment professionals, in order to receive feedback on potential areas of improvement as well as to validate main aspects of the study. After including the feedback received in the final study design, in a second step, the link to access the survey is send to n = 454 relevant venture capital investment professionals, which are considered to represent the vast majority of relevant venture capital investors in German-speaking Europe (cf. section 4.3.2.3.1). The survey was carried out in the 3 months period spanning from July 2018 to November 2018. Out of these, n = 40 investment professionals participated in the survey and classify as “full participants”. This corresponds to a response ratio of 8.81%. Even though this is slightly below the response rate attained in section 4.3.2.4.2.2, the relatively high response rate is interpreted as the survey being understandable and comprehensible, thereby supporting the overall reliability of the attained results. 4.3.3.3.3 Data Analysis Having retrieved the data from n = 40 full participants in the survey, the subsequent analysis was performed by means of the dedicated software conjoint.ly. Further, the analysis fully respected the initially described estimation of partial utility values and calculation of total utility values. Thus, the attained data is considered to show a high degree of reliability and validity.

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4.3.3.3.3.1 Sample Description The participants fulfilled the relevant requirements necessary to meaningfully respond to RQ3 and RQ4, thus • Being an active venture capital investor, either as a representative of a venture capital fund, a corporate venture capital organization or a business angel; • Having an investment focus on NTBF, in particular in the early stage of corporate development; • Having an investment focus on German-speaking countries. The sample had an overall size of n = 40 and consisted of 67.5% of participants that described themselves as being a representative of a venture capital fund or a corporate venture capital organization, whereas the remaining 32.5% identified themselves as business angels. 4.3.3.3.3.2 Data Analysis The retrieved data from n = 40 participants was quantitatively analyzed following a three-step approach. First, with the objective of improving comparability, the partial utility values of each determinant manifestation for each respondent were normalized β j,n norm , following the subtraction of the mean value of all determinant values of the same determinant β¯ J from their respective values β j,n . . Next, the significance weights for each respondent attached to each determinant were determined relative to each other (cf. equation 4.13). 



max β jnorm − min β jnorm w j = I J



 ∗ 100 i=1 max β jnorm − min β jnorm

(4.13)

Second, “a ‘level value’ was calculated for each determinant value [in order to] relate its normalized partial utility value to the range of the corresponding determinant and its relative weight” (Wessendorf, Schneider, Gresch, et al., 2020). Level value j =

β j,n norm 

 ∗ wj max β jnorm − min β jnorm

(4.14)

Finally, Wessendorf et al. (2020) state that “the normalized partial utility values of all determinant manifestations, their relative significance weights and level values were determined by means of a simple average calculation across all respondents. According to the assumed linear-additive utility model, the total utility of all stimuli

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results from the sum of the normalized partial utility values (over the entire sample) of the determinant values constituting them” (cf. equation 4.15). Uk =



β j,n norm ∗ x j,n

j∈J j∈N j

W ith:Uk = T otal utilit y value f or stimulus k β j,n norm = N or mali zed par tial utilit y value f or value n o f deter minant j x j,n = Dummy − variable with value 1, i f determinant j f or stimulus k exists, other wise value 0 (4.15) 4.3.3.3.3.3 Quality Assessment In order to measure and assess quality and thus validity of the attained results, the present research project applies McFadden’s Pseudo-R2 , which is considered a common measure in conjoint analyses. McFadden’s Pseudo-R2 particularly evaluates the ability of the chosen model to predict the respondents’ answers or its fit to the observations made. Thereby, similar to the coefficient of determination R2 , which is often used as a measure of validity for linear regressions, it assumes values between 0 and 1. However, in contrast to the coefficient of determination, the McFadden’s R2 cannot be interpreted as a proportion of the declared scatter. As a consequence, it bears the additional designation “pseudo” (Backhaus et al., 2015, p. 210). According to Backhaus et al. (2015, p. 210) a good fit for empirical studies is considered to attain a McFadden’s Pseudo-R2 value between 20 and 40%. The present research project results in a McFadden’s pseudo-R2 of 72%, which is interpreted as a strong fit. Thereby, the study’s internal validity is classified as high.

4.3.3.3.4 Data Interpretation The results of Data Analysis serve as a basis for finding initial answers to the defined research questions. By interpreting this data, the last step of the CBC is reached. For the sake of easy reference and clear structure, the data processed according to CBC methodology will be interpreted and discussed in greater detail within the subsequent section 4.3.3.4 “Findings”.

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4.3.3.4 Findings The impact of determinants on early-stage NTBF valuation is expressed within the calculated level values, which specify how much a determinant manifestation is preferred compared to another manifestation of the same determinant. The level values thus indicate which determinant manifestations influence the valuation behavior of venture capital investment professionals and to what extent. Therefore, to provide a reading example of table 4.17, a strong level of “USP” has a strong positive influence (8.47) on valuation, while a weak level of “USP” has a strong negative influence (-10.89). Respective statements can be made about all determinant manifestations. Turning to the attained results, the highest level values are observed for the extremes of “Entrepreneurial Spirit”, both in a positive (i.e. strong) and negative (i.e. weak) direction. In contrast, the level values of the determinant “Alliances” are particularly weak for all levels. The results of all value-driving determinants are synthesized in table 4.17. Within this table, the stated 90% confidence intervals of the level values reveal an additional peculiarity—a sign change in the confidence intervals of the “intermediate” manifestation of the determinant “Alliances”. In general, underlying estimates are more uncertain, the larger the range of values in the confidence intervals. According to Backhaus et al. (2006, p. 97) this applies in particular when sign changes are observed. Since this sign change is the only one to be observed in the present values, “the respective value range is relatively close to zero and the ranges in the confidence intervals for all other level values are not excessively large, the estimates are nevertheless to be assessed positively against the background of a rather small sample. Further, all weak values have negative level values, which corresponds to the assumed correlations” (Wessendorf, Schneider, Gresch, et al., 2020). As previous research refrains from specifying level values that reflect positive as well as negative levels of impact on early-stage NTBF valuation, the present findings are discussed without direct benchmark. Nevertheless, all resulting level values correspond to the assumed correlations (i.e. positive values have positive level values and negative values have negative level values), which is considered a good indicator of the level values’ validity. With regard to constructing an artifact improving the indication of value in early-stage NTBF valuation (InVESt-NTBF), while addressing the set out requirements (cf . section 4.2) the following has been achieved:

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Table 4.17 Level values of determinants of early-stage NTBF valuation following CBC analysis (n = 40) (Wessendorf, Schneider, Gresch, et al., 2020) Attribute

Level (i.e. manifestation)

Relative level preference score

Lower bound of 90% confidence interval

Upper bound of 90% confidence interval

Alliances

weak

−1.80

−2.94

−0.72

Alliances

intermediate

−0.02

−0.90

0.97

Alliances

strong

1.82

0.88

2.81

Entrepreneurial Spirit

weak

−18.68

−19.87

−17.17

Entrepreneurial Spirit

intermediate

3.98

2.54

5.11

Entrepreneurial Spirit

strong

14.71

14.04

15.44

Founder Experience

weak

−6.19

-6.72

−5.72

Founder Experience

intermediate

0.85

0.18

1.59

Founder Experience

strong

5.34

4.44

6.24

Patents and Applications

weak

−7.31

−7.87

−6.76

Patents and Applications

intermediate

0.94

0.18

1.81

Patents and Applications

strong

6.37

5.15

7.43

Market Growth

weak

−9.93

−11.11

−8.75

Market Growth

intermediate

1.43

0.42

2.15

Market Growth

strong

8.50

7.02

10.22

USP

weak

−10.89

−12.10

−9.47

USP

intermediate

2.42

1.18

3.45

USP

strong

8.47

7.69

9.19

1. A clear understanding of non-financial determinants’ impact on early-stage NTBF valuation was achieved. This allows for the construction of an artifact accounting for the specific challenges in valuation of early-stage NTBF by fulfilling the set out requirements (cf. section 4.2.1), in particular, with regard to a formalized and structured assessment of value-drivers.

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4.3.4

171

Applicable Discount Rates for Early-Stage NTBF Valuation

This section is based on: Wessendorf, C. P., Schneider, J., Shen, K. and Terzidis, O. (2019) Valuation of EarlyStage Technology Ventures – A Model to Determine the Discount Rate in Present Value Valuation Methods, EntFin 2019, 4th EntFin (Entrepreneurial Finance) Conference, Trier. Wessendorf, C. P., Schneider, J., Shen, K. and Terzidis, O. (2021) Valuation of EarlyStage Technology Ventures – An Approach to Derive the Discount Rate, The Journal of Alternative Investments, Winter 2021, 23(3), pp. 32–44. https://doi.org/10.3905/jai. 2020.1.114

An understanding of discount rates to be applied in early-stage NTBF valuation is indispensable, as the discount rate appears to be a strong instrument to integrate subjective, non-financial valuation information into accepted valuation methodologies (Damodaran, 2009; Festel et al., 2013). This will further address the set out requirements (cf . section 4.2.1). A clear understanding of respective discount rates and the integration of subjective, non-financial valuation information will reflect the required implementation into an accepted valuation method.

4.3.4.1 Approaching Applicable Discount Rates for Early-Stage Venture Valuation from Previous Literature Previous literature already extensively treated discount rates to be applied to present value valuation methods. Even though it is hypothesized that the majority of research in this field does not focus on early-stage ventures, the investigation for appropriate discount rates will start with a systematic review of relevant literature.

4.3.4.1.1 Systematic Literature Review (SLR) As previously introduced (cf. section 4.3.1) this SLR follows major studies outlining the methodological approach of an SLR (Tranfield et al., 2003; Kitchenham, 2004; Budgen and Brereton, 2006; Kitchenham et al., 2009; Crossan and Apaydin, 2010; Tahir et al., 2016), and is thus subdivided into three main steps: Planning, Conducting, and Reporting. As before, the planning phase needs to establish the necessity for an SLR, leading to the formulation of a research question and the definition of a review protocol. The conducting phase describes the review itself, further detailing the identification and selection of primary studies as well as the

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analysis and discussion of relevant content. Finally, the reporting phase addresses the result presentation and thereby concludes the review. 4.3.4.1.1.1 Planning Phase The planning phase of an SLR is comprised of three steps that will be elaborated in the following: necessity of the SLR, research questions, and review protocol: 1. Necessity of the SLR: The necessity for an SLR in this field of research is mainly established by its high relevance for both academic environment and venture capital practice (cf. section 1.2). Yet, no comparable review of existing literature can be identified, thereby revealing a gap in terms of a lacking metaanalysis on discount rates to be applied in early-stage venture valuation. Along with a high complexity of the topic, this motivates the need for an SLR in which various publications are identified and presented in a structured and comprehensible manner. 2. Research questions: Based on the theoretical background related to discount rates to be applied in early-stage venture valuation, which specifies different value concepts and the respective derivation of discount rates (cf. section 2.5), one research question emerges to be addressed in this SLR: RQ5: What is the average discount rate used by venture capitalists in early-stage venture valuation?

With the research question and thereby scope of the analysis defined, the process of analysis has to be specified within the review protocol. 3. Review protocol: In line with previous SLR (cf. section 4.3.1.1.1) the review protocol is defined along six sections, that all have a clear link to the review process and the defined scope. a. Search process: According to García et al. (2006), finding relevant publications might be threatened by a lack of consistency within the search process (i.e. with regard to terminology). Therefore, appropriate concepts and terminology in the field of study as well as relevant keywords related to the defined research questions were identified in the first step. Next, relevant business dictionaries and thesauri (i.e. Wirtschaftslexikon Gabler and www.thesaurus.com, www.openthesaurus.de) were consulted to gather appropriate synonyms for these keywords. The applied search string consisted of three elements all connected by the Boolean operator “AND”: (1) the research object, e.g. “discount rate”, (2) the research purpose, e.g. “valuation”, and (3) the valued object, e.g. “NTBF”. Within

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these elements, the Boolean operator “OR” in combination with wildcard operator “*” were used to consider relevant synonyms, both in English and German (cf. figure 4.5). For the sake of an example, this resulted in a search string such as: [(Diskont*satz) OR (Diskont*rate) OR […] OR (Risikoprämie)] AND [(Bewertung) OR (Unternehmensbewertung) OR […] OR (Unternehmenswert)] AND [(Start-Up*) OR (Startup) OR […] OR (NTBF)]. The search and selection of relevant publications followed a three-step approach. First, the defined search string was used to search relevant databases (i.e. Business Source Premier, EconBiz, WISO, EconPapers, SSRN and Google Scholar). Based on the title’s fit to the search string, a long list of relevant publications was created. Next, the resulting publications’ abstract and conclusion sections were analyzed in order to further establish relevance of the publications to the intended search. Subsequently, the below detailed study inclusion and exclusion criteria as well as quality assessment criteria were applied to the identified publications. Next, relevant publications identified were registered with the literature reference software Mendeley. Lastly, relevant references within the selected publications were screened (i.e. “snowball tracking”) in order to reduce the risk of omitting relevant primary studies and to further increase the sample size (Horsley et al., 2009).

b. Study inclusion criteria: A study’s fit to the defined research question and research object can be seen as the fundamental selection criterion. Thus, the publications selected all needed to discuss discount rates and target returns for venture valuation in an early stage of the venture’s life cycle. Further, a substantial discussion on discount rates and target return needed to be identified in order to provide answers to RQ5. Next, with the aim of increasing practical relevance, publications that derived or observed discount rates by analyzing a specified set of own primary data (i.e. empirical evaluation) were of particular interest. In consequence, publications deriving their results from other publications (i.e. secondary data) were omitted. c. Study exclusion criteria: The quality and thus detailed analysis of the selected publications is deemed critical to the SLR’s validity. Therefore, selected primary studies needed to be available in full as well as written in English or German. In consequence, studies requiring paid access were excluded. Further, as discount rates are considered to vary over time, it was decided to exclude studies that were carried out before 2009, thus before the global financial crisis and therefore relate to a likely different interest rate environment.

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I

II

III

RESEARCH OBJECT

RESEARCH PURPOSE

VALUATION OBJECT

Diskonerungszinssatz

Bewertung

Start-Up*

OR

OR

OR

Diskont*satz

Unternehmensbewertung

Startup*

OR

OR

OR

Diskont*rate

Wert

Startup-Unternehmen

OR Start-Up-Unternehmen

OR

OR

Abzinsungsrate

OR Abzinsungsfaktor

Größe

OR Unternehmensgröße

AND

OR Unternehmenswert

OR

AND

OR Diskont*faktor

Neue Unternehmen

OR Junge Unternehmen

OR

OR

OR

Abzinsung

DCF

Wachstumsunternehmen

OR

OR

OR

Zielrendite

Discounted Cash Flow

Frühphasenunternehmen

OR

OR

OR

Internal Rate of Return

Venture Capital Methode

Technologie-Startup*

OR

OR

OR

IRR

VC-Methode

Technologie-Start-Up*

OR

OR

OR

CAPM

Barwert

Tech-Startup

OR

OR

OR

Capital Asset Pricing Model

Gegenwartswert

Tech-Start-Up

OR

OR

OR

-Beta*

Barwert-Methode

NTBF

Figure 4.5 Search string structure for Systematic Literature Review—German Keywords displayed only. (own illustration)

d. Quality assessment criteria: In order to ensure high quality as a basis for reliable results and conclusions, the size of the empirical studies’ sample and population as well as potential selection bias were investigated. A subjective assessment by the researcher was performed in order to decide if the sample size is deemed large enough and if a potential selection bias can be ignored. Further, the empirical approach chosen was assessed in the light of its fit to answer the defined research question RQ5. The reference management software Mendeley was used to manage the selected primary studies. e. Data extraction: A data extraction form was designed using Microsoft Excel in order to extract data in a structured, consistent, and uniform manner. The relevant data from relevant publications was recorded and analyzed in a subsequent step.

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f. Information on empirical study: The data to be gathered needs to be defined beforehand in order to allow for a concise database. Thus, data captured from the selected publications were assigned to three sections: general information (i.e. title, author, year of publication, type of publication, country of investment, research method, sample size), specific information (i.e. investor type covered, investment focus as technology vs general, methodology, discount rates, type of discount rate, perspective of discount rates development) as well as own comments on the publication. 4.3.4.1.1.2 Conduction Phase The conduction phase, as the name implies, presents the major aspects of how the SLR was conducted, following the previously defined SLR plan. 1. Search and selection of primary studies: The defined systematic search for relevant publications discussing discount rates in early-stage (technology) venture valuation was carried out in accordance with the research protocol in the three months period spanning from December 2018 to February 2019. A Microsoft Excel search documentation table was created to provide a transparent overview of the work progress at any time. The number of results of the initial search is n = 18.276. As noted in figure 4.6, the search term was consequently adjusted by adding the following English and German terms: “required roi”, “hurdle rate”, “start-up pricing”, “capitalization rate”, “expected IRR”, “cost of capital”, “erwartete Rendite”, “Kapitalisierungszinsrate” and “Kapitalkosten”. These search results were not included in the number of results listed above. The first selection process based on title and abstract reduced the number of relevant publications to n = 125, while for Google Scholar only the first 600 search results were screened, as a correlation between further search results and strongly decreasing relevance could be observed. The search results in the remaining databases were all checked individually for relevance. Ten duplications were identified, which reduced the relevant amount of publications to n = 115. After applying the inclusion and exclusion criteria and quality assurance criteria, the number of relevant publications was further reduced. This is mainly attributable to the fact that many publications in the target field do either not focus on start-ups or not specifically on the discount rate. Only 4 out of 16 relevant publications were identified in this process. By performing the snowball tracking, another 12 publications were identified. Yet, when applying the above stated inclusion, exclusion and quality assessment criteria, 5 relevant studies remained for analysis.

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Search results

Assessment of tle and abstract

Screening of content

Applicaon of inclusion and exclusion criteria

Applicaon of quality assessment criteria

if relevant

Add to final literature selecon

Snowball tracking based on idenfied literature

if relevant

Add to final literature selecon

Figure 4.6 Selection process within Systematic Literature Review. (own illustration)

2. Data extraction and analysis: Data was extracted according to the previously prepared data extraction forms (cf . section 4.3.4.1.1.1 (3) e). Data of 5 publications dealing with discount rates applicable for early-stage venture valuation were gathered (cf. table 4.18).

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Table 4.18 Selection of relevant studies resulting from a systematic literature review on discount rates applicable for early-stage venture valuation (own illustration in reference to Wessendorf, Schneider, et al. (2012); * stages defined according to Kazanjian and Drazin (1990)) Author

Year

Geo-graphy

General vs Tech

Type of Return

Beaton

2010

USA

General

Target

Stage 1*

Stage 2*

Data w/t split 50.0%

Vara

2013

USA

General

N/A

+ 45.0%

+ 45.0%

Everett

2018

USA

General

Expected

52.5%

40.0%

Everett

2018

USA

General

Expected

41.5%

33.0%

Honold et al.

2018

GER

General

N/A

55.0%

28.8%

4.3.4.1.1.3 Reporting Phase As a result of the identified relevant studies, an average discount rate of 45.2% is applied within the early stage of a venture (i.e. stage “Conception and Development” and “Commercialization” according to Kazanjian and Drazin, 1990). Wessendorf, Schneider, Shen, & Terzidis (2019, 2021) find that “Vara (2013) reports the highest discount rate of + 45% over a normal risk discount factor based on expert interviews. In addition, Honold, Hümmer and Prengel (2018) empirically observe the highest discount rate of 55.0% as well as the lowest discount rate of 28.8% based on a survey among business angels and venture capitalists.” The attained results are consistent with an earlier but comparable study by Achleitner and Nathusius (2004, 199), which reports an average discount rate of 40.3% for early-stage venture valuation.

4.3.4.1.2 Findings The analysis of previous literature deemed relevant to answer RQ5, reveals a clear and quantifiable picture. RQ5 asking for the average discount rate to be applied to early-stage venture valuation results in a discount rate of 45.2%. Yet, this average is based on values ranging from 55.0% to 28.8%, thereby leaving a relatively broad spectrum of potential discount rates. In terms of a subsequent valuation, this range is considered to have a tremendous effect. In addition, even though RQ5 was successfully answered, in the context of this research project, the discount rates to be applied to early-stage NTBF remain mostly unclear. Therefore, the subsequent steps of analysis in order to determine an appropriate discount rate for early-stage NTBF valuation will on one hand focus particularly on NTBF, and on the other, on more recent data potentially

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limiting the range of values or at least provide the underlying rationale for the observable range.

4.3.4.2 Empirical Approach to Identify Applicable Discount Rates for Early-Stage NTBF Valuation As outlined in the previous section 4.3.4.1.2, the results of the performed SLR provide initial but strong results on discount rates to be used within early-stage venture valuation. Yet, the attained results miss out on some crucial aspects: first, recent publications do not provide information originating from NTBF, and, second, a clear view on maximum and minimum values as well as an understanding for the respective benchmark venture are not available. Thus, RQ6 was defined as follows: RQ6: What is the average maximum and minimum discount rate used by venture capitalists in early-stage NTBF valuation?

Therefore, in the context of this research project, an empirical approach to identify applicable discount rates for early-stage NTBF valuation is followed in order to achieve insights to be integrated in a valuation tool (i.e. the artifact). Similar to previous empirical analysis, this follows a four-step approach including the study design, data collection, data analysis, and data interpretation.

4.3.4.2.1 Study Design In the context of the above detailed Choice-based Conjoint Analysis (CBC; cf. section 4.3.3.3.1), the participants are first asked to read through the explanatory documentation to get a methodological understanding of CBC as well as an understanding of the valuation context created within the experiment. Next, the different choice sets are presented, whereby the relevant participants select one stimulus each. At the end of the questions related to selecting presented stimuli, the participants were asked two questions with regard to target return for the specific NTBF valuation context of the study: 1. What is the maximum target return you would set for high-tech start-ups of the type described in the notes and under the conditions described in the notes? (Figures in % p.a.) 2. What is the minimum target return you would set for high-tech start-ups of the type described in the notes and under the conditions described in the notes? (Figures in % p.a.)

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Following these two questions, it was intended to attain a comprehensive and very recent spread of discount rates to be specifically applied to early-stage NTBF. It needs to be pointed out that “target return” is intentionally asked for, as it is considered to represent the discount rate concept of choice within the subsequently developed artifact. This decision is mainly driven by target return’s “suitability to the venture capital method as well as its high practicability and apt reflection of venture capital reality” (A.-K. Achleitner, 2001; A.-K. Achleitner & Nathusius, 2004; Scherlis & Sahlman, 1989; J. K. Smith & Smith, 2000).

4.3.4.2.2 Data Collection Data collection is identical to the steps outlined in section 4.3.3.3.2 and thus included the same early validation by six expert interviews (i.e. pre-test) as well as the invitation to n = 454 relevant venture capital investment professionals in German-speaking Europe to participate in the survey. Out of these, n = 40 investment professionals participated in the survey between July 2018 and September 2018 and classify as “full participants”. 4.3.4.2.3 Data Analysis Analogue to section 4.3.3.3.3, the retrieved data from n = 40 full participants in the survey was subsequently analyzed. The attained data is considered to show a high degree of reliability and validity. 4.3.4.2.3.1 Sample Description The participants fulfilled the relevant requirements necessary to meaningfully respond to RQ6, thus • Being an active venture capital investor, either as a representative of a venture capital fund, a corporate venture capital organization or a business angel; • Having an investment focus on NTBF, in particular in the early stage of corporate development; • Having an investment focus on German-speaking countries. The sample had an overall size of n = 40 and consisted of 67.5% of participants that described themselves as being a representative of a venture capital fund or a corporate venture capital organization, whereas the remaining 32.5% identified themselves as business angels.

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4.3.4.2.3.2 Data Analysis The retrieved data from n = 40 participants was registered and quantitatively analyzed. In this context, the median of the received maximum and minimum values was computed in order to avoid distortions from extreme values or outliers. Thus, the median of maximum target return applied by the participants, being relevant venture capital investors, to value early-stage NTBF, e.g. by means of the venture capital method, amounts to 40.0% p.a. The median of the respective minimum target return amounts to 17.5% p.a.

4.3.4.2.4 Data Interpretation Having analyzed the retrieved data, the attained results are subsequently interpreted in light of existing literature and appear plausible. For the sake of easy reference and clear structure, the data processed in this empirical survey will be interpreted and discussed in greater detail within the subsequent section 4.3.4.2.5 “Findings”. 4.3.4.2.5 Findings The median of maximum target return applied by relevant venture capital investors, to value early-stage NTBF amounts to 40.0% p.a. The median of the respective minimum target return amounts to 17.5% p.a. In light of existing literature, this appears plausible. Whereas the upper limit can be located within the ranges resulting from the previous SLR (cf. section 4.3.4.1.2), the lower limit is supported by Cotton & Schinski (1999) who find a target return of 16.3% for technology investments, even though this work is not considered as recent. In addition, Kaplan (2019) reports a median required return of 30% for early-stage venture capital investments, which underpins the above stated range. In consequence, RQ6 is successfully answered by the attained results. With regard to constructing an artifact improving the indication of value in early-stage NTBF valuation (InVESt-NTBF), while addressing the set out requirements (cf . section 4.2) the following has been achieved: 1. A spread of applicable recent discount rates to be used for implementation of subjective, non-financial valuation information was identified. 2. The discount rate concept of choice, i.e. target return, was identified based on its high practicability and apt reflection of venture capital reality.

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4.3.4.3 Approaching Applicable Discount Rate Structure for Early-Stage NTBF Valuation from a Theoretical Point of View Having identified recent maximum and minimum target return to be applied to early-stage NTBF valuation by relevant venture capital investors, an important step towards defining an interface between the artifact to be developed and conventional valuation methods widely accepted in venture capital practice was taken. In order to allow for a comprehensive integration of target return as a concept to capture value within the artifact, the target return’s overall structure between maximum and minimum value has to be investigated. From a theoretical point of view, three archetypes of discount rate structures can be differentiated to reflect an investor’s valuation behavior. This is a rational, risk-seeking and risk-averse archetype of discount rate structure, which can be described by a mathematical function each. This results in a rational investor’s behavior to be expressed “by a linear function, a risk-seeking investor behavior by a [convex] function, and a risk-averse investor behavior by an [concave] function, whereby the theoretical foundation of the [concave function] can be found in prospect theory” (Wessendorf, Schneider, Shen, et al., 2021). The following reflections on a potential structure of discount rates between a maximum target return, which would be applied to a suboptimal NTBF valuation profile in the context of a present value-based valuation, and a minimum target return, which would be applied to an optimal NTBF valuation profile in the context of a present value-based valuation, are of theoretical nature. This research project will follow theoretical considerations, which will need to be validated at a later stage in the artifact development process (cf. section 4.4.3), as the empirical testing of hypotheses directly in the context of the subject under research appeared most efficient and meaningful.

4.3.4.3.1 Theoretical Archetypes of Discount Rate Structures 4.3.4.3.1.1 Linear Discount Rate Structure for Rational Investment Behavior An investor, who will follow a rational investment behavior, is considered to precisely and rationally value each change in risk underlying her investment. Thus, her rational investment behavior is reflected by accounting for a change in the valuation score of the NTBF valuation profile under investigation by a proportional change in risk (i.e. the discount rate). In consequence, rational investment behavior follows the hypothesis that discount rates between a suboptimal and an optimal NTBF valuation profile can be expresd by a linear function (cf. equation 4.16). Therefore, a change of the valuation score observed for a given NTBF valuation profile from 80 to 60 results in a proportional change of risk from

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24.47% t33.43%. This proportional change is negatively proportional as a decrease in valuation score (i.e. a decreasing attractivity assessment of the venture) reflects an increase of risk and thus results in an increasing discount rate serving as a measure of risk. In consequence, a linear discount rate structure describes the behavior of “an investor [who] is acting rationally in a valuation context and thus values each normalized valuation score derived with an equal unit of target return for discounting purposes” (Wessendorf, Schneider, Shen, et al., 2021). This effect is supposed to be mathematically expressed by accounting for each change in x norm , directly influencing the difference of xnor m max − xnor m , with a respective change in the discount rate r. This is achieved by relating the difference −rmin of xnor m max − xnor m . to the discount rate r through rxmax . , which is to be norm max added to the lower frontier of the discount rate range r min in order to compute the discount rate r to be applied in valuation (cf. equation 4.16). r = rmin +



 rmax − rmin xnor m max − xnor m ∗ xnor m max



W ith: r = Discount rate xnor m = N or mali zed valuation scor e

(4.16)

The quality of the computed result and thus the linear function as a measure for rational investment behavior, is expressed by the quality of its fit to the target returns observed in valuation reality, which will be evaluated in section 4.4.3. Values to be applied to r max and r min have to be chosen in accordance with the context a valuation is performed in. This present work builds upon the empirical values specified in section 4.3.4.2.3.2 4.3.4.3.1.2 Convex Discount Rate Structure for Risk-Affine Investment Behavior An investor, who will follow a risk-affine investment behavior, is considered to (intentionally) underestimate the risk (i.e. subjective risk) underlying her investment and thereby to embrace high levels of risk (i.e. objective risk) in her investment decisions at the condition of a high valuation (i.e. low subjective risk leads to low discount rates and thus high valuation). Thus, her risk-affine investment behavior is defined as accounting for a change in the valuation score observed in the NTBF valuation profile under investigation by an over proportional change in risk (i.e. the discount rate) until the measure of risk is approaching a subjectively defined threshold. After this threshold, only minor changes in risk can be accounted for as the risk assessment is approaching a minimum. In consequence, this behavior can be described by a convex function (cf. equation 4.17),

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which also accounts for a change of a variable x by an over proportional change of a variable y, until a defined threshold. Thereby, an investor behavior reflected by a convex discount rate structure would classify as risk-affine, as each increase in x norm results in an over proportionally diminishing level of target return. This investor will therefore estimate the underlying risk for a venture that improves the valuation score from a minimum valuation score of e.g. 1 to a slightly improved score of e.g. 5 to be significantly reduced from 37.53% to 30.06%. Thus, in the context of a present value-based valuation, an increasing level of x norm translated in r following a convex discount rate structure, would lead to a potential overvaluation in absolute terms. “In practice, such an investor would be willing to significantly lower the discount rate r [until] a subjectively defined threshold with regard to the present valuation determinants and its associated risk is reached. Thereby, the valuation performed would account for high-risk investments by a relatively high [but rapidly decreasing] discount factor, while any lower levels of risk [after a subjectively defined threshold], even if only incremental, are not significantly differentiated. The investor will thereby accept higher levels of risk without significantly adjusting [her] valuation, thereby increasing [her] exposure to risk by a structural overvaluation of the venture in a certain, not yet defined range of risk profiles“ (Wessendorf, Schneider, Shen, et al., 2021). This effect is expected to be mathematically expressed by locating the normalized valuation score x norm on the curve, strongly shaped by the threshold value c, which will define the gradient of the convex discount rate structure. As a convex function can be considered an inverse exponential function, this inversion (i.e. the gradient becoming negative), is realized by the natural logarithm of e. Putting this in relation with the defined range (rmax − rmin ). , this will lead to a change in discount rate to be added to the lower limit r min in order to compute the discount rate r to be applied (cf. equation 4.17).   r = rmin + (rmax − rmin ) ∗ eln(c∗xnorm ) W ith: r = Discount rate xnor m = N or mali zed valuation scor e c = T hr eshold value to ex pr ess appr oachs f it to valuation r ealit y (to be subjectively ad justed accor ding to investment f ocus, time and geography)

(4.17)

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As result from these theoretical considerations, a risk-affine investment behavior appears to be well reflected by a convex function. Even if the overall structure is thereby defined for risk-affine investment behavior, the individual expression of this function remains subjective. As outlined above, a subjectively defined threshold that marks the frontier between strongly decreasing levels of subjective risk and only incrementally decreasing levels of subjective risk, is a crucial property of the defined function that needs to be chosen individually. This threshold is reflected by the variable c, which is intended to account for a specific investment focus, time, geography and other market conditions to be factored in by the investor. As investment behavior is generally shaped by subjective impressions, this variable is needed to fit the defined discount rate structure to the investor’s individual preferences, believes and areas of focus. The quality of the computed result and thus the convex function as a measure for risk-affine investment behavior, is expressed by the quality of its fit to the target returns observed in valuation reality, which will be evaluated in section 4.4.3. Values to be applied to r max and r min have to be chosen in accordance with the context a valuation is performed in. This present work builds upon the empirical values specified in section 4.3.4.2.3.2 4.3.4.3.1.3 Concave Discount Rate Structure for Risk-Averse Investment Behavior An investor, who will follow a risk-averse investment behavior, is considered to (intentionally) overestimate the risk (i.e. subjective risk) underlying her investment and thereby seek low levels of risk (i.e. objective risk) in her investment decisions at the condition of a low valuation (i.e. high subjective risk leads to high discount rates and thus low valuation). Thus, her risk-averse investment behavior is defined as accounting for a change in the valuation score of the NTBF valuation profile under investigation by an under proportional change in risk (i.e. the discount rate) until the measure of risk is approaching a subjectively defined threshold. After this threshold, risk can change over proportionally. In consequence, this behavior can be described by an concave function (cf. equation 4.18), which also accounts for a change of a variable x by an under proportional change of a variable y, until a defined threshold. Thereby, an investor behavior reflected by an concave discount rate structure would classify as risk-averse, as each increase in x norm results in an under proportionally diminishing level of target return. This investor will therefore estimate the underlying risk for a venture that improves the valuation score significantly from e.g. 50 to e.g. 70 to be only incrementally reduced from 38.79% to 37.36%.

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185

Thus, in the context of a present value-based valuation, an increasing level of x norm would lead to a potential undervaluation in absolute terms. The valuation behavior, expressed by an concave function, is considered to be related to risk aversion within Prospect Theory (D. Kahneman & Tversky, 1979; Daniel Kahneman & Tversky, 1984; Tversky & Kahneman, 1992). Thus, an investor having a positive impression of the venture’s development and prospects (i.e. a high valuation score within the NTBF valuation profile), can act risk-averse if she underestimates the certainty of this positive impression to materialize. She will thus prefer a sure gain to an uncertain but higher gain. “In practice, such an investor would be very cautious, even if the venture valuation profile suggests a diminishing associated risk. This investor will not significantly lower the discount factor r until [she] reaches a subjectively defined threshold with regard to the present valuation determinants and its associated risk that assures [her] of the venture’s future success. Thereby, the valuation performed would account for a wide range of investment risk by a relatively high discount factor, thereby leading to a structural undervaluation of the venture […] for a certain, not yet defined range of risk profiles.” (Wessendorf, Schneider, Shen, et al., 2021). This appears plausible, as with higher valuation, the potential gain becomes more uncertain, if the share in a venture is considered to be bought at a premium. This effect is considered to be mathematically described by a negative expo1 nential function expressed by e− x . x contains variables defining the intersection point of the curve with the x-axis (x norm s ), thereby reflecting the lower limit r min , its difference with the normalized valuation score x norm , derived from the NTBF valuation profile, which needs to be located on the curve and k, that expresses the curvature reflecting the subjective risk of the investor. This will then become the denominator in a function to calculate the discount rate r where i, reflecting the upper limit r max by defining the intersection with the y-axis, is the numerator (cf. equation 4.18). r=



i −

1

e k ∗ (xnorm −xnorm W ith: r = Discount rate

 s)

xnor m = N or mali zed valuation scor e xnor m

s

= N or mali zed valuation scor e as inter section point with the x − axis

i = I nter section with the y − axis re f lecting the upper limit o f r

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k = Cur vatur e f actor (to be ad justed accor ding to investment f ocus, time, geography)

(4.18)

As result from these theoretical considerations, a risk-averse investment behavior appears to be well reflected by an concave function. Even if the overall structure is thereby defined for risk-averse investment behavior, the individual expression of this function remains subjective. As outlined above, a subjectively defined threshold that marks the frontier between only incrementally decreasing levels of subjective risk and strongly decreasing levels of subjective risk, is a crucial property of the defined function that needs to be chosen individually. This threshold is reflected by the curvature k, which is intended to account for a specific investment focus, time, geography and other market conditions to be factored in by the investor. As investment behavior is generally shaped by subjective impressions, these variables are needed to fit the defined discount rate structure to the investor’s individual preferences, believes and areas of focus. The quality of the computed result and thus the concave function as a measure for risk-averse investment behavior, is expressed by the quality of its fit to the target returns observed in valuation reality, which will be evaluated in section 4.4.3. Values to be applied to r max and r min have to be chosen in accordance with the context a valuation is performed in. This present work builds upon the empirical values specified in section 4.3.4.2.3.2

4.3.4.3.2 Findings Three different archetypes of valuation behavior were expressed by a respective mathematical function. This is rational valuation behavior following a linear function, risk-affine valuation behavior following a convex function and risk-averse valuation behavior following an concave function. These respective functions were expressed mathematically and plotted for easy understanding of the general concept (cf. figure 4.7). Further, these functions were linked to prevailing behavioral theories, also applied in behavioral finance, such as Prospect Theory (D. Kahneman & Tversky, 1979; Daniel Kahneman & Tversky, 1984; Tversky & Kahneman, 1992). Yet, no preferred concept was identified from a theoretical point of view. Within this research project, the identification of a preferred discount rate structure, matching venture valuation reality, will be addressed by an empirical analysis in section 4.4.3.

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With regard to constructing an artifact improving the indication of value in early-stage NTBF valuation (InVESt-NTBF), while addressing the set out requirements (cf . section 4.2) the following has been achieved: 1. An understanding of potential forms of discount rate structures between the two previously defined maximum and minimum values was developed, thereby facilitating the development of the set out artifact according to defined requirements.

Target Return r used as Discount Rate [in %]

17.5%

Normalized Valuaon Score xnorm

Concave

Linear

Convex

Figure 4.7 Discount rates applicable to analyzed case studies following a linear, convex and concave structure (own representation, based on Wessendorf, Schneider, Shen, et al. (2021))

188

4.4

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Application and Results

Demonstration of the Artifact

This section is based on: Wessendorf, C. P., Schneider, J., Shen, K. and Terzidis, O. (2019) Valuation of EarlyStage Technology Ventures – A Model to Determine the Discount Rate in Present Value Valuation Methods, EntFin 2019, 4th EntFin (Entrepreneurial Finance) Conference, Trier. Wessendorf, C. P., Schneider, J., Shen, K. and Terzidis, O. (2021) Valuation of EarlyStage Technology Ventures – An Approach to Derive the Discount Rate, The Journal of Alternative Investments, Winter 2021, 23(3), pp. 32–44. https://doi.org/10.3905/jai. 2020.1.114

Having designed and developed the individual but complementary components of the intended artifact (cf. sections 4.3.1—4.3.4), these need to be merged and integrated in an overarching tool for value indication in early-stage NTBF investment. This will be addressed in three steps: first, a presentation of the subject of investigation, second, the approach to merge and integrate the single components into an overarching tool and lastly, a description of its observable results.

4.4.1

Subject of Investigation

Previous empirical analysis as well as existing research (cf. section 4.2.1 for summary) suggest a high level of subjectivity within early-stage venture valuation. Further, with venture capital funds and respective investment volume constantly increasing in recent years and the related valuations augmenting, the need for an objectifiable valuation becomes highly relevant. The overall volume of funds as well as the valuation of individual companies drive the interest of venture capital funds’ limited partners to optimally manage funds provided and to make investment and valuation decisions in a transparent manner. These findings reveal a set of industry customs in valuation practice as well as challenges that are at the core of the problem to be addressed in this research project. It therefore aims for constructing an artifact that will improve the indication of value in early-stage NTBF (InVESt-NTBF), while addressing the following requirements as defined in section 4.2.1:

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1. Functional requirements: a. The artifact must allow for an orientation towards the future development of a NTBF representing the core of value creation; b. The artifact must reflect special features of early-stage NTBF development in the valuation method’s fundamental approach; c. The artifact must allow for subjective assessment and valuation based on non-financial valuation determinants; d. The artifact must provide a clear ranking of relevance for valuation determinants to allow for efficient valuation; e. The artifact must allow for the subjective assessment of value drivers to be formalized and structured for easy understanding; f. The artifact must allow for the formalized and structured value drivers to be implemented into an accepted valuation method; g. The artifact must allow the reduction of complexity in order to provide a practical and operationalizable approach to valuation practice. 2. Structural requirements: a. The artifact must be coherent; b. The Artifact must be concise. 3. Environmental requirements: a. The artifact must be easy to use; b. The artifact must be comprehensible; c. The artifact must be complete; d. The artifact must be adequately complex; e. The artifact must be efficient; f. The artifact must be effective; g. The artifact must be accountable. 4. Effect requirements: a. The artifact must provide some advantage to status quo. These requirements will be respected during the development and validation of the artifact. In order to demonstrate that the defined requirements were respected during the artifact’s development, individual requirements respected are marked in bold within section 4.4.2.1 to 4.4.2.3. The evaluation of the artifact, along these defined requirements, will be detailed in the following section 4.5.

190

4.4.2

4

Application and Results

Development of the Artifact

The envisaged development of an artifact that is able to improve the indication of value of early-stage NTBF (InVESt-NTBF) and fulfilling the set out requirements (cf . section 4.2) can be subdivided into three main steps. First, non-financial valuation determinants relevant for early-stage NTBF valuation were identified by reviewing existing literature (cf. section 4.3.1) and triangulating different but complementary empirical analysis (cf. section 4.3.2). In this step, an investor will need to review the venture to be valued in order to provide a good assessment of these determinant’s presence within the venture. This is required to meaningfully reflect the subjectivity inherent in the respective valuation in order to draw relevant conclusions (cf. section 4.4.2.1). Second, the investigated determinants’ impact on a NTBF’s value has to be quantified and normalized in order to be used as a valuation score within the tool. As previously, subjective information was meaningfully taken into consideration originating for empirical analysis by Choice-based Conjoint (CBC) analysis (cf. sections 4.3.3). A simple process of building the sum of relevant level values for the determinants’ observed presence derives a valuation score to be further processed in the tool (cf. section 4.4.2.2). Third, the derived valuation score needs to be matched to a maximum and minimum discount rate applicable to present value valuation methods. “In line with Smith and Smith (2000), the hereafter developed valuation [tool] is based on the assumption that uncertainty about future cash flows should be reflected within the discount rate applied. In terms of practicability and operationalizability, this approach appears most suitable, as it allows the reflection of subjective impressions while limiting the adjustment effort of business forecasts. Therefore, target return appears to be the discount rate concept most suitable as inherent subjectivity in the valuation process (e.g. cash flow uncertainty and assessment) as well as venture capital reality (e.g. limited diversification opportunities, illiquidity of assets) can be properly reflected” (Wessendorf, Schneider, Shen, et al., 2021). Next, a discount rate structure is modeled for intermediate valuation scores in order to allow for a meaningful and venture specific valuation within present value valuation methods (cf. section 4.4.2.3). Again, as with previous data shaping the artifact’s development, minimum and maximum discount rates for early-stage NTBF as well as a suitable discount rate structure have their origin in the review of relevant literature and empirical analysis (cf. section 4.3.4). In this context, the VCM is preferred and envisaged to be the method of choice during artifact development, as subjectivity is accounted for by the method’s design (Achleitner and Nathusius, 2004,

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p. 181 f). In addition, VCM is broadly used by practitioners, thereby suggesting a strong acceptance level as well as practicability. The three elements of the artifact, that were outlined in the three steps above, will be detailed in the following three sections. Figure 4.8 provides an overview of these elements as well as their order within the artifact.

4.4.2.1 Assessment of Non-Financial Valuation Determinants Relevant for NTBF Previous literature finds that the specific characteristics of early-stage ventures in general, and early-stage NTBF in particular, mostly prevent the application of conventional valuation methods, as crucial aspects of the valuation cannot be sufficiently reflected. In contrast to established companies, the lack of a corporate and financial history, recognized products and experienced management teams as well as missing stable turnovers, profits and cash flows increases risk and uncertainty (Kaserer et al., 2007). Therefore, the valuation of Start-Ups, especially in the early stages of the corporate life cycle remains “a difficult and often subjective process” (A.-K. Achleitner, 2001). In consequence of a lack of meaningful corporate history and data, valuation of early-stage NTBF has to focus on non-financial determinants. Yet, this limitation in scope is not considered to compromise the results, as Sievers et al. (2013) find that “the variation in value explainable by solely non-financial information is strongly comparable to the variation in value explainable by solely financial information”. Thus, in order to properly reflect these special characteristics of early-stage NTBF while allowing for a practical and operationalizable approach to comprehensively assess and value a NTBF based on non-financial determinants, the artifact builds on the selection of most relevant non-financial valuation determinants. Building on a systematic literature review to identify non-financial valuation determinants for NTBF (cf. section 4.3.1), a subset of six most relevant and strong impact determinants was derived by empirical analysis. This empirical analysis triangulates different but complementary research methods (Analytical Hierarchy Process, AHP; Choice-based Conjoint Analysis, CBC; cf. section 4.3.2) and thereby suggests that the valuation should be based on the following six determinants in order to ensure a meaningful valuation, while keeping data collection efforts at a minimum: 1. Entrepreneurial Spirit 2. USP 3. Market Growth

(importance weighting of 33.4%) (importance weighting of 19.4%) (importance weighting of 18.4%)

Input

Input

Valuation Score x

Market Growth5 USP6

Patents and

-1.80

2.42

Σ

1.43

-9.93

0.94

0.85

3.98

-0.02

-10.89

-7.31

-6.19

-18.68

1.82

__.__

8.47

8.50

6.37

5.34

14.71

Input Systematic Literature Review (SLR; section 4.3.4) with n=5 relevant studies Survey (section 4.3.4.2) with n=40 relevant investors

=

=

+ ( +

− =

−

−

∗(

∗

)∗

1 −

ln ( ∗

)

)

__.__ −

Target Return r indicates risk and associated value and can be used within conventional present valuebased valuation methods (e.g. VCM)

Output

Risk-Averse Investor

Risk-Affine Investor

Rational Investor

Normalized Valuation Score (xnorm = x+54.799)

Transfer of calculated valuation score into a discount rate for present value-based valuation – Alternative discount rate structures hypothesized.

Section 4.4.2.3

•

•

4

Figure 4.8 Structural elements of the artifact “Indication of Value in Early-Stage NTBF” (InVESt-NTBF) including an overview of most relevant input and output components

Market Growth5 USP6

Patents and Applications4

Founder Experience3

Founder Experience3

Applications4

Alliances1 Entrepreneurial Spirit2

Entrepreneurial Spirit2

Relevant Determinants

Relevant Determinants

Alliances1

Calculation of valuation score based on relevant valuation determinants’ impact8 – Assessment of previous step to result in valuation score.

Choice-based Conjoint (CBC) Analysis (section 4.3.3) with n=40 relevant investors

Section 4.4.2.2

•

Assessment of non-financial valuation determinants relevant for NTBF – Respective manifestation to be selected.

Systematic Literature Review (SLR; section 4.3.1) with n=45 relevant studies Triangulation of Analytical Hierarchy Process (AHP) and Choice-based Conjoint (CBC) Analysis (section 4.3.2) with n=75 relevant investors

Section 4.4.2.1

•

•

STRUCTUAL ELEMENTS OF SECTION 4.4.2: INDICATION OF VALUE IN EARLY-STAGE NEW TECHNOLOGY-BASED FIRMS (InVESt NTBF)

192 Application and Results

Figure 4.8 (continued)

The following determinants were scientifically investigated and proved to have the highest impact on early-stage NTBF valuation 1Alliances are supplier, customer and other strategic partnerships that are essential for the business of the NTBF. 2Entrepreneurial Spirit describes the personality traits of the founders, such as perseverance, persuasiveness or resistance to stress. 3Founder Experience is the entirety of relevant experience available in the founding team, incl. start-up, management and industry experience. 4Patents and Applications describe all important protective measures that can protect the product, technology or service of the NTBF from imitation. 5Market growth reflects the current or future growth of the target market in comparison to the growth of a benchmark market. 6USP refers to NTBF’s uniqueness regarding the product, service 7

or technology as well as the application possibilities. Alliances: Weak – relations do not yet exist. Intermediate – important relations are developing but not yet established. Strong – important contractual relations are established. Entrepreneurial Spirit: Weak – characteristics of founding team is not suitable for starting a company. Intermediate – founding team combines only partially qualified characteristics for starting a company. Strong – founding team combines all characteristics highly suitable for starting a company. Founder Experience: Weak – the founding team does not dispose of relevant experience Intermediate – the founding team combines a lower degree of relevant experience in industry, management and start-ups. Strong – the founding team combines a high degree of relevant experience in industry, management and start-ups. Patents and Applications: Weak – no protective measures exist. Intermediate – the protectability is not clearly given or the protective measures are not yet finally implemented. Strong 9

8

7

– the offer is fully protected against imitation and the protective measures have already been implemented. Market growth: Weak – market growth is currently below average compared to a benchmark and is expected to remain below average in the future. Intermediate – market growth is currently average compared to a benchmark or it is expected to be average in the future. Strong – market growth currently shows disproportionately high values compared to a benchmark or these can be expected with high certainty in the future. USP: Weak – the respective desirable uniqueness is very doubtful. Intermediate – the uniqueness is only given with concessions. Strong – the uniqueness in each of the areas relevant to the venture is unrestricted. Respective values are preference levels originating from a choice-based conjoint analysis performed with n=40 relevant venture capital investment professionals. Correction by 54.79 necessary to normalize the valuation score to a scale ranging from 0 to 100.

4.4 Demonstration of the Artifact 193

194

4. Patents and Applications 5. Founder Experience 6. Alliances

4

Application and Results

(importance weighting of 13.7%) (importance weighting of 11.5%) (importance weighting of 3.6%)

In line with the findings of Wessendorf & Hammes (2018), who suggest that early-stage investors will base valuation on subjectively assessed determinants at most, or even rely on experience, “gut feeling” or personal impression, this subjective valuation information needs to be captured and formalized. Therefore, a venture capital investor performing the valuation of an early-stage NTBF will assess the above stated six determinants with regard to their manifestation on a three-step scale, i.e. the determinant presents itself as weak (under proportionally present), intermediate (in line with the benchmark) or strong (over proportionally present) as displayed in figure 4.8 and table 4.19. This assessment, which will be referred to as a NTBF valuation profile, will be matched to the identified level values specifying the determinant’s impact on valuation (cf. section 4.3.3), thereby providing a transparent understanding of value drivers. In order to provide an example, an investor will first need to evaluate the above stated six determinants with regard to their manifestation within a given venture as weak, intermediate or strong. Table 4.19 provides such an exemplary valuation table, whereby the individual determinants’ manifestation as selected by the investor are marked in grey. In this specific case, the investor is dealing with the valuation of a venture that has alliances that are developing but not yet established (intermediate) as well as disposes of IPR that does not show a clearly given protectability or the implementation of suitable measures (intermediate) compared to relevant benchmark ventures. Compared to these benchmark ventures, the venture under investigation has a strong and motivated team that combines all characteristics, which are highly suitable for starting a company (strong). Further, market growth currently shows disproportionally high values or these can be expected with high certainty in the future (strong). Additionally, the uniqueness of the offering in each of the areas relevant to the venture is unrestricted (strong). Yet, the founders’ experience is below the benchmark and therefore considered weak. Each of the indicated cells is further assigned a defined level value, which originates from the empirical analysis in section 4.3.3.4, that will later serve as a mean to derive risk underlying the venture to be investigated. In a subsequent step, the identified NTBF valuation profile needs to be transformed into an interpretable valuation score that reflects the venture’s attractivity and the risk underlying an investment.

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Table 4.19 Selection of an exemplary NTBF valuation profile (selection made in grey) out of valuation determinants’ level values following CBC analysis (Wessendorf, Schneider, Shen, et al., 2021)

4.4.2.2 Calculation of Valuation Score Based on Relevant Valuation Determinants’ Impact Following the creation of a NTBF valuation profile that is capturing subjective valuation information, a formalized and structured approach to calculate a valuation score, expressing the gathered valuation information has to be followed. This approach has to be simple to understand and to perform, in order to provide a practical and operationalizable tool for valuation practice. Based on the data resulting from the NTBF valuation profile, the sum of the respective selected level values defines the Valuation Score x, which amounts to 26.41 in the following example (cf. table 4.20). This valuation score will be normalized in a consecutive step in order to allow for a simplified interpretation. Normalization will transform the calculated valuation score x ranging from -54.79 to 45.21 to an easy to be interpreted normalized valuation score x norm ranging from 0 to 100. Therefore, normalization is realized by accounting for a correction value of 54.79, which represents the negative deviation, thus x nor m = x + 54.79 As a result, a normalized valuation score ranging from 0 to 100 is more suitable for communication, understanding and interpretation as well as subsequent calculation. It is considered as being more convenient than an asymmetric scale as present for the valuation score x. The resulting normalized valuation score x norm (in the above example amounting to 81.20) will need to be transferred into an appropriate discount rate in order to ensure its integration into valuation methods, widely accepted in valuation practice.

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Table 4.20 Calculation of a normalized valuation score of an exemplary NTBF valuation profile (selection made in grey) out of valuation determinants’ level values following CBC analysis (Wessendorf, Schneider, Shen, et al., 2021)

4.4.2.3 Transfer of Calculated Valuation Score Into a Discount Rate for Present Value-based Valuation With reference to section 2.4.5.1.4 only the DCF (cf. section 2.4.2.2) and VCM (cf. section 2.4.4.1) valuation methods fulfill the necessary requirement of earlystage venture capital valuation with regard to comprehensibility of the valuation result, flexibility in accounting for venture-specific information within the valuation (i.e. orientation towards the future) as well as practicability. Common to both of these two valuation methods is the fact that they perform a present valuebased valuation. Thus, the discount rate included in both is considered to represent a strong instrument to implement the gathered subjective valuation information in a conventional and widely accepted valuation method (Festel et al., 2013; Wessendorf & Hammes, 2018). Yet, in order to better deliver on the defined requirement of reducing complexity and thereby allowing for easy operationalization and practical use, the VCM represents the valuation model of choice within this research project. Thus, target return represents the preferred discount rate concept, which is also supported by its high practicability and apt reflection of venture capital reality (A.-K. Achleitner, 2001; A.-K. Achleitner & Nathusius, 2004; Scherlis & Sahlman, 1989; J. K. Smith & Smith, 2000). Having identified maximum target return r max and minimum target return r min to be applied as a discount rate in early-stage NTBF valuation (cf. section 4.3.4.2) the previously calculated normalized valuation score x norm will be transformed into a rate of target return. Therefore, the defined equation for a linear, convex and concave discount rate structure (reflecting rational, risk-affine and risk-averse investment behavior) will be used as follows(cf . section 4.3.4.3): Linear Discount Rate Structure for Rational Investment Behavior

4.4 Demonstration of the Artifact

r = rmin +



197

xnor m max − xnor m



rmax − rmin ∗ xnor m max



W ith: r = Discount rate xnor m = N or mali zed valuation scor e

(4.19)

Convex Discount Rate Structure for Risk-Affine Investment Behavior   r = rmin + (rmax − rmin ) ∗ eln(c∗xnorm ) W ith: r = Discount rate xnor m = N or mali zed valuation scor e c = T hr eshold value to ex pr ess appr oach  s f it to valuation r ealit y (to be subjectively ad justed accor ding to investment f ocus, time and geography)

(4.20)

Concave Discount Rate Structure for Risk-Averse Investment Behavior r=



i −

1 k ∗ (xnorm −xnorm s )



e W ith: r = Discount rate

xnor m = N or mali zed valuation scor e xnor m

s

= N or mali zed valuation scor e as I nter section point with the x − axis

i = I nter section with the y − axis re f lecting the upper limit o f r k = Cur vatur e f actor (to be ad justed accor ding to investment f ocus, time, geography)

(4.21)

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4

Application and Results

For the above given example, with a normalized valuation score of 81.20, this results in a discount rate of 21.73%, 17.50% or 35.56% respectively. At this stage, there is no clear preference for a discount rate structure to be derived from investment theory. Thus, a validation of the valuation approach by real-life investment cases is considered a necessary next step to better understand how to model target return (cf. section 4.4.3). The resulting target return (i.e. the artifact’s output) will then be included as a discount rate in the VCM (cf. section 2.4.4.1 for VCM calculation) and allow an improved indication of value as it is derived based on venture specifics instead of a rule of thumb or a generalized rate of return.

4.4.3

Validation of valuation approach in practical valuation setting

Having outlined the different components and their interplay in the previous section, the approach’s results, representing the benchmark for its ability to indicate value of early-stage NTBF fulfilling the set out requirements (cf . section 4.2), i.e. the artifact, need to be analyzed and assessed. As the developed approach is based on existing literature, empirical data, theoretical considerations and hypotheses, it has to be assessed and validated within a practical valuation setting. “This was realized by analyzing the considerations and assumptions within three early-stage [NTBF] investments (hereafter referred to as case studies) that recently (Q3/2018 and Q1/2019) closed with a 6-figure to a considerable 7-figure venture capital investment volume each” (Wessendorf, Schneider, Shen, et al., 2021) (cf. table 4.21). Table 4.21 Description of analyzed early-stage NTBF in the context of discount rate tool validation (Wessendorf, Schneider, Shen, et al., 2021)

CASE 1

CASE 2

CASE 3

Year of foundation

2016

2017

2016

Country of HQ

Germany

Germany

Germany

Sector

Greentech/ Cleantech

Micro Systems Technology

Internet of Things

Funding Stage

Seed

Seed

Seed

4.4 Demonstration of the Artifact

199

Deal information, in general, is considered as highly confidential and therefore difficult to attain. Therefore, the investors in the above stated NTBF evaluated the ventures themselves according to the approach elaborated in the previous section. Additional deal information provided allowed to derive target returns applied within the respective venture’s valuation, subsequently serving as a real-life benchmark to the modeled values. The validation of the developed valuation approach (cf. section 4.4.2) is comprised of the above detailed structural elements, which define the process of valuation, as summarized in figure 4.9. Based on the received NTBF valuation profiles (cf. section 4.4.2.1 or table 4.19 for general structure and level values), target return rates were calculated according to the previously elaborated approach along three defined steps. Thus, in a first step, the individual ventures (Case 1, Case 2, Case 3) were assessed by its respective investors with the objective of creating a NTBF valuation profile (cf. section 4.4.2.1). In a second step, level values derived from previous empirical analysis (cf. section 4.3.3.3) were assigned to the resulting NTBF valuation profile in order to sum up a valuation score x (cf. section 4.4.2.2). Remarkably, the investor feedback on different NTBF demonstrated a highly comparable valuation score for both a mean valuation score x¯ and median valuation score x˜ (cf. table 4.21). Third, with the valuation score being normalized to allow for easy interpretation and calculation by accounting for a correction value of 54.79, thus xnor m = x +54.79, various target return rates r (also abbreviated as TR within the following tables) were modeled following the discount rate structures specified in section 4.3.4.3 and 4.4.2.3. The modelled target return according to the respective equation for a rational, risk-affine and risk-averse investment behavior based on the reported valuation score is detailed in table 4.22 (cf. grey area). Independently of using the modeled target return’s mean or median, specific patterns become apparent. Wessendorf, Schneider, Shen, et al. (2021) find that “in case of a linear discount rate structure, […] the [modeled] target returns are clearly reduced [compared to real target returns observed, with] a strong [and significant] deviation of [-39.34% to -42.37%. This leads] to the assumption that a linear discount rate structure is not suitable [to the developed] approach. Comparable findings are made for a [convex] discount rate structure, leading to strongly reduced target returns [with] deviation to the real target returns [between -47.10% to 49.15%]. When analyzing the modeled values following [a concave] discount rate structure, the […] results are in the area of 32.2% to 34.5%, thereby strongly comparable to the actual target returns observable […]. This leads to a deviation from values observed in the range of [-5.58% to 0.24%].”

VALUATION SCORE CALCULATION

TARGET RETURN CALCULATION

4

Figure 4.9 Structural elements of the artifact “Indication of Value in Early-Stage NTBF” (InVESt-NTBF)

NTBF VALUATION PROFILE

200 Application and Results

4.4 Demonstration of the Artifact

201

Table 4.22 Description of analyzed early-stage NTBF in the context of discount rate approach validation, with x = mean and x˜ = median (Wessendorf, Schneider, Shen, et al., 2021)

Note: A deviation to target return (Dev. to TR) indicates the deviation of modelled target returns from the real target return observed (in percent). Thus, e.g. for case 1, a modelled target return (TR) that follows a linear interest structure of 19.68% is -40.5% lower than the real target return observed (33.08%). In contrast, a modelled target return (TR) that follows an concave interest rate structure of 32.17% is -2.77% lower than the real target return observed and therefore a better fit to reality.

To conclude, the attained modeled target return rates and their subsequent comparison to real target return data for the NTBF analyzed suggest that an exponential discount rate structure is the most appropriate one to be used within the developed approach to derive a discount rate to be applied in conventional valuation methods for early-stage NTBF valuation. The comparison between real target returns observed and modelled target returns shows a set of highly comparable target returns, thereby suggesting that the InVESt-NTBF approach will indeed improve the indication of value in early-stage NTBF. This result can be considered as a further hint on the approach’s validity. Further, as the resulting modeled target return rates can easily be integrated into the widely accepted VCM, the artifact that will improve the indication of value in early-stage NTBF (InVESt-NTBF), while addressing the previously set out requirements, is successfully demonstrated in its first iteration. Additionally, the tool can easily be implemented in conventional table calculation programs,

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such as Microsoft Excel, commonly used among investment professionals, or other mathematical software.

4.5

Evaluation of the Artifact

As the single components of the artifact are now defined and detailed as well as merged to the artifact itself, an evaluation of the artifact with regard to its fulfillment of the set out requirements and objectives needs to be performed. The underlying questions guiding this evaluation are “Is the developed artifact improving the indication of value in early-stage NTBF valuation?” and “How can the developed artifact be further improved, if necessary?”. In consequence, this evaluation follows formative evaluation goals.

4.5.1

Research Design

In light of the artifact being evaluated for the first time in its entirety and the defined questions guiding this evaluation, a qualitative research approach is followed to profoundly investigate the impact and perception of the artifact. Thus, this evaluation follows formative evaluation goals. Explorative expert interviews are chosen in terms of research design (Bogner & Menz, 2005, p. 37), which can be classified as a mix of descriptive and analytical methods. This research design uses informed arguments from the newly created knowledge base, and a static examination of the artifact to explore the fulfilment of set out requirements (Hevner, March, Park, & Ram, 2004). It needs to be stressed that the interviewed experts are not the object of investigation itself, but rather take up the role of a source of specialist knowledge on the subject to be researched (Gläser & Laudel, 2010, p. 12). This specific knowledge stems from the expert’s professional or business field of activity and refers not only to technical or special, but also to practical or operational knowledge (Bogner & Menz, 2005, p. 44). In this case, especially their practical, operational and professional knowledge with regard to indicating value in early-stage NTBF valuation is requested. The expert interviews are held in the format of semi-structured interviews, which are then transcribed and coded. This is followed by a content analysis using the “Gioia-Method” (Gioia, Corley, & Hamilton, 2012; Gioia & Pitre, 1990) according to the process described in section 4.5.4.4.

4.5 Evaluation of the Artifact

4.5.2

203

Selection of Experts for Interviews

The selection of suitable experts to be interviewed obliges to assess the existence and extent of desired knowledge for the present research project. Thus, in order to retrieve all relevant information, a perspective triangulation approach will be followed. Thereby, different experts deemed to be relevant, each representing different backgrounds and different perspectives on the topic of value indication in early-stage NTBF, thus, providing complementary evidence, will be questioned. In this context, it is a prerequisite that the experts are also able to and willing to give precise information (Gläser & Laudel, 2010, p. 117). Overall, five experts had agreed to evaluate the present artifact. Table 4.23 presents the background of each expert interviewed. As some experts represent more than one background, the most relevant background or function with regard to this research project is marked with “*”. The relevant backgrounds for this interview series are venture capital investors, entrepreneurs, advisors in due diligence and M&A transactions, as well as researchers. Table 4.23 Background of interviewed experts within artifact evaluation Expert Code Name

Background of Interviewee (main background marked with *) Venture Capitalist

Entrepreneur

E1 E2

Researcher

x* x*

x

E3

x

E4

x*

E5

DD/ M&A Advisor

x*

x*

In total, three interviewees have a background shaped by entrepreneurial experience as they founded a venture previous to their current role or as they are still operatively working within that venture. These will provide the venture perspective within the evaluation. Two of the interviewees are venture capitalists, thereby providing the investor perspective within the evaluation. Yet, as one made experiences as an entrepreneur, her understanding for the venture perspective might enrich the results of this evaluation. One of the experts interviewed has a background in advising due diligence processes as well as M&A transactions, also for small and medium-sized companies, thereby regularly working on value drivers

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and proper reflection of firm value. One of the interviewed experts has a research background in the field of entrepreneurship and entrepreneurial finance. To summarize, the perspective triangulation includes the perspective of the venture, of the investor as well as third parties overlooking the entire process of valuation within an investment in a company.

4.5.3

Data Points

A semi-structured interview is conducted with each expert identified, either as a personal interview or over the telephone/ video call. The average interview lasted 00:57:12, leading to a total of 04:46:02 recorded (cf. table 4.24). The interviews were conducted between March 22nd , 2020 and April 7th , 2020.

Table 4.24 Overview of expert interviews conducted Expert Code Name

Interview length (hh:mm:ss)

Interview type

E1

00:34:17

Personal Interview

E2

01:09:06

Telephone Interview

E3

01:14:25

Telephone Interview

E4

01:00:20

Telephone Interview

E5

00:47:54

Telephone Interview

Sum

04:46:02

Average

00:57:12

4.5.4

Data Collection and Analysis Method

In order to collect relevant data for the artifact’s evaluation, semi-structured interviews with relevant interviewees, as outlined in the following section 4.5.4.2, are conducted. In the subsequent data analysis, a structured content analysis (Kuckartz, 2018, p. 97 ff.) aiming to summarize the material created (Mayring, 2015, p. 69 ff.) is performed. The required categories are built in a hybrid form (Kuckartz, 2018, p. 95 f.). On one hand, the inductively emerging categories are built following the Gioia Method (Clark, Gioia, Ketchen, & Thomas, 2010; Corley & Gioia, 2004; Gioia & Chittipeddi, 1991; Gioia et al., 2012) (cf. section 4.5.4.4),

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205

whereas, on the other hand, the a priori deductive category building is based on the research questions and the artifact’s defined requirements (cf. section 4.2.1). As a formative evaluation, the analysis is not intended to derive a structure and potential relationships out of the data analyzed but will focus primarily on improvement potential (cf. table 4.25). The a priori codes will be used to define the area of improvement. Table 4.25 Deductive categories for artifact evaluation A Priori Code (Areas)

Related to Formative Evaluation Part

Purpose of the artifact, as this will provide differentiation/ advantage to the status quo

• Fulfillment of functional requirements • Fulfillment of effect requirements

Process and Content

• Coherence • Completeness • Conciseness

Efficiency

• Adequate complexity • Efficiency

Operationalizability

• • • •

Accountability Comprehensibility Ease of use Effectiveness

Due to time restrictions, this evaluation was performed manually using Microsoft Excel instead of using a qualitative analysis software.

4.5.4.1 Interview Setting Prior to the interview, a schematic process flow chart of the artifact including examples from the case studies in section 4.4.3 as well as the interview guideline was sent to the interviewees. It was decided to use a canvas in order to discuss the approach in a first step, as this promised to be much more directed and easier compared to a Microsoft Excel tool. At the start of the interview, if required by the interviewee, a short introduction to the artifact as well as the motivation for its development was given. The interview was then performed by going through the steps of the defined process and the respective examples, as stated in figure 4.10.

4.5.4.2 Interview Guideline The interview follows the format of a semi-structured interview. Thus, the interview guideline will contain a list of open-ended questions and serves as a basic framework to ensure comparability of the interviews. In addition, such an interview guide offers a flexible handling of the interview situation with regard to

Market Growth5 USP6

Market Growth5 USP6

and Applicaons describe all important protecve measures that can protect the product, technology or service of the NTBF from imitaon. growth reflects the current or future growth of the target market in comparison to the growth of a benchmark market. 6USP refers to NTBF’s uniqueness regarding the product, service or technology as well as the applicaon possibilies. 5Market

4Patents

Figure 4.10 Process Canvas “Indication of Value in Early-Stage NTBF” (InVESt-NTBF)

2Entrepreneurial

1Alliances

104

0.2908

k xnorm s

41.345

c

For German NTBF, use the following values8

8

Example values originate from analyzed case studies and need to be adjusted depending on target industry. 9 Example: A turnover mulple of 4 applied to a turnover of 100 results in VT = 400

7

CALCULATE FIRM VALUE WITH THE VENTURE CAPITAL METHOD

__.__

__.__

8.47

8.50

6.37

5.34

14.71

Integrate the derived discount rate r (step 3) and the Future Firm Value V at me T (step 5) in the Venture Capital Method to calculate present firm value.

4

Normalized Valuaon Score7 (xnorm)

2.42

-10.89

Σ

1.43

-9.93

3.98

DERIVE A DISCOUNT RATE

Please solve the below stated formula by inserng the calculated valuaon score xnorm from step 2 in order to derive a discount rate.

3a

4

are supplier, customer and other strategic partnerships that are essenal for the business of the NTBF. Spirit describes the personality traits of the founders, such as perseverance, persuasiveness or resistance to stress. 3Founder Experience is the enrety of relevant experience available in the founding team, incl. start-up, management and industry experience.

Project the future firm value9 V at me T by mulplying an average mulplier (from a relevant peer group) M with the respecve firm rao X.

PROJECT THE FUTURE FIRM VALUE

Patents and Applicaons4 -7.31

Patents and Applicaons4

3b

0.94

Founder Experience3

Founder Experience3

Valuaon Score

0.85

-6.19

Entrepreneurial Spirit2

1.82

Alliances1

Entrepreneurial Spirit2

-0.02

-1.80 -18.68

Relevant Determinants

Relevant Determinants

CALCULATE THE VALUATION SCORE

Alliances1

2 Please transfer your assessment from step 1 to the respecve point in the table below and sum up the resulng six figures in the boxes.

CREATE AN NTBF VALUATION PROFILE

Please assess the NTBF to be valued along the following determinants and specify the present level as weak, intermediate or strong.

1

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207

word choice, order of questions, and/ or additional questions. In consequence, the interviewer is allowed to ask several clarification questions until the meaning of the answers provided is understood. Further, the order of questions asked, even if determined prior to the interview, can be adapted to the course of the discussion if necessary (Döring & Bortz, 2016, 372; Schnell et al., 1999, p. 379). With the structure of questions set, the intended content to be addressed is key. Thus, defined content topics and research questions further shape the development of the interview guideline (Döring & Bortz, 2016, p. 372). Following recommendations of respective methodological literature, 8–15 questions on a maximum of two pages will be determined prior to the interview (Gläser & Laudel, 2010, p. 144). These respect four criteria for productive interview material (Hopf, 1978; Merton, Fiske, & Kendall, 1956; Przyborski & Wohlrab-Sahr, 2014): • range/openness, i.e. to maximize the interviewee’s reaction and memory by open questions; • specificity, i.e. to address specific aspects mentioned by the interviewee; • depth, i.e. to support the interviewee in describing selected aspects in detail; • personal context, i.e. to capture the personal, social and situational context. The guide supporting the interview carried out to evaluate the artifact of this research project consists of six parts. In the warm-up phase commencing the interview (Gläser & Laudel, 2010, p. 148 f.) the interviewees were thanked for taking their time to answer the defined questions and supporting the research project. It further provides background information about the interview partners (Döring & Bortz, 2016, p. 372) and a short but detailed description of the research project. At this stage, the interviewees had the opportunity to give a first uninfluenced impression about the artifact, based on the information and illustration they received in advance. The following four main parts of the interview are directly related to the requirements under evaluation (Döring & Bortz, 2016, p. 372; Leedy & Ormrod, 2013, p. 154 f.). Subsequently, the interviewee is asked open questions, e.g. if important aspects form the point of view of the interviewee were omitted to be mentioned, in order to increase the openness of the interview (Gläser & Laudel, 2010, p. 149). The last part closes the interview and consists of an open question inquiring, if important aspects are remaining of the topic that have not been considered before. This is intended to increase the openness of the interview (Gläser & Laudel, 2010, p. 149). The interview guideline used for the evaluation of the developed artifact (cf. table 4.26) includes key questions as well as optional questions (Friedrichs, 1990, p. 227). The latter are only asked if the conversation process allows it.

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Table 4.26 Interview guideline for expert interviews Part

Interview Questions

Requirement Coverage

Part 1: • Warm-Up phase of the interview (background Introduction information on interview partners and research project) • What was your first impression of the approach for “Indication of value of early-stage NTBF” (InVESt-NTBF)?

Comprehensibility

Part 2: Process and content

Questions to be asked at each step: • Do you understand what needs to be done? • Do you consider this step to be complete? • Do you consider parts of the step as being irrelevant? • Do you see potential to improve the step? • Do you consider the step to build upon the previous step?

Coherence Completeness Comprehensibility Conciseness

Part 3: Purpose

• Does the InVESt-NTBF cover all related objectives and requirements that are relevant for indicating value of early-stage NTBF, i.e. ◯ Orientation towards the future; ◯ Reflection of features specific to early-stage NTBF; ◯ Possibility to include subjective assessments in the valuation; ◯ Transparency by following a structured, formalized and relevance-driven approach in an uncertain valuation setting; ◯ Possibility to integrate InVESt-NTBF into an accepted valuation method; ◯ Reduction of complexity to allow for practical use and operationalizability. • Are there goals that are not covered? • Do you think it is possible with the InVESt-NTBF to ◯ understand the value assigned to early-stage NTBF? ◯ validate an early-stage NTBF valuation? • Do you consider InVESt-NTBF to provide an advantage to the status quo?

Functional requirements Effect requirements

Part 4: Operationalizability

• How easy was it for you to understand the usage/ process of InVESt-NTBF? (scale: 1 = very difficult to 6 = very easy) • Was the provided graphical guideline helpful? Is something missing in this guideline? • Do you feel to get more reliable value indications? • Do you feel to get more meaningful value indications?

Accountability Comprehensibility Ease of use Effectiveness (continued)

4.5 Evaluation of the Artifact

209

Table 4.26 (continued) Part

Interview Questions

Requirement Coverage

Part 5: Efficiency

• Do you believe that InVESt-NTBF enhances the efficiency of investors and entrepreneurs in valuation?

Adequate complexity Efficiency

Part 6: Closing remarks

• Do you have any additional remarks regarding the InVESt-NTBF, its usage/ design/ content/ usefulness?

All of the above

In order to ensure comprehensibility, completeness and an acceptable interview duration, the defined questions were pretested with interviewees disposing of a relevant background, as outlined in section 4.5.2 (Berger-Grabner, 2016, p. 142; Döring & Bortz, 2016, 372; Friedrichs, 1990, p. 221 f.). The subsequent analysis of the pretest led to a changed formulation and order of the questions.

4.5.4.3 Transcription System The aim in choosing the transcription system was to reduce the material in a reasonable way. An interview of about one hour’s duration yields 25 to 60 pages of text material in transcribed form (Kuckartz, 2010). In order to generate a compact and structured form, the focus was placed on apparently relevant passages during transcription. This corresponds to a content-analytical transcription in which, in addition to pure transcription, initial content-analytical evaluation steps are included (Höld, 2009). A “selective protocol” is therefore chosen as the transcription system (Mayring, 2015). In contrast to a word-for-word approach, only those parts of the material relevant to the evaluation of the artifact’s requirements are selected and transcribed. This improves the readability of the material for analysis purposes and the presentation of content in a more structured way.

4.5.4.4 Data Analysis Method Following the transcription of interviews and accounting for documents related to the presented artifact, the subsequent data analysis was performed based on “pattern matching” (Yin, 2014, p. 133 ff.) using a “systematic inductive approach to concept development” (Gioia et al., 2012, p. 16), namely the Gioia Method. The Gioia Method is based on relevant literature on grounded theory (Glaser & Strauss, 1967; Lincoln & Guba, 1985; Strauss & Corbin, 1990) and has been used in a variety of studies published in renowned journals (Gioia et al., 2012).

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The Gioia Method assumes knowledgeable agents, thus that “people know what they are trying to do and can explain their thoughts, intentions, and actions” (Gioia et al., 2012, p. 17). All parties of a research project, thus not only the participants but also the researcher itself, are considered knowledgeable agents, who are able to “figure out patterns in the data, enabling [the researcher] to surface concepts and relationships that might escape the awareness of the informants” (Gioia et al., 2012, p. 17). Hence, in order to prominently represent the participants’ experience, prior constructs or theories are avoided with the aim of preventing a priori explanations. The procedure of data analysis and theory articulation through the use of the Gioia Method can be structured in four consecutive steps (Clark et al., 2010; Corley & Gioia, 2004; Gioia & Chittipeddi, 1991; Gioia et al., 2012): • First-order analysis: Initial data coding uses informant-centric terms and codes to develop a compendium of first-order categories. This unveils key elements of the informants’ meaning systems but does not investigate the deeper patterns or relationships in the data (Gioia & Chittipeddi, 1991). • Second-order analysis: The developed informant-centric categories are refined by seeking similarities and differences, and subsequently labelling the emergent themes using researcher-centric concepts, themes, and dimensions. If appropriate, these second-order themes might be further aggregated into overarching theoretical dimensions, so called aggregate dimensions. • Terms or categories, themes, and dimensions are transformed into a “data structure”, including first-order concepts, second-order themes and an aggregated dimension. • The attained static data structure is transformed into a dynamic model by formulation of dynamic relationships among the second-order concepts. The proper and refined articulation of emerging concepts and relationships might require the consultation of additional literature. Instead of a qualitative analysis software, Microsoft Excel is used to support in coding and analyzing the coded transcripts throughout the entire research process.

4.5 Evaluation of the Artifact

4.5.5

211

Findings

The attained data originating from a selective protocol transcription of five expert interviews is subsequently coded. The codes used reflect the deductively derived categories, so called first-order categories, as detailed in table 4.27. A total of 153 statements were included in the coding, whereby 145 statements remained for subsequent analysis due to duplication. In order to assign the first-order statements to first-order categories and subsequently derive second order themes as well as aggregated dimensions, the mind map function of the tool Zenkit was used. This enabled to structure thoughts and easily develop suitable categories deductively. The result is a rather large mind map, whose structure is detailed in section 4.5.5.2 (cf . figure 4.11). The derived first-order categories and second-order themes have been further condensed in aggregated dimensions. The resulting structure of categories, themes and dimensions is detailed in figures 4.12, 4.13, 4.14, 4.15 and 4.16.

Table 4.27 Supporting data for each first-order category First-order category

Representative first-order data

Interviewee

A dynamic perspective to account for potential and growth is beneficial

“[…] auch eine neue Ebene die zukünftiges Potenzial abgleicht—je nachdem wie man das einschätzt—manche Bewerter vermutlich eher statisch in der Einschätzung, andere eher dynamisch. ”

E3

A marketvaluation is always more objective as an individual valuation

“Diese subjektive Bewertung ist zwar richtig und wichtig, E5 steht jedoch hinter der objektiven Bewertung aus dem Markt. Eine objektive Bewertung ist letztlich nur eine wirkliche Marktbewertung und somit näher am finalen Preis als am eigentlichen Wert.”

Ambiguous expressions complicate the proper execution of the approach

“jedoch ist “solve” vielleicht schlechter Ausdruck und missverständlich. “Please calculate r” ist gut.”

“Vielleicht [ist der Ansatz] auch dynamisch durchsetzbar, um E3 den Verlauf zu tracken bzw. den Erfolg im Verlauf.”

E4

(continued)

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Table 4.27 (continued) First-order category

Representative first-order data

Interviewee

Approach reveals the mechanics of valuation and allows for a structured and objectified discussion

“Ja, in Bezug auf die Quantifizierung von Risiko scheint es besser [als der Status Quo der Frühphasen-Bewertung].”

E2

“Dem mir bekannten Status Quo gegenüber [bringt dieser Ansatz] auf jeden Fall [einen Vorteil]. Die aktuellen Bewertungsmethoden in dem Bereich [Venture Capital] sind eher Hanebüchen.”

E4

“Dennoch ist ein strukturiertes Vorgehen sehr hilfreich. Viele E5 Bewertungen werden im VC-Bereich stiefmütterlich angegangen, da ist jeder strukturierte Ansatz vorteilhaft.” “Die Struktur unterstützt dabei, dass keine relevanten Bereiche vergessen werden und eine möglichst objektive Sichtweise und Bewertung des early-stage NTBF sichergestellt werden kann.”

E1

“Ja, der Ansatz ist verlässlicher im Vergleich zum Status Quo, da man weiß, worauf der Wert basiert.”

E3

“Ja, gegenüber “Bauchgefühl” [stellvertretend für den Status Quo in der Bewertung] in der frühen Phase auf jeden Fall.”

E3

Approach allows for a quantification of subjective valuation outcome

“Ja, ich sehe einen Vorteil gegenüber. dem Status Quo, weil E1 es nach wie vor erlaubt subjektive Faktoren zu diskutieren, zu bewerten und eine Struktur/ Framework bietet, sich einer Bewertung bestmöglich objektiv zu nähern oder zu validieren.”

Approach allows for an informed indication of value

“Für eine Indikation ist der Ansatz sehr gut, bietet aber keine E5 Argumente für eine Verhandlung. Der Ansatz kann helfen, aber mehr auch nicht. Ich denke jedoch, dass es gerade für den Gründer sehr interessant sein kann, eine schnelle Indikation zu bekommen. Für den erfahrenen Investor vermutlich nicht.” “Ja, wäre sicher möglich und kann helfen, als “kognitiver Anker”, die in die Diskussion geworfenen Zahlen zu validieren.”

E3

(continued)

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213

Table 4.27 (continued) First-order category

Representative first-order data

Interviewee

Approach “Jedoch bin ich als Investor schneller in der Quantifizierung E2 enables des Risikos, was gut ist. Die schlussendliche Bewertung läuft higher anders und ist dennoch zeitaufwändig.” efficiency in assessment and quantification of risk Approach enables higher efficiency in determining a first indication on value

“Definitiv [gibt es hier einen Effizienzgewinn], weil man relativ einfach einen Wert berechnen und den Prozess beschleunigen kann.”

E4

Approach enables higher efficiency pursuing focused discussion

“Ja, weil die Diskussion zielgerichtet läuft, da man sich nur E1 an bestimmten Kriterien orientiert. Dies spart vermutlich Zeit und ermöglicht eine zielgerichtete Diskussion.”

Approach focusses on a companyperspective and not on market/ hype

“Angebot und Nachfrage an einem Venture/ Team ist hier E2 nicht abgebildet. Dies ist aber wertentscheidend, da viel Interesse von Investoren den Wert in die Höhe treibt. Dies ist jedoch der Bewertung nachgelagert. Dennoch ist der letztliche Wert bzw. Preis im stärkeren Interesse des Investors. “Indication” ist hier somit eine gute und nicht zu unterschätzende Konkretisierung des Ansatzes.”

“Der Ansatz scheint hervorragend um einen ersten Aufschlag E2 [für eine Bewertung] zu erreichen, da er nicht komplex in der Umsetzung ist. Das ist schon sehr spannend. Ebenso scheint er gut einsetzbar.”

“Die Standardisierung erlaubt eine gute Abbildung der E5 Bewertung. Allerdings steht an erster Stelle die Frage ob ich investieren möchte oder nicht. Wenn ja, ist der Wert aus Investor-Sicht sekundär im Vgl. zum letztlichen Preis. Dieser ist jedoch durch Angebot und Nachfrage, Verhandlung etc. beeinflusst.” “In unserem Reporting vermerken wir die Bewertung anhand E5 der letzten Finanzierungsrunde unter Investorenbeteiligung. Diese Standards stellen für uns eine objektive Marktbewertung dar, was sich jedoch nur schwer mit dem vorgestellten Ansatz kombinieren lässt.” (continued)

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Table 4.27 (continued) First-order category

Representative first-order data

Interviewee

“Marktrisiko ist in meinen Augen noch nicht richtig E4 abgebildet bzw. ein Market Faktor der eine Kompensation für Hypethemen ermöglicht.” “Mir kommt hier in den Sinn, dass die Realität ggf. anders E5 aussieht. Es kann schließlich sein, dass nur eine Determinante ausschlaggebend ist für eine Investition, weil dies z. B. die große Besonderheit oder den großen Vorteil in einem Markt darstellt. Dies wird hier nicht berücksichtigt. Dieser einzelne Faktor kann jedoch ausschlaggebend sein für die Entscheidung ein Investment zu machen oder nicht.” “Weiter ist die hier eingenommene Perspektive sehr E5 unternehmenslastig. Die Marktsituation für Investitionen ist nicht ausreichend komplett dargestellt zum Beispiel Angebot und Nachfrage oder Hype-Themen.” “Wie eingangs erwähnt, sind einzelne standardisierte Werte ggf. sinnvoll, andererseits kann es jedoch sein, dass einzelne Standardwerte die Realität nicht abbilden (zum Beispiel bei Hypethemen oder unausgeglichener Nachfrage und Angebot).” Approach is oriented towards the future

E5

“Der Zukunftsbezug ist theoretisch im Modell abgebildet. E5 Allerdings ist es in der Umsetzung ggf. problematisch, zum Beispiel wenn es Unternehmen zur Bewertung gibt, die noch keine Peers haben und somit auch keinen Zukunftsbezug zulassen. Der Schwachpunkt ist somit die Wahl des richtigen Multiples.” “Eher Abbildung Status Quo als Entwicklung [mit Bezug zur E4 Zukunft]. [Dennoch] Zinskurve als Marktfaktor und Future Firm Value […] mit Bezug zur Zukunft, jedoch die Determinanten beziehen sich mehrheitlich auf den aktuellen Stand.” “Grundsätzlich ist die gewisse Orientierung in die Zukunft abgebildet, einerseits aufgrund der Ausrichtung der Determinanten und deren Wahrscheinlichkeit des Erfolges. Ebenso durch die mathematische Betrachtung des zukünftigen Firmenwertes (VT)”

E1

“Ja, eine Orientierung in die Zukunft wird ermöglicht. Die Trajektorie und das Risiko werden deutlich, der zukünftige Wert jedoch nicht.”

E2

(continued)

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Table 4.27 (continued) First-order category

Representative first-order data

Interviewee

Approach reflects early-stage NTBFspecific characteristics

“Grundsätzlich besteht die Möglichkeit, dies [die Bewertung des NTBF anhand NTBF-spezifischer Kriterien] über die Determinanten zu tun.”

E4

“Ja, diese Eigenschaften werden einerseits über die Determinanten und vermutlich auch das ausgewählte Multiple in Schritt 3b abgebildet.”

E1

“Ja, durch die Determinanten ist [die Berücksichtigung von NTBF-spezifischen Kriterien] gegeben. Vielleicht müsste Geschäftsmodell oder Skalierbarkeit aufgenommen werden—andererseits ist das durch USP abgebildet.”

E3

“Ja, NTBF-spezifische Charakteristika sind durch Schritt 1 und 2 abgedeckt.”

E5

“Ja, wichtige Faktoren sind durch die Determinanten reflektiert. Die Patente werden jedoch vermutlich später erst relevant.”

E2

Calculation of “Ich verstehe, dass ich einen besseren Wert durch r erhalte. future value Dennoch sehe ich VT weiter kritisch.” by multiples might be seen critical

E2

Clear “Jedoch stellt sich die Frage ob die Variablen alle gegeben description of sind oder ob die immer berechnet werden müssen.” tasks and process steps for understanding

E3

Combination with existing valuation methods

“Definitiv [kann dieser Ansatz die Effizienz steigern], da ich E3 nun einen Ansatz an die Hand bekomme (als Gründer) um hier mal einen erfahrenen/ informierten Aufschlag zu machen. Bei einem Investor ist es evtl. gut da die Varianz der Bewertungen durch verschiedene Investment Manager geringer ausfallen würde. Vielleicht aber auch Varianzen durch andere Gründe. “ [Schwierigkeiten im Prozess können] vielleicht umgangen E1 werden, in dem mehrere Parteien im Investment-Prozess eine Bewertung vornehmen und dies im Anschluss gemittelt wird, um Extremwerte zu vermeiden.” (continued)

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Table 4.27 (continued) First-order category

Representative first-order data

Interviewee

“Interessant wäre zu verstehen, ob in der Praxis die Methode alleinig für eine Bewertung herangezogen oder komplementär genutzt werden kann. Gegebenenfalls muss man noch andere Ansätze hinzunehmen, z. B. eine SWOT. Diese ist jedoch komplementär zum Wert, als Begründung desselben. Es handelt sich dabei um eine Detaillierung der Wertbegründung.”

E1

“Ja, das könnte funktionieren. Man müsste das jedoch mal E2 testen und selbst einige Fälle rechnen um das zu prüfen. Hier fehlt mir noch das Gefühl dazu. Ebenso muss der Mehrwert getestet werden im Vgl. zu den Bewertungsergebnissen, welche ich sonst erhalte.” “Ja, sofort klar gegeben [dass man diesen Ansatz mit einer gängigen Bewertungsmethode kombinieren kann]—kann als Diskontierungsrate in DCF oder VCM eingesetzt werden.” Complexity is limited to facilitate implementation and practical use

E4

“Die augenscheinliche Komplexität ist relativ gering im Vgl. E1 zu normalerweise benutzen Bewertungsmethoden. Somit ist der Ansatz vermutlich gut operationalisierbar.” “Die einzelnen Schritte im Ansatz sind einfach auszufüllen und gedanklich zu verfolgen. Also auch gut durchführbar.”

E4

“Ja, definitiv [ist die Komplexität reduziert]—die Darstellung E3 des Ablaufs ist auch als One Pager möglich.” “Ja, die vorhandene Komplexität ermöglicht die praktische Nutzung.”

E5

“Sie [die Methode] erscheint mir alltagstauglich und wenig komplex.”

E1

Comprehensibility increased by clear value mechanics

“[…] Verständnis schaffen, welche Auswirkung die Auswahl E3 auf das Gesamtergebnis hat.”

Definitions in foot notes limit the ease of use

“Die Determinanten jedoch zu bewerten ist nicht einfach und E1 muss diskutiert werden […] Die Diskussion ist hier, was ist der Referenzpunkt und wie stehe ich im Vgl. zum Referenzpunkt.”

“Ich glaube schon, dass man die Bewertung besser verstehen E1 kann, weil eine Methodik geschaffen wurde, um vergleichbar zu bewerten und einen in sich konsistent Ansatz zu verfolgen.”

(continued)

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Table 4.27 (continued) First-order category

Representative first-order data

Interviewee

“Fußnoten werden jedoch leicht übersehen. Vielleicht kann E4 man diese grafisch hervorheben, damit diese nicht übersehen werden.” “Konkretisierung durch Fußnoten ist essentiell wichtig.”

E4

Derivation of a suitable multiple should be explained

“Herleitung für M [in Schritt 3b] wäre interessant—vielleicht E2 in Anlehnung zur Score Card Methode. Dabei sind 4 verschiedene Firmen und deren Wert bekannt. Der Durchschnitt dieser Werte stellt dann die Basis dessen, zu dem sich das zu bewertende Venture differenzieren muss.”

aDesign could become more intuitive and easier to follow

“[…] könnte man den Schritt auch unterteilen in Quantifizierung und dann Ausrechnen des Valuation Score.”

E3

“[…] könnte man die Nähe der Variablen in der Formel zu deren Definition noch vereinfachen. Vielleicht kann man das schon eintragen, sodass der Nutzer das nicht mehr machen muss.”

E4

“Die Anweisung [z. B. Schritt 3b und 4] könnte kürzer sein, da bereits verständlich beziehungsweise Wiederholung”

E4

“Vielleicht auch Berechnung nicht über Fußnote sondern direkt in den Schritt einbauen.”

E3

Determinants should be detailed and broken down in sub-elements

“Ggf. sollte man Unterpunkte für die einzelnen Determinanten aufführen, um eine noch strukturiertere Einschätzung vornehmen zu können. Damit hat man es schwerer sich auszutricksen und einen Bias einzubringen.”

E2

Existing knowledge required to follow the process i.e. in valuation methods

“Ja, auch dieser Schritt [3b] ist klar, erfordert aber gegebenenfalls Vorwissen in der VCM.”

E1

Further detailing of presented scale is not necessary but advantageous

“Detaillierung der Ausprägung “weak” etc. der einzelnen Determinanten ist eher nicht unbedingt erforderlich, da es sich um eine qualitative Einschätzung handelt.”

E4

“Drei Unterscheidungen [der Ausprägungen] finde ich ausreichend, dass es einen zwingt, sich für eine Tendenz zu entscheiden. Dies ist in der Frühphase vermutlich wichtiger als Nuancen abzubilden.”

E2

(continued)

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Table 4.27 (continued) First-order category

Representative first-order data

Graphical “Beschreibung/ Nummerierung in der Beschreibung ist nicht representation klar und müsste angepasst werden.” confuses “Pfeil verwirrt etwas und bricht die Reihenfolge. Durch Schritt 3b, der nicht auf 3a aufbaut, werfen sich viele zusätzliche Fragen auf.” “Vielleicht muss die Formel am Anfang des Pfeils in eine Box gesetzt werden, damit klar wird, dass komplett r unten eingesetzt werden.”

Interviewee E3 E4

E4

Graphical “Der Pfeil hat viel geholfen die Zusammenhänge zu E2 representation verstehen.” supports the “Ja die grafische Darstellung hat geholfen und war sehr gut.” E3 approach “Ja, die grafische Darstellung hat sehr geholfen.” E5 “Ja, diese [grafische Darstellung] war sehr hilfreich—und kann mit den Verbesserungsvorschlägen noch hilfreicher werden.” High comprehensibility induced by the approach’s design

E4

“5, also ist die Anwendung recht einfach.”

E5

“6, da Darstellung sehr einfach und gut war.”

E3

“Der Ansatz ist einigermaßen einfach—zumindest Schritt 1 und 2.”

E4

“Die einzelnen Schritte sind gut beschrieben.”

E3

“Die Prozessdarstellung ist gut und relativ einfach zu verstehen.”

E3

“Es war leicht [den Prozess zu verstehen], also eine 5, da zu wenig Definitionen gegeben waren und Fußnoten leicht übersehbar sind.”

E4

“Es war leicht, also eine 5. Dies liegt daran, weil x norm erst einmal unklar war.”

E2

“Ja, der Schritt ist verstanden.”

E4

“Ja, der Schritt ist verständlich.”

E3

“Ja, der Schritt ist verständlich.”

E5

“Wenn die Determinanten bewertet sind, dann ist [die Durchführbarkeit des Prozesses] eine 6—also sehr einfach.”

E1 (continued)

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Table 4.27 (continued) First-order category

Representative first-order data

Interviewee

Increase transparency on underlying model/ calculations

“Ja, der Schritt [2] erscheint vollständig. Wobei keine Aussage zu den Zahlen getroffen werden kann.”

E1

“Ja, vermutlich gibt es hier jedoch noch Interesse wie das E3 funktioniert mit k, c, x norm s—aber das ist vermutlich nicht im Fokus der Bewertenden.” “r müsste noch transparenter werden, damit die Akzeptanz des Modells gesichert ist.”

E5

“Zur Validierung muss auch das dahinter liegende Modell gut E4 verstanden werden. Das ist hier so nicht ersichtlich und kann sicher nur durch Tests mit Fallstudien gut getestet werden.” Integration in accepted valuation method possible

Lack of definitions complicates the execution of the approach

“Grundsätzlich kann der Ansatz in eine akzeptierte Methode überführt werden, z. B. in die Venture Capital Methode.”

E1

“Ja, durch VCM—gegebenenfalls auch später auch durch DCF. Die Stärke des Ansatzes ist es jedoch r rauszubekommen.”

E3

“Ja, es ist möglich eine bestehende Bewertung zu validieren, sofern ich die Daten für eine Bewertung habe. Als ergänzende Methode ist dies durch den vorgestellten Ansatz definitiv möglich.”

E1

““NTBF” ist jedoch nicht direkt verständlich—müsste vermutlich erklärt werden.”

E3

“[…] vermutlich müsste X und M spezifiziert werden.”

E3

“Die Variable x sollte noch definiert werden im Modell, da sonst die Fußnote 7 nicht klar wird.”

E3

“Eventuell müsste noch darauf hingewiesen werden, dass das E3 pre-money ist also eine “indication of pre-money value”.” “Present Firm Value und V0 sind nicht zusammenhängend. V0 sollte somit definiert werden. Begrifflichkeiten kann man klarer formulieren.”

E4

“Vielleicht müsste der Term “Valuation Score” ausgeführt werden.”

E2

“Vielleicht sollte man hier eine Legende und Definition der Variablen einführen.”

E3

“X könnte noch besser erklärt werden zum Beispiel durch eine Alternative für “ratio”, da dieser Ausdruck missverständlich ist.”

E5

(continued)

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Application and Results

Table 4.27 (continued) First-order category

Representative first-order data

Interviewee

Limit approach and stop at the calculation of r, as further steps increase uncertainty and room for interpretation

“Die Schritte 1 und 2 finde ich vorteilhaft, da sie eine strukturierte und eine gleichmäßige Bewertung ermöglichen. Vielleicht sogar objektivierbar. Der Preis wird letztlich anders bestimmt. Daher könnte ich mir vorstellen, dass Schritt 2 oder 3 das Ende des Prozesses darstellen sollten, da sonst der Interpretationsspielraum zu groß wird.”

E5

“Ggf. sollte man bei r [also Schritt 3a] aufhören, da man noch E2 mehr Interpretationsspielraum [in 3b] wegnimmt und damit die Nützlichkeit des Tools zunimmt.” “VT ist meines Erachtens jedoch gar nicht notwendig, sondern dass ich r als entscheidenenden Parameter habe um Risiko und Chance abzuwägen. Das geht schnell und einfach—und ist auch aussagekräftig.”

E2

Marketrelated determinants are missing (i.e. competition, market size)

“Die Determinante Market Size ist m.E. auch relevant und vielleicht hier nicht richtig abgebildet. Ebenso Markt-Potenzial, auch wenn sich dies ggf. in Market Growth abbilden lässt.”

E5

“Grundsätzlich erscheint der Schritt [1] vollständig, aber vielleicht müsste man Wettbewerb hier noch aufnehmen.”

E1

Methodological ambiguities create uncertainty in the approach

“[Bei Schritt 3b] gibt es methodische Unklarheiten, z. B. welchen X-Wert man hier nehmen sollte (Status Quo vs zukünftig). Das muss geklärt beziehungsweise auf Zukunft [der Einschätzung] hingewiesen werden.”

E3

“Guidance fehlt, wie ich diesen Schritt [3b] genau angehen E4 soll. Hier stellen sich Fragen wie ich das errechnen soll beziehungsweise wo ich die Werte herbekomme. Diese Fragen stellen sich jedoch grundsätzlich bei der Methodik der VCM beziehungsweise der Berechnung von VT mit Multiples.” “Man benötigt jedoch einen gewissen Background um das zu E3 verstehen.” “Was ist VT und wie wird dieser genau berechnet? Dies lässt E2 dem Bewerter jedoch viel Spielraum und somit auch die Möglichkeit für den Eintrag eines Bias. VT sollte man auch strukturiert erheben können.” (continued)

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221

Table 4.27 (continued) First-order category

Representative first-order data

Interviewee

Methodological background should be integrated into the process description

“[…] sollte auch die Frage beantwortet werden, warum gerade diese Determinanten gewählt wurden.”

E3

Objective of the process is not clear

“Ein DCF-ähnliches Modell wird angewendet. Macht nun auch den vorherigen Schritt klarer. Vielleicht sollte die Zielsetzung am Anfang einmal direkt beschrieben werden.”

E4

“Ich frage mich warum—dennoch würde ich jetzt einfach weiter machen und dem Prozess folgen [Trust the Process].”

E4

“Ja, aber es war bisher nicht klar, dass wir hier eine Diskontierungsrate berechnen.”

E2

“Ja, ich verstehe was gemacht werden muss, ich verstehe jedoch nicht warum.”

E2

“Allerdings bietet sich jedoch die Möglichkeit zur Objektivierung und Quantifizierung der subjektiven Einschätzung in diesem Schritt.”

E1

Opportunity to include subjective valuation assessment

“Es fehlt jedoch die Erklärung der Ausprägungsstufen—insb. E1 für unerfahrene Bewerter. Wenn Erfahrung vorhanden ist, dann kann diese Einschätzung auf Erfahrung beruhen. Eine Erklärung ist dann nicht notwendig. Unternehmen und Markt ist in der early-stage immer unsicher und schwer zu bewerten, somit ist es vielleicht vorteilhaft, die Ausprägung frei und unvoreingenommen zu diskutieren, weil der Bewerter sich in der Tiefe mit der Einschätzung auseinander setzen muss.”

“Grundsätzlich ist die subjektive Einschätzung gegeben. Dies E1 über die Bewertung der Determinanten in Schritt 1.” “Ja [die subjektive Bewertung] ist durch die Auswahl der Ausprägungen der Determinanten in Schritt 1 gegeben.”

E3

“Ja, die Möglichkeit subjektive Assessments in die E5 Bewertung aufzunehmen ist durch Schritt 1 und 2 abgedeckt.” “Ja, durch Auswahl der Ausprägungen bei den verschiedenen E2 Determinanten in Schritt 1 kann ich eine subjektive Einschätzung vornehmen. In der Praxis ist dies ggf. noch weiter zu detaillieren.” (continued)

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Table 4.27 (continued) First-order category

Representative first-order data

Interviewee

“Ja, durch die Auswahl der Ausprägungen in Schritt 1 [können subjektive Kriterien in die Bewertung einfließen].”

E4

Qualitative “[Der Ansatz] ist jedoch in seiner Grundlage sehr qualitativ, nature of daher natürlich etwas unsicher in der Messbarkeit.” approach results in a certain level of uncertainty

E4

r needs to be detailed and put in riskreturn-context for understanding

“Manche Investoren haben “target ownership”. In Seed ist es E2 oft 15–25% Target. Somit ist kann es sein, dass der Wert nicht passend zu meiner Strategie beziehungsweise sogar irrelevant in der diskutierten Form ist. Außer wenn er in den definierten Grenzen liegt. […] Generell kann man aber sagen, dass r im Sweet Spot sein sollte, dann ist das ein interessantes Tool.” “r verstehe ich besser. V0 verstehe ich jedoch nicht besser, da E2 ich nicht weiß, wie VT zustande kommt. Daher kann ich den Wert nur bedingt besser verstehen. Vielleicht ist r bereits ausreichend und resultiert in einer stärkeren Aussage.” “Weiter variiert r mit der Qualität des Investments. Hohes E5 Risiko und uninteressantes Investment bedingen somit hohes r, was aber nicht intuitiv ist, wenn ich an r als Return denke. Unter Risiko-Aspekten jedoch nachvollziehbar.”

Relevance of shown determinants should be established

“Die Relevanz der Kriterien wird hinterfragt, ebenso deren Abgrenzung.”

Relevant “Ja, der Schritt baut auf dem vorherigen auf.” process steps “Ja, die Schritte bauen aufeinander auf.” follow a logic “Ja, er baut auf dem vorherigen Schritt auf.” sequence Risk of a bias is present when investor decides on investing before valuation

E4

E5 E4 E1

“Wenn ich mich entschieden habe in ein Venture zu E2 investieren, dann bin ich biased. Jedoch mache ich mir erst zu diesem Zeitpunkt Gedanken über die Bewertung. Daher gibt es vermutlich auch einen Bias bei der Einschätzung. Man kann hier in Versuchung kommen, diesen Ansatz “für Bare Münze” zu nehmen und den eigenen Bias einfließen zu lassen.” (continued)

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223

Table 4.27 (continued) First-order category

Representative first-order data

Interviewee

Some determinants might not be important to all NTBF (i.e. alliances, IP)

“[…] kann man Patents and Applications eher in IP umbenennen, da dies weiter greift. In einigen Bereichen, z. B. Software, spielen Patente keine große Rolle.”

E3

“Mir erschien der Punkt “Allianzen” erst nicht ganz schlüssig, aber leuchtet mir nun ein. In meinen Augen kann das relevant sein, muss es aber nicht.”

E5

Structured and transparent process results in comprehensible results

“Bauchgefühl kann quantifiziert werden. Dies ist nachvollziehbar und transparent. Man gibt sich selbst die Möglichkeit, sich kritisch zu hinterfragen.”

E2

“Die Art der Bewertung ist nachvollziehbar, schlüssig und vollständig.”

E1

“Ja, weil die Faktoren/ Determinanten begründet E1 wurden—diese sind die entscheidenden Werttreiber. Dadurch wird das Ergebnis aussagekräftig.” “Transparenz ist gegeben durch klaren Ablauf und Struktur.” E3

Testing the approach with real cases would improve interpretability

“Transparenz ist grundsätzlich dadurch gegeben, dass alle Schritte eindeutig und zurückverfolgbar sind.”

E1

“Ja die Bewertung ist verständlicher geworden—allerdings stellen sich die o.g. Fragen zur Funktion des Ansatzes und zur Genauigkeit. Diese müsste selbst einmal anhand von einigen Fallstudien getestet werden.”

E4

“Man müsste das Modell erst testen für 4–5 Fälle und dann einmal in die Hintergründe eintauchen—dies würden helfen, um zu verstehen wir verlässlich [und aussagekräftig] das Model ist.”

E4

The described “Ebenso erscheint der Ansatz vollständig.” process suggests a high level of completeness The presented scale appears sufficient, yet it might be wise to differentiate positive aspects more

E1

“Die gewählte Skala scheint auf den ersten Blick vielleicht E5 sinnvoll, aber vielleicht ist es auch notwendig, positive Aspekte stärker zu differenzieren. Die ganz negativen Punkte würde ich mir ja gar nicht für eine Bewertung ansehen, sondern schon vorher verwerfen.”

(continued)

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Table 4.27 (continued) First-order category

Representative first-order data

Interviewee

The resulting rate of return needs to be interpreted in a risk-returncontext

“Weiter gibt es jedoch nicht die Aussage, das hohes Risiko schlecht ist—hier hat jeder Investor seinen eigenen Sweet Spot. Man muss Risiko vs Chance abwägen.”

E2

Transparency is limited to the process but does not include entire approach

“Der Ablauf bzw. Prozess der Bewertung ist transparent. E5 Allerdings sind die Faktorgewichtungen bisher nicht klar und daher derzeit nur eingeschränkt transparent. Ebenso die Zinsstruktur, welche sich bei ersten Lesen nicht erschließt.”

Use of Microsoft Excel would be more beneficial

Value drivers and implications allow for an interpretability of the approach

“Ich sehe hier eine interessante Methode um Transparenz in Bezug auf Kriterien, Gewichtung und Bewertung zu erhalten.”

E1

“Schwer eine Aussage zu treffen. Ist alles logisch nachvollziehbar. Für eine Transparenz in der Bewertung selbst müsste ich das einige Male testen und andere Firmen bewerten.”

E4

“Excel erscheint mir hier jedoch besser [als die grafische E2 Darstellung], da man als Nutzer dann nicht weiß, welche Auswahl wie stark in die Bewertung einfließt. Man kann sich also wieder nicht austricksen und ggf. einen Bias einfließen lassen, weil man unbedingt ein Investment machen will.” “Excel macht Benutzung besser/ verlässlicher. Allerdings ist das schwerer für die Diskussion.”

E2

“Excel wäre besser für die Nutzung.”

E2

“Excel wäre besser, für eine Diskussion ist die grafische Darstellung jedoch gut.”

E2

“Excel würde hier wieder der Übersichtlichkeit beitragen.”

E2

“Ja, ebenso [ist der Ansatz aussagekräftig], da ich die Wertreiber kenne und interpretieren kann.”

E3

“Ja, ich habe das Gefühl eine verlässlichere Bewertung zu erhalten, weil ich eine subjektive Einschätzung in einen objektiven Kontext bringe und das Ergebnis quantifiziere. Dieses kann man vergleichen.”

E1

“Ja, weil man die Werttreiber sehen kann und weiß, was den Wert entsprechend beeinflusst. Die Bewertung wird somit klar verständlich.”

E3

Figure 4.11 Mind map created with Zenkit to deductively derive first- and second-order themes as well as aggregated dimensions from the attained statements

AGGREGATED DIMENSIONS

SECOND-ORDER THEMES

FIRST-ORDER CATEGORIES

ANSWERS RECEIVED

4.5 Evaluation of the Artifact 225

Figure 4.12 Data structure of first-order statements originating from relevant expert interviews with regard to first iteration—Part I

Exisng knowledge required to follow the process i.e. in valuaon methods

Guidance

AGGREGATED DIMENSIONS

4

Ambiguous expressions complicate the proper execuon of the approach

Definions in foot notes limit the ease of use

Lack of definions complicates the execuon of the approach

Methodological ambiguies create uncertainty in the approach

Graphical representaon confuses

Clear descripon of tasks and process steps are for understanding

The described process suggests a high level of completeness

High comprehensibility induced by the approach's design

Unclear acvity descripon/ wording

Clear structure/ process

Relevant process steps follow a logic sequence

Graphical representaon supports the approach

SECOND-ORDER THEMES

FIRST-ORDER CATEGORIES

226 Application and Results

Unfavorable Design

Operaonalizability of developed approach

User Experience

AGGREGATED DIMENSIONS

Figure 4.13 Data structure of first-order statements originating from relevant expert interviews with regard to first iteration—Part II

Methodological background should be integrated into the process descripon

Objecve of the process is not clear

Use of MS EXCEL would be more beneficial

Design could become more intuive and easy to follow

Complexity is limited to facilitate implementaon and praccal use

Combinaon with exisng valuaon methods

Approach enables higher efficiency in determining a first indicaon on value

Approach enables higher efficiency in assessment and quanficaon of risk

Approach enables higher efficiency pursuing focused discussion

Efficiency resulng from a structured approach

Transparency of process and approach

Structured and transparent process results in comprehensible results

Transparency is limited to the process but does not include enre approach

SECOND-ORDER THEMES

FIRST-ORDER CATEGORIES

4.5 Evaluation of the Artifact 227

Figure 4.14 Data structure of first-order statements originating from relevant expert interviews with regard to first iteration—Part III

The resulng rate of return needs to be interpreted in a risk-return-context

Quality of value indicaon

User Experience

AGGREGATED DIMENSIONS

4

A market-valuaon is always more subjecve as an individual valuaon

Qualitave nature of approach results in a certain level of uncertainty

Risk of a bias is present when investor decides on invesng before valuaon

Calculaon of future value by mulples might be seen crical

r needs to be detailed and put in riskreturn-context for understanding

Increase transparency on underlying model/ calculaons

Integraon in accepted valuaon method possible

Relevance of shown determinants should be established

Risk of potenal flaws

Interpretability of value indicaon

Value drivers and implicaons allow for an interpretability of the approach

Tesng the approach with real cases would improve interpretability

SECOND-ORDER THEMES

FIRST-ORDER CATEGORIES

228 Application and Results

Gain in understanding

Missing aspects/ determinants

Quality of value indicaon

AGGREGATED DIMENSIONS

Figure 4.15 Data structure of first-order statements originating from relevant expert interviews with regard to first iteration—Part IV

Approach allows for an informed indicaon of value

Comprehensibility increased by clear value mechanics

Market-related determinants are missing (i.e. compeon, market size)

Approach focusses on a companyperspecve and not on market/ hype

A dynamic perspecve to account for potenal and growth is beneficial

Some determinants might not be important to all NTBF (i.e. alliances, IP)

Approach reveals value mechanics and allows structured/ objecfied debate

Approach allows for quanficaon of subjecve valuaon outcome

Opportunity to include subjecve valuaon assessment Advantage over the status quo

Purpose of value indicaon

Approach reflects early-stage NTBFspecific characteriscs

Approach is oriented towards the future

SECOND-ORDER THEMES

FIRST-ORDER CATEGORIES

4.5 Evaluation of the Artifact 229

Granularity of value indicaon

Determinants should be detailed and broken down in sub-elements

Redundant content

Limit approach to calculaon of r, as further steps increase uncertainty

Content

Quality of value indicaon

AGGREGATED DIMENSIONS

4

Figure 4.16 Data structure of first-order statements originating from relevant expert interviews with regard to first iteration—Part V

Missing content/ funcons

Derivaon of a suitable mulple should be explained

Presented scale sufficient, yet differrenaon of posive aspects valuable

Further detailing of presented scale is not necessary but advantageous

SECOND-ORDER THEMES

FIRST-ORDER CATEGORIES

230 Application and Results

4.5 Evaluation of the Artifact

231

4.5.5.1 Descriptive statistics The interview with five relevant interviewees led to 145 first-order statements. These statements were coded and assigned to deductively derived first-order categories and second-order themes. When analyzing the allocation of first-order statements to the derived categories (cf. table 4.28) it can be observed that the large majority deals with User Experience (36.6%) and the closely related category Guidance (27.6%). Thus, the design and representation of the approach itself as well as its process need be in focus for a subsequent iteration. The remaining statements relate to the InVESt-NTBF approach’s underlying model or other observable aspects that impact the quality of resulting value indications (33.1%) as well as the approach’s content (2.8%). Also, these statements will need to be taken into account within a subsequent iteration of developing the InVESt-NTBF approach in order to further improve the indication of value. Yet, the qualitative analysis of the attained first-order statements, as well as the respective first-order categories and second-order themes will provide an avenue for future improvements.

4.5.5.2 Qualitative Analysis The qualitative analysis of data gathered will be structured along the four identified aggregated dimensions. In each dimension, the underlying data will be specified and relevant actions to improve the InVESt-NTBF approach identified.

4.5.5.2.1 User Experience User Experience was addressed by 53 first-order statements, structured in 17 first-order categories and five second-order themes. Overall, the experts were convinced that the presented approach would positively impact the efficiency of the valuation process as well as the value indication’s interpretability, transparency and operationalizability. Yet, some aspects were pointed-out in order to improve the approach. First, the design of the canvas and by this also the process itself could become more intuitive, by e.g. implementing information from the foot notes directly in the process, displaying variables right at the point of calculation or by shortening the descriptions to the essential, non-distracting minimum. Further, selected information on the underlying model should be communicated already at the process stage, i.e. definition of determinants’ values or specification on the determinants’ relevance. This also resulted in the impression that the transparency on the process itself is good, yet the transparency of the logic underneath is very limited. In consequence, some interviewees mentioned that they would ideally test the approach on real

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Table 4.28 Descriptive statistics of attained first-order statements and deductively derived categories Second-order themes

First-order category

User Experience

53

36,6% 11,0%

Unfavorable Design

15

10,3%

Operationalizability of developed approach

10

6,9%

Transparency of process and approach

8

5,5%

Efficiency resulting from a structured approach

4

2,8%

48

33,1%

Purpose of the value indication

15

10,3%

Missing aspects/ determinants

13

9,0%

Advantage over the status quo

7

4,8%

Risk of potential flaws

5

3,4%

Gain in understanding

4

2,8%

Granularity of value indication

4

2,8%

40

27,6%

Unclear activity description/ wording

21

14,5%

Clear structure/ process

19

13,1%

Guidance

Content

Total

Share of total statements given

16

Interpretability of value indication

Quality of value indication

Number of first-order statements

4

2,8%

Redundant Content

3

2,1%

Missing content/ functions

1

0,7%

145

100,0%

4.5 Evaluation of the Artifact

233

cases in order to get a better understanding of the underlying mechanics. Additionally, four statements indicate that the objective of the process and thus partially the individual steps were not clear enough. Next, the question on an interpretable context of r was raised, which was considered to improve the understanding of the approach’s benefit. Finally, the representation of the InVESt-NTBF approach on a canvas was considered as positive with regard to a discussion of individual steps. Yet, the implementation of the process in a computer-based tool, e.g. in Microsoft Excel was strongly favored. Most of the mentioned aspects for improvement can easily be adapted for a future iteration (cf. section 4.5.5.3). With regard to the transparency of the underlying logic, a different format, e.g. a tool working in a Microsoft Excel environment, would be more beneficial. Yet, the implementation of the approach in Microsoft Excel is considered simple and a prerequisite for implementation in valuation practice.

4.5.5.2.2 Quality of Value Indication Quality of value indication was addressed by 48 first-order statements, structured in 19 first-order categories and six second-order themes. Overall, the experts stated that the InVESt-NTBF approach reflects critical aspects relevant to earlystage technology venture valuation, e.g. the integration of NTBF-specific features in the valuation, their subjective assessment as well as subsequent objectification and quantification, the integration of a future-oriented perspective in order to account for the venture’s potential as well as a clear understanding and comprehensibility of the mechanics underlying the resulting value indication. Yet, some aspects were pointed-out in order to improve the approach. First, the approach was considered to have a strong focus on venture-specifics and does not account for offer and demand in an investment setting as well as implication on value resulting from hype topics. In a following discussion between the respective interviewees and the authors with regard to a differentiation on value (i.e. specific to the venture) and price (i.e. resulting from investment competition, negotiation etc.) the interviewees agreed that such a differentiation is correct and important. However, the crucial information to an investor is represented by the price of an investment. Furthermore, concerns have been raised with regard to the qualitative nature of the approach. Even though it is accepted that a qualitative approach is necessary in an early-stage valuation setting, risks from correctly measuring value were pointed out. These are inherent in such an early-stage valuation. Additionally, the reliability as well as meaningfulness of the venture capital method, and more precisely the determination of a future firm value by means of multiples, were questioned. This resulted in the suggestion of some interviewees

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to limit the InVESt-NTBF process to the calculation of r and omit its integration in the venture capital method, as this last step would further increase the scope for interpretation and thereby uncertainty related to the results. Finally, a risk of bias was raised by a venture capital investor interviewed. This bias might result from the fact that an investor normally decides on investing in a venture before valuing the venture. Thus, with a positive investment decision already made, the investor might be likely to seek ways on how to improve the valuation to match her particular interests. This aspect is intriguing and should be analyzed in further detail in future research. Some of the mentioned aspects for improvement can easily be adapted for a future iteration, e.g. the process’s limit to calculate r (cf. section 4.5.5.3 and step 3a in figure 4.8). Other, more substantial aspects might require larger adjustments to the approach and subsequent multiple iterations to be tested.

4.5.5.2.3 Guidance Guidance was addressed by 40 first-order statements, structured in 11 first-order categories and two second-order themes. Overall, the experts were convinced that the presented approach follows a logical sequence, provides a feeling of completeness and allows for a high comprehensibility by design. Yet, some aspects were pointed out in order to improve the approach. First, a more prominent representation of definitions would positively impact the ease of use. In that context, definitions and specifications should be provided more extensively. Further, clear descriptions of tasks and process steps avoiding ambiguous expressions were emphasized. Finally, methodological ambiguities were pointedout, which exclusively relate to the calculation of a future firm value by multiples and the subsequent application of the venture capital method. In line with the findings of section 4.5.5.2.2 it was suggested to limit the InVESt-NTBF process to the calculation of r and omit its integration in the venture capital method. Most of the mentioned aspects for improvement can easily be adapted for a future iteration (cf. section 4.5.5.3). Other, more substantial aspects might require larger adjustments to the approach and subsequent multiple iterations to be tested. 4.5.5.2.4 Content Content was addressed by four first-order statements, structured in two first-order categories and two second-order themes. In line with the previously detailed aggregated dimensions, some aspects were pointed-out in order to improve the approach. First, the derivation of a suitable multiple in order to allow for the calculation of future firm value should be explained. Yet, the valuation by multiples was seen critically. Additionally, the

4.5 Evaluation of the Artifact

235

interviewees confirmed the usefulness and strengths over the status quo of steps 1 to 3a, in particular 1 to 2, and thus suggested to limit the InVESt-NTBF process to the calculation of r and omit its integration in the venture capital method.

4.5.5.3 Implications The previously detailed findings (cf. sections 4.5.5.2.1 to 4.5.5.2.4) suggest several aspects for improvement that would need to be taken into account in future iterations of the InVESt-NTBF’s development. Due to constraints in resources, the author previously decided on limiting the present work on a first iteration of the approach. Yet, the findings suggest two categories of improvement aspects that can be treated differently. The first category includes all mentioned aspects that are not directly related to the process’s representation in form of a canvas, such as the implementation in Microsoft Excel or aspects relating to the underlying valuation logic as well as the logic’s extension. These aspects can particularly be found within the aggregated dimension Quality of value indication (cf. section 4.5.5.2.2). Due to the high level of necessary adjustments, these aspects will need to be implemented and tested in a future iteration. The second category includes all mentioned aspects that are directly related to the process’s representation in form of a canvas and do not relate to the underlying logic of the InVEStNTBF approach. These aspects can easily be implemented (cf. figure 4.15) but, nevertheless, need to be tested in a future iteration. The author decided to already include these easy to account for improvements in the canvas in order to leverage the benefit of having conducted expert interviews by himself and thus have the right understanding of the improvements’ objective (cf . figure 4.17).

Patents and Applicaons4 -7.31 Market Growth5 USP6

Patents and Applicaons4

Market Growth5 USP6

7

2.42

-10.89 __.__

8.47

8.50

6.37

5.34

14.71

Alliances: Weak – relaons do not yet exist. Intermediate – important relaons are developing but not yet established. Strong – important contractual relaons are established. Entrepreneurial Spirit: Weak – characteriscs of founding team is not suitable for starng a company. Intermediate – founding team combines only parally qualified characteriscs for starng a company. Strong – founding team combines all characteriscs highly suitable for starng a company. Founder Experience: Weak – the founding team does not dispose of relevant experience Intermediate – the founding team combines a lower degree of relevant experience in industry, management and start-ups. Strong – the founding team combines a high degree of relevant experience in industry, management and start-ups. Patents and Applicaons: Weak – no protecve measures exist. Intermediate – the protectability is not clearly given or the protecve measures are not yet finally implemented. Strong – the offer is fully protected against imitaon and the protecve measures have already been implemented.

Σ

1.43

0.94

0.85

3.98

-9.93

-6.19

-18.68

DERIVE A DISCOUNT RATE

Market growth: Weak – market growth is currently below average compared to a benchmark and is expected to remain below average in the future. Intermediate – market growth is currently average compared to a benchmark or it is expected to be average in the future. Strong – market growth currently shows disproporonately high values compared to a benchmark or these can be expected with high certainty in the future. USP: Weak – the respecve desirable uniqueness is very doubul. Intermediate – the uniqueness is only given with concessions. Strong – the uniqueness in each of the areas relevant to the venture is unrestricted. 8 Respecve values are preference levels originang from a choice-based conjoint analysis performed with n=75 relevant venture capital investment professionals. 9 Example values included in the formula to calculate r describe a suitable interest rate structure and originate from analyzed case studies (sample consists of German NTBF only). 10 Correcon by necessary to normalize the valuaon score to a scale ranging from 0 to 100. 7

r indicates risk and associated value and cannot per se be classified as advantageous or disadvantageous as this differenaon depends on the pursued investment strategy.

Normalized Valuaon Score (xnorm = x+54.7910) __.__

Please compute the below stated formulas9 by inserng the calculated valuaon score x from step 2 in order to derive a discount rate r.

3

4

Figure 4.17 Improved Process Canvas “Indication of Value in Early-Stage NTBF” (InVESt-NTBF)—Version 1.5

The following determinants were scienfically invesgated and proved to have the highest impact on early-stage NTBF valuaon 1Alliances are supplier, customer and other strategic partnerships that are essenal for the business of the NTBF. 2Entrepreneurial Spirit describes the personality traits of the founders, such as perseverance, persuasiveness or resistance to stress. 3Founder Experience is the enrety of relevant experience available in the founding team, incl. start-up, management and industry experience. 4Patents and Applicaons describe all important protecve measures that can protect the product, technology or service of the NTBF from imitaon. 5Market growth reflects the current or future growth of the target market in comparison to the growth of a benchmark market. 6USP refers to NTBF’s uniqueness regarding the product, service or technology as well as the applicaon possibilies.

Founder Experience3

Valuaon Score x

Entrepreneurial Spirit2

Founder Experience3

1.82

Entrepreneurial Spirit2

-0.02

Alliances1 -1.80

Relevant Determinants

Relevant Determinants

CALCULATE THE VALUATION SCORE

Alliances1

2 Please transfer your assessment from step 1 to the respecve point in the table below and sum up the resulng six figures8.

CREATE AN NTBF VALUATION PROFILE

Please assess the NTBF to be valued along the following determinants and specify the present level as weak, intermediate or strong.

1

CANVAS: INDICATION OF VALUE IN EARLY-STAGE NEW TECHNOLOGY-BASED FIRMS (InVESt NTBF)

236 Application and Results

4.7 Communication

4.6

237

Summary of DSR Project Elements

The evaluation of the artifact in the context of this research project is completed with the first evaluation iteration. Even though there are relevant suggestions for improvement that were not yet taken into account developing the artifact, a further development and evaluation iteration will be omitted due to resource restrictions. Nevertheless, these suggestions are thoroughly documented in order to allow for a seamless linkage to potential future research. A summary of the DSR project elements along the defined stages of the DSR project is given in table 4.29. Numerous expert interviews, questionnaires and extensive previous literature were analyzed following different but complementary research methods, thereby combining quantitative and qualitative research. The resulting data provided a strong foundation to build an artifact that addresses all laid-out requirements and can already be implemented in an initial version.

4.7

Communication

Communication in DSR is considered a crucial activity in order to receive necessary input external to the research project itself. A. Hevner & Chatterjee (2010, p. 12) and A. R. Hevner et al. (2004) even determine effective communication of DSR to technology- as well as management-oriented audiences as a guideline for conducting good design science research. Yet, the content of this communication will influence the value add of effective communication to a DSR project. Thus, Peffers et al. (2007, p. 56) suggest to focus communication activities on “the problem and its importance, the artifact, its utility and novelty, the rigor of its design, and its effectiveness”. This communication should not be limited to “practicing professionals”, but also include researchers and other relevant audiences. With regard to this DSR project, communication at each step of the defined DSR process (cf. section 3.2.1) to either the practitioner (i.e. relevant investment professionals or entrepreneurs), the research community or both has been conducted. In addition, the developed artifact was evaluated and thus subject to evaluation-based communication (cf. section 4.5). The feedback gathered from this communication served as a relevant input throughout the present work. With regard to informal communication of this DSR project, other researchers and practitioners (in particular NTBF entrepreneurs) were consulted to discuss the overall problem and its importance as well as single components of the artifact, in particular its utility, effectiveness and rigor of design. In addition, intense

238

4

Application and Results

Table 4.29 Summary of DSR Project Elements DSR Stage Problem specification

Creation of archival knowledge base

Development of artifact

Evaluation of artifact

Empirical survey

Empirical survey

SLR, AHP, CBC and case studies

Expert interviews

Number of n = 54 cases/ experts participants

n = 54 participants

AHP: n = 35; CBC: n = 40; 3 case studies

n = 5 experts with relevant background

Setting

Online questionnaire and interviews

Online Online Presentation questionnaire questionnaire of artifact and and interviews interviews

Main data points

n = 54 completed surveys

n = 54 completed surveys

n = 75 n=5 completed interviews questionnaires (04:46.02 length)

Data analysis method

Statistical analysis (quantitative)

Statistical analysis (quantitative)

Systematic Content Literature analysis Review (SLR); method Analytical Hierarchy Process (AHP); Choice-based Conjoint Analysis (CBC)

Section

4.1.1

4.1.1.2

4.3 and 4.4

Element Method

4.5

discussions with researchers from the Karlsruhe Institute of Technology (KIT), namely the Institute for Entrepreneurship, Technology-Management and Innovation (EnTechnon) and the Institute of Management (IBU) on overall DSR design as well as methodology to be followed to develop the single components of the artifact were pursued.

4.7 Communication

239

Formal communication in terms of the underlying problem and its importance as well as the artifact’s single components’ “utility and novelty, the rigor of its design, and its effectiveness” (Peffers et al., 2007, p. 56) was ensured by publication of attained results at the Karlsruhe Institute of Technology (KIT) as well as in peer-reviewed academic journals: Wessendorf, C. P. and Hammes, C. (2018) Methods and Criteria affecting Early-Stage Venture Valuation. https://doi.org/10.5445/IR/1000079690. Wessendorf, C. P., Kegelmann, J. and Terzidis, O. (2019) Determinants of Early-Stage Technology Venture Valuation by Business Angels and Venture Capitalists, International Journal of Entrepreneurial Venturing, 11(5), pp. 489–520. https://doi.org/10.1504/ IJEV.2019.102259. Wessendorf, C. P., Schneider, J., Gresch, M. A. and Terzidis, O. (2020) What matters most in Technology Venture Valuation? Importance and Impact of NonFinancial Determinants for Early-Stage Venture Valuation, International Journal of Entrepreneurial Venturing, 12(5), pp. 490–521. https://doi.org/10.1504/IJEV.2020. 111536

Further, the artifact as a whole including “its utility and novelty, the rigor of its design, and its effectiveness” (Peffers et al., 2007, p. 56) as well as its underlying problem and its importance was subject to the following conference contributions and publication in a peer-reviewed academic journal: Wessendorf, C. P., Schneider, J., Shen, K. and Terzidis, O. (2019) Valuation of EarlyStage Technology Ventures – A Model to Determine the Discount Rate in Present Value Valuation Methods, EntFin 2019, 4th EntFin (Entrepreneurial Finance) Conference, Trier. Wessendorf, C. P., Schneider, J., Shen, K. and Terzidis, O. (2021) Valuation of EarlyStage Technology Ventures – An Approach to Derive the Discount Rate, The Journal of Alternative Investments, Winter 2021, 23(3), pp. 32–44. https://doi.org/10.3905/jai. 2020.1.114

Moreover, the extent of communication suggested by Peffers et al. (2007, p. 56) is fulfilled by publication of this dissertation.

5

Discussion

During the development of the artifact, this DSR project elaborated major research results that will be outlined in the following chapter. Further, its contribution to relevant theory and implication to venture capital valuation practice are detailed as well as the work’s limitations. To conclude this chapter, avenues for future research will be highlighted.

5.1

Major Research Results

In order to grasp the extent to which the present research project achieved relevant results, it is necessary to look at both the different components of the research process as well as at the actual artifact developed, which represents the sum of its parts. Analyzing the results achieved by following the research process chronologically, all research results will be taken into account. First, the Systematic Literature Review (SLR) performed in order to identify relevant non-financial determinants for early-stage technology venture (NTBF) valuation resulted in a gain in insight compared to existing literature. The relevant field of research mainly developed in the early 2000s and the financial crisis of 2009 and continued to grow in terms of attention. Today, numerous studies exist that focus on the identification and description of (non-financial) determinants for early-stage NTBF valuation. As a consequence, the field reflects a high level of complexity, which was not yet addressed by any comprehensive study aiming for a structured and comprehensible review. Within this research project, such a structured review was conducted and led to the identification and thorough description of 18 non-financial determinants for early-stage venture valuation that are currently known and understood by existing research. 14 of these determinants are © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2021 C. P. Wessendorf, Indicating Value in Early-Stage Technology Venture Valuation, Schriften zum europäischen Management, https://doi.org/10.1007/978-3-658-34944-8_5

241

242

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Discussion

particularly important for early-stage NTBF valuation. The identified determinants are interdependent and contextualize each other. The analysis further suggests that non-financial information is of importance during the early-stage of the organizational life cycle as financial information is scarce and provides a limited level of insights. Thus, it appears that non-financial information explains a comparable part of the variation in pre-money valuation as does financial information. Second, the analyses following an Analytical Hierarchy Process (AHP) and a Choice-based Conjoint Analysis (CBC) resulted in a clear ranking of relative importance of a selection of previously identified non-financial valuation determinants. Previous literature, even though it identifies and discusses nonfinancial determinants for early-stage venture valuation, refrains from specifying a level of relevance of the determinants covered. It is thus limited to the determinants’ description and the discovery of an existing but unspecified influence on value. Within this research project, n = 75 relevant investment professionals were interviewed about their valuation behavior and preferences, which led to the identification of a subset of most relevant non-financial valuation determinants that were further put in a clear order of relevance. The six most relevant determinants used within the development of the artifact are, in descending order of importance, Entrepreneurial Spirit (relative weight of 33.4%), Unique Selling Proposition (USP) (19.4%), Market Growth (18.4%), Patents and Applications (13.7%), Founder Experience (11.5%) and Alliances (3.6%). In this context, it needs to be pointed out that relevant previous publications mention differences in venture capitalist and business angel investment and valuation behavior. These differences might originate from the respective institutional background, investment motivation, and objectives. Yet, the findings of the conducted AHP and CBC analyses are strongly differing from these previous findings. The findings of the AHP analysis, across all answers given, reflect a consistency ratio of CR = 0.07, which proves that the results derived are highly comparable, independently of the investor type. The CBC analysis establishes similarities in the most important determinant (Entrepreneurial Spirit demonstrating an importance weight of 33.0% for venture capitalists and 34.3% for business angels) as well as the least important determinant (Alliances demonstrating an importance weight of 3.6% for venture capitalists and 4.0% for business angels). The order of the remaining determinants varies slightly, with differences in the range of 0.8 to 2.9 percentage points. Hence, a comparable behavior in early-stage NTBF valuation is observed among venture capitalists and business angels. Third, in line with shortcomings of existing literature stated in the previous paragraph, the impact of relevant non-financial determinants for early-stage NTBF valuation was not quantified, therefore preventing a measurable linkage to the

5.1 Major Research Results

243

valuation outcome. Within this research project, n = 40 relevant investment professionals participated in an experiment following a Choice-based Conjoint analysis approach. In this experiment, they were shown the simplified profile of three different early-stage NTBF out which they had to choose the most valuable one to invest in. Based on the attained data, preference levels for the different non-financial determinants that characterized the presented venture profiles were calculated. This resulted in a quantified impact of each determinant, dependent on its observable presence (i.e., a weak, intermediate, or strong manifestation of a given determinant). This research project thereby successfully quantified nonfinancial determinants’ impact on early-stage NTBF valuation based on a solid empiricism (cf. table 4.17). Finally, a major achievement of the present research project is the development and integration of above-stated results into an artifact that allows for the indication of value in early-stage NTBF (also referred to InVESt-NTBF). In this context, the presented results were integrated into a multi-step assessment process. In a first step, the party performing the valuation would create a valuation profile of an NTBF by assessing the present level of the six most relevant nonfinancial determinants in the venture. In a second step, the impact level values corresponding to the identified valuation profile would be aggregated to a valuation score. This valuation score is, in a third step, normalized and, taking into account an appropriate interest rate structure validated by real-life valuation case studies, transformed into a discount rate r. A suitable discount rate structure was identified by investigating different archetypes of investor behavior, which reflect central elements of prospect theory. This discount rate is a strong approximation of risk inherent to an investment in the respective early-stage NTBF and further provides an indication of a thus required rate of return. This can be integrated into a DCF and or VCM valuation allowing the investor to perform a valuation based on the assessed risk. The developed approach to indicate value thereby demonstrates characteristics that are not yet reflected as a whole by any other valuation method used in venture capital practice. First, the InVESt-NTBF approach allows for the subjective assessment of early-stage (NTBF) specific features, which will then be quantified in the course of the process. Second, due to its clear and relatively simple structure, the InVESt-NTBF approach is characterized by a high level of transparency, thereby allowing for interpretable results. This is mainly due to the fact that the underlying valuation logic is transparent, and the valuing party can reconstruct the individual drivers leading to a certain valuation result. Third, the said level of limited complexity further leads to a high level of practicability and consequently,

244

5

Discussion

operationalizability in order to use this valuation approach in venture capital practice. Fourth, the approach does inherently reflect a certain orientation towards the future, which is crucial in the valuation of young companies with a large growth potential. Turning to the foundations of company valuation, the presented approach follows subjective value theory (cf. section 2.1.1). If the resulting discount rate r is used within DCF, the underlying concept is the present value of expected surplus (cf. section 2.1.2.6), whereas its application in VCM would result in a mixed concept of present value of expected surplus (cf. Section 2.1.2.6) and market capitalization (cf. section 2.1.2.2). As the motivation for such a valuation is most likely linked to a change in shareholder structure, the approach follows a non-dominant, decision-dependent motive, thereby support valuation advisory and argumentation (cf. section 2.1.3). To conclude, the knowledge contribution classifies as a tool, as it augments the capabilities of its respective user in terms of functionality, efficacy, and efficiency. Further, all set out functional requirements (cf. section 4.2.1), which justify a clear advantage over the status quo, are fulfilled within the developed artifact. Also, the remaining requirements are integrated to a large extent but will need to be further improved in future iterations.

5.2

Theoretical Contribution

The attained results within this research project contribute to existing literature on early-stage technology venture (NTBF) valuation within the field of entrepreneurial finance. Analyzing previous works in that field and also entrepreneurship research in general, three main conclusions can be drawn. First, a significant part of relevant existing research investigated the influence of different determinants (both financial and non-financial in nature) in a venture capital investment context. Yet, the majority of these studies did not specifically focus on valuation of early-stage ventures but venture capital decision-making and underlying factors as well as ventures’ success factors. Still, a subsegment of relevant literature focusing on determinants in a venture valuation context can be identified. Interestingly, these publications focus on ventures in general and only a limited few have a focus on technology ventures, which become more and more present nowadays (Wessendorf, Kegelmann, et al., 2019). Second, analyzing these publications, a great number of valuation determinants and their underlying rationale for driving value becomes apparent. However, the present studies mostly refrain from specifying a clear ranking of importance among these determinants as well as

5.2 Theoretical Contribution

245

quantifying their impact. Therefore, the findings of previous literature are yet hard to be used in valuation practice and thus require further research within the field of entrepreneurial finance. Lastly, when analyzing valuation approaches for early-stage venture capital that strongly account for non-financial valuation determinants, previous research is scarce. Only one relevant publication can be identified, which suggests a refined valuation approach accounting for different static levels of relevant valuation determinants, thereby expanding on the existing research in this field (Festel et al., 2013). However, the fundamentals are questionable with regard to using a CAPM-like approach to derive a discount rate because the use of CAPM in a venture capital context is not fully accepted by researchers. Further, the suggestion that all valuation determinants included in the approach are of equal importance and have theoretically the same impact on value is questionable. In this context, the present work contributes to research in entrepreneurship and entrepreneurial finance by adding relevant insights in the field of early-stage venture valuation. The major contributions are threefold. First, with early-stage ventures being increasingly driven by new technology, this study provides a differentiated view on early-stage technology venture investments and their determinants of value. Thereby, 18 non-financial determinants for early-stage venture valuation were identified (cf. section 4.3.1). Second, by focusing on non-financial determinants only in order to account for the specific characteristics of earlystage ventures, this work further contributes by investigating the determinants’ relative importance (cf. section 4.3.2) and provides a quantifiable impact on value (cf. section 4.3.3). Thereby, this work allows for the quantitative measurement of valuation determinants that are otherwise mainly driven by subjective impressions. Lastly, the attained findings are implemented into a comprehensive approach that allows for an objectivized assessment leading to a discount rate, which can be used for valuation in the context of accepted valuation methods, such as the Venture Capital Method (c.f. section 2.4.4.1). Further, the theoretical contribution of this research project differs from relevant previous publications that mention differences in venture capitalist and business angel investment and valuation behavior. These differences might originate from the respective institutional background, investment motivation, and objectives. The findings of the AHP analysis conducted within this research project, across all answers given, reflect a consistency ratio of CR = 0.07, which proves that the results derived are highly comparable, independently of the investor type. The CBC analysis conducted within this research project establishes similarities in the most important determinant (Entrepreneurial Spirit demonstrating an importance weight of 33.0% for venture capitalists and 34.3% for business

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Discussion

angels) as well as the least important determinant (Alliances demonstrating an importance weight of 3.6% for venture capitalists and 4.0% for business angels). The order of the remaining determinants varies slightly, with differences in the range of 0.8 to 2.9 percentage points. Thus, a comparable behavior in early-stage NTBF valuation is observed among venture capitalists and business angels.

5.3

Practical Implications

The practical implications originating from the developed artifact need to be considered in the context of venture capital valuation reality. The clear trend of a strongly increasing level of funds provided to venture capital investors, as well as the subsequent high volume of investments and high valuations per deal, establish the importance of a thorough valuation (cf . sections section 1.1 and 1.2). With venture capital investments being, as many other investments and asset classes, driven by a general investment pressure due to the prevailing low interest environment, competition among investors gets fierce, potentially resulting in higher valuations and quicker investment decisions. It is therefore in the best interest of venture capital investors as well as potential limited partners in a fund, to follow a structured valuation approach that does account for the specifics of earlystage NTBF, which is easy to operationalize (i.e., time-efficient) and provides a comprehensive valuation. With regard to nowadays’ valuation practice, investors cannot rely on conventional valuation methods, as these are mostly not suitable for early-stage ventures. These methods are in general not sufficiently able to account for the large growth potential of a young venture and are mostly driven by financial data and corporate history—which cannot yet be provided by an early-stage venture. Other methods, like e.g. the venture capital method, provide an alternative to tackle these mentioned flaws but are often not able to provide a precise assessment due to a lack of structure and proven approach to derive the many assumptions needed. Therefore, investors often follow their own “experience” or “gut feeling” or some other form of subjective impression, in a mostly unstructured way (Wessendorf & Hammes, 2018). These subjective impressions are not negative per se and can be a good approximation for a venture’s value. Yet, they are not comprehensible, not transparent and therefore hard to trust in or rely on for entrepreneurs sitting at the other side of the table or third parties involved in the transaction (e.g. limited partners). In this context, the present research contributes to early-stage technology venture valuation practice by the development of a tool that is specifically designed

5.4 Limitations

247

for early-stage NTBF, hence accounting for respective venture-specifics, potentially large growth potential, and is further easy to operationalize. In addition, this tool provides a clear structure and transparent approach to collect necessary information for the valuation. The valuation follows a clear process, starting with the assessment of a defined set of non-financial valuation determinants that prove to be the most relevant value drivers in early-stage venture capital. This does not only enable an easy-to-operationalize approach to data collection but also allows the investor to provide subjective input data (i.e. by deciding on the observed intensity of the individual determinants in a NTBF). Next, the subjectively assessed intensity of most relevant non-financial valuation determinants will be objectivized following a transparent and easy-to-understand process. At this step, the scientific background underlying the process’s rationale originates strongly from the present research and is thus well documented. Hence, clear statements can be made about how different subjective assessments of different valuation determinants impact the value of a venture. The objectivized valuation assessment will then be transformed to a discount rate, following an interest rate structure that can be adjusted to the specific industries the investor is active in. Thereby, the investor is in a position to reflect not only venture-specific information but also implicit industry-specific information. The resulting discount rate is a good approximation of the risk inherent in the NTBF as well as a required rate of return. Finally, the investor can use this discount rate in her own investment calculations, compare it against her target return or decide on using it within a conventional and accepted valuation method, such as the venture capital method. To the best of the author’s knowledge and proven by the present work, no such valuation approach is broadly used in early-stage venture capital and consequently provides a new way on how to value a NTBF. It thereby offers a solution to the observable challenges of valuing early-stage NTBF, mainly shaped by unsuitable conventional valuation methods and resulting non-transparent valuations driven by “gut feeling”. Therefore, these findings are relevant to practitioners of the buy- and sell-side of an early-stage NTBF investment as they provide a comprehensible, transparent, structured and proven approach to uncover value, which can subsequently be used in tried and tested valuation methods.

5.4

Limitations

Each step in the DSR project is designed and performed individually and is therefore considered independently with regard to possible limitations.

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5

Discussion

First, in the context of the Systematic Literature Review performed to identify non-financial determinants proven to have an effect on early-stage venture valuation in general, and early-stage NTBF valuation in particular, certain limitations emerge. These can be assigned to two groups: the interpretation of results as well as the methods chosen. With regard to the first, the interpretation of results, the limited sample size of 15 relevant publications dealing with early-stage NTBF valuation in particular raises the question, whether identified determinants of early-stage NTBF valuation are not considered to replace the general valuation determinants, but to be applied in a complementary manner. Yet, a clear answer cannot be given due to the limited sample. With regard to the methodological limitations, “[…] the specific steps of a SLR and thus decisions on how to proceed within the analysis are on one hand strengthening consistency and rigor of the work. One the other hand, they might impose certain additional limitations. In this context, the specific focus of the SLR, the defined review protocol and the selection of primary studies have to be considered, [in particular] the defined inclusion and exclusion criteria […]. Thus, the focus on publications in English and German […] can be considered to potentially overrepresented studies from English-speaking countries and/ or underrepresented studies from non-English-speaking countries. This potentially influences the set of determinants identified, as American venture capital is considered to follow different dynamics than European venture capital. Indeed, out of 45 studies analyzed, 27 (60%) have a focus on North American ventures and/ or venture investors […]. […] [Yet], the determinant selection by geography is highly comparable, with exception of ‘alliances’, ‘investor reputation’ and ‘patents’ that are mainly analyzed within a North American research focus. Nevertheless, this does not necessarily reflect a specialty of the North American venture capital industry or research, but is rather a result of the very strong representation of North American studies. Additionally, the databases chosen potentially represent a source of bias […]. Depending on the databases’ scope, a different sample of publications could potentially have emerged, providing a differing set of determinants subject to different valuation dynamics […]. A further segmentation […] of the research scope would have resulted in a further reduction of an already limited sample size […].” (Wessendorf, Kegelmann, & Terzidis, 2019)

Second, in the context of non-financial determinants’ relevance and impact, various limitations and sources of bias potentially originate from the high degree of discretion and the hard-to-access small population of investors for early-stage NTBF in German-speaking Europe. This results in a smaller and potentially more homogenous but differing sample compared to other important geographies. The specific geographic focus should be extended to provide an exhaustive view on venture capital valuation practice. Additionally, it can be argued that NTBF valuation is driven by the same valuation determinants, independently of the specific

5.4 Limitations

249

technology underlying the venture. This position is taken by the present research project, as the valuation process itself is primarily focusing on valuing technology and thus hypothesized to face a comparable set of requirements and objectives. However, in the absence of sufficient and relevant data, this hypothesis can neither be validated nor falsified. Yet, in terms of valuation determinants’ impact, the existence of different level values for different fields of technology appears reasonable. Next, even though a comprehensive literature review to account for all determinants relevant to early-stage technology venture valuation preceded the analysis of relevance and impact, the determinants selected represent only a subset of potential determinants assessed in a valuation context. In consequence, a different set of determinants potentially leads to differing results. Nevertheless, following the objective of developing an operationalizable artifact to indicate value in early-stage NTBF, a limited scope with regard to determinant selection appears reasonable at this stage. “[Third], the limited number of case studies available to validate the presented valuation model imposes a distinct potential for bias. Even though all three case studies fit the designated objective of testing [an approach to indicate value in early-stage NTBF], they represent a single geography at a single point in time only. […] However, it needs to be pointed out that [relevant] deal information […] is not publicly available and therefore strongly confidential. The possibility to even analyze three case studies in depth was highly appreciated. Nevertheless, these case studies are a good fit to each other and allow for relatively firm conclusions and an initial validation of the model”, (Wessendorf, Schneider, Shen, et al., 2021). Overall, a greater empiricism across the different steps of the design and development phase as well as the validation phase would improve the meaningfulness and generalizability of the developed approach.

Fourth, the topic of approximating valuation reality, in particular with regard to the variables c and k within the derived equations describing potential discount rate structures, needs to be reconsidered in the individual valuation context of an investor. Thus, as stated previously, c and k reflect an investor’s individual portfolio or investment strategy, which is shaped by its investment focus, geography, market conditions at the point in time when a valuation is performed as well as subjective impressions of the valuation environment. Within the present dissertation, these variables were defined to fit a very specific selection of technology ventures. Due to a limited availability of case studies, the values assigned to these variables are not representative to all technology ventures. Therefore, an investor wishing to apply this valuation approach might be well advised to calibrate the presented valuation approach to his individual portfolio and preferences. Finally, due to constraints in (time) resources, the development of the artifact is limited to one iteration. The feedback for improvement gathered within

250

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Discussion

the evaluation cycle of this first iteration was partially implemented. Yet, some more fundamental adjustments were not accounted for. In consequence, the present artifact has to be evaluated in this context and is limited to a first development iteration.

5.5

Future Research

Future research should resume at the outlined limitations. Therefore, in a first step, limitations in determinant identification and selection could be addressed by following a different but complementary set of defined SLR criteria, e.g. different databases and papers, different inclusion and exclusion criteria (e.g. language focus) or by adjusting the research focus (e.g. focusing on specific fields of technology or a more refined view on the early stage). Second, future research should focus on increasing the sample size within its analyses on determinants’ relevance and impact. In this context, a clear limit of the present work is its specific geographic focus on German-speaking Europe, which would need to be extended. Further, the determinants analyzed should be extended in order to capture additional value aspects of early-stage NTBF. Next, the focus on a specific field of technology or cluster (i.e. hardware vs software) might further improve the understanding of non-financial determinants’ relevance and impact on NTBF valuation. Third, future research should also have the ambition of increasing “the sample size of early-stage technology ventures that recently underwent valuation and analyze the observed target returns applied as well as the valuation score derived according to the developed model. This would, on the one hand, increase the significance of relevant empiricism for validation. On the other hand, an increased sample size would potentially lower the risk of underlying biases with regard to geography, timing and other company-specific characteristics. […] This might potentially increase the operationalizability and informative value of the model.” (Wessendorf, Schneider, Shen, et al., 2021) Lastly, the feedback gathered within the evaluation cycle of the artifact’s first development iteration should be fully implemented in a second development cycle. In consequence, the performance of further development cycles in order to improve the artifact might significantly add to the overall objective of indicating value in early-stage NTBF valuation. These development cycles might not only include the feedback gathered, but also extend on the above-stated suggestions to focus on in future research. Thereby, a continuation of the DSR project to a broader extent provides a rich avenue for further research.

6

Conclusion

One crucial step in the investment process of a venture capital investor or business angel is the valuation of the target company. Investors today, as in the past, are faced with the great challenge of valuing a young company without a corporate history and a firm customer relationship or even without a business model that can be realized in the short term, while still taking into account the potentially enormous growth potential. While many different techniques have been developed to value companies in general, most of them are not applicable for early-stage venture valuation. Missing financial time series or even non-existent sales and cash flows lead to unreliable valuation results. Existing research observes that the valuation of ventures, especially in the early stages of the corporate life cycle remains “a difficult and often subjective process” (A.-K. Achleitner, 2001). As a consequence, most venture capital investors will rely on “experience” and “gut feeling” when valuing a young venture, thereby creating a highly subjective value that is not necessarily transparent to other parties involved (Wessendorf & Hammes, 2018). This phenomenon is particularly pronounced for the valuation of early-stage NTBF, which require not only the valuation of the ventures future market potential but also its technological feasibility as well as the technology’s suitability for commercialization. An objectification of the subjective valuation result by means of a comprehensible, operationalizable procedure has not yet been sufficiently achieved. Especially with regard to the valuation of young technology companies, there is a need for action here. However, with venture capital funds (i.e. USD 13bn raised by European VCs in 2018 (Atomico, 2019)) and respective investment volume constantly increasing in recent years (i.e. USD 11.6bn in Q2/2019 (Atomico, 2019)) and the related valuations augmenting, the need for an objectifiable valuation reinforces its high relevance. The overall volume of funds, as well as © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2021 C. P. Wessendorf, Indicating Value in Early-Stage Technology Venture Valuation, Schriften zum europäischen Management, https://doi.org/10.1007/978-3-658-34944-8_6

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Conclusion

the valuation of individual companies, drives the interest of venture capital funds’ limited partners to optimally manage funds provided and to make investment and valuation decisions in a transparent and objective manner. Based on existing scientific literature as well as relevant empiricism, this DSR research project developed an artifact enabling the indication of value in early-stage NTBF, while respecting various requirements crucial for an academically sound and operationalizable approach. These requirements include the approach’s orientation towards the future in order to account for future growth potential of the venture, the reflection of subjective assessments typical to the early-stage as well as early-stage NTBF-specific features, a limited complexity allowing for easy operationalizability and acceptance from valuation practice due to its complementarity to existing and widely-used valuation methods. “[The development of the artifact] followed four distinct steps. First, non-financial valuation determinants relevant for early-stage [NTBF] valuation were identified, [which] appear to reflect the subjectivity inherent in the respective valuation meaningfully. [Second], the determinants’ impact on a NTBF’s value was quantified [representing an information that was not yet treated sufficiently by previous literature]. This [allows] to normalize the derived relationship and to use it as a valuation score. [Third], […] an empirically suggested maximum and minimum discount rate applicable to present value valuation methods [was matched] with the derived valuation score, thereby increasing the validity of the approach’s results. Finally, a discount rate structure was modeled for intermediate valuation scores.” (Wessendorf, Schneider, Shen, et al., 2021)

The functionality of the thereby developed artifact was subsequently examined by comparing the attained valuation results with the observable valuation of three early-stage NTBF based in Germany, that recently underwent valuation for funding. This comparison led to a high level of consistency marked by a deviation of under 2 percentage points between the observable real valuation and the attained modeled valuation. In a final step, the developed artifact was presented to five experts disposing of a relevant background (e.g. venture capital investor or NTBF entrepreneur). These experts were interviewed with regard to the artifact’s functional, structural and environmental requirements as well as its effect. Besides extensive positive feedback, in particular to the overall function and structure of the artifact, many ideas for improvement were generated. These ideas for improvement were well documented and, to a certain extent, already integrated in a revised artifact. Yet, the artifact needs to undergo a second full development cycle, which will in particular need to reflect the more fundamental feedback received.

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Conclusion

253

To conclude, at the end of this research project stands a “first iteration artifact” that was successfully examined in its operational environment and is validated by relevant experts. Further, this artifact already fulfills the functional, structural and environmental requirements set out, e.g. its high operationalizability as well as the structured and formalized subjective assessment of early-stage NTBF-specific features. Future research will need to focus on the artifact’s subsequent development cycles accounting for limitations identified within this research project.

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